From 8729f403a50d0793fb70c75de40c70ed0c0a6fa2 Mon Sep 17 00:00:00 2001 From: renan-francisco Date: Fri, 8 Mar 2024 13:59:53 -0500 Subject: [PATCH 01/87] Refactorings after frontier setup --- flowcept/__init__.py | 15 +-- flowcept/analytics/__init__.py | 2 +- flowcept/configs.py | 21 +++-- .../adapters/dask/dask_interceptor.py | 2 + flowcept/flowceptor/telemetry_capture.py | 94 +++++++++++++------ 5 files changed, 93 insertions(+), 41 deletions(-) diff --git a/flowcept/__init__.py b/flowcept/__init__.py index aea5b0f5..94368532 100644 --- a/flowcept/__init__.py +++ b/flowcept/__init__.py @@ -12,13 +12,14 @@ WorkflowObject, ) -try: - from flowcept.instrumentation.decorators.responsible_ai import ( - model_explainer, - model_profiler, - ) -except: - pass +# These resp_ai imports below are adding long wait in flowcept imports! +# try: +# from flowcept.instrumentation.decorators.responsible_ai import ( +# #model_explainer, +# #model_profiler, +# ) +# except: +# pass if Vocabulary.Settings.ZAMBEZE_KIND in flowcept.configs.ADAPTERS: try: diff --git a/flowcept/analytics/__init__.py b/flowcept/analytics/__init__.py index 93130ff2..d0ad0a8c 100644 --- a/flowcept/analytics/__init__.py +++ b/flowcept/analytics/__init__.py @@ -9,4 +9,4 @@ describe_cols, ) -from flowcept.analytics.plot import heatmap, scatter2d_with_colors +#from flowcept.analytics.plot import heatmap, scatter2d_with_colors diff --git a/flowcept/configs.py b/flowcept/configs.py index 4305351d..29d24f85 100644 --- a/flowcept/configs.py +++ b/flowcept/configs.py @@ -123,15 +123,24 @@ N_GPUS = dict() if TELEMETRY_CAPTURE.get("gpu", False): try: - from pynvml import nvmlDeviceGetCount - - N_GPUS["nvidia"] = nvmlDeviceGetCount() + visible_devices_var = os.environ.get("CUDA_VISIBLE_DEVICES", None) + if visible_devices_var is not None: + visible_devices = [int(i) for i in visible_devices_var.split(",")] + N_GPUS["nvidia"] = visible_devices + else: + from pynvml import nvmlDeviceGetCount + N_GPUS["nvidia"] = list(range(0, nvmlDeviceGetCount())) except: pass try: - import pyamdgpuinfo - - N_GPUS["amd"] = pyamdgpuinfo.detect_gpus() + visible_devices_var = os.environ.get("ROCR_VISIBLE_DEVICES", None) + if visible_devices_var is not None: + visible_devices = [int(i) for i in visible_devices_var.split(",")] + N_GPUS["amd"] = visible_devices + else: + import pyamdgpuinfo + N_GPUS["amd"] = list(range(0, pyamdgpuinfo.detect_gpus())) + except: pass diff --git a/flowcept/flowceptor/adapters/dask/dask_interceptor.py b/flowcept/flowceptor/adapters/dask/dask_interceptor.py index 89edb9c0..986554f8 100644 --- a/flowcept/flowceptor/adapters/dask/dask_interceptor.py +++ b/flowcept/flowceptor/adapters/dask/dask_interceptor.py @@ -132,6 +132,8 @@ def setup_worker(self, worker): """ self._worker = worker super().__init__(self._plugin_key) + self._generated_workflow_id = True # TODO: :refactor: This is to avoid workers to register workflows. The schedulers do that. + self._registered_workflow = True super().start(bundle_exec_id=self._worker.scheduler.address) # Note that both scheduler and worker get the exact same input. # Worker does not resolve intermediate inputs, just like the scheduler. diff --git a/flowcept/flowceptor/telemetry_capture.py b/flowcept/flowceptor/telemetry_capture.py index 5ce491be..eba25688 100644 --- a/flowcept/flowceptor/telemetry_capture.py +++ b/flowcept/flowceptor/telemetry_capture.py @@ -28,6 +28,10 @@ class TelemetryCapture: + + _gpu_unsuccessful_queries = dict() # TODO: refactor; I need this to avoid querying GPU stuff that is generating errors. The idea is to try once and if it fails, add this in this dictionary to avoid trying again. The mapping will be {gpu_device_id: {query_type: True or False}}; False if it found that it's unsuccessful. If it's mapping to an empty dict, the whole GPU is bad for capture. + + def __init__(self, conf=TELEMETRY_CAPTURE): self.conf = conf self.logger = FlowceptLogger() @@ -35,7 +39,6 @@ def __init__(self, conf=TELEMETRY_CAPTURE): def capture(self) -> Telemetry: if self.conf is None: return None - tel = Telemetry() if self.conf.get("process_info", False): tel.process = self._capture_process_info() @@ -196,86 +199,123 @@ def __get_gpu_info_nvidia(self, gpu_ix: int = 0): ), "power_usage": nvmlDeviceGetPowerUsage(handle), "name": nvmlDeviceGetName(handle), + "device_ix": gpu_ix } - return flowcept_gpu_info + + def __register_unsuccessful_gpu_query(self, gpu_ix, gpu_info_key): + self.logger.error(f"Error to get {gpu_info_key} for the GPU device ix {gpu_ix}") + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries: + TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] = {} + TelemetryCapture._gpu_unsuccessful_queries[gpu_ix][gpu_info_key] = True + + # TODO: finish adding the else: None def __get_gpu_info_amd(self, gpu_ix: int = 0): flowcept_gpu_info = {} try: amd_info = pyamdgpuinfo.get_gpu(gpu_ix) except Exception as e: self.logger.exception(e) + TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] = {} return flowcept_gpu_info + flowcept_gpu_info["device_ix"] = gpu_ix + + flowcept_gpu_info["gpu_id"] = amd_info.gpu_id + memory_info = amd_info.memory_info.copy() try: - flowcept_gpu_info["total"] = memory_info.pop("vram_size") - except Exception as e: + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "total" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["total"] = memory_info.pop("vram_size") + except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "total") self.logger.exception(e) try: - flowcept_gpu_info["temperature"] = amd_info.query_temperature() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "temperature" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["temperature"] = amd_info.query_temperature() except Exception as e: + flowcept_gpu_info["temperature"] = None + self.__register_unsuccessful_gpu_query(gpu_ix, "temperature") self.logger.exception(e) try: - flowcept_gpu_info["power_usage"] = amd_info.query_power() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "power_usage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["power_usage"] = amd_info.query_power() except Exception as e: + flowcept_gpu_info["power_usage"] = None + self.__register_unsuccessful_gpu_query(gpu_ix, "power_usage") self.logger.exception(e) try: - flowcept_gpu_info["used"] = amd_info.query_vram_usage() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "used" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["used"] = amd_info.query_vram_usage() except Exception as e: + flowcept_gpu_info["used"] = None + self.__register_unsuccessful_gpu_query(gpu_ix, "used") self.logger.exception(e) try: - max_clocks = amd_info.query_max_clocks() - flowcept_gpu_info["max_shader_clock"] = max_clocks["sclk_max"] - flowcept_gpu_info["max_memory_clock"] = max_clocks["mclk_max"] + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "max_shader_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + max_clocks = amd_info.query_max_clocks() + flowcept_gpu_info["max_shader_clock"] = max_clocks["sclk_max"] + flowcept_gpu_info["max_memory_clock"] = max_clocks["mclk_max"] except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "max_shader_clock") self.logger.exception(e) try: - flowcept_gpu_info["shader_clock"] = amd_info.query_sclk() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "shader_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["shader_clock"] = amd_info.query_sclk() except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "shader_clock") self.logger.exception(e) try: - flowcept_gpu_info["memory_clock"] = amd_info.query_mclk() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "memory_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["memory_clock"] = amd_info.query_mclk() except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "memory_clock") self.logger.exception(e) try: - flowcept_gpu_info["gtt_usage"] = amd_info.query_gtt_usage() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "gtt_usage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["gtt_usage"] = amd_info.query_gtt_usage() except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "gtt_usage") self.logger.exception(e) try: - flowcept_gpu_info["load"] = amd_info.query_load() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "load" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["load"] = amd_info.query_load() except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "load") self.logger.exception(e) try: - flowcept_gpu_info[ - "graphics_voltage" - ] = amd_info.query_graphics_voltage() + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "graphics_voltage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + flowcept_gpu_info["graphics_voltage"] = amd_info.query_graphics_voltage() except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "graphics_voltage") self.logger.exception(e) flowcept_gpu_info.update(memory_info) try: - name = amd_info.name - if name is not None: - flowcept_gpu_info["name"] = name + if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "name" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + name = amd_info.name + if name is not None: + flowcept_gpu_info["name"] = name except Exception as e: + self.__register_unsuccessful_gpu_query(gpu_ix, "name") self.logger.exception(e) return flowcept_gpu_info def _capture_gpu(self): - try: + try: + self.logger.debug(f"These are the visible GPUs by Flowcept Capture: {N_GPUS}") if len(N_GPUS) == 0: self.logger.exception( "You are trying to capture telemetry GPU info, but we" @@ -284,13 +324,13 @@ def _capture_gpu(self): ) return None - n_nvidia_gpus = N_GPUS.get("nvidia", 0) - n_amd_gpus = N_GPUS.get("amd", 0) + n_nvidia_gpus = N_GPUS.get("nvidia", []) + n_amd_gpus = N_GPUS.get("amd", []) - if n_nvidia_gpus > 0: + if len(n_nvidia_gpus) > 0: n_gpus = n_nvidia_gpus gpu_capture_func = self.__get_gpu_info_nvidia - elif n_amd_gpus > 0: + elif len(n_amd_gpus) > 0: n_gpus = n_amd_gpus gpu_capture_func = self.__get_gpu_info_amd else: @@ -298,8 +338,8 @@ def _capture_gpu(self): return None gpu_telemetry = {} - for i in range(0, n_gpus): - gpu_telemetry[i] = gpu_capture_func(i) + for gpu_ix in n_gpus: + gpu_telemetry[gpu_ix] = gpu_capture_func(gpu_ix) return gpu_telemetry except Exception as e: From 9a0f2446df76a18dacdebb17bf1fbcf32a8c9922 Mon Sep 17 00:00:00 2001 From: Renan Souza Date: Fri, 8 Mar 2024 19:13:20 -0500 Subject: [PATCH 02/87] Major commit to fix workflow saving and removing some decorator imports from __init__ --- flowcept/analytics/__init__.py | 2 - flowcept/commons/daos/mq_dao.py | 6 +- .../flowcept_dataclasses/task_object.py | 21 +- .../flowcept_dataclasses/workflow_object.py | 97 +- flowcept/commons/utils.py | 8 - flowcept/configs.py | 11 +- flowcept/flowcept_api/db_api.py | 3 +- .../flowceptor/adapters/base_interceptor.py | 122 +- .../adapters/dask/dask_interceptor.py | 28 +- .../flowceptor/consumers/document_inserter.py | 90 +- flowcept/flowceptor/telemetry_capture.py | 106 +- notebooks/analytics.ipynb | 8083 ++++++++++++++++- resources/sample_settings.yaml | 8 +- tests/adapters/test_dask.py | 19 +- tests/api/dbapi_test.py | 3 +- tests/decorator_tests/ml_tests/dl_trainer.py | 6 +- .../ml_tests/llm_tests/llm_decorator_test.py | 4 +- .../ml_tests/llm_tests/llm_trainer.py | 3 +- tests/telemetry_test.py | 2 - 19 files changed, 8363 insertions(+), 259 deletions(-) diff --git a/flowcept/analytics/__init__.py b/flowcept/analytics/__init__.py index d0ad0a8c..6bf2c5a6 100644 --- a/flowcept/analytics/__init__.py +++ b/flowcept/analytics/__init__.py @@ -8,5 +8,3 @@ describe_col, describe_cols, ) - -#from flowcept.analytics.plot import heatmap, scatter2d_with_colors diff --git a/flowcept/commons/daos/mq_dao.py b/flowcept/commons/daos/mq_dao.py index 71b4e04b..1bd20a46 100644 --- a/flowcept/commons/daos/mq_dao.py +++ b/flowcept/commons/daos/mq_dao.py @@ -131,10 +131,14 @@ def _flush(self): json.dumps(message, cls=MQDao.ENCODER), ) except Exception as e: + self.logger.exception(e) self.logger.error( "Critical error as some messages couldn't be flushed! Check the messages' contents!" ) - self.logger.exception(e) + self.logger.error( + f"Message that caused error: {message}" + ) + t0 = 0 if PERF_LOG: t0 = time() diff --git a/flowcept/commons/flowcept_dataclasses/task_object.py b/flowcept/commons/flowcept_dataclasses/task_object.py index 92c6471f..58b47e41 100644 --- a/flowcept/commons/flowcept_dataclasses/task_object.py +++ b/flowcept/commons/flowcept_dataclasses/task_object.py @@ -20,6 +20,7 @@ def get_finished_statuses(): # Not a dataclass because a dataclass stores keys even when there's no value, # adding unnecessary overhead. class TaskObject: + type = "task" task_id: AnyStr = None # Any way to identify a task utc_timestamp: float = None adapter_id: AnyStr = None @@ -45,12 +46,9 @@ class TaskObject: public_ip: AnyStr = None private_ip: AnyStr = None hostname: AnyStr = None - extra_metadata: Dict = None - sys_name: AnyStr = None address: AnyStr = None dependencies: List = None dependents: List = None - flowcept_version: str = None @staticmethod def get_dict_field_names(): @@ -71,9 +69,14 @@ def workflow_id_field(): return "workflow_id" def to_dict(self): - ret = self.__dict__ - if self.telemetry_at_start is not None: - ret["telemetry_at_start"] = self.telemetry_at_start.to_dict() - if self.telemetry_at_end is not None: - ret["telemetry_at_end"] = self.telemetry_at_end.to_dict() - return ret + result_dict = {} + for attr, value in self.__dict__.items(): + if value is not None: + if attr == "telemetry_at_start": + result_dict[attr] = self.telemetry_at_start.to_dict() + elif attr == "telemetry_at_end": + result_dict[attr] = self.telemetry_at_end.to_dict() + else: + result_dict[attr] = value + result_dict["type"] = "task" + return result_dict diff --git a/flowcept/commons/flowcept_dataclasses/workflow_object.py b/flowcept/commons/flowcept_dataclasses/workflow_object.py index 52a80d7c..d529ef98 100644 --- a/flowcept/commons/flowcept_dataclasses/workflow_object.py +++ b/flowcept/commons/flowcept_dataclasses/workflow_object.py @@ -4,46 +4,75 @@ # Not a dataclass because a dataclass stores keys even when there's no value, # adding unnecessary overhead. class WorkflowObject: - def __init__( - self, - workflow_id: AnyStr = None, - parent_workflow_id: AnyStr = None, - machine_info: Dict = None, - flowcept_settings: Dict = None, - flowcept_version: AnyStr = None, - utc_timestamp: float = None, - user: AnyStr = None, - campaign_id: AnyStr = None, - adapter_id: AnyStr = None, - interceptor_ids: List[AnyStr] = None, - name: AnyStr = None, - custom_metadata: Dict = None, - ): - self.workflow_id = workflow_id - self.parent_workflow_id = parent_workflow_id - self.machine_info = machine_info - self.flowcept_settings = flowcept_settings - self.flowcept_version = flowcept_version - self.utc_timestamp = utc_timestamp - self.user = user - self.campaign_id = campaign_id - self.adapter_id = adapter_id - self.interceptor_ids = interceptor_ids - self.name = name - self.custom_metadata = custom_metadata + workflow_id: AnyStr = None + parent_workflow_id: AnyStr = None + machine_info: Dict = None + flowcept_settings: Dict = None + flowcept_version: AnyStr = None + utc_timestamp: float = None + user: AnyStr = None + campaign_id: AnyStr = None + adapter_id: AnyStr = None + interceptor_ids: List[AnyStr] = None + name: AnyStr = None + custom_metadata: Dict = None + environment_id: str = None + sys_name: str = None + extra_metadata: str = None + + # def __init__( + # self, + # workflow_id: AnyStr = None, + # parent_workflow_id: AnyStr = None, + # machine_info: Dict = None, + # flowcept_settings: Dict = None, + # flowcept_version: AnyStr = None, + # utc_timestamp: float = None, + # user: AnyStr = None, + # campaign_id: AnyStr = None, + # adapter_id: AnyStr = None, + # interceptor_ids: List[AnyStr] = None, + # name: AnyStr = None, + # custom_metadata: Dict = None, + # environment_id: str = None, + # sys_name: str = None, + # extra_metadata: str = None, + # ): + # self.type = "workflow" + # self.workflow_id = workflow_id + # self.environment_id = environment_id + # self.parent_workflow_id = parent_workflow_id + # self.machine_info = machine_info + # self.flowcept_settings = flowcept_settings + # self.flowcept_version = flowcept_version + # self.utc_timestamp = utc_timestamp + # self.user = user + # self.campaign_id = campaign_id + # self.adapter_id = adapter_id + # self.interceptor_ids = interceptor_ids + # self.name = name + # self.custom_metadata = custom_metadata + # self.sys_name = sys_name + # self.extra_metadata = extra_metadata @staticmethod def workflow_id_field(): return "workflow_id" + @staticmethod + def from_dict(dict_obj: Dict) -> "WorkflowObject": + wf_obj = WorkflowObject() + for k, v in dict_obj.items(): + setattr(wf_obj, k, v) + return wf_obj + def to_dict(self): - ret = self.__dict__ - # I'm just leaving these comments below in case we need to add specific to_dict stuff - # if self.telemetry_at_start is not None: - # ret["telemetry_at_start"] = self.telemetry_at_start.to_dict() - # if self.telemetry_at_end is not None: - # ret["telemetry_at_end"] = self.telemetry_at_end.to_dict() - return ret + result_dict = {} + for attr, value in self.__dict__.items(): + if value is not None: + result_dict[attr] = value + result_dict["type"] = "workflow" + return result_dict def __repr__(self): return ( diff --git a/flowcept/commons/utils.py b/flowcept/commons/utils.py index 19c330ac..23e85d54 100644 --- a/flowcept/commons/utils.py +++ b/flowcept/commons/utils.py @@ -61,14 +61,6 @@ def get_status_from_str(status_str: str) -> Status: return Status.UNKNOWN -def fill_with_basic_workflow_info(workflow_obj: WorkflowObject): - workflow_obj.campaign_id = CAMPAIGN_ID - workflow_obj.utc_timestamp = get_utc_now() - workflow_obj.user = FLOWCEPT_USER - workflow_obj.flowcept_settings = settings - workflow_obj.flowcept_version = __version__ - - def get_adapter_exception_msg(adapter_kind): return ( f"You have an adapter for {adapter_kind} in" diff --git a/flowcept/configs.py b/flowcept/configs.py index 29d24f85..d9e8d5a6 100644 --- a/flowcept/configs.py +++ b/flowcept/configs.py @@ -128,8 +128,9 @@ visible_devices = [int(i) for i in visible_devices_var.split(",")] N_GPUS["nvidia"] = visible_devices else: - from pynvml import nvmlDeviceGetCount - N_GPUS["nvidia"] = list(range(0, nvmlDeviceGetCount())) + from pynvml import nvmlDeviceGetCount + + N_GPUS["nvidia"] = list(range(0, nvmlDeviceGetCount())) except: pass try: @@ -138,9 +139,10 @@ visible_devices = [int(i) for i in visible_devices_var.split(",")] N_GPUS["amd"] = visible_devices else: - import pyamdgpuinfo + import pyamdgpuinfo + N_GPUS["amd"] = list(range(0, pyamdgpuinfo.detect_gpus())) - + except: pass @@ -156,6 +158,7 @@ sys_metadata = settings.get("sys_metadata", None) if sys_metadata is not None: + ENVIRONMENT_ID = sys_metadata.get("environment_id", None) SYS_NAME = sys_metadata.get("sys_name", None) NODE_NAME = sys_metadata.get("node_name", None) LOGIN_NAME = sys_metadata.get("login_name", None) diff --git a/flowcept/flowcept_api/db_api.py b/flowcept/flowcept_api/db_api.py index c70b7d8a..50370cd4 100644 --- a/flowcept/flowcept_api/db_api.py +++ b/flowcept/flowcept_api/db_api.py @@ -53,7 +53,8 @@ def workflow_query(self, filter) -> WorkflowObject: return None if len(results): try: - return WorkflowObject(**results[0]) + wf_dict = results[0] + return WorkflowObject.from_dict(wf_dict) except Exception as e: self.logger.exception(e) return None diff --git a/flowcept/flowceptor/adapters/base_interceptor.py b/flowcept/flowceptor/adapters/base_interceptor.py index 37f6a4a3..6dfc03aa 100644 --- a/flowcept/flowceptor/adapters/base_interceptor.py +++ b/flowcept/flowceptor/adapters/base_interceptor.py @@ -5,7 +5,7 @@ WorkflowObject, ) from flowcept.flowcept_api.db_api import DBAPI -from flowcept.commons.utils import get_utc_now, fill_with_basic_workflow_info +from flowcept.commons.utils import get_utc_now from flowcept.configs import ( FLOWCEPT_USER, SYS_NAME, @@ -17,6 +17,8 @@ HOSTNAME, EXTRA_METADATA, ENRICH_MESSAGES, + ENVIRONMENT_ID, + settings, ) from flowcept.commons.flowcept_logger import FlowceptLogger from flowcept.commons.daos.mq_dao import MQDao @@ -49,29 +51,41 @@ def __init__(self, plugin_key): self._bundle_exec_id = None self._interceptor_instance_id = str(id(self)) self.telemetry_capture = TelemetryCapture() + self._saved_workflows = set() self._generated_workflow_id = False - self._registered_workflow = False + # self._registered_workflow = False - def _enrich_task_message(self, settings_key, task_msg: TaskObject): - if task_msg.utc_timestamp is None: - task_msg.utc_timestamp = get_utc_now() + @staticmethod + def _enrich_workflow_message(workflow_obj: WorkflowObject): + workflow_obj.utc_timestamp = get_utc_now() + workflow_obj.flowcept_settings = settings + + if workflow_obj.user is None: + workflow_obj.user = FLOWCEPT_USER + + if workflow_obj.campaign_id is None: + workflow_obj.campaign_id = CAMPAIGN_ID - if task_msg.adapter_id is None: - task_msg.adapter_id = settings_key + if workflow_obj.environment_id is None and ENVIRONMENT_ID is not None: + workflow_obj.environment_id = ENVIRONMENT_ID - if task_msg.user is None: - task_msg.user = FLOWCEPT_USER + if workflow_obj.sys_name is None and SYS_NAME is not None: + workflow_obj.sys_name = SYS_NAME - if task_msg.campaign_id is None: - task_msg.campaign_id = CAMPAIGN_ID + if workflow_obj.extra_metadata is None and EXTRA_METADATA is not None: + workflow_obj.extra_metadata = EXTRA_METADATA - if task_msg.sys_name is None: - task_msg.sys_name = SYS_NAME + if workflow_obj.flowcept_version is None: + workflow_obj.flowcept_version = __version__ + + def _enrich_task_message(self, task_msg: TaskObject): + if task_msg.utc_timestamp is None: + task_msg.utc_timestamp = get_utc_now() - if task_msg.node_name is None: + if task_msg.node_name is None and NODE_NAME is not None: task_msg.node_name = NODE_NAME - if task_msg.login_name is None: + if task_msg.login_name is None and LOGIN_NAME is not None: task_msg.login_name = LOGIN_NAME if task_msg.public_ip is None and PUBLIC_IP is not None: @@ -83,12 +97,6 @@ def _enrich_task_message(self, settings_key, task_msg: TaskObject): if task_msg.hostname is None and HOSTNAME is not None: task_msg.hostname = HOSTNAME - if task_msg.extra_metadata is None and EXTRA_METADATA is not None: - task_msg.extra_metadata = EXTRA_METADATA - - if task_msg.flowcept_version is None: - task_msg.flowcept_version = __version__ - if task_msg.workflow_id is None and not self._generated_workflow_id: task_msg.workflow_id = str(uuid.uuid4()) self._generated_workflow_id = True @@ -136,26 +144,64 @@ def callback(self, *args, **kwargs): """ raise NotImplementedError() - def register_workflow(self, task_msg: TaskObject): - self._registered_workflow = True - if task_msg.workflow_id is None: + # def register_workflow(self, task_msg: TaskObject): + # self._registered_workflow = True + # if task_msg.workflow_id is None: + # return + # + # workflow_obj = WorkflowObject() + # workflow_obj.workflow_id = task_msg.workflow_id + # fill_with_basic_workflow_info(workflow_obj) + # workflow_obj.interceptor_ids = [self._interceptor_instance_id] + # + # machine_info = self.telemetry_capture.capture_machine_info() + # if machine_info is not None: + # if workflow_obj.machine_info is None: + # workflow_obj.machine_info = {} + # # TODO :refactor-base-interceptor: we might want to register machine info even when there's no observer + # workflow_obj.machine_info[ + # self._interceptor_instance_id + # ] = machine_info + + # self._db_api.insert_or_update_workflow(workflow_obj) + + def send_workflow_message(self, workflow_obj: WorkflowObject): + wf_id = workflow_obj.workflow_id + if wf_id is None: + self.logger.warning( + f"Workflow_id is empty, we can't save this workflow_obj: {workflow_obj}" + ) return - - workflow_obj = WorkflowObject() - workflow_obj.workflow_id = task_msg.workflow_id - fill_with_basic_workflow_info(workflow_obj) + if wf_id in self._saved_workflows: + return + self._saved_workflows.add(wf_id) + if ( + self._mq_dao._buffer is None + ): # TODO :base-interceptor-refactor: :code-reorg: :usability: + raise Exception( + f"This interceptor {id(self)} has never been started!" + ) workflow_obj.interceptor_ids = [self._interceptor_instance_id] - machine_info = self.telemetry_capture.capture_machine_info() if machine_info is not None: if workflow_obj.machine_info is None: - workflow_obj.machine_info = {} + workflow_obj.machine_info = dict() # TODO :refactor-base-interceptor: we might want to register machine info even when there's no observer workflow_obj.machine_info[ self._interceptor_instance_id ] = machine_info - self._db_api.insert_or_update_workflow(workflow_obj) + if ENRICH_MESSAGES: + if self.settings is not None: + # TODO :base-interceptor-refactor: :code-reorg: :usability: revisit all times we assume settings is not none + workflow_obj.adapter_id = self.settings.key + BaseInterceptor._enrich_workflow_message(workflow_obj) + _msg = workflow_obj.to_dict() + self.logger.debug( + f"Going to send to Redis an WORKFLOW message:" + f"\n\t[BEGIN_MSG]{_msg}\n[END_MSG]\t" + ) + self._mq_dao.publish(_msg) def intercept(self, task_msg: TaskObject): if ( @@ -166,16 +212,10 @@ def intercept(self, task_msg: TaskObject): ) if ENRICH_MESSAGES: - if ( - self.settings is not None - ): # TODO :base-interceptor-refactor: :code-reorg: :usability: revisit all times we assume settings is not none - key = self.settings.key - else: - key = None - self._enrich_task_message(key, task_msg) - - if not self._registered_workflow: - self.register_workflow(task_msg) + self._enrich_task_message(task_msg) + + # if not self._registered_workflow: + # self.register_workflow(task_msg) _msg = task_msg.to_dict() self.logger.debug( diff --git a/flowcept/flowceptor/adapters/dask/dask_interceptor.py b/flowcept/flowceptor/adapters/dask/dask_interceptor.py index 986554f8..3fc8f393 100644 --- a/flowcept/flowceptor/adapters/dask/dask_interceptor.py +++ b/flowcept/flowceptor/adapters/dask/dask_interceptor.py @@ -1,5 +1,8 @@ import pickle +from flowcept.commons.flowcept_dataclasses.workflow_object import ( + WorkflowObject, +) from flowcept.commons.flowcept_dataclasses.task_object import ( TaskObject, Status, @@ -95,11 +98,7 @@ def callback(self, task_id, start, finish, *args, **kwargs): if ts.state == "waiting": task_msg = TaskObject() task_msg.task_id = task_id - task_msg.custom_metadata = { - "scheduler": self._scheduler.address_safe, - "scheduler_id": self._scheduler.id, - "scheduler_pid": self._scheduler.proc.pid, - } + task_msg.status = Status.SUBMITTED if self.settings.scheduler_create_timestamps: task_msg.submitted_at = get_utc_now() @@ -112,6 +111,22 @@ def callback(self, task_id, start, finish, *args, **kwargs): if self.settings.scheduler_should_get_input: if hasattr(ts, "run_spec"): get_run_spec_data(task_msg, ts.run_spec) + + wf_obj = WorkflowObject() + if task_msg.workflow_id: + wf_obj.workflow_id = task_msg.workflow_id + wf_obj.custom_metadata = { + "scheduler": self._scheduler.address_safe, + "scheduler_id": self._scheduler.id, + "scheduler_pid": self._scheduler.proc.pid, + } + self.send_workflow_message(wf_obj) + else: + # TODO: we can't do much if the user doesn't specify a workflow_id + # The reason why I'm marking this as TODO is because + # there might be some clever way that I couldn't think of now. + pass + self.intercept(task_msg) except Exception as e: @@ -132,8 +147,7 @@ def setup_worker(self, worker): """ self._worker = worker super().__init__(self._plugin_key) - self._generated_workflow_id = True # TODO: :refactor: This is to avoid workers to register workflows. The schedulers do that. - self._registered_workflow = True + self._generated_workflow_id = True # TODO: :refactor: This is just to avoid the auto-generation of workflow id, which doesnt make sense in Dask case.. super().start(bundle_exec_id=self._worker.scheduler.address) # Note that both scheduler and worker get the exact same input. # Worker does not resolve intermediate inputs, just like the scheduler. diff --git a/flowcept/flowceptor/consumers/document_inserter.py b/flowcept/flowceptor/consumers/document_inserter.py index 0ba8aed0..0c418138 100644 --- a/flowcept/flowceptor/consumers/document_inserter.py +++ b/flowcept/flowceptor/consumers/document_inserter.py @@ -4,6 +4,9 @@ from typing import Dict from datetime import datetime +from flowcept.commons.flowcept_dataclasses.workflow_object import ( + WorkflowObject, +) from flowcept.commons.utils import GenericJSONDecoder from flowcept.commons.flowcept_dataclasses.task_object import TaskObject from flowcept.configs import ( @@ -47,7 +50,6 @@ def __init__(self, check_safe_stops=True): self._curr_max_buffer_size = MONGO_MAX_BUFFER_SIZE self._lock = Lock() self.check_safe_stops = check_safe_stops - # self._safe_to_stop = not check_safe_stops def _set_buffer_size(self): if not MONGO_ADAPTIVE_BUFFER_SIZE: @@ -104,6 +106,8 @@ def handle_task_message(self, message: Dict): if DEBUG_MODE: message["debug"] = True + message.pop("type") + self.logger.debug( f"Received following msg in DocInserter:" f"\n\t[BEGIN_MSG]{message}\n[END_MSG]\t" @@ -116,6 +120,37 @@ def handle_task_message(self, message: Dict): self.logger.debug("Docs buffer exceeded, flushing...") self._flush() + def handle_workflow_message(self, message: Dict): + message.pop("type") + + self.logger.debug( + f"Received following msg in DocInserter:" + f"\n\t[BEGIN_MSG]{message}\n[END_MSG]\t" + ) + if MONGO_REMOVE_EMPTY_FIELDS: + remove_empty_fields_from_dict(message) + + wf_obj = WorkflowObject.from_dict(message) + inserted = self._doc_dao.workflow_insert_or_update(wf_obj) + return inserted + + def handle_control_message(self, message): + if message["info"] == "mq_dao_thread_stopped": + exec_bundle_id = message.get("exec_bundle_id", None) + interceptor_instance_id = message.get("interceptor_instance_id") + self.logger.debug( + f"Received mq_dao_thread_stopped message " + f"in DocInserter from the interceptor " + f"{'' if exec_bundle_id is None else exec_bundle_id}_{interceptor_instance_id}!" + ) + self._mq_dao.register_time_based_thread_end( + interceptor_instance_id, exec_bundle_id + ) + return "continue" + elif message["info"] == "stop_document_inserter": + self.logger.info("Document Inserter is stopping...") + return "stop" + def time_based_flushing(self, event: Event): while not event.is_set(): if len(self._buffer): @@ -149,44 +184,24 @@ def _start(self): self.logger.debug("Doc inserter Received a message!") if message["type"] in MQDao.MESSAGE_TYPES_IGNORE: continue + _dict_obj = json.loads( message["data"], cls=DocumentInserter.DECODER ) - if ( - "type" in _dict_obj - and _dict_obj["type"] == "flowcept_control" - ): - if _dict_obj["info"] == "mq_dao_thread_stopped": - exec_bundle_id = _dict_obj.get( - "exec_bundle_id", None - ) - interceptor_instance_id = _dict_obj.get( - "interceptor_instance_id" - ) - self.logger.debug( - f"Received mq_dao_thread_stopped message " - f"in DocInserter from the interceptor " - f"{''if exec_bundle_id is None else exec_bundle_id}_{interceptor_instance_id}!" - ) - self._mq_dao.register_time_based_thread_end( - interceptor_instance_id, exec_bundle_id - ) - # if self._mq_dao.all_time_based_threads_ended( - # exec_bundle_id - # ): - # self._safe_to_stop = True - # self.logger.debug("It is safe to stop.") - - elif _dict_obj["info"] == "stop_document_inserter": - self.logger.info( - "Document Inserter is stopping..." - ) + msg_type = _dict_obj.get("type") + if msg_type == "flowcept_control": + r = self.handle_control_message(_dict_obj) + if r == "stop": stop_event.set() self._flush() should_continue = False break - else: + elif msg_type == "task": self.handle_task_message(_dict_obj) + elif msg_type == "workflow": + self.handle_workflow_message(_dict_obj) + else: + self.logger.error("Unexpected message type") self.logger.debug( "Processed all MQ msgs in doc_inserter we got so far. " "Now waiting (hopefully not forever!) on the " @@ -215,16 +230,3 @@ def stop(self, bundle_exec_id=None): self._mq_dao.stop_document_inserter() self._main_thread.join() self.logger.info("Document Inserter is stopped.") - - # def stop(self): - # while not self._safe_to_stop: - # sleep_time = 3 - # self.logger.debug( - # f"It's still not safe to stop DocInserter. " - # f"Checking again in {sleep_time} secs." - # ) - # sleep(sleep_time) - # - # self._mq_dao.stop_document_inserter() - # self._main_thread.join() - # self.logger.info("Document Inserter is stopped.") diff --git a/flowcept/flowceptor/telemetry_capture.py b/flowcept/flowceptor/telemetry_capture.py index eba25688..a5f8f4f8 100644 --- a/flowcept/flowceptor/telemetry_capture.py +++ b/flowcept/flowceptor/telemetry_capture.py @@ -28,10 +28,10 @@ class TelemetryCapture: + _gpu_unsuccessful_queries = ( + dict() + ) # TODO: refactor; I need this to avoid querying GPU stuff that is generating errors. The idea is to try once and if it fails, add this in this dictionary to avoid trying again. The mapping will be {gpu_device_id: {query_type: True or False}}; False if it found that it's unsuccessful. If it's mapping to an empty dict, the whole GPU is bad for capture. - _gpu_unsuccessful_queries = dict() # TODO: refactor; I need this to avoid querying GPU stuff that is generating errors. The idea is to try once and if it fails, add this in this dictionary to avoid trying again. The mapping will be {gpu_device_id: {query_type: True or False}}; False if it found that it's unsuccessful. If it's mapping to an empty dict, the whole GPU is bad for capture. - - def __init__(self, conf=TELEMETRY_CAPTURE): self.conf = conf self.logger = FlowceptLogger() @@ -89,7 +89,7 @@ def capture_machine_info(self): "platform": platform_info, "cpu": processor_info, "network": network_info, - "environment": os.environ, + "environment": dict(os.environ), "hostname": HOSTNAME, "login_name": LOGIN_NAME, "process": self._capture_process_info().__dict__, @@ -199,16 +199,19 @@ def __get_gpu_info_nvidia(self, gpu_ix: int = 0): ), "power_usage": nvmlDeviceGetPowerUsage(handle), "name": nvmlDeviceGetName(handle), - "device_ix": gpu_ix + "device_ix": gpu_ix, } return flowcept_gpu_info - def __register_unsuccessful_gpu_query(self, gpu_ix, gpu_info_key): - self.logger.error(f"Error to get {gpu_info_key} for the GPU device ix {gpu_ix}") + self.logger.error( + f"Error to get {gpu_info_key} for the GPU device ix {gpu_ix}" + ) if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries: TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] = {} - TelemetryCapture._gpu_unsuccessful_queries[gpu_ix][gpu_info_key] = True + TelemetryCapture._gpu_unsuccessful_queries[gpu_ix][ + gpu_info_key + ] = True # TODO: finish adding the else: None def __get_gpu_info_amd(self, gpu_ix: int = 0): @@ -223,33 +226,50 @@ def __get_gpu_info_amd(self, gpu_ix: int = 0): flowcept_gpu_info["device_ix"] = gpu_ix flowcept_gpu_info["gpu_id"] = amd_info.gpu_id - memory_info = amd_info.memory_info.copy() try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "total" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "total" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["total"] = memory_info.pop("vram_size") - except Exception as e: + except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "total") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "temperature" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: - flowcept_gpu_info["temperature"] = amd_info.query_temperature() + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "temperature" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): + flowcept_gpu_info[ + "temperature" + ] = amd_info.query_temperature() except Exception as e: flowcept_gpu_info["temperature"] = None self.__register_unsuccessful_gpu_query(gpu_ix, "temperature") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "power_usage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "power_usage" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["power_usage"] = amd_info.query_power() except Exception as e: flowcept_gpu_info["power_usage"] = None - self.__register_unsuccessful_gpu_query(gpu_ix, "power_usage") + self.__register_unsuccessful_gpu_query(gpu_ix, "power_usage") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "used" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "used" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["used"] = amd_info.query_vram_usage() except Exception as e: flowcept_gpu_info["used"] = None @@ -257,7 +277,11 @@ def __get_gpu_info_amd(self, gpu_ix: int = 0): self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "max_shader_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "max_shader_clock" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): max_clocks = amd_info.query_max_clocks() flowcept_gpu_info["max_shader_clock"] = max_clocks["sclk_max"] flowcept_gpu_info["max_memory_clock"] = max_clocks["mclk_max"] @@ -266,36 +290,58 @@ def __get_gpu_info_amd(self, gpu_ix: int = 0): self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "shader_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "shader_clock" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["shader_clock"] = amd_info.query_sclk() except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "shader_clock") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "memory_clock" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "memory_clock" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["memory_clock"] = amd_info.query_mclk() except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "memory_clock") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "gtt_usage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "gtt_usage" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["gtt_usage"] = amd_info.query_gtt_usage() except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "gtt_usage") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "load" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "load" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): flowcept_gpu_info["load"] = amd_info.query_load() except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "load") self.logger.exception(e) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "graphics_voltage" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: - flowcept_gpu_info["graphics_voltage"] = amd_info.query_graphics_voltage() + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "graphics_voltage" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): + flowcept_gpu_info[ + "graphics_voltage" + ] = amd_info.query_graphics_voltage() except Exception as e: self.__register_unsuccessful_gpu_query(gpu_ix, "graphics_voltage") self.logger.exception(e) @@ -303,7 +349,11 @@ def __get_gpu_info_amd(self, gpu_ix: int = 0): flowcept_gpu_info.update(memory_info) try: - if gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries or "name" not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix]: + if ( + gpu_ix not in TelemetryCapture._gpu_unsuccessful_queries + or "name" + not in TelemetryCapture._gpu_unsuccessful_queries[gpu_ix] + ): name = amd_info.name if name is not None: flowcept_gpu_info["name"] = name @@ -314,8 +364,10 @@ def __get_gpu_info_amd(self, gpu_ix: int = 0): return flowcept_gpu_info def _capture_gpu(self): - try: - self.logger.debug(f"These are the visible GPUs by Flowcept Capture: {N_GPUS}") + try: + self.logger.debug( + f"These are the visible GPUs by Flowcept Capture: {N_GPUS}" + ) if len(N_GPUS) == 0: self.logger.exception( "You are trying to capture telemetry GPU info, but we" @@ -338,7 +390,7 @@ def _capture_gpu(self): return None gpu_telemetry = {} - for gpu_ix in n_gpus: + for gpu_ix in n_gpus: gpu_telemetry[gpu_ix] = gpu_capture_func(gpu_ix) return gpu_telemetry diff --git a/notebooks/analytics.ipynb b/notebooks/analytics.ipynb index 80634791..62c4289b 100644 --- a/notebooks/analytics.ipynb +++ b/notebooks/analytics.ipynb @@ -2,31 +2,24 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "222b4132-fc10-4503-a108-592d5e742515", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n" - ] - } - ], + "outputs": [], "source": [ "from datetime import datetime\n", "import numpy as np\n", "import pandas as pd\n", "import flowcept.analytics as analytics\n", + "import flowcept.analytics.plot as flow_plot\n", "from flowcept import TaskQueryAPI" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "c7b11fbf-ec74-46e7-9824-4685a9288c55", "metadata": { "tags": [] @@ -61,33 +54,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "176f01c5-5e59-44e3-ad65-409fcfdc2f9b", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'e02f8776-3777-4856-952b-5b0cfbed2165'" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Need to run only if this is the first time.\n", - "wf_id = ingest_mock_data()\n", - "wf_id" + "#wf_id = ingest_mock_data()\n", + "#wf_id" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "96442d46-7ebb-470d-962b-11b65e7aca12", "metadata": { "tags": [] }, "outputs": [], "source": [ - "# wf_id = '100faab4-ff4c-4f78-92a7-6f20ec1fad83'" + "wf_id = '100faab4-ff4c-4f78-92a7-6f20ec1fad83'" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "e41fe652-d7e8-4e3d-a780-dfec4e5142b0", "metadata": { "tags": [] @@ -107,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "2c3cd6d6-fc22-4155-80e0-da7ffc9f8e0e", "metadata": { "tags": [] @@ -122,12 +126,208 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d2c04cbe-4b78-49ee-b74d-5e7680a4478f", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
task_idsubmitted_atactivity_idworkflow_idutc_timestampadapter_idusercampaign_idsys_namenode_name...telemetry_diff.network.netio_per_interface.bridge100.bytes_senttelemetry_diff.network.netio_per_interface.bridge100.bytes_recvtelemetry_diff.network.netio_per_interface.bridge100.packets_senttelemetry_diff.network.netio_per_interface.bridge100.packets_recvtelemetry_diff.network.netio_per_interface.bridge100.errintelemetry_diff.network.netio_per_interface.bridge100.errouttelemetry_diff.network.netio_per_interface.bridge100.dropintelemetry_diff.network.netio_per_interface.bridge100.dropoutstatuselapsed_time
06b1209fe-e078-4572-b082-db14d0de025e2024-02-09 01:05:28.202881024wrapper100faab4-ff4c-4f78-92a7-6f20ec1fad832024-02-09 01:06:27.422988032daskrootsuper_campaignDarwinMAC132633...0.00.02.00.00.00.00.00.0FINISHED59.133646
18646acc7-bdd7-4504-bfb4-3768b97912d62024-02-09 01:05:28.206701056wrapper100faab4-ff4c-4f78-92a7-6f20ec1fad832024-02-09 01:06:29.350380800daskrootsuper_campaignDarwinMAC132633...0.00.02.00.00.00.00.00.0FINISHED61.062001
2d36a4538-7e52-49df-b8c9-332506160d5b2024-02-09 01:05:28.210365952wrapper100faab4-ff4c-4f78-92a7-6f20ec1fad832024-02-09 01:08:17.270892032daskrootsuper_campaignDarwinMAC132633...1024.00.010.00.00.00.00.00.0FINISHED168.981788
\n", + "

3 rows × 334 columns

\n", + "
" + ], + "text/plain": [ + " task_id submitted_at \\\n", + "0 6b1209fe-e078-4572-b082-db14d0de025e 2024-02-09 01:05:28.202881024 \n", + "1 8646acc7-bdd7-4504-bfb4-3768b97912d6 2024-02-09 01:05:28.206701056 \n", + "2 d36a4538-7e52-49df-b8c9-332506160d5b 2024-02-09 01:05:28.210365952 \n", + "\n", + " activity_id workflow_id \\\n", + "0 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", + "1 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", + "2 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", + "\n", + " utc_timestamp adapter_id user campaign_id sys_name \\\n", + "0 2024-02-09 01:06:27.422988032 dask root super_campaign Darwin \n", + "1 2024-02-09 01:06:29.350380800 dask root super_campaign Darwin \n", + "2 2024-02-09 01:08:17.270892032 dask root super_campaign Darwin \n", + "\n", + " node_name ... \\\n", + "0 MAC132633 ... \n", + "1 MAC132633 ... \n", + "2 MAC132633 ... \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_sent \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 1024.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_recv \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_sent \\\n", + "0 2.0 \n", + "1 2.0 \n", + "2 10.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_recv \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.errin \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.errout \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.dropin \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.dropout status \\\n", + "0 0.0 FINISHED \n", + "1 0.0 FINISHED \n", + "2 0.0 FINISHED \n", + "\n", + " elapsed_time \n", + "0 59.133646 \n", + "1 61.062001 \n", + "2 168.981788 \n", + "\n", + "[3 rows x 334 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.head(3)" ] @@ -142,12 +342,290 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8a8c1dd7-9647-4e7a-82e3-f7db7752f824", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of columns originally: 334\n", + "Number of columns later: 39\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
used.max_epochsgenerated.lossgenerated.accuracygenerated.responsible_ai_metrics.shap_sumgenerated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.paramsgenerated.responsible_ai_metrics.max_widthgenerated.responsible_ai_metrics.depthgenerated.responsible_ai_metrics.n_fc_layersgenerated.responsible_ai_metrics.n_cv_layers...telemetry_diff.memory.swap.souttelemetry_diff.disk.disk_usage.freeused.conv_in_outs_sumused.conv_kernel_sizes_sumused.conv_pool_sizes_sumused.fc_in_outs_sumused.softmax_dims_sumtelemetry_diff.network.activitytelemetry_diff.disk.activitytelemetry_diff.process.activity
01.00.01472940.750.02.188019e+07162990.0100.012.05.07.0...5390336.0-1.067229e+0941.029.02.0220.01.0156472.43751.714730e+081997778.0
11.00.04032611.350.04.727514e+07359840.0400.016.09.07.0...5390336.0-1.067397e+0941.029.02.01620.03.0162635.81251.724063e+082044184.0
21.00.05815711.350.05.405073e+0942184840.04000.024.017.07.0...14172160.0-1.065861e+0941.029.02.032020.07.0369630.81252.518569e+084531840.0
31.00.01824210.280.03.241957e+081890690.0100.016.05.011.0...21512192.0-1.069761e+09181.030.03.0260.01.0650028.62502.726195e+086284750.0
41.00.04031211.350.03.498467e+082089540.0400.020.09.011.0...22134784.0-1.070207e+09181.030.03.01660.03.0652738.75002.752950e+086311004.0
\n", + "

5 rows × 39 columns

\n", + "
" + ], + "text/plain": [ + " used.max_epochs generated.loss generated.accuracy \\\n", + "0 1.0 0.014729 40.75 \n", + "1 1.0 0.040326 11.35 \n", + "2 1.0 0.058157 11.35 \n", + "3 1.0 0.018242 10.28 \n", + "4 1.0 0.040312 11.35 \n", + "\n", + " generated.responsible_ai_metrics.shap_sum \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "\n", + " generated.responsible_ai_metrics.flops \\\n", + "0 2.188019e+07 \n", + "1 4.727514e+07 \n", + "2 5.405073e+09 \n", + "3 3.241957e+08 \n", + "4 3.498467e+08 \n", + "\n", + " generated.responsible_ai_metrics.params \\\n", + "0 162990.0 \n", + "1 359840.0 \n", + "2 42184840.0 \n", + "3 1890690.0 \n", + "4 2089540.0 \n", + "\n", + " generated.responsible_ai_metrics.max_width \\\n", + "0 100.0 \n", + "1 400.0 \n", + "2 4000.0 \n", + "3 100.0 \n", + "4 400.0 \n", + "\n", + " generated.responsible_ai_metrics.depth \\\n", + "0 12.0 \n", + "1 16.0 \n", + "2 24.0 \n", + "3 16.0 \n", + "4 20.0 \n", + "\n", + " generated.responsible_ai_metrics.n_fc_layers \\\n", + "0 5.0 \n", + "1 9.0 \n", + "2 17.0 \n", + "3 5.0 \n", + "4 9.0 \n", + "\n", + " generated.responsible_ai_metrics.n_cv_layers ... \\\n", + "0 7.0 ... \n", + "1 7.0 ... \n", + "2 7.0 ... \n", + "3 11.0 ... \n", + "4 11.0 ... \n", + "\n", + " telemetry_diff.memory.swap.sout telemetry_diff.disk.disk_usage.free \\\n", + "0 5390336.0 -1.067229e+09 \n", + "1 5390336.0 -1.067397e+09 \n", + "2 14172160.0 -1.065861e+09 \n", + "3 21512192.0 -1.069761e+09 \n", + "4 22134784.0 -1.070207e+09 \n", + "\n", + " used.conv_in_outs_sum used.conv_kernel_sizes_sum \\\n", + "0 41.0 29.0 \n", + "1 41.0 29.0 \n", + "2 41.0 29.0 \n", + "3 181.0 30.0 \n", + "4 181.0 30.0 \n", + "\n", + " used.conv_pool_sizes_sum used.fc_in_outs_sum used.softmax_dims_sum \\\n", + "0 2.0 220.0 1.0 \n", + "1 2.0 1620.0 3.0 \n", + "2 2.0 32020.0 7.0 \n", + "3 3.0 260.0 1.0 \n", + "4 3.0 1660.0 3.0 \n", + "\n", + " telemetry_diff.network.activity telemetry_diff.disk.activity \\\n", + "0 156472.4375 1.714730e+08 \n", + "1 162635.8125 1.724063e+08 \n", + "2 369630.8125 2.518569e+08 \n", + "3 650028.6250 2.726195e+08 \n", + "4 652738.7500 2.752950e+08 \n", + "\n", + " telemetry_diff.process.activity \n", + "0 1997778.0 \n", + "1 2044184.0 \n", + "2 4531840.0 \n", + "3 6284750.0 \n", + "4 6311004.0 \n", + "\n", + "[5 rows x 39 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "cleaned_df = analytics.clean_dataframe(\n", " df,\n", @@ -162,12 +640,184 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "5497b4c8-ba90-4ae4-82d7-0ef821fe2f4f", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
used.conv_in_outsused.conv_kernel_sizesused.conv_pool_sizesused.fc_in_outsused.softmax_dimsused.max_epochsgenerated.lossgenerated.accuracygenerated.responsible_ai_metrics.shap_sumgenerated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.paramsgenerated.responsible_ai_metrics.max_widthgenerated.responsible_ai_metrics.depthgenerated.responsible_ai_metrics.n_fc_layersgenerated.responsible_ai_metrics.n_cv_layersgenerated.responsible_ai_metrics.convolutional_layersgenerated.responsible_ai_metrics.fully_connected_layers
0[[1, 10], [10, 20]][1, 28][1, 1][[20, 50], [50, 100]][None, 1]10.01472940.750.02.188019e+07162990.0100.012.05.07.0Sequential(\\n (0): Conv2d(1, 10, kernel_size=...Sequential(\\n (0): Linear(in_features=20, out...
6[[1, 30], [30, 60], [60, 90], [90, 120]][1, 1, 1, 28][1, 1, 1, 1][[120, 50], [50, 100]][None, 1]10.01820810.090.01.810793e+098485880.0120.020.05.015.0Sequential(\\n (0): Conv2d(1, 30, kernel_size=...Sequential(\\n (0): Linear(in_features=120, ou...
3[[1, 20], [20, 40], [40, 60]][1, 1, 28][1, 1, 1][[60, 50], [50, 100]][None, 1]10.01824210.280.03.241957e+081890690.0100.016.05.011.0Sequential(\\n (0): Conv2d(1, 20, kernel_size=...Sequential(\\n (0): Linear(in_features=60, out...
\n", + "
" + ], + "text/plain": [ + " used.conv_in_outs used.conv_kernel_sizes \\\n", + "0 [[1, 10], [10, 20]] [1, 28] \n", + "6 [[1, 30], [30, 60], [60, 90], [90, 120]] [1, 1, 1, 28] \n", + "3 [[1, 20], [20, 40], [40, 60]] [1, 1, 28] \n", + "\n", + " used.conv_pool_sizes used.fc_in_outs used.softmax_dims \\\n", + "0 [1, 1] [[20, 50], [50, 100]] [None, 1] \n", + "6 [1, 1, 1, 1] [[120, 50], [50, 100]] [None, 1] \n", + "3 [1, 1, 1] [[60, 50], [50, 100]] [None, 1] \n", + "\n", + " used.max_epochs generated.loss generated.accuracy \\\n", + "0 1 0.014729 40.75 \n", + "6 1 0.018208 10.09 \n", + "3 1 0.018242 10.28 \n", + "\n", + " generated.responsible_ai_metrics.shap_sum \\\n", + "0 0.0 \n", + "6 0.0 \n", + "3 0.0 \n", + "\n", + " generated.responsible_ai_metrics.flops \\\n", + "0 2.188019e+07 \n", + "6 1.810793e+09 \n", + "3 3.241957e+08 \n", + "\n", + " generated.responsible_ai_metrics.params \\\n", + "0 162990.0 \n", + "6 8485880.0 \n", + "3 1890690.0 \n", + "\n", + " generated.responsible_ai_metrics.max_width \\\n", + "0 100.0 \n", + "6 120.0 \n", + "3 100.0 \n", + "\n", + " generated.responsible_ai_metrics.depth \\\n", + "0 12.0 \n", + "6 20.0 \n", + "3 16.0 \n", + "\n", + " generated.responsible_ai_metrics.n_fc_layers \\\n", + "0 5.0 \n", + "6 5.0 \n", + "3 5.0 \n", + "\n", + " generated.responsible_ai_metrics.n_cv_layers \\\n", + "0 7.0 \n", + "6 15.0 \n", + "3 11.0 \n", + "\n", + " generated.responsible_ai_metrics.convolutional_layers \\\n", + "0 Sequential(\\n (0): Conv2d(1, 10, kernel_size=... \n", + "6 Sequential(\\n (0): Conv2d(1, 30, kernel_size=... \n", + "3 Sequential(\\n (0): Conv2d(1, 20, kernel_size=... \n", + "\n", + " generated.responsible_ai_metrics.fully_connected_layers \n", + "0 Sequential(\\n (0): Linear(in_features=20, out... \n", + "6 Sequential(\\n (0): Linear(in_features=120, ou... \n", + "3 Sequential(\\n (0): Linear(in_features=60, out... " + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sort = [\n", " (\"generated.loss\", TaskQueryAPI.ASC),\n", @@ -196,12 +846,283 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "c669ac40-60b4-49e0-ae62-a2cda2c5815a", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of columns originally: 334\n", + "Number of columns later: 58\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
used.max_epochsgenerated.lossgenerated.accuracygenerated.responsible_ai_metrics.shap_sumgenerated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.paramsgenerated.responsible_ai_metrics.max_widthgenerated.responsible_ai_metrics.depthgenerated.responsible_ai_metrics.n_fc_layersgenerated.responsible_ai_metrics.n_cv_layers...telemetry_diff.network.netio_per_interface.en0.bytes_senttelemetry_diff.network.netio_per_interface.en0.packets_senttelemetry_diff.network.netio_per_interface.utun4.bytes_senttelemetry_diff.network.netio_per_interface.utun4.bytes_recvtelemetry_diff.network.netio_per_interface.utun4.packets_senttelemetry_diff.network.netio_per_interface.utun4.packets_recvtelemetry_diff.network.netio_per_interface.vmenet0.bytes_senttelemetry_diff.network.netio_per_interface.vmenet0.packets_senttelemetry_diff.network.netio_per_interface.bridge100.bytes_senttelemetry_diff.network.netio_per_interface.bridge100.packets_sent
01.00.01472940.750.02.188019e+07162990.0100.012.05.07.0...243712.0534.0199680.0205824.0507.0780.00.01.00.02.0
11.00.04032611.350.04.727514e+07359840.0400.016.09.07.0...245760.0546.0200704.0208896.0519.0806.00.01.00.02.0
21.00.05815711.350.05.405073e+0942184840.04000.024.017.07.0...626688.01355.0514048.0493568.01288.01960.00.05.01024.010.0
31.00.01824210.280.03.241957e+081890690.0100.016.05.011.0...1671168.02556.0812032.0799744.01994.03078.01024.010.02048.020.0
\n", + "

4 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " used.max_epochs generated.loss generated.accuracy \\\n", + "0 1.0 0.014729 40.75 \n", + "1 1.0 0.040326 11.35 \n", + "2 1.0 0.058157 11.35 \n", + "3 1.0 0.018242 10.28 \n", + "\n", + " generated.responsible_ai_metrics.shap_sum \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "\n", + " generated.responsible_ai_metrics.flops \\\n", + "0 2.188019e+07 \n", + "1 4.727514e+07 \n", + "2 5.405073e+09 \n", + "3 3.241957e+08 \n", + "\n", + " generated.responsible_ai_metrics.params \\\n", + "0 162990.0 \n", + "1 359840.0 \n", + "2 42184840.0 \n", + "3 1890690.0 \n", + "\n", + " generated.responsible_ai_metrics.max_width \\\n", + "0 100.0 \n", + "1 400.0 \n", + "2 4000.0 \n", + "3 100.0 \n", + "\n", + " generated.responsible_ai_metrics.depth \\\n", + "0 12.0 \n", + "1 16.0 \n", + "2 24.0 \n", + "3 16.0 \n", + "\n", + " generated.responsible_ai_metrics.n_fc_layers \\\n", + "0 5.0 \n", + "1 9.0 \n", + "2 17.0 \n", + "3 5.0 \n", + "\n", + " generated.responsible_ai_metrics.n_cv_layers ... \\\n", + "0 7.0 ... \n", + "1 7.0 ... \n", + "2 7.0 ... \n", + "3 11.0 ... \n", + "\n", + " telemetry_diff.network.netio_per_interface.en0.bytes_sent \\\n", + "0 243712.0 \n", + "1 245760.0 \n", + "2 626688.0 \n", + "3 1671168.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.en0.packets_sent \\\n", + "0 534.0 \n", + "1 546.0 \n", + "2 1355.0 \n", + "3 2556.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.bytes_sent \\\n", + "0 199680.0 \n", + "1 200704.0 \n", + "2 514048.0 \n", + "3 812032.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.bytes_recv \\\n", + "0 205824.0 \n", + "1 208896.0 \n", + "2 493568.0 \n", + "3 799744.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.packets_sent \\\n", + "0 507.0 \n", + "1 519.0 \n", + "2 1288.0 \n", + "3 1994.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.packets_recv \\\n", + "0 780.0 \n", + "1 806.0 \n", + "2 1960.0 \n", + "3 3078.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.vmenet0.bytes_sent \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 1024.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.vmenet0.packets_sent \\\n", + "0 1.0 \n", + "1 1.0 \n", + "2 5.0 \n", + "3 10.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_sent \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 1024.0 \n", + "3 2048.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_sent \n", + "0 2.0 \n", + "1 2.0 \n", + "2 10.0 \n", + "3 20.0 \n", + "\n", + "[4 rows x 58 columns]" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clauses = [\n", " (\"telemetry_diff.process.cpu_times.user\", \"<\", 0.5),\n", @@ -243,12 +1164,2723 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "066717e4-2110-4d62-aedd-005c1198cefa", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
used.max_epochsgenerated.lossgenerated.accuracygenerated.responsible_ai_metrics.shap_sumgenerated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.paramsgenerated.responsible_ai_metrics.max_widthgenerated.responsible_ai_metrics.depthgenerated.responsible_ai_metrics.n_fc_layersgenerated.responsible_ai_metrics.n_cv_layers...telemetry_diff.network.netio_per_interface.en0.bytes_senttelemetry_diff.network.netio_per_interface.en0.packets_senttelemetry_diff.network.netio_per_interface.utun4.bytes_senttelemetry_diff.network.netio_per_interface.utun4.bytes_recvtelemetry_diff.network.netio_per_interface.utun4.packets_senttelemetry_diff.network.netio_per_interface.utun4.packets_recvtelemetry_diff.network.netio_per_interface.vmenet0.bytes_senttelemetry_diff.network.netio_per_interface.vmenet0.packets_senttelemetry_diff.network.netio_per_interface.bridge100.bytes_senttelemetry_diff.network.netio_per_interface.bridge100.packets_sent
used.max_epochsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
generated.lossNaN1.000000-0.570400NaN0.8198080.8234720.8688190.8961289.669773e-01-0.479819...-0.271887-0.149934-0.055562-0.080247-0.043729-0.053281-0.479819-0.116829-6.767772e-02-0.116829
generated.accuracyNaN-0.5704001.000000NaN-0.345728-0.338603-0.349589-0.649192-4.485456e-01-0.365087...-0.477162-0.525894-0.551055-0.546526-0.557710-0.557186-0.365087-0.533059-5.469569e-01-0.533059
generated.responsible_ai_metrics.shap_sumNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
generated.responsible_ai_metrics.flopsNaN0.819808-0.345728NaN1.0000000.9998930.9942330.9368849.340567e-01-0.284137...-0.0176970.1265000.2377110.2078000.2479570.235496-0.2841370.1682332.247585e-010.168233
generated.responsible_ai_metrics.paramsNaN0.823472-0.338603NaN0.9998931.0000000.9954200.9347359.370909e-01-0.298127...-0.0323160.1119810.2234820.1934740.2337640.221259-0.2981270.1538002.104857e-010.153800
generated.responsible_ai_metrics.max_widthNaN0.868819-0.349589NaN0.9942330.9954201.0000000.9385199.649505e-01-0.367405...-0.1061280.0381370.1502850.1201290.1610270.148537-0.3674050.0798601.370506e-010.079860
generated.responsible_ai_metrics.depthNaN0.896128-0.649192NaN0.9368840.9347350.9385191.0000009.365858e-01-0.132453...0.1229450.2580130.3580050.3319350.3689810.358651-0.1324530.2945473.458572e-010.294547
generated.responsible_ai_metrics.n_fc_layersNaN0.966977-0.448546NaN0.9340570.9370910.9649510.9365861.000000e+00-0.471405...-0.231315-0.0939790.013152-0.0153650.0248210.013332-0.471405-0.0551741.639908e-16-0.055174
generated.responsible_ai_metrics.n_cv_layersNaN-0.479819-0.365087NaN-0.284137-0.298127-0.367405-0.132453-4.714045e-011.000000...0.9636620.9151320.8637580.8788080.8583770.8648761.0000000.8973158.703883e-010.897315
telemetry_diff.cpu.times_avg.userNaN-0.035758-0.560502NaN0.2562440.2420820.1695310.3768313.337242e-020.853946...0.9619080.9912580.9997950.9987530.9999630.9997700.8539460.9959139.994417e-010.995913
telemetry_diff.cpu.times_avg.systemNaN0.004163-0.569578NaN0.3009530.2869760.2151230.4168647.788061e-020.828804...0.9481460.9840450.9978220.9953540.9984810.9976900.8288040.9906479.968501e-010.990647
telemetry_diff.cpu.times_avg.idleNaN-0.201370-0.507239NaN0.0660490.051451-0.0225270.201962-1.519078e-010.937919...0.9964880.9981550.9849100.9897600.9830310.9852950.9379190.9946969.871205e-010.994696
telemetry_diff.process.memory.rssNaN-0.2868020.642277NaN-0.569155-0.557103-0.496968-0.667256-3.729260e-01-0.624431...-0.809753-0.885564-0.931697-0.920466-0.935748-0.931360-0.624431-0.903897-9.267632e-01-0.903897
telemetry_diff.process.memory.vmsNaN-0.5737170.451121NaN-0.893210-0.886669-0.843689-0.870138-7.144685e-01-0.168978...-0.425057-0.550086-0.640690-0.616711-0.648229-0.638209-0.168978-0.585088-6.305982e-01-0.585088
telemetry_diff.process.memory.pfaultsNaN0.026389-0.574134NaN0.3255360.3116750.2402800.4387541.025762e-010.814044...0.9396030.9791110.9957890.9925300.9967200.9955980.8140440.9867899.944736e-010.986789
telemetry_diff.process.memory.pageinsNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
telemetry_diff.process.cpu_times.userNaN-0.034090-0.561064NaN0.2579910.2438350.1713230.3784753.516914e-020.853002...0.9614110.9910180.9997570.9986610.9999450.9997300.8530020.9957479.993796e-010.995747
telemetry_diff.process.cpu_times.systemNaN0.018441-0.573210NaN0.3162300.3023240.2308070.4307839.348204e-020.819705...0.9429140.9810580.9966330.9936770.9974650.9964720.8197050.9883249.954462e-010.988324
telemetry_diff.process.num_open_file_descriptorsNaN0.595114-0.999426NaN0.3604640.3537580.3674050.6622664.714045e-010.333333...0.4481560.4992740.5266250.5214900.5335600.5327930.3333330.5071785.222330e-010.507178
telemetry_diff.process.num_connectionsNaN0.595114-0.999426NaN0.3604640.3537580.3674050.6622664.714045e-010.333333...0.4481560.4992740.5266250.5214900.5335600.5327930.3333330.5071785.222330e-010.507178
telemetry_diff.process.num_open_filesNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
telemetry_diff.process.num_threadsNaN0.4798190.365087NaN0.2841370.2981270.3674050.1324534.714045e-01-1.000000...-0.963662-0.915132-0.863758-0.878808-0.858377-0.864876-1.000000-0.897315-8.703883e-01-0.897315
telemetry_diff.process.num_ctx_switches.voluntaryNaN0.013872-0.572264NaN0.3111860.2972550.2256440.4262868.840923e-020.822737...0.9446700.9820730.9970530.9942590.9978290.9969040.8227370.9891189.959365e-010.989118
telemetry_diff.memory.virtual.availableNaN0.650383-0.268278NaN0.9661330.9634500.9335370.8695608.108192e-01-0.104999...0.1596490.2969410.4015160.3732390.4099410.397912-0.1049990.3371583.898045e-010.337158
telemetry_diff.memory.virtual.usedNaN-0.8433800.195404NaN-0.969549-0.973005-0.983837-0.865558-9.500208e-010.508791...0.2604600.1188280.0058690.036619-0.0045410.0083700.5087910.0767071.913862e-020.076707
telemetry_diff.memory.virtual.freeNaN0.762274-0.206719NaN0.9894890.9903490.9818220.8765729.016455e-01-0.344248...-0.0833290.0594400.1713720.1409230.1809820.168082-0.3442480.1018261.585301e-010.101826
telemetry_diff.memory.virtual.activeNaN-0.797255-0.012427NaN-0.849813-0.857370-0.887187-0.701887-8.871182e-010.742773...0.5378270.4113870.3058190.3350190.2961260.3085190.7427730.3723203.183437e-010.372320
telemetry_diff.memory.virtual.inactiveNaN-0.492605-0.234913NaN-0.163899-0.177951-0.261248-0.088462-4.214716e-010.969478...0.9653010.9333580.8961680.9069740.8908400.8952380.9694780.9216799.015499e-010.921679
telemetry_diff.memory.virtual.wiredNaN-0.4429960.664942NaN-0.699543-0.689120-0.638971-0.786457-5.302289e-01-0.479494...-0.694941-0.790574-0.853364-0.837588-0.859256-0.853010-0.479494-0.814837-8.463404e-01-0.814837
telemetry_diff.memory.swap.totalNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
telemetry_diff.memory.swap.usedNaN-0.0735440.576533NaN-0.381632-0.368075-0.297423-0.485662-1.571348e-01-0.777778...-0.917412-0.965147-0.988536-0.983428-0.990051-0.988140-0.777778-0.975343-9.864401e-01-0.975343
telemetry_diff.memory.swap.freeNaN0.073544-0.576533NaN0.3816320.3680750.2974230.4856621.571348e-010.777778...0.9174120.9651470.9885360.9834280.9900510.9881400.7777780.9753439.864401e-010.975343
telemetry_diff.memory.swap.sinNaN0.211144-0.592704NaN0.5249520.5124500.4465460.6105183.074307e-010.666893...0.8417080.9106950.9515220.9416350.9547070.9507920.6668930.9273149.473410e-010.927314
telemetry_diff.memory.swap.soutNaN-0.028649-0.556661NaN0.2689310.2548180.1819550.3856204.363225e-020.847046...0.9582460.9894260.9994780.9980080.9997400.9993550.8470460.9946759.989610e-010.994675
telemetry_diff.disk.disk_usage.freeNaN0.7130560.167844NaN0.6623850.6732510.7221270.5021207.668184e-01-0.905250...-0.759478-0.658216-0.568944-0.594005-0.560470-0.571158-0.905250-0.625691-5.797646e-01-0.625691
telemetry_diff.disk.io_sum.read_countNaN0.189411-0.593114NaN0.5014970.4887920.4221150.5911942.831263e-010.687004...0.8561460.9216480.9595680.9504850.9624880.9589150.6870040.9371899.557312e-010.937189
telemetry_diff.disk.io_sum.write_countNaN-0.053648-0.574445NaN0.2195640.2052830.1340150.3526325.305484e-030.872431...0.9709710.9950590.9992270.9994280.9991490.9995190.8724310.9979829.993365e-010.997982
telemetry_diff.disk.io_sum.read_bytesNaN0.200516-0.592808NaN0.5136140.5010120.4347150.6011332.956057e-010.676720...0.8487970.9160980.9555180.9460200.9585740.9548240.6767200.9321969.515041e-010.932196
telemetry_diff.disk.io_sum.write_bytesNaN-0.041367-0.576577NaN0.2345410.2203070.1491050.3656321.954089e-020.864883...0.9672710.9935400.9994520.9991760.9995270.9996950.8648830.9971199.993589e-010.997119
telemetry_diff.disk.io_sum.read_timeNaN0.122188-0.588540NaN0.4298280.4165790.3476650.5300982.087714e-010.743555...0.8951520.9500290.9791910.9725170.9812930.9787360.7435550.9623329.763949e-010.962332
telemetry_diff.disk.io_sum.write_timeNaN-0.022095-0.570804NaN0.2656040.2514790.1796990.3885574.564447e-020.848745...0.9590940.9898500.9994390.9981320.9997660.9994770.8487450.9948579.989421e-010.994857
telemetry_diff.network.netio_sum.bytes_sentNaN-0.151213-0.528206NaN0.1223810.1078550.0342620.255617-9.669159e-020.916774...0.9901320.9999820.9930910.9962350.9918300.9933900.9167740.9988809.945481e-010.998880
telemetry_diff.network.netio_sum.bytes_recvNaN-0.042983-0.565808NaN0.2419290.2277160.1556250.3673182.226364e-020.861425...0.9657210.9930140.9998420.9992860.9999110.9999380.8614250.9969939.996702e-010.996993
telemetry_diff.network.netio_sum.packets_sentNaN-0.065661-0.554730NaN0.2207150.2064290.1336090.345677-8.667188e-040.872387...0.9712410.9954090.9998030.9998920.9995990.9998840.8723870.9985119.999324e-010.998511
telemetry_diff.network.netio_sum.packets_recvNaN-0.038843-0.563210NaN0.2497810.2355950.1632710.3726582.845319e-020.857359...0.9636680.9920900.9998530.9990320.9999780.9998850.8573590.9964389.995813e-010.996438
telemetry_diff.network.netio_per_interface.lo0.bytes_sentNaN-0.017335-0.578592NaN0.2651850.2510620.1798930.3913324.810721e-020.848764...0.9589990.9897280.9992320.9979670.9996080.9993530.8487640.9946659.987263e-010.994665
telemetry_diff.network.netio_per_interface.lo0.packets_sentNaN-0.030336-0.566697NaN0.2581660.2440120.1719300.3808673.735422e-020.852840...0.9612920.9909480.9996810.9986000.9999090.9997100.8528400.9956489.992907e-010.995648
telemetry_diff.network.netio_per_interface.en0.bytes_sentNaN-0.271887-0.477162NaN-0.017697-0.032316-0.1061280.122945-2.313148e-010.963662...1.0000000.9895670.9669760.9743380.9642200.9675270.9636620.9826149.702818e-010.982614
telemetry_diff.network.netio_per_interface.en0.packets_sentNaN-0.149934-0.525894NaN0.1265000.1119810.0381370.258013-9.397918e-020.915132...0.9895671.0000000.9935950.9966000.9923540.9938490.9151320.9990969.950035e-010.999096
telemetry_diff.network.netio_per_interface.utun4.bytes_sentNaN-0.055562-0.551055NaN0.2377110.2234820.1502850.3580051.315250e-020.863758...0.9669760.9935951.0000000.9995240.9999300.9999630.8637580.9974829.999104e-010.997482
telemetry_diff.network.netio_per_interface.utun4.bytes_recvNaN-0.080247-0.546526NaN0.2078000.1934740.1201290.331935-1.536456e-020.878808...0.9743380.9966000.9995241.0000000.9991470.9995830.8788080.9991799.998390e-010.999179
telemetry_diff.network.netio_per_interface.utun4.packets_sentNaN-0.043729-0.557710NaN0.2479570.2337640.1610270.3689812.482132e-020.858377...0.9642200.9923540.9999300.9991471.0000000.9999140.8583770.9966569.996919e-010.996656
telemetry_diff.network.netio_per_interface.utun4.packets_recvNaN-0.053281-0.557186NaN0.2354960.2212590.1485370.3586511.333218e-020.864876...0.9675270.9938490.9999630.9995830.9999141.0000000.8648760.9975979.998925e-010.997597
telemetry_diff.network.netio_per_interface.vmenet0.bytes_sentNaN-0.479819-0.365087NaN-0.284137-0.298127-0.367405-0.132453-4.714045e-011.000000...0.9636620.9151320.8637580.8788080.8583770.8648761.0000000.8973158.703883e-010.897315
telemetry_diff.network.netio_per_interface.vmenet0.packets_sentNaN-0.116829-0.533059NaN0.1682330.1538000.0798600.294547-5.517373e-020.897315...0.9826140.9990960.9974820.9991790.9966560.9975970.8973151.0000009.983382e-011.000000
telemetry_diff.network.netio_per_interface.bridge100.bytes_sentNaN-0.067678-0.546957NaN0.2247590.2104860.1370510.3458571.639908e-160.870388...0.9702820.9950030.9999100.9998390.9996920.9998930.8703880.9983381.000000e+000.998338
telemetry_diff.network.netio_per_interface.bridge100.packets_sentNaN-0.116829-0.533059NaN0.1682330.1538000.0798600.294547-5.517373e-020.897315...0.9826140.9990960.9974820.9991790.9966560.9975970.8973151.0000009.983382e-011.000000
\n", + "

58 rows × 58 columns

\n", + "
" + ], + "text/plain": [ + " used.max_epochs \\\n", + "used.max_epochs NaN \n", + "generated.loss NaN \n", + "generated.accuracy NaN \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops NaN \n", + "generated.responsible_ai_metrics.params NaN \n", + "generated.responsible_ai_metrics.max_width NaN \n", + "generated.responsible_ai_metrics.depth NaN \n", + "generated.responsible_ai_metrics.n_fc_layers NaN \n", + "generated.responsible_ai_metrics.n_cv_layers NaN \n", + "telemetry_diff.cpu.times_avg.user NaN \n", + "telemetry_diff.cpu.times_avg.system NaN \n", + "telemetry_diff.cpu.times_avg.idle NaN \n", + "telemetry_diff.process.memory.rss NaN \n", + "telemetry_diff.process.memory.vms NaN \n", + "telemetry_diff.process.memory.pfaults NaN \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user NaN \n", + "telemetry_diff.process.cpu_times.system NaN \n", + "telemetry_diff.process.num_open_file_descriptors NaN \n", + "telemetry_diff.process.num_connections NaN \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads NaN \n", + "telemetry_diff.process.num_ctx_switches.voluntary NaN \n", + "telemetry_diff.memory.virtual.available NaN \n", + "telemetry_diff.memory.virtual.used NaN \n", + "telemetry_diff.memory.virtual.free NaN \n", + "telemetry_diff.memory.virtual.active NaN \n", + "telemetry_diff.memory.virtual.inactive NaN \n", + "telemetry_diff.memory.virtual.wired NaN \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used NaN \n", + "telemetry_diff.memory.swap.free NaN \n", + "telemetry_diff.memory.swap.sin NaN \n", + "telemetry_diff.memory.swap.sout NaN \n", + "telemetry_diff.disk.disk_usage.free NaN \n", + "telemetry_diff.disk.io_sum.read_count NaN \n", + "telemetry_diff.disk.io_sum.write_count NaN \n", + "telemetry_diff.disk.io_sum.read_bytes NaN \n", + "telemetry_diff.disk.io_sum.write_bytes NaN \n", + "telemetry_diff.disk.io_sum.read_time NaN \n", + "telemetry_diff.disk.io_sum.write_time NaN \n", + "telemetry_diff.network.netio_sum.bytes_sent NaN \n", + "telemetry_diff.network.netio_sum.bytes_recv NaN \n", + "telemetry_diff.network.netio_sum.packets_sent NaN \n", + "telemetry_diff.network.netio_sum.packets_recv NaN \n", + "telemetry_diff.network.netio_per_interface.lo0.... NaN \n", + "telemetry_diff.network.netio_per_interface.lo0.... NaN \n", + "telemetry_diff.network.netio_per_interface.en0.... NaN \n", + "telemetry_diff.network.netio_per_interface.en0.... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.vmen... NaN \n", + "telemetry_diff.network.netio_per_interface.vmen... NaN \n", + "telemetry_diff.network.netio_per_interface.brid... NaN \n", + "telemetry_diff.network.netio_per_interface.brid... NaN \n", + "\n", + " generated.loss \\\n", + "used.max_epochs NaN \n", + "generated.loss 1.000000 \n", + "generated.accuracy -0.570400 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.819808 \n", + "generated.responsible_ai_metrics.params 0.823472 \n", + "generated.responsible_ai_metrics.max_width 0.868819 \n", + "generated.responsible_ai_metrics.depth 0.896128 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.966977 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.479819 \n", + "telemetry_diff.cpu.times_avg.user -0.035758 \n", + "telemetry_diff.cpu.times_avg.system 0.004163 \n", + "telemetry_diff.cpu.times_avg.idle -0.201370 \n", + "telemetry_diff.process.memory.rss -0.286802 \n", + "telemetry_diff.process.memory.vms -0.573717 \n", + "telemetry_diff.process.memory.pfaults 0.026389 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user -0.034090 \n", + "telemetry_diff.process.cpu_times.system 0.018441 \n", + "telemetry_diff.process.num_open_file_descriptors 0.595114 \n", + "telemetry_diff.process.num_connections 0.595114 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.479819 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.013872 \n", + "telemetry_diff.memory.virtual.available 0.650383 \n", + "telemetry_diff.memory.virtual.used -0.843380 \n", + "telemetry_diff.memory.virtual.free 0.762274 \n", + "telemetry_diff.memory.virtual.active -0.797255 \n", + "telemetry_diff.memory.virtual.inactive -0.492605 \n", + "telemetry_diff.memory.virtual.wired -0.442996 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.073544 \n", + "telemetry_diff.memory.swap.free 0.073544 \n", + "telemetry_diff.memory.swap.sin 0.211144 \n", + "telemetry_diff.memory.swap.sout -0.028649 \n", + "telemetry_diff.disk.disk_usage.free 0.713056 \n", + "telemetry_diff.disk.io_sum.read_count 0.189411 \n", + "telemetry_diff.disk.io_sum.write_count -0.053648 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.200516 \n", + "telemetry_diff.disk.io_sum.write_bytes -0.041367 \n", + "telemetry_diff.disk.io_sum.read_time 0.122188 \n", + "telemetry_diff.disk.io_sum.write_time -0.022095 \n", + "telemetry_diff.network.netio_sum.bytes_sent -0.151213 \n", + "telemetry_diff.network.netio_sum.bytes_recv -0.042983 \n", + "telemetry_diff.network.netio_sum.packets_sent -0.065661 \n", + "telemetry_diff.network.netio_sum.packets_recv -0.038843 \n", + "telemetry_diff.network.netio_per_interface.lo0.... -0.017335 \n", + "telemetry_diff.network.netio_per_interface.lo0.... -0.030336 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.271887 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.149934 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.055562 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.080247 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.043729 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.053281 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.479819 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.116829 \n", + "telemetry_diff.network.netio_per_interface.brid... -0.067678 \n", + "telemetry_diff.network.netio_per_interface.brid... -0.116829 \n", + "\n", + " generated.accuracy \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.570400 \n", + "generated.accuracy 1.000000 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops -0.345728 \n", + "generated.responsible_ai_metrics.params -0.338603 \n", + "generated.responsible_ai_metrics.max_width -0.349589 \n", + "generated.responsible_ai_metrics.depth -0.649192 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.448546 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.365087 \n", + "telemetry_diff.cpu.times_avg.user -0.560502 \n", + "telemetry_diff.cpu.times_avg.system -0.569578 \n", + "telemetry_diff.cpu.times_avg.idle -0.507239 \n", + "telemetry_diff.process.memory.rss 0.642277 \n", + "telemetry_diff.process.memory.vms 0.451121 \n", + "telemetry_diff.process.memory.pfaults -0.574134 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user -0.561064 \n", + "telemetry_diff.process.cpu_times.system -0.573210 \n", + "telemetry_diff.process.num_open_file_descriptors -0.999426 \n", + "telemetry_diff.process.num_connections -0.999426 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.365087 \n", + "telemetry_diff.process.num_ctx_switches.voluntary -0.572264 \n", + "telemetry_diff.memory.virtual.available -0.268278 \n", + "telemetry_diff.memory.virtual.used 0.195404 \n", + "telemetry_diff.memory.virtual.free -0.206719 \n", + "telemetry_diff.memory.virtual.active -0.012427 \n", + "telemetry_diff.memory.virtual.inactive -0.234913 \n", + "telemetry_diff.memory.virtual.wired 0.664942 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used 0.576533 \n", + "telemetry_diff.memory.swap.free -0.576533 \n", + "telemetry_diff.memory.swap.sin -0.592704 \n", + "telemetry_diff.memory.swap.sout -0.556661 \n", + "telemetry_diff.disk.disk_usage.free 0.167844 \n", + "telemetry_diff.disk.io_sum.read_count -0.593114 \n", + "telemetry_diff.disk.io_sum.write_count -0.574445 \n", + "telemetry_diff.disk.io_sum.read_bytes -0.592808 \n", + "telemetry_diff.disk.io_sum.write_bytes -0.576577 \n", + "telemetry_diff.disk.io_sum.read_time -0.588540 \n", + "telemetry_diff.disk.io_sum.write_time -0.570804 \n", + "telemetry_diff.network.netio_sum.bytes_sent -0.528206 \n", + "telemetry_diff.network.netio_sum.bytes_recv -0.565808 \n", + "telemetry_diff.network.netio_sum.packets_sent -0.554730 \n", + "telemetry_diff.network.netio_sum.packets_recv -0.563210 \n", + "telemetry_diff.network.netio_per_interface.lo0.... -0.578592 \n", + "telemetry_diff.network.netio_per_interface.lo0.... -0.566697 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.477162 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.525894 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.551055 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.546526 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.557710 \n", + "telemetry_diff.network.netio_per_interface.utun... -0.557186 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.365087 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.533059 \n", + "telemetry_diff.network.netio_per_interface.brid... -0.546957 \n", + "telemetry_diff.network.netio_per_interface.brid... -0.533059 \n", + "\n", + " generated.responsible_ai_metrics.shap_sum \\\n", + "used.max_epochs NaN \n", + "generated.loss NaN \n", + "generated.accuracy NaN \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops NaN \n", + "generated.responsible_ai_metrics.params NaN \n", + "generated.responsible_ai_metrics.max_width NaN \n", + "generated.responsible_ai_metrics.depth NaN \n", + "generated.responsible_ai_metrics.n_fc_layers NaN \n", + "generated.responsible_ai_metrics.n_cv_layers NaN \n", + "telemetry_diff.cpu.times_avg.user NaN \n", + "telemetry_diff.cpu.times_avg.system NaN \n", + "telemetry_diff.cpu.times_avg.idle NaN \n", + "telemetry_diff.process.memory.rss NaN \n", + "telemetry_diff.process.memory.vms NaN \n", + "telemetry_diff.process.memory.pfaults NaN \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user NaN \n", + "telemetry_diff.process.cpu_times.system NaN \n", + "telemetry_diff.process.num_open_file_descriptors NaN \n", + "telemetry_diff.process.num_connections NaN \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads NaN \n", + "telemetry_diff.process.num_ctx_switches.voluntary NaN \n", + "telemetry_diff.memory.virtual.available NaN \n", + "telemetry_diff.memory.virtual.used NaN \n", + "telemetry_diff.memory.virtual.free NaN \n", + "telemetry_diff.memory.virtual.active NaN \n", + "telemetry_diff.memory.virtual.inactive NaN \n", + "telemetry_diff.memory.virtual.wired NaN \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used NaN \n", + "telemetry_diff.memory.swap.free NaN \n", + "telemetry_diff.memory.swap.sin NaN \n", + "telemetry_diff.memory.swap.sout NaN \n", + "telemetry_diff.disk.disk_usage.free NaN \n", + "telemetry_diff.disk.io_sum.read_count NaN \n", + "telemetry_diff.disk.io_sum.write_count NaN \n", + "telemetry_diff.disk.io_sum.read_bytes NaN \n", + "telemetry_diff.disk.io_sum.write_bytes NaN \n", + "telemetry_diff.disk.io_sum.read_time NaN \n", + "telemetry_diff.disk.io_sum.write_time NaN \n", + "telemetry_diff.network.netio_sum.bytes_sent NaN \n", + "telemetry_diff.network.netio_sum.bytes_recv NaN \n", + "telemetry_diff.network.netio_sum.packets_sent NaN \n", + "telemetry_diff.network.netio_sum.packets_recv NaN \n", + "telemetry_diff.network.netio_per_interface.lo0.... NaN \n", + "telemetry_diff.network.netio_per_interface.lo0.... NaN \n", + "telemetry_diff.network.netio_per_interface.en0.... NaN \n", + "telemetry_diff.network.netio_per_interface.en0.... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.utun... NaN \n", + "telemetry_diff.network.netio_per_interface.vmen... NaN \n", + "telemetry_diff.network.netio_per_interface.vmen... NaN \n", + "telemetry_diff.network.netio_per_interface.brid... NaN \n", + "telemetry_diff.network.netio_per_interface.brid... NaN \n", + "\n", + " generated.responsible_ai_metrics.flops \\\n", + "used.max_epochs NaN \n", + "generated.loss 0.819808 \n", + "generated.accuracy -0.345728 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 1.000000 \n", + "generated.responsible_ai_metrics.params 0.999893 \n", + "generated.responsible_ai_metrics.max_width 0.994233 \n", + "generated.responsible_ai_metrics.depth 0.936884 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.934057 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.284137 \n", + "telemetry_diff.cpu.times_avg.user 0.256244 \n", + "telemetry_diff.cpu.times_avg.system 0.300953 \n", + "telemetry_diff.cpu.times_avg.idle 0.066049 \n", + "telemetry_diff.process.memory.rss -0.569155 \n", + "telemetry_diff.process.memory.vms -0.893210 \n", + "telemetry_diff.process.memory.pfaults 0.325536 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.257991 \n", + "telemetry_diff.process.cpu_times.system 0.316230 \n", + "telemetry_diff.process.num_open_file_descriptors 0.360464 \n", + "telemetry_diff.process.num_connections 0.360464 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.284137 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.311186 \n", + "telemetry_diff.memory.virtual.available 0.966133 \n", + "telemetry_diff.memory.virtual.used -0.969549 \n", + "telemetry_diff.memory.virtual.free 0.989489 \n", + "telemetry_diff.memory.virtual.active -0.849813 \n", + "telemetry_diff.memory.virtual.inactive -0.163899 \n", + "telemetry_diff.memory.virtual.wired -0.699543 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.381632 \n", + "telemetry_diff.memory.swap.free 0.381632 \n", + "telemetry_diff.memory.swap.sin 0.524952 \n", + "telemetry_diff.memory.swap.sout 0.268931 \n", + "telemetry_diff.disk.disk_usage.free 0.662385 \n", + "telemetry_diff.disk.io_sum.read_count 0.501497 \n", + "telemetry_diff.disk.io_sum.write_count 0.219564 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.513614 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.234541 \n", + "telemetry_diff.disk.io_sum.read_time 0.429828 \n", + "telemetry_diff.disk.io_sum.write_time 0.265604 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.122381 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.241929 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.220715 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.249781 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.265185 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.258166 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.017697 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.126500 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.237711 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.207800 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.247957 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.235496 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.284137 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.168233 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.224759 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.168233 \n", + "\n", + " generated.responsible_ai_metrics.params \\\n", + "used.max_epochs NaN \n", + "generated.loss 0.823472 \n", + "generated.accuracy -0.338603 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.999893 \n", + "generated.responsible_ai_metrics.params 1.000000 \n", + "generated.responsible_ai_metrics.max_width 0.995420 \n", + "generated.responsible_ai_metrics.depth 0.934735 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.937091 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.298127 \n", + "telemetry_diff.cpu.times_avg.user 0.242082 \n", + "telemetry_diff.cpu.times_avg.system 0.286976 \n", + "telemetry_diff.cpu.times_avg.idle 0.051451 \n", + "telemetry_diff.process.memory.rss -0.557103 \n", + "telemetry_diff.process.memory.vms -0.886669 \n", + "telemetry_diff.process.memory.pfaults 0.311675 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.243835 \n", + "telemetry_diff.process.cpu_times.system 0.302324 \n", + "telemetry_diff.process.num_open_file_descriptors 0.353758 \n", + "telemetry_diff.process.num_connections 0.353758 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.298127 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.297255 \n", + "telemetry_diff.memory.virtual.available 0.963450 \n", + "telemetry_diff.memory.virtual.used -0.973005 \n", + "telemetry_diff.memory.virtual.free 0.990349 \n", + "telemetry_diff.memory.virtual.active -0.857370 \n", + "telemetry_diff.memory.virtual.inactive -0.177951 \n", + "telemetry_diff.memory.virtual.wired -0.689120 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.368075 \n", + "telemetry_diff.memory.swap.free 0.368075 \n", + "telemetry_diff.memory.swap.sin 0.512450 \n", + "telemetry_diff.memory.swap.sout 0.254818 \n", + "telemetry_diff.disk.disk_usage.free 0.673251 \n", + "telemetry_diff.disk.io_sum.read_count 0.488792 \n", + "telemetry_diff.disk.io_sum.write_count 0.205283 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.501012 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.220307 \n", + "telemetry_diff.disk.io_sum.read_time 0.416579 \n", + "telemetry_diff.disk.io_sum.write_time 0.251479 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.107855 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.227716 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.206429 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.235595 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.251062 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.244012 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.032316 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.111981 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.223482 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.193474 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.233764 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.221259 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.298127 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.153800 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.210486 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.153800 \n", + "\n", + " generated.responsible_ai_metrics.max_width \\\n", + "used.max_epochs NaN \n", + "generated.loss 0.868819 \n", + "generated.accuracy -0.349589 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.994233 \n", + "generated.responsible_ai_metrics.params 0.995420 \n", + "generated.responsible_ai_metrics.max_width 1.000000 \n", + "generated.responsible_ai_metrics.depth 0.938519 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.964951 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.367405 \n", + "telemetry_diff.cpu.times_avg.user 0.169531 \n", + "telemetry_diff.cpu.times_avg.system 0.215123 \n", + "telemetry_diff.cpu.times_avg.idle -0.022527 \n", + "telemetry_diff.process.memory.rss -0.496968 \n", + "telemetry_diff.process.memory.vms -0.843689 \n", + "telemetry_diff.process.memory.pfaults 0.240280 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.171323 \n", + "telemetry_diff.process.cpu_times.system 0.230807 \n", + "telemetry_diff.process.num_open_file_descriptors 0.367405 \n", + "telemetry_diff.process.num_connections 0.367405 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.367405 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.225644 \n", + "telemetry_diff.memory.virtual.available 0.933537 \n", + "telemetry_diff.memory.virtual.used -0.983837 \n", + "telemetry_diff.memory.virtual.free 0.981822 \n", + "telemetry_diff.memory.virtual.active -0.887187 \n", + "telemetry_diff.memory.virtual.inactive -0.261248 \n", + "telemetry_diff.memory.virtual.wired -0.638971 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.297423 \n", + "telemetry_diff.memory.swap.free 0.297423 \n", + "telemetry_diff.memory.swap.sin 0.446546 \n", + "telemetry_diff.memory.swap.sout 0.181955 \n", + "telemetry_diff.disk.disk_usage.free 0.722127 \n", + "telemetry_diff.disk.io_sum.read_count 0.422115 \n", + "telemetry_diff.disk.io_sum.write_count 0.134015 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.434715 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.149105 \n", + "telemetry_diff.disk.io_sum.read_time 0.347665 \n", + "telemetry_diff.disk.io_sum.write_time 0.179699 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.034262 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.155625 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.133609 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.163271 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.179893 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.171930 \n", + "telemetry_diff.network.netio_per_interface.en0.... -0.106128 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.038137 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.150285 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.120129 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.161027 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.148537 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.367405 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.079860 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.137051 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.079860 \n", + "\n", + " generated.responsible_ai_metrics.depth \\\n", + "used.max_epochs NaN \n", + "generated.loss 0.896128 \n", + "generated.accuracy -0.649192 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.936884 \n", + "generated.responsible_ai_metrics.params 0.934735 \n", + "generated.responsible_ai_metrics.max_width 0.938519 \n", + "generated.responsible_ai_metrics.depth 1.000000 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.936586 \n", + "generated.responsible_ai_metrics.n_cv_layers -0.132453 \n", + "telemetry_diff.cpu.times_avg.user 0.376831 \n", + "telemetry_diff.cpu.times_avg.system 0.416864 \n", + "telemetry_diff.cpu.times_avg.idle 0.201962 \n", + "telemetry_diff.process.memory.rss -0.667256 \n", + "telemetry_diff.process.memory.vms -0.870138 \n", + "telemetry_diff.process.memory.pfaults 0.438754 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.378475 \n", + "telemetry_diff.process.cpu_times.system 0.430783 \n", + "telemetry_diff.process.num_open_file_descriptors 0.662266 \n", + "telemetry_diff.process.num_connections 0.662266 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 0.132453 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.426286 \n", + "telemetry_diff.memory.virtual.available 0.869560 \n", + "telemetry_diff.memory.virtual.used -0.865558 \n", + "telemetry_diff.memory.virtual.free 0.876572 \n", + "telemetry_diff.memory.virtual.active -0.701887 \n", + "telemetry_diff.memory.virtual.inactive -0.088462 \n", + "telemetry_diff.memory.virtual.wired -0.786457 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.485662 \n", + "telemetry_diff.memory.swap.free 0.485662 \n", + "telemetry_diff.memory.swap.sin 0.610518 \n", + "telemetry_diff.memory.swap.sout 0.385620 \n", + "telemetry_diff.disk.disk_usage.free 0.502120 \n", + "telemetry_diff.disk.io_sum.read_count 0.591194 \n", + "telemetry_diff.disk.io_sum.write_count 0.352632 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.601133 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.365632 \n", + "telemetry_diff.disk.io_sum.read_time 0.530098 \n", + "telemetry_diff.disk.io_sum.write_time 0.388557 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.255617 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.367318 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.345677 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.372658 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.391332 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.380867 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.122945 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.258013 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.358005 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.331935 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.368981 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.358651 \n", + "telemetry_diff.network.netio_per_interface.vmen... -0.132453 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.294547 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.345857 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.294547 \n", + "\n", + " generated.responsible_ai_metrics.n_fc_layers \\\n", + "used.max_epochs NaN \n", + "generated.loss 9.669773e-01 \n", + "generated.accuracy -4.485456e-01 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 9.340567e-01 \n", + "generated.responsible_ai_metrics.params 9.370909e-01 \n", + "generated.responsible_ai_metrics.max_width 9.649505e-01 \n", + "generated.responsible_ai_metrics.depth 9.365858e-01 \n", + "generated.responsible_ai_metrics.n_fc_layers 1.000000e+00 \n", + "generated.responsible_ai_metrics.n_cv_layers -4.714045e-01 \n", + "telemetry_diff.cpu.times_avg.user 3.337242e-02 \n", + "telemetry_diff.cpu.times_avg.system 7.788061e-02 \n", + "telemetry_diff.cpu.times_avg.idle -1.519078e-01 \n", + "telemetry_diff.process.memory.rss -3.729260e-01 \n", + "telemetry_diff.process.memory.vms -7.144685e-01 \n", + "telemetry_diff.process.memory.pfaults 1.025762e-01 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 3.516914e-02 \n", + "telemetry_diff.process.cpu_times.system 9.348204e-02 \n", + "telemetry_diff.process.num_open_file_descriptors 4.714045e-01 \n", + "telemetry_diff.process.num_connections 4.714045e-01 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads 4.714045e-01 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 8.840923e-02 \n", + "telemetry_diff.memory.virtual.available 8.108192e-01 \n", + "telemetry_diff.memory.virtual.used -9.500208e-01 \n", + "telemetry_diff.memory.virtual.free 9.016455e-01 \n", + "telemetry_diff.memory.virtual.active -8.871182e-01 \n", + "telemetry_diff.memory.virtual.inactive -4.214716e-01 \n", + "telemetry_diff.memory.virtual.wired -5.302289e-01 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -1.571348e-01 \n", + "telemetry_diff.memory.swap.free 1.571348e-01 \n", + "telemetry_diff.memory.swap.sin 3.074307e-01 \n", + "telemetry_diff.memory.swap.sout 4.363225e-02 \n", + "telemetry_diff.disk.disk_usage.free 7.668184e-01 \n", + "telemetry_diff.disk.io_sum.read_count 2.831263e-01 \n", + "telemetry_diff.disk.io_sum.write_count 5.305484e-03 \n", + "telemetry_diff.disk.io_sum.read_bytes 2.956057e-01 \n", + "telemetry_diff.disk.io_sum.write_bytes 1.954089e-02 \n", + "telemetry_diff.disk.io_sum.read_time 2.087714e-01 \n", + "telemetry_diff.disk.io_sum.write_time 4.564447e-02 \n", + "telemetry_diff.network.netio_sum.bytes_sent -9.669159e-02 \n", + "telemetry_diff.network.netio_sum.bytes_recv 2.226364e-02 \n", + "telemetry_diff.network.netio_sum.packets_sent -8.667188e-04 \n", + "telemetry_diff.network.netio_sum.packets_recv 2.845319e-02 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 4.810721e-02 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 3.735422e-02 \n", + "telemetry_diff.network.netio_per_interface.en0.... -2.313148e-01 \n", + "telemetry_diff.network.netio_per_interface.en0.... -9.397918e-02 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.315250e-02 \n", + "telemetry_diff.network.netio_per_interface.utun... -1.536456e-02 \n", + "telemetry_diff.network.netio_per_interface.utun... 2.482132e-02 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.333218e-02 \n", + "telemetry_diff.network.netio_per_interface.vmen... -4.714045e-01 \n", + "telemetry_diff.network.netio_per_interface.vmen... -5.517373e-02 \n", + "telemetry_diff.network.netio_per_interface.brid... 1.639908e-16 \n", + "telemetry_diff.network.netio_per_interface.brid... -5.517373e-02 \n", + "\n", + " generated.responsible_ai_metrics.n_cv_layers \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.479819 \n", + "generated.accuracy -0.365087 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops -0.284137 \n", + "generated.responsible_ai_metrics.params -0.298127 \n", + "generated.responsible_ai_metrics.max_width -0.367405 \n", + "generated.responsible_ai_metrics.depth -0.132453 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.471405 \n", + "generated.responsible_ai_metrics.n_cv_layers 1.000000 \n", + "telemetry_diff.cpu.times_avg.user 0.853946 \n", + "telemetry_diff.cpu.times_avg.system 0.828804 \n", + "telemetry_diff.cpu.times_avg.idle 0.937919 \n", + "telemetry_diff.process.memory.rss -0.624431 \n", + "telemetry_diff.process.memory.vms -0.168978 \n", + "telemetry_diff.process.memory.pfaults 0.814044 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.853002 \n", + "telemetry_diff.process.cpu_times.system 0.819705 \n", + "telemetry_diff.process.num_open_file_descriptors 0.333333 \n", + "telemetry_diff.process.num_connections 0.333333 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -1.000000 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.822737 \n", + "telemetry_diff.memory.virtual.available -0.104999 \n", + "telemetry_diff.memory.virtual.used 0.508791 \n", + "telemetry_diff.memory.virtual.free -0.344248 \n", + "telemetry_diff.memory.virtual.active 0.742773 \n", + "telemetry_diff.memory.virtual.inactive 0.969478 \n", + "telemetry_diff.memory.virtual.wired -0.479494 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.777778 \n", + "telemetry_diff.memory.swap.free 0.777778 \n", + "telemetry_diff.memory.swap.sin 0.666893 \n", + "telemetry_diff.memory.swap.sout 0.847046 \n", + "telemetry_diff.disk.disk_usage.free -0.905250 \n", + "telemetry_diff.disk.io_sum.read_count 0.687004 \n", + "telemetry_diff.disk.io_sum.write_count 0.872431 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.676720 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.864883 \n", + "telemetry_diff.disk.io_sum.read_time 0.743555 \n", + "telemetry_diff.disk.io_sum.write_time 0.848745 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.916774 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.861425 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.872387 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.857359 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.848764 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.852840 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.963662 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.915132 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.863758 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.878808 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.858377 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.864876 \n", + "telemetry_diff.network.netio_per_interface.vmen... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.897315 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.870388 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.897315 \n", + "\n", + " ... \\\n", + "used.max_epochs ... \n", + "generated.loss ... \n", + "generated.accuracy ... \n", + "generated.responsible_ai_metrics.shap_sum ... \n", + "generated.responsible_ai_metrics.flops ... \n", + "generated.responsible_ai_metrics.params ... \n", + "generated.responsible_ai_metrics.max_width ... \n", + "generated.responsible_ai_metrics.depth ... \n", + "generated.responsible_ai_metrics.n_fc_layers ... \n", + "generated.responsible_ai_metrics.n_cv_layers ... \n", + "telemetry_diff.cpu.times_avg.user ... \n", + "telemetry_diff.cpu.times_avg.system ... \n", + "telemetry_diff.cpu.times_avg.idle ... \n", + "telemetry_diff.process.memory.rss ... \n", + "telemetry_diff.process.memory.vms ... \n", + "telemetry_diff.process.memory.pfaults ... \n", + "telemetry_diff.process.memory.pageins ... \n", + "telemetry_diff.process.cpu_times.user ... \n", + "telemetry_diff.process.cpu_times.system ... \n", + "telemetry_diff.process.num_open_file_descriptors ... \n", + "telemetry_diff.process.num_connections ... \n", + "telemetry_diff.process.num_open_files ... \n", + "telemetry_diff.process.num_threads ... \n", + "telemetry_diff.process.num_ctx_switches.voluntary ... \n", + "telemetry_diff.memory.virtual.available ... \n", + "telemetry_diff.memory.virtual.used ... \n", + "telemetry_diff.memory.virtual.free ... \n", + "telemetry_diff.memory.virtual.active ... \n", + "telemetry_diff.memory.virtual.inactive ... \n", + "telemetry_diff.memory.virtual.wired ... \n", + "telemetry_diff.memory.swap.total ... \n", + "telemetry_diff.memory.swap.used ... \n", + "telemetry_diff.memory.swap.free ... \n", + "telemetry_diff.memory.swap.sin ... \n", + "telemetry_diff.memory.swap.sout ... \n", + "telemetry_diff.disk.disk_usage.free ... \n", + "telemetry_diff.disk.io_sum.read_count ... \n", + "telemetry_diff.disk.io_sum.write_count ... \n", + "telemetry_diff.disk.io_sum.read_bytes ... \n", + "telemetry_diff.disk.io_sum.write_bytes ... \n", + "telemetry_diff.disk.io_sum.read_time ... \n", + "telemetry_diff.disk.io_sum.write_time ... \n", + "telemetry_diff.network.netio_sum.bytes_sent ... \n", + "telemetry_diff.network.netio_sum.bytes_recv ... \n", + "telemetry_diff.network.netio_sum.packets_sent ... \n", + "telemetry_diff.network.netio_sum.packets_recv ... \n", + "telemetry_diff.network.netio_per_interface.lo0.... ... \n", + "telemetry_diff.network.netio_per_interface.lo0.... ... \n", + "telemetry_diff.network.netio_per_interface.en0.... ... \n", + "telemetry_diff.network.netio_per_interface.en0.... ... \n", + "telemetry_diff.network.netio_per_interface.utun... ... \n", + "telemetry_diff.network.netio_per_interface.utun... ... \n", + "telemetry_diff.network.netio_per_interface.utun... ... \n", + "telemetry_diff.network.netio_per_interface.utun... ... \n", + "telemetry_diff.network.netio_per_interface.vmen... ... \n", + "telemetry_diff.network.netio_per_interface.vmen... ... \n", + "telemetry_diff.network.netio_per_interface.brid... ... \n", + "telemetry_diff.network.netio_per_interface.brid... ... \n", + "\n", + " telemetry_diff.network.netio_per_interface.en0.bytes_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.271887 \n", + "generated.accuracy -0.477162 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops -0.017697 \n", + "generated.responsible_ai_metrics.params -0.032316 \n", + "generated.responsible_ai_metrics.max_width -0.106128 \n", + "generated.responsible_ai_metrics.depth 0.122945 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.231315 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.963662 \n", + "telemetry_diff.cpu.times_avg.user 0.961908 \n", + "telemetry_diff.cpu.times_avg.system 0.948146 \n", + "telemetry_diff.cpu.times_avg.idle 0.996488 \n", + "telemetry_diff.process.memory.rss -0.809753 \n", + "telemetry_diff.process.memory.vms -0.425057 \n", + "telemetry_diff.process.memory.pfaults 0.939603 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.961411 \n", + "telemetry_diff.process.cpu_times.system 0.942914 \n", + "telemetry_diff.process.num_open_file_descriptors 0.448156 \n", + "telemetry_diff.process.num_connections 0.448156 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.963662 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.944670 \n", + "telemetry_diff.memory.virtual.available 0.159649 \n", + "telemetry_diff.memory.virtual.used 0.260460 \n", + "telemetry_diff.memory.virtual.free -0.083329 \n", + "telemetry_diff.memory.virtual.active 0.537827 \n", + "telemetry_diff.memory.virtual.inactive 0.965301 \n", + "telemetry_diff.memory.virtual.wired -0.694941 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.917412 \n", + "telemetry_diff.memory.swap.free 0.917412 \n", + "telemetry_diff.memory.swap.sin 0.841708 \n", + "telemetry_diff.memory.swap.sout 0.958246 \n", + "telemetry_diff.disk.disk_usage.free -0.759478 \n", + "telemetry_diff.disk.io_sum.read_count 0.856146 \n", + "telemetry_diff.disk.io_sum.write_count 0.970971 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.848797 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.967271 \n", + "telemetry_diff.disk.io_sum.read_time 0.895152 \n", + "telemetry_diff.disk.io_sum.write_time 0.959094 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.990132 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.965721 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.971241 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.963668 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.958999 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.961292 \n", + "telemetry_diff.network.netio_per_interface.en0.... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.989567 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.966976 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.974338 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.964220 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.967527 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.963662 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.982614 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.970282 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.982614 \n", + "\n", + " telemetry_diff.network.netio_per_interface.en0.packets_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.149934 \n", + "generated.accuracy -0.525894 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.126500 \n", + "generated.responsible_ai_metrics.params 0.111981 \n", + "generated.responsible_ai_metrics.max_width 0.038137 \n", + "generated.responsible_ai_metrics.depth 0.258013 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.093979 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.915132 \n", + "telemetry_diff.cpu.times_avg.user 0.991258 \n", + "telemetry_diff.cpu.times_avg.system 0.984045 \n", + "telemetry_diff.cpu.times_avg.idle 0.998155 \n", + "telemetry_diff.process.memory.rss -0.885564 \n", + "telemetry_diff.process.memory.vms -0.550086 \n", + "telemetry_diff.process.memory.pfaults 0.979111 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.991018 \n", + "telemetry_diff.process.cpu_times.system 0.981058 \n", + "telemetry_diff.process.num_open_file_descriptors 0.499274 \n", + "telemetry_diff.process.num_connections 0.499274 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.915132 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.982073 \n", + "telemetry_diff.memory.virtual.available 0.296941 \n", + "telemetry_diff.memory.virtual.used 0.118828 \n", + "telemetry_diff.memory.virtual.free 0.059440 \n", + "telemetry_diff.memory.virtual.active 0.411387 \n", + "telemetry_diff.memory.virtual.inactive 0.933358 \n", + "telemetry_diff.memory.virtual.wired -0.790574 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.965147 \n", + "telemetry_diff.memory.swap.free 0.965147 \n", + "telemetry_diff.memory.swap.sin 0.910695 \n", + "telemetry_diff.memory.swap.sout 0.989426 \n", + "telemetry_diff.disk.disk_usage.free -0.658216 \n", + "telemetry_diff.disk.io_sum.read_count 0.921648 \n", + "telemetry_diff.disk.io_sum.write_count 0.995059 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.916098 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.993540 \n", + "telemetry_diff.disk.io_sum.read_time 0.950029 \n", + "telemetry_diff.disk.io_sum.write_time 0.989850 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.999982 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.993014 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.995409 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.992090 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.989728 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.990948 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.989567 \n", + "telemetry_diff.network.netio_per_interface.en0.... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.993595 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.996600 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.992354 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.993849 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.915132 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.999096 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.995003 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999096 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.bytes_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.055562 \n", + "generated.accuracy -0.551055 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.237711 \n", + "generated.responsible_ai_metrics.params 0.223482 \n", + "generated.responsible_ai_metrics.max_width 0.150285 \n", + "generated.responsible_ai_metrics.depth 0.358005 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.013152 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.863758 \n", + "telemetry_diff.cpu.times_avg.user 0.999795 \n", + "telemetry_diff.cpu.times_avg.system 0.997822 \n", + "telemetry_diff.cpu.times_avg.idle 0.984910 \n", + "telemetry_diff.process.memory.rss -0.931697 \n", + "telemetry_diff.process.memory.vms -0.640690 \n", + "telemetry_diff.process.memory.pfaults 0.995789 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.999757 \n", + "telemetry_diff.process.cpu_times.system 0.996633 \n", + "telemetry_diff.process.num_open_file_descriptors 0.526625 \n", + "telemetry_diff.process.num_connections 0.526625 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.863758 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.997053 \n", + "telemetry_diff.memory.virtual.available 0.401516 \n", + "telemetry_diff.memory.virtual.used 0.005869 \n", + "telemetry_diff.memory.virtual.free 0.171372 \n", + "telemetry_diff.memory.virtual.active 0.305819 \n", + "telemetry_diff.memory.virtual.inactive 0.896168 \n", + "telemetry_diff.memory.virtual.wired -0.853364 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.988536 \n", + "telemetry_diff.memory.swap.free 0.988536 \n", + "telemetry_diff.memory.swap.sin 0.951522 \n", + "telemetry_diff.memory.swap.sout 0.999478 \n", + "telemetry_diff.disk.disk_usage.free -0.568944 \n", + "telemetry_diff.disk.io_sum.read_count 0.959568 \n", + "telemetry_diff.disk.io_sum.write_count 0.999227 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.955518 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.999452 \n", + "telemetry_diff.disk.io_sum.read_time 0.979191 \n", + "telemetry_diff.disk.io_sum.write_time 0.999439 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.993091 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.999842 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.999803 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.999853 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999232 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999681 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.966976 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.993595 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999524 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999930 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999963 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.863758 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.997482 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999910 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.997482 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.bytes_recv \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.080247 \n", + "generated.accuracy -0.546526 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.207800 \n", + "generated.responsible_ai_metrics.params 0.193474 \n", + "generated.responsible_ai_metrics.max_width 0.120129 \n", + "generated.responsible_ai_metrics.depth 0.331935 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.015365 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.878808 \n", + "telemetry_diff.cpu.times_avg.user 0.998753 \n", + "telemetry_diff.cpu.times_avg.system 0.995354 \n", + "telemetry_diff.cpu.times_avg.idle 0.989760 \n", + "telemetry_diff.process.memory.rss -0.920466 \n", + "telemetry_diff.process.memory.vms -0.616711 \n", + "telemetry_diff.process.memory.pfaults 0.992530 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.998661 \n", + "telemetry_diff.process.cpu_times.system 0.993677 \n", + "telemetry_diff.process.num_open_file_descriptors 0.521490 \n", + "telemetry_diff.process.num_connections 0.521490 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.878808 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.994259 \n", + "telemetry_diff.memory.virtual.available 0.373239 \n", + "telemetry_diff.memory.virtual.used 0.036619 \n", + "telemetry_diff.memory.virtual.free 0.140923 \n", + "telemetry_diff.memory.virtual.active 0.335019 \n", + "telemetry_diff.memory.virtual.inactive 0.906974 \n", + "telemetry_diff.memory.virtual.wired -0.837588 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.983428 \n", + "telemetry_diff.memory.swap.free 0.983428 \n", + "telemetry_diff.memory.swap.sin 0.941635 \n", + "telemetry_diff.memory.swap.sout 0.998008 \n", + "telemetry_diff.disk.disk_usage.free -0.594005 \n", + "telemetry_diff.disk.io_sum.read_count 0.950485 \n", + "telemetry_diff.disk.io_sum.write_count 0.999428 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.946020 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.999176 \n", + "telemetry_diff.disk.io_sum.read_time 0.972517 \n", + "telemetry_diff.disk.io_sum.write_time 0.998132 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.996235 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.999286 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.999892 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.999032 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.997967 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.998600 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.974338 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.996600 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999524 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999147 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999583 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.878808 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.999179 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999839 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999179 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.packets_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.043729 \n", + "generated.accuracy -0.557710 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.247957 \n", + "generated.responsible_ai_metrics.params 0.233764 \n", + "generated.responsible_ai_metrics.max_width 0.161027 \n", + "generated.responsible_ai_metrics.depth 0.368981 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.024821 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.858377 \n", + "telemetry_diff.cpu.times_avg.user 0.999963 \n", + "telemetry_diff.cpu.times_avg.system 0.998481 \n", + "telemetry_diff.cpu.times_avg.idle 0.983031 \n", + "telemetry_diff.process.memory.rss -0.935748 \n", + "telemetry_diff.process.memory.vms -0.648229 \n", + "telemetry_diff.process.memory.pfaults 0.996720 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.999945 \n", + "telemetry_diff.process.cpu_times.system 0.997465 \n", + "telemetry_diff.process.num_open_file_descriptors 0.533560 \n", + "telemetry_diff.process.num_connections 0.533560 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.858377 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.997829 \n", + "telemetry_diff.memory.virtual.available 0.409941 \n", + "telemetry_diff.memory.virtual.used -0.004541 \n", + "telemetry_diff.memory.virtual.free 0.180982 \n", + "telemetry_diff.memory.virtual.active 0.296126 \n", + "telemetry_diff.memory.virtual.inactive 0.890840 \n", + "telemetry_diff.memory.virtual.wired -0.859256 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.990051 \n", + "telemetry_diff.memory.swap.free 0.990051 \n", + "telemetry_diff.memory.swap.sin 0.954707 \n", + "telemetry_diff.memory.swap.sout 0.999740 \n", + "telemetry_diff.disk.disk_usage.free -0.560470 \n", + "telemetry_diff.disk.io_sum.read_count 0.962488 \n", + "telemetry_diff.disk.io_sum.write_count 0.999149 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.958574 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.999527 \n", + "telemetry_diff.disk.io_sum.read_time 0.981293 \n", + "telemetry_diff.disk.io_sum.write_time 0.999766 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.991830 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.999911 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.999599 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.999978 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999608 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999909 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.964220 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.992354 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999930 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999147 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999914 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.858377 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.996656 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999692 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.996656 \n", + "\n", + " telemetry_diff.network.netio_per_interface.utun4.packets_recv \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.053281 \n", + "generated.accuracy -0.557186 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.235496 \n", + "generated.responsible_ai_metrics.params 0.221259 \n", + "generated.responsible_ai_metrics.max_width 0.148537 \n", + "generated.responsible_ai_metrics.depth 0.358651 \n", + "generated.responsible_ai_metrics.n_fc_layers 0.013332 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.864876 \n", + "telemetry_diff.cpu.times_avg.user 0.999770 \n", + "telemetry_diff.cpu.times_avg.system 0.997690 \n", + "telemetry_diff.cpu.times_avg.idle 0.985295 \n", + "telemetry_diff.process.memory.rss -0.931360 \n", + "telemetry_diff.process.memory.vms -0.638209 \n", + "telemetry_diff.process.memory.pfaults 0.995598 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.999730 \n", + "telemetry_diff.process.cpu_times.system 0.996472 \n", + "telemetry_diff.process.num_open_file_descriptors 0.532793 \n", + "telemetry_diff.process.num_connections 0.532793 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.864876 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.996904 \n", + "telemetry_diff.memory.virtual.available 0.397912 \n", + "telemetry_diff.memory.virtual.used 0.008370 \n", + "telemetry_diff.memory.virtual.free 0.168082 \n", + "telemetry_diff.memory.virtual.active 0.308519 \n", + "telemetry_diff.memory.virtual.inactive 0.895238 \n", + "telemetry_diff.memory.virtual.wired -0.853010 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.988140 \n", + "telemetry_diff.memory.swap.free 0.988140 \n", + "telemetry_diff.memory.swap.sin 0.950792 \n", + "telemetry_diff.memory.swap.sout 0.999355 \n", + "telemetry_diff.disk.disk_usage.free -0.571158 \n", + "telemetry_diff.disk.io_sum.read_count 0.958915 \n", + "telemetry_diff.disk.io_sum.write_count 0.999519 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.954824 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.999695 \n", + "telemetry_diff.disk.io_sum.read_time 0.978736 \n", + "telemetry_diff.disk.io_sum.write_time 0.999477 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.993390 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.999938 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.999884 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.999885 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999353 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.999710 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.967527 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.993849 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999963 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999583 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999914 \n", + "telemetry_diff.network.netio_per_interface.utun... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.864876 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.997597 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.999893 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.997597 \n", + "\n", + " telemetry_diff.network.netio_per_interface.vmenet0.bytes_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.479819 \n", + "generated.accuracy -0.365087 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops -0.284137 \n", + "generated.responsible_ai_metrics.params -0.298127 \n", + "generated.responsible_ai_metrics.max_width -0.367405 \n", + "generated.responsible_ai_metrics.depth -0.132453 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.471405 \n", + "generated.responsible_ai_metrics.n_cv_layers 1.000000 \n", + "telemetry_diff.cpu.times_avg.user 0.853946 \n", + "telemetry_diff.cpu.times_avg.system 0.828804 \n", + "telemetry_diff.cpu.times_avg.idle 0.937919 \n", + "telemetry_diff.process.memory.rss -0.624431 \n", + "telemetry_diff.process.memory.vms -0.168978 \n", + "telemetry_diff.process.memory.pfaults 0.814044 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.853002 \n", + "telemetry_diff.process.cpu_times.system 0.819705 \n", + "telemetry_diff.process.num_open_file_descriptors 0.333333 \n", + "telemetry_diff.process.num_connections 0.333333 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -1.000000 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.822737 \n", + "telemetry_diff.memory.virtual.available -0.104999 \n", + "telemetry_diff.memory.virtual.used 0.508791 \n", + "telemetry_diff.memory.virtual.free -0.344248 \n", + "telemetry_diff.memory.virtual.active 0.742773 \n", + "telemetry_diff.memory.virtual.inactive 0.969478 \n", + "telemetry_diff.memory.virtual.wired -0.479494 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.777778 \n", + "telemetry_diff.memory.swap.free 0.777778 \n", + "telemetry_diff.memory.swap.sin 0.666893 \n", + "telemetry_diff.memory.swap.sout 0.847046 \n", + "telemetry_diff.disk.disk_usage.free -0.905250 \n", + "telemetry_diff.disk.io_sum.read_count 0.687004 \n", + "telemetry_diff.disk.io_sum.write_count 0.872431 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.676720 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.864883 \n", + "telemetry_diff.disk.io_sum.read_time 0.743555 \n", + "telemetry_diff.disk.io_sum.write_time 0.848745 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.916774 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.861425 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.872387 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.857359 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.848764 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.852840 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.963662 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.915132 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.863758 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.878808 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.858377 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.864876 \n", + "telemetry_diff.network.netio_per_interface.vmen... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.897315 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.870388 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.897315 \n", + "\n", + " telemetry_diff.network.netio_per_interface.vmenet0.packets_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -0.116829 \n", + "generated.accuracy -0.533059 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.168233 \n", + "generated.responsible_ai_metrics.params 0.153800 \n", + "generated.responsible_ai_metrics.max_width 0.079860 \n", + "generated.responsible_ai_metrics.depth 0.294547 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.055174 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.897315 \n", + "telemetry_diff.cpu.times_avg.user 0.995913 \n", + "telemetry_diff.cpu.times_avg.system 0.990647 \n", + "telemetry_diff.cpu.times_avg.idle 0.994696 \n", + "telemetry_diff.process.memory.rss -0.903897 \n", + "telemetry_diff.process.memory.vms -0.585088 \n", + "telemetry_diff.process.memory.pfaults 0.986789 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.995747 \n", + "telemetry_diff.process.cpu_times.system 0.988324 \n", + "telemetry_diff.process.num_open_file_descriptors 0.507178 \n", + "telemetry_diff.process.num_connections 0.507178 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.897315 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.989118 \n", + "telemetry_diff.memory.virtual.available 0.337158 \n", + "telemetry_diff.memory.virtual.used 0.076707 \n", + "telemetry_diff.memory.virtual.free 0.101826 \n", + "telemetry_diff.memory.virtual.active 0.372320 \n", + "telemetry_diff.memory.virtual.inactive 0.921679 \n", + "telemetry_diff.memory.virtual.wired -0.814837 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.975343 \n", + "telemetry_diff.memory.swap.free 0.975343 \n", + "telemetry_diff.memory.swap.sin 0.927314 \n", + "telemetry_diff.memory.swap.sout 0.994675 \n", + "telemetry_diff.disk.disk_usage.free -0.625691 \n", + "telemetry_diff.disk.io_sum.read_count 0.937189 \n", + "telemetry_diff.disk.io_sum.write_count 0.997982 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.932196 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.997119 \n", + "telemetry_diff.disk.io_sum.read_time 0.962332 \n", + "telemetry_diff.disk.io_sum.write_time 0.994857 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.998880 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.996993 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.998511 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.996438 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.994665 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.995648 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.982614 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.999096 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.997482 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999179 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.996656 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.997597 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.897315 \n", + "telemetry_diff.network.netio_per_interface.vmen... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.998338 \n", + "telemetry_diff.network.netio_per_interface.brid... 1.000000 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_sent \\\n", + "used.max_epochs NaN \n", + "generated.loss -6.767772e-02 \n", + "generated.accuracy -5.469569e-01 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 2.247585e-01 \n", + "generated.responsible_ai_metrics.params 2.104857e-01 \n", + "generated.responsible_ai_metrics.max_width 1.370506e-01 \n", + "generated.responsible_ai_metrics.depth 3.458572e-01 \n", + "generated.responsible_ai_metrics.n_fc_layers 1.639908e-16 \n", + "generated.responsible_ai_metrics.n_cv_layers 8.703883e-01 \n", + "telemetry_diff.cpu.times_avg.user 9.994417e-01 \n", + "telemetry_diff.cpu.times_avg.system 9.968501e-01 \n", + "telemetry_diff.cpu.times_avg.idle 9.871205e-01 \n", + "telemetry_diff.process.memory.rss -9.267632e-01 \n", + "telemetry_diff.process.memory.vms -6.305982e-01 \n", + "telemetry_diff.process.memory.pfaults 9.944736e-01 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 9.993796e-01 \n", + "telemetry_diff.process.cpu_times.system 9.954462e-01 \n", + "telemetry_diff.process.num_open_file_descriptors 5.222330e-01 \n", + "telemetry_diff.process.num_connections 5.222330e-01 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -8.703883e-01 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 9.959365e-01 \n", + "telemetry_diff.memory.virtual.available 3.898045e-01 \n", + "telemetry_diff.memory.virtual.used 1.913862e-02 \n", + "telemetry_diff.memory.virtual.free 1.585301e-01 \n", + "telemetry_diff.memory.virtual.active 3.183437e-01 \n", + "telemetry_diff.memory.virtual.inactive 9.015499e-01 \n", + "telemetry_diff.memory.virtual.wired -8.463404e-01 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -9.864401e-01 \n", + "telemetry_diff.memory.swap.free 9.864401e-01 \n", + "telemetry_diff.memory.swap.sin 9.473410e-01 \n", + "telemetry_diff.memory.swap.sout 9.989610e-01 \n", + "telemetry_diff.disk.disk_usage.free -5.797646e-01 \n", + "telemetry_diff.disk.io_sum.read_count 9.557312e-01 \n", + "telemetry_diff.disk.io_sum.write_count 9.993365e-01 \n", + "telemetry_diff.disk.io_sum.read_bytes 9.515041e-01 \n", + "telemetry_diff.disk.io_sum.write_bytes 9.993589e-01 \n", + "telemetry_diff.disk.io_sum.read_time 9.763949e-01 \n", + "telemetry_diff.disk.io_sum.write_time 9.989421e-01 \n", + "telemetry_diff.network.netio_sum.bytes_sent 9.945481e-01 \n", + "telemetry_diff.network.netio_sum.bytes_recv 9.996702e-01 \n", + "telemetry_diff.network.netio_sum.packets_sent 9.999324e-01 \n", + "telemetry_diff.network.netio_sum.packets_recv 9.995813e-01 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 9.987263e-01 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 9.992907e-01 \n", + "telemetry_diff.network.netio_per_interface.en0.... 9.702818e-01 \n", + "telemetry_diff.network.netio_per_interface.en0.... 9.950035e-01 \n", + "telemetry_diff.network.netio_per_interface.utun... 9.999104e-01 \n", + "telemetry_diff.network.netio_per_interface.utun... 9.998390e-01 \n", + "telemetry_diff.network.netio_per_interface.utun... 9.996919e-01 \n", + "telemetry_diff.network.netio_per_interface.utun... 9.998925e-01 \n", + "telemetry_diff.network.netio_per_interface.vmen... 8.703883e-01 \n", + "telemetry_diff.network.netio_per_interface.vmen... 9.983382e-01 \n", + "telemetry_diff.network.netio_per_interface.brid... 1.000000e+00 \n", + "telemetry_diff.network.netio_per_interface.brid... 9.983382e-01 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_sent \n", + "used.max_epochs NaN \n", + "generated.loss -0.116829 \n", + "generated.accuracy -0.533059 \n", + "generated.responsible_ai_metrics.shap_sum NaN \n", + "generated.responsible_ai_metrics.flops 0.168233 \n", + "generated.responsible_ai_metrics.params 0.153800 \n", + "generated.responsible_ai_metrics.max_width 0.079860 \n", + "generated.responsible_ai_metrics.depth 0.294547 \n", + "generated.responsible_ai_metrics.n_fc_layers -0.055174 \n", + "generated.responsible_ai_metrics.n_cv_layers 0.897315 \n", + "telemetry_diff.cpu.times_avg.user 0.995913 \n", + "telemetry_diff.cpu.times_avg.system 0.990647 \n", + "telemetry_diff.cpu.times_avg.idle 0.994696 \n", + "telemetry_diff.process.memory.rss -0.903897 \n", + "telemetry_diff.process.memory.vms -0.585088 \n", + "telemetry_diff.process.memory.pfaults 0.986789 \n", + "telemetry_diff.process.memory.pageins NaN \n", + "telemetry_diff.process.cpu_times.user 0.995747 \n", + "telemetry_diff.process.cpu_times.system 0.988324 \n", + "telemetry_diff.process.num_open_file_descriptors 0.507178 \n", + "telemetry_diff.process.num_connections 0.507178 \n", + "telemetry_diff.process.num_open_files NaN \n", + "telemetry_diff.process.num_threads -0.897315 \n", + "telemetry_diff.process.num_ctx_switches.voluntary 0.989118 \n", + "telemetry_diff.memory.virtual.available 0.337158 \n", + "telemetry_diff.memory.virtual.used 0.076707 \n", + "telemetry_diff.memory.virtual.free 0.101826 \n", + "telemetry_diff.memory.virtual.active 0.372320 \n", + "telemetry_diff.memory.virtual.inactive 0.921679 \n", + "telemetry_diff.memory.virtual.wired -0.814837 \n", + "telemetry_diff.memory.swap.total NaN \n", + "telemetry_diff.memory.swap.used -0.975343 \n", + "telemetry_diff.memory.swap.free 0.975343 \n", + "telemetry_diff.memory.swap.sin 0.927314 \n", + "telemetry_diff.memory.swap.sout 0.994675 \n", + "telemetry_diff.disk.disk_usage.free -0.625691 \n", + "telemetry_diff.disk.io_sum.read_count 0.937189 \n", + "telemetry_diff.disk.io_sum.write_count 0.997982 \n", + "telemetry_diff.disk.io_sum.read_bytes 0.932196 \n", + "telemetry_diff.disk.io_sum.write_bytes 0.997119 \n", + "telemetry_diff.disk.io_sum.read_time 0.962332 \n", + "telemetry_diff.disk.io_sum.write_time 0.994857 \n", + "telemetry_diff.network.netio_sum.bytes_sent 0.998880 \n", + "telemetry_diff.network.netio_sum.bytes_recv 0.996993 \n", + "telemetry_diff.network.netio_sum.packets_sent 0.998511 \n", + "telemetry_diff.network.netio_sum.packets_recv 0.996438 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.994665 \n", + "telemetry_diff.network.netio_per_interface.lo0.... 0.995648 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.982614 \n", + "telemetry_diff.network.netio_per_interface.en0.... 0.999096 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.997482 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.999179 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.996656 \n", + "telemetry_diff.network.netio_per_interface.utun... 0.997597 \n", + "telemetry_diff.network.netio_per_interface.vmen... 0.897315 \n", + "telemetry_diff.network.netio_per_interface.vmen... 1.000000 \n", + "telemetry_diff.network.netio_per_interface.brid... 0.998338 \n", + "telemetry_diff.network.netio_per_interface.brid... 1.000000 \n", + "\n", + "[58 rows x 58 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.corr()" ] @@ -265,12 +3897,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "e03dab0b-1a03-46a7-bbb2-4d16339abfe1", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of columns originally: 334\n", + "Number of columns later: 39\n" + ] + } + ], "source": [ "df = query_api.df_query(_filter, calculate_telemetry_diff=True)\n", "df = analytics.clean_dataframe(df, aggregate_telemetry=True, sum_lists=True)" @@ -288,12 +3929,145 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "89923e60-b251-45aa-8723-d42c548f8ea1", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
0generated.lossgenerated.accuracy0.18
1generated.lossgenerated.responsible_ai_metrics.flops0.43
2generated.lossgenerated.responsible_ai_metrics.params0.43
3generated.lossgenerated.responsible_ai_metrics.max_width0.83
4generated.lossgenerated.responsible_ai_metrics.depth0.49
............
625used.softmax_dims_sumtelemetry_diff.disk.activity0.04
626used.softmax_dims_sumtelemetry_diff.process.activity-0.10
627telemetry_diff.network.activitytelemetry_diff.disk.activity0.93
628telemetry_diff.network.activitytelemetry_diff.process.activity1.00
629telemetry_diff.disk.activitytelemetry_diff.process.activity0.93
\n", + "

630 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " col_1 \\\n", + "0 generated.loss \n", + "1 generated.loss \n", + "2 generated.loss \n", + "3 generated.loss \n", + "4 generated.loss \n", + ".. ... \n", + "625 used.softmax_dims_sum \n", + "626 used.softmax_dims_sum \n", + "627 telemetry_diff.network.activity \n", + "628 telemetry_diff.network.activity \n", + "629 telemetry_diff.disk.activity \n", + "\n", + " col_2 correlation \n", + "0 generated.accuracy 0.18 \n", + "1 generated.responsible_ai_metrics.flops 0.43 \n", + "2 generated.responsible_ai_metrics.params 0.43 \n", + "3 generated.responsible_ai_metrics.max_width 0.83 \n", + "4 generated.responsible_ai_metrics.depth 0.49 \n", + ".. ... ... \n", + "625 telemetry_diff.disk.activity 0.04 \n", + "626 telemetry_diff.process.activity -0.10 \n", + "627 telemetry_diff.disk.activity 0.93 \n", + "628 telemetry_diff.process.activity 1.00 \n", + "629 telemetry_diff.process.activity 0.93 \n", + "\n", + "[630 rows x 3 columns]" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations(df)" ] @@ -310,82 +4084,1206 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "fe702218-c80c-4e78-a642-8a91b5571b1d", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
0generated.lossgenerated.responsible_ai_metrics.max_width0.93
1generated.lossgenerated.responsible_ai_metrics.n_fc_layers0.94
2generated.lossused.softmax_dims_sum0.94
3generated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.params1.00
4generated.responsible_ai_metrics.flopsgenerated.responsible_ai_metrics.depth0.98
............
176used.conv_pool_sizes_sumtelemetry_diff.disk.activity0.94
177used.fc_in_outs_sumused.softmax_dims_sum0.95
178telemetry_diff.network.activitytelemetry_diff.disk.activity0.98
179telemetry_diff.network.activitytelemetry_diff.process.activity1.00
180telemetry_diff.disk.activitytelemetry_diff.process.activity0.98
\n", + "

181 rows × 3 columns

\n", + "
" + ], + "text/plain": [ + " col_1 \\\n", + "0 generated.loss \n", + "1 generated.loss \n", + "2 generated.loss \n", + "3 generated.responsible_ai_metrics.flops \n", + "4 generated.responsible_ai_metrics.flops \n", + ".. ... \n", + "176 used.conv_pool_sizes_sum \n", + "177 used.fc_in_outs_sum \n", + "178 telemetry_diff.network.activity \n", + "179 telemetry_diff.network.activity \n", + "180 telemetry_diff.disk.activity \n", + "\n", + " col_2 correlation \n", + "0 generated.responsible_ai_metrics.max_width 0.93 \n", + "1 generated.responsible_ai_metrics.n_fc_layers 0.94 \n", + "2 used.softmax_dims_sum 0.94 \n", + "3 generated.responsible_ai_metrics.params 1.00 \n", + "4 generated.responsible_ai_metrics.depth 0.98 \n", + ".. ... ... \n", + "176 telemetry_diff.disk.activity 0.94 \n", + "177 used.softmax_dims_sum 0.95 \n", + "178 telemetry_diff.disk.activity 0.98 \n", + "179 telemetry_diff.process.activity 1.00 \n", + "180 telemetry_diff.process.activity 0.98 \n", + "\n", + "[181 rows x 3 columns]" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations(df, method='spearman', threshold=0.9)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "f68a5db8-fcbe-4762-83fe-255a19a3ccc8", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
2generated.lossused.fc_in_outs_sum0.64
3generated.lossused.softmax_dims_sum0.87
8generated.accuracyused.conv_in_outs_sum-0.57
9generated.accuracyused.conv_kernel_sizes_sum-0.57
10generated.accuracyused.conv_pool_sizes_sum-0.57
25generated.responsible_ai_metrics.flopsused.fc_in_outs_sum0.79
26generated.responsible_ai_metrics.flopsused.softmax_dims_sum0.62
41generated.responsible_ai_metrics.paramsused.fc_in_outs_sum0.79
42generated.responsible_ai_metrics.paramsused.softmax_dims_sum0.62
47generated.responsible_ai_metrics.max_widthused.fc_in_outs_sum0.91
48generated.responsible_ai_metrics.max_widthused.softmax_dims_sum0.96
59generated.responsible_ai_metrics.depthused.fc_in_outs_sum0.87
60generated.responsible_ai_metrics.depthused.softmax_dims_sum0.70
63generated.responsible_ai_metrics.n_fc_layersused.fc_in_outs_sum0.87
64generated.responsible_ai_metrics.n_fc_layersused.softmax_dims_sum1.00
83generated.responsible_ai_metrics.n_cv_layersused.conv_in_outs_sum1.00
84generated.responsible_ai_metrics.n_cv_layersused.conv_kernel_sizes_sum1.00
85generated.responsible_ai_metrics.n_cv_layersused.conv_pool_sizes_sum1.00
\n", + "
" + ], + "text/plain": [ + " col_1 col_2 \\\n", + "2 generated.loss used.fc_in_outs_sum \n", + "3 generated.loss used.softmax_dims_sum \n", + "8 generated.accuracy used.conv_in_outs_sum \n", + "9 generated.accuracy used.conv_kernel_sizes_sum \n", + "10 generated.accuracy used.conv_pool_sizes_sum \n", + "25 generated.responsible_ai_metrics.flops used.fc_in_outs_sum \n", + "26 generated.responsible_ai_metrics.flops used.softmax_dims_sum \n", + "41 generated.responsible_ai_metrics.params used.fc_in_outs_sum \n", + "42 generated.responsible_ai_metrics.params used.softmax_dims_sum \n", + "47 generated.responsible_ai_metrics.max_width used.fc_in_outs_sum \n", + "48 generated.responsible_ai_metrics.max_width used.softmax_dims_sum \n", + "59 generated.responsible_ai_metrics.depth used.fc_in_outs_sum \n", + "60 generated.responsible_ai_metrics.depth used.softmax_dims_sum \n", + "63 generated.responsible_ai_metrics.n_fc_layers used.fc_in_outs_sum \n", + "64 generated.responsible_ai_metrics.n_fc_layers used.softmax_dims_sum \n", + "83 generated.responsible_ai_metrics.n_cv_layers used.conv_in_outs_sum \n", + "84 generated.responsible_ai_metrics.n_cv_layers used.conv_kernel_sizes_sum \n", + "85 generated.responsible_ai_metrics.n_cv_layers used.conv_pool_sizes_sum \n", + "\n", + " correlation \n", + "2 0.64 \n", + "3 0.87 \n", + "8 -0.57 \n", + "9 -0.57 \n", + "10 -0.57 \n", + "25 0.79 \n", + "26 0.62 \n", + "41 0.79 \n", + "42 0.62 \n", + "47 0.91 \n", + "48 0.96 \n", + "59 0.87 \n", + "60 0.70 \n", + "63 0.87 \n", + "64 1.00 \n", + "83 1.00 \n", + "84 1.00 \n", + "85 1.00 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations_between(df, col_pattern1=\"generated.\", col_pattern2=\"used.\", threshold=0.5)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "43601683-a091-412a-bbc6-e66f78546fc9", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
2generated.lossused.softmax_dims_sum0.87
7generated.responsible_ai_metrics.max_widthused.fc_in_outs_sum0.91
8generated.responsible_ai_metrics.max_widthused.softmax_dims_sum0.96
9generated.responsible_ai_metrics.depthused.fc_in_outs_sum0.87
10generated.responsible_ai_metrics.n_fc_layersused.fc_in_outs_sum0.87
11generated.responsible_ai_metrics.n_fc_layersused.softmax_dims_sum1.00
26generated.responsible_ai_metrics.n_cv_layersused.conv_in_outs_sum1.00
27generated.responsible_ai_metrics.n_cv_layersused.conv_kernel_sizes_sum1.00
28generated.responsible_ai_metrics.n_cv_layersused.conv_pool_sizes_sum1.00
\n", + "
" + ], + "text/plain": [ + " col_1 col_2 \\\n", + "2 generated.loss used.softmax_dims_sum \n", + "7 generated.responsible_ai_metrics.max_width used.fc_in_outs_sum \n", + "8 generated.responsible_ai_metrics.max_width used.softmax_dims_sum \n", + "9 generated.responsible_ai_metrics.depth used.fc_in_outs_sum \n", + "10 generated.responsible_ai_metrics.n_fc_layers used.fc_in_outs_sum \n", + "11 generated.responsible_ai_metrics.n_fc_layers used.softmax_dims_sum \n", + "26 generated.responsible_ai_metrics.n_cv_layers used.conv_in_outs_sum \n", + "27 generated.responsible_ai_metrics.n_cv_layers used.conv_kernel_sizes_sum \n", + "28 generated.responsible_ai_metrics.n_cv_layers used.conv_pool_sizes_sum \n", + "\n", + " correlation \n", + "2 0.87 \n", + "7 0.91 \n", + "8 0.96 \n", + "9 0.87 \n", + "10 0.87 \n", + "11 1.00 \n", + "26 1.00 \n", + "27 1.00 \n", + "28 1.00 " + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations_used_vs_generated(df, threshold=0.8)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "01ea1a46-7fe7-4334-b546-b23943af98e4", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
42telemetry_diff.cpu.times_avg.userused.conv_in_outs_sum0.87
43telemetry_diff.cpu.times_avg.userused.conv_kernel_sizes_sum0.87
44telemetry_diff.cpu.times_avg.userused.conv_pool_sizes_sum0.87
57telemetry_diff.cpu.times_avg.systemused.conv_in_outs_sum0.87
58telemetry_diff.cpu.times_avg.systemused.conv_kernel_sizes_sum0.87
59telemetry_diff.cpu.times_avg.systemused.conv_pool_sizes_sum0.87
71telemetry_diff.cpu.times_avg.idleused.conv_in_outs_sum0.87
72telemetry_diff.cpu.times_avg.idleused.conv_kernel_sizes_sum0.87
73telemetry_diff.cpu.times_avg.idleused.conv_pool_sizes_sum0.87
84telemetry_diff.process.memory.rssused.conv_in_outs_sum-0.87
85telemetry_diff.process.memory.rssused.conv_kernel_sizes_sum-0.87
86telemetry_diff.process.memory.rssused.conv_pool_sizes_sum-0.87
97telemetry_diff.process.memory.pfaultsused.conv_in_outs_sum0.87
98telemetry_diff.process.memory.pfaultsused.conv_kernel_sizes_sum0.87
99telemetry_diff.process.memory.pfaultsused.conv_pool_sizes_sum0.87
108telemetry_diff.process.cpu_times.userused.conv_in_outs_sum0.87
109telemetry_diff.process.cpu_times.userused.conv_kernel_sizes_sum0.87
110telemetry_diff.process.cpu_times.userused.conv_pool_sizes_sum0.87
118telemetry_diff.process.cpu_times.systemused.conv_in_outs_sum0.87
119telemetry_diff.process.cpu_times.systemused.conv_kernel_sizes_sum0.87
120telemetry_diff.process.cpu_times.systemused.conv_pool_sizes_sum0.87
125telemetry_diff.memory.virtual.usedused.conv_in_outs_sum0.88
126telemetry_diff.memory.virtual.usedused.conv_kernel_sizes_sum0.88
127telemetry_diff.memory.virtual.usedused.conv_pool_sizes_sum0.88
128telemetry_diff.memory.virtual.activeused.conv_in_outs_sum0.88
129telemetry_diff.memory.virtual.activeused.conv_kernel_sizes_sum0.88
130telemetry_diff.memory.virtual.activeused.conv_pool_sizes_sum0.88
133telemetry_diff.memory.virtual.inactiveused.conv_in_outs_sum0.87
134telemetry_diff.memory.virtual.inactiveused.conv_kernel_sizes_sum0.87
135telemetry_diff.memory.virtual.inactiveused.conv_pool_sizes_sum0.87
139telemetry_diff.memory.swap.usedused.conv_in_outs_sum-0.96
140telemetry_diff.memory.swap.usedused.conv_kernel_sizes_sum-0.96
141telemetry_diff.memory.swap.usedused.conv_pool_sizes_sum-0.96
147telemetry_diff.memory.swap.freeused.conv_in_outs_sum0.96
148telemetry_diff.memory.swap.freeused.conv_kernel_sizes_sum0.96
149telemetry_diff.memory.swap.freeused.conv_pool_sizes_sum0.96
154telemetry_diff.memory.swap.sinused.conv_in_outs_sum0.88
155telemetry_diff.memory.swap.sinused.conv_kernel_sizes_sum0.88
156telemetry_diff.memory.swap.sinused.conv_pool_sizes_sum0.88
160telemetry_diff.memory.swap.soutused.conv_in_outs_sum0.90
161telemetry_diff.memory.swap.soutused.conv_kernel_sizes_sum0.90
162telemetry_diff.memory.swap.soutused.conv_pool_sizes_sum0.90
168used.conv_in_outs_sumtelemetry_diff.network.activity0.87
169used.conv_in_outs_sumtelemetry_diff.disk.activity0.87
171used.conv_kernel_sizes_sumtelemetry_diff.network.activity0.87
172used.conv_kernel_sizes_sumtelemetry_diff.disk.activity0.87
173used.conv_pool_sizes_sumtelemetry_diff.network.activity0.87
174used.conv_pool_sizes_sumtelemetry_diff.disk.activity0.87
\n", + "
" + ], + "text/plain": [ + " col_1 col_2 \\\n", + "42 telemetry_diff.cpu.times_avg.user used.conv_in_outs_sum \n", + "43 telemetry_diff.cpu.times_avg.user used.conv_kernel_sizes_sum \n", + "44 telemetry_diff.cpu.times_avg.user used.conv_pool_sizes_sum \n", + "57 telemetry_diff.cpu.times_avg.system used.conv_in_outs_sum \n", + "58 telemetry_diff.cpu.times_avg.system used.conv_kernel_sizes_sum \n", + "59 telemetry_diff.cpu.times_avg.system used.conv_pool_sizes_sum \n", + "71 telemetry_diff.cpu.times_avg.idle used.conv_in_outs_sum \n", + "72 telemetry_diff.cpu.times_avg.idle used.conv_kernel_sizes_sum \n", + "73 telemetry_diff.cpu.times_avg.idle used.conv_pool_sizes_sum \n", + "84 telemetry_diff.process.memory.rss used.conv_in_outs_sum \n", + "85 telemetry_diff.process.memory.rss used.conv_kernel_sizes_sum \n", + "86 telemetry_diff.process.memory.rss used.conv_pool_sizes_sum \n", + "97 telemetry_diff.process.memory.pfaults used.conv_in_outs_sum \n", + "98 telemetry_diff.process.memory.pfaults used.conv_kernel_sizes_sum \n", + "99 telemetry_diff.process.memory.pfaults used.conv_pool_sizes_sum \n", + "108 telemetry_diff.process.cpu_times.user used.conv_in_outs_sum \n", + "109 telemetry_diff.process.cpu_times.user used.conv_kernel_sizes_sum \n", + "110 telemetry_diff.process.cpu_times.user used.conv_pool_sizes_sum \n", + "118 telemetry_diff.process.cpu_times.system used.conv_in_outs_sum \n", + "119 telemetry_diff.process.cpu_times.system used.conv_kernel_sizes_sum \n", + "120 telemetry_diff.process.cpu_times.system used.conv_pool_sizes_sum \n", + "125 telemetry_diff.memory.virtual.used used.conv_in_outs_sum \n", + "126 telemetry_diff.memory.virtual.used used.conv_kernel_sizes_sum \n", + "127 telemetry_diff.memory.virtual.used used.conv_pool_sizes_sum \n", + "128 telemetry_diff.memory.virtual.active used.conv_in_outs_sum \n", + "129 telemetry_diff.memory.virtual.active used.conv_kernel_sizes_sum \n", + "130 telemetry_diff.memory.virtual.active used.conv_pool_sizes_sum \n", + "133 telemetry_diff.memory.virtual.inactive used.conv_in_outs_sum \n", + "134 telemetry_diff.memory.virtual.inactive used.conv_kernel_sizes_sum \n", + "135 telemetry_diff.memory.virtual.inactive used.conv_pool_sizes_sum \n", + "139 telemetry_diff.memory.swap.used used.conv_in_outs_sum \n", + "140 telemetry_diff.memory.swap.used used.conv_kernel_sizes_sum \n", + "141 telemetry_diff.memory.swap.used used.conv_pool_sizes_sum \n", + "147 telemetry_diff.memory.swap.free used.conv_in_outs_sum \n", + "148 telemetry_diff.memory.swap.free used.conv_kernel_sizes_sum \n", + "149 telemetry_diff.memory.swap.free used.conv_pool_sizes_sum \n", + "154 telemetry_diff.memory.swap.sin used.conv_in_outs_sum \n", + "155 telemetry_diff.memory.swap.sin used.conv_kernel_sizes_sum \n", + "156 telemetry_diff.memory.swap.sin used.conv_pool_sizes_sum \n", + "160 telemetry_diff.memory.swap.sout used.conv_in_outs_sum \n", + "161 telemetry_diff.memory.swap.sout used.conv_kernel_sizes_sum \n", + "162 telemetry_diff.memory.swap.sout used.conv_pool_sizes_sum \n", + "168 used.conv_in_outs_sum telemetry_diff.network.activity \n", + "169 used.conv_in_outs_sum telemetry_diff.disk.activity \n", + "171 used.conv_kernel_sizes_sum telemetry_diff.network.activity \n", + "172 used.conv_kernel_sizes_sum telemetry_diff.disk.activity \n", + "173 used.conv_pool_sizes_sum telemetry_diff.network.activity \n", + "174 used.conv_pool_sizes_sum telemetry_diff.disk.activity \n", + "\n", + " correlation \n", + "42 0.87 \n", + "43 0.87 \n", + "44 0.87 \n", + "57 0.87 \n", + "58 0.87 \n", + "59 0.87 \n", + "71 0.87 \n", + "72 0.87 \n", + "73 0.87 \n", + "84 -0.87 \n", + "85 -0.87 \n", + "86 -0.87 \n", + "97 0.87 \n", + "98 0.87 \n", + "99 0.87 \n", + "108 0.87 \n", + "109 0.87 \n", + "110 0.87 \n", + "118 0.87 \n", + "119 0.87 \n", + "120 0.87 \n", + "125 0.88 \n", + "126 0.88 \n", + "127 0.88 \n", + "128 0.88 \n", + "129 0.88 \n", + "130 0.88 \n", + "133 0.87 \n", + "134 0.87 \n", + "135 0.87 \n", + "139 -0.96 \n", + "140 -0.96 \n", + "141 -0.96 \n", + "147 0.96 \n", + "148 0.96 \n", + "149 0.96 \n", + "154 0.88 \n", + "155 0.88 \n", + "156 0.88 \n", + "160 0.90 \n", + "161 0.90 \n", + "162 0.90 \n", + "168 0.87 \n", + "169 0.87 \n", + "171 0.87 \n", + "172 0.87 \n", + "173 0.87 \n", + "174 0.87 " + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations_used_vs_telemetry_diff(df, threshold=0.8)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "5b3c1356-6209-4d92-b87f-d84f00b20041", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
col_1col_2correlation
12generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.cpu.times_avg.user0.87
13generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.cpu.times_avg.system0.87
14generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.cpu.times_avg.idle0.87
15generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.process.memory.rss-0.87
16generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.process.memory.pfaults0.87
17generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.process.cpu_times.user0.87
18generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.process.cpu_times.system0.87
19generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.virtual.used0.88
20generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.virtual.active0.88
21generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.virtual.inactive0.87
22generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.swap.used-0.96
23generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.swap.free0.96
24generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.swap.sin0.88
25generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.memory.swap.sout0.90
29generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.network.activity0.87
30generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.disk.activity0.87
31generated.responsible_ai_metrics.n_cv_layerstelemetry_diff.process.activity0.87
\n", + "
" + ], + "text/plain": [ + " col_1 \\\n", + "12 generated.responsible_ai_metrics.n_cv_layers \n", + "13 generated.responsible_ai_metrics.n_cv_layers \n", + "14 generated.responsible_ai_metrics.n_cv_layers \n", + "15 generated.responsible_ai_metrics.n_cv_layers \n", + "16 generated.responsible_ai_metrics.n_cv_layers \n", + "17 generated.responsible_ai_metrics.n_cv_layers \n", + "18 generated.responsible_ai_metrics.n_cv_layers \n", + "19 generated.responsible_ai_metrics.n_cv_layers \n", + "20 generated.responsible_ai_metrics.n_cv_layers \n", + "21 generated.responsible_ai_metrics.n_cv_layers \n", + "22 generated.responsible_ai_metrics.n_cv_layers \n", + "23 generated.responsible_ai_metrics.n_cv_layers \n", + "24 generated.responsible_ai_metrics.n_cv_layers \n", + "25 generated.responsible_ai_metrics.n_cv_layers \n", + "29 generated.responsible_ai_metrics.n_cv_layers \n", + "30 generated.responsible_ai_metrics.n_cv_layers \n", + "31 generated.responsible_ai_metrics.n_cv_layers \n", + "\n", + " col_2 correlation \n", + "12 telemetry_diff.cpu.times_avg.user 0.87 \n", + "13 telemetry_diff.cpu.times_avg.system 0.87 \n", + "14 telemetry_diff.cpu.times_avg.idle 0.87 \n", + "15 telemetry_diff.process.memory.rss -0.87 \n", + "16 telemetry_diff.process.memory.pfaults 0.87 \n", + "17 telemetry_diff.process.cpu_times.user 0.87 \n", + "18 telemetry_diff.process.cpu_times.system 0.87 \n", + "19 telemetry_diff.memory.virtual.used 0.88 \n", + "20 telemetry_diff.memory.virtual.active 0.88 \n", + "21 telemetry_diff.memory.virtual.inactive 0.87 \n", + "22 telemetry_diff.memory.swap.used -0.96 \n", + "23 telemetry_diff.memory.swap.free 0.96 \n", + "24 telemetry_diff.memory.swap.sin 0.88 \n", + "25 telemetry_diff.memory.swap.sout 0.90 \n", + "29 telemetry_diff.network.activity 0.87 \n", + "30 telemetry_diff.disk.activity 0.87 \n", + "31 telemetry_diff.process.activity 0.87 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.analyze_correlations_generated_vs_telemetry_diff(df, threshold=0.8)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "bae58142-3f70-4df8-a57a-4f75eef0cca8", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "{'label': 'generated.loss',\n", + " 'mean': '0.04',\n", + " 'std': '0.02',\n", + " 'min': '0.01',\n", + " '25%': '0.02',\n", + " '50%': '0.04',\n", + " '75%': '0.04',\n", + " 'max': '0.06'}" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.describe_col(df, col='generated.loss')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "f9a5bdd4-b5ed-441d-9b96-bdd57fe89bd6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
labelmeanstdmin25%50%75%max
0Loss0.040.020.010.020.040.040.06
1#Params13.47M18.57M162.99K1.51M5.29M17.06M43.93M
\n", + "
" + ], + "text/plain": [ + " label mean std min 25% 50% 75% max\n", + "0 Loss 0.04 0.02 0.01 0.02 0.04 0.04 0.06\n", + "1 #Params 13.47M 18.57M 162.99K 1.51M 5.29M 17.06M 43.93M" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "analytics.describe_cols(df, cols=['generated.loss','generated.responsible_ai_metrics.params'], col_labels=['Loss', '#Params'])" ] @@ -402,12 +5300,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "id": "c369915d-b12d-4bf7-b0f4-02e5b5be8a9b", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of columns originally: 334\n", + "Number of columns later: 39\n" + ] + } + ], "source": [ "_filter = {\n", " \"workflow_id\": wf_id\n", @@ -417,14 +5324,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "e765a93d-a005-4d77-9d42-4acde84bb72a", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "analytics.heatmap(df)" + "flow_plot.heatmap(df)" ] }, { @@ -437,17 +5355,2861 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6bb073fa-6e17-403e-8c9f-3884b86119f5", + "execution_count": 10, + "id": "4613ace3-ab5a-4553-9629-ac94daa30c0f", "metadata": { "tags": [] }, "outputs": [], + "source": [ + "df.to_csv('sample_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6bb073fa-6e17-403e-8c9f-3884b86119f5", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "customdata": [ + [ + "0.01", + "397.45", + "162.99K" + ], + [ + "0.04", + "411.42", + "359.84K" + ], + [ + "0.06", + "1.18K", + "42.18M" + ], + [ + "0.02", + "1.86K", + "1.89M" + ], + [ + "0.04", + "1.88K", + "2.09M" + ], + [ + "0.06", + "2.38K", + "43.93M" + ], + [ + "0.02", + "4.17K", + "8.49M" + ], + [ + "0.04", + "4.17K", + "8.69M" + ], + [ + "nan", + "nan", + "nan" + ] + ], + "hovertemplate": "Loss: %{customdata[0]}
User CPU: %{customdata[1]}
#Params: %{customdata[2]}", + "marker": { + "color": [ + 162990, + 359840, + 42184840, + 1890690, + 2089540, + 43930540, + 8485880, + 8687730, + null + ], + "colorbar": { + "orientation": "v", + "title": { + "text": "#Params" + } + }, + "colorscale": [ + [ + 0, + "rgb(255,245,240)" + ], + [ + 0.125, + "rgb(254,224,210)" + ], + [ + 0.25, + "rgb(252,187,161)" + ], + [ + 0.375, + "rgb(252,146,114)" + ], + [ + 0.5, + "rgb(251,106,74)" + ], + [ + 0.625, + "rgb(239,59,44)" + ], + [ + 0.75, + "rgb(203,24,29)" + ], + [ + 0.875, + "rgb(165,15,21)" + ], + [ + 1, + "rgb(103,0,13)" + ] + ], + "opacity": 0.8, + "reversescale": false, + "size": 5 + }, + "mode": "markers", + "name": "", + "type": "scatter", + "x": [ + 0.014728536784648895, + 0.040325844478607174, + 0.05815730080604553, + 0.018241909003257752, + 0.040312224340438844, + 0.05813799858093262, + 0.018207813382148743, + 0.04012699522972107, + null + ], + "y": [ + 397.4499999999971, + 411.41999999999825, + 1179.7599999999948, + 1862.6100000000006, + 1875.5800000000017, + 2384.9100000000035, + 4169.770000000004, + 4172.880000000005, + null + ] + }, + { + "marker": { + "color": "darkred", + "opacity": 0.5, + "size": 1 + }, + "mode": "markers", + "name": "", + "type": "scatter", + "x": [ + 0.014728536784648895, + 0.014776844642292272, + 0.01482515249993565, + 0.014873460357579029, + 0.014921768215222406, + 0.014970076072865783, + 0.015018383930509163, + 0.01506669178815254, + 0.015114999645795917, + 0.015163307503439295, + 0.015211615361082672, + 0.015259923218726051, + 0.015308231076369428, + 0.015356538934012806, + 0.015404846791656185, + 0.015453154649299562, + 0.01550146250694294, + 0.015549770364586317, + 0.015598078222229694, + 0.01564638607987307, + 0.01569469393751645, + 0.01574300179515983, + 0.015791309652803207, + 0.015839617510446585, + 0.015887925368089962, + 0.01593623322573334, + 0.015984541083376717, + 0.016032848941020094, + 0.016081156798663475, + 0.016129464656306852, + 0.01617777251395023, + 0.016226080371593607, + 0.016274388229236984, + 0.01632269608688036, + 0.01637100394452374, + 0.016419311802167116, + 0.016467619659810494, + 0.016515927517453875, + 0.016564235375097252, + 0.01661254323274063, + 0.016660851090384007, + 0.016709158948027384, + 0.01675746680567076, + 0.01680577466331414, + 0.01685408252095752, + 0.016902390378600897, + 0.016950698236244274, + 0.01699900609388765, + 0.01704731395153103, + 0.017095621809174406, + 0.017143929666817784, + 0.01719223752446116, + 0.01724054538210454, + 0.01728885323974792, + 0.017337161097391297, + 0.017385468955034674, + 0.01743377681267805, + 0.01748208467032143, + 0.017530392527964806, + 0.017578700385608183, + 0.017627008243251564, + 0.01767531610089494, + 0.01772362395853832, + 0.017771931816181696, + 0.017820239673825074, + 0.01786854753146845, + 0.01791685538911183, + 0.017965163246755206, + 0.018013471104398583, + 0.018061778962041964, + 0.01811008681968534, + 0.01815839467732872, + 0.018206702534972096, + 0.018255010392615473, + 0.01830331825025885, + 0.018351626107902228, + 0.01839993396554561, + 0.018448241823188986, + 0.018496549680832364, + 0.01854485753847574, + 0.01859316539611912, + 0.018641473253762496, + 0.018689781111405873, + 0.01873808896904925, + 0.018786396826692628, + 0.018834704684336005, + 0.018883012541979386, + 0.018931320399622763, + 0.01897962825726614, + 0.019027936114909518, + 0.019076243972552896, + 0.019124551830196276, + 0.019172859687839654, + 0.01922116754548303, + 0.01926947540312641, + 0.019317783260769786, + 0.019366091118413163, + 0.01941439897605654, + 0.019462706833699918, + 0.019511014691343295, + 0.019559322548986673, + 0.01960763040663005, + 0.01965593826427343, + 0.019704246121916808, + 0.019752553979560186, + 0.019800861837203563, + 0.01984916969484694, + 0.01989747755249032, + 0.0199457854101337, + 0.019994093267777076, + 0.020042401125420453, + 0.02009070898306383, + 0.020139016840707208, + 0.020187324698350585, + 0.020235632555993963, + 0.02028394041363734, + 0.020332248271280717, + 0.020380556128924095, + 0.020428863986567476, + 0.020477171844210853, + 0.02052547970185423, + 0.020573787559497608, + 0.020622095417140985, + 0.020670403274784366, + 0.020718711132427743, + 0.02076701899007112, + 0.020815326847714498, + 0.020863634705357875, + 0.020911942563001253, + 0.02096025042064463, + 0.021008558278288007, + 0.021056866135931385, + 0.021105173993574762, + 0.02115348185121814, + 0.02120178970886152, + 0.021250097566504898, + 0.021298405424148275, + 0.021346713281791652, + 0.02139502113943503, + 0.02144332899707841, + 0.021491636854721788, + 0.021539944712365165, + 0.021588252570008543, + 0.02163656042765192, + 0.021684868285295297, + 0.021733176142938675, + 0.021781484000582052, + 0.02182979185822543, + 0.021878099715868807, + 0.021926407573512184, + 0.021974715431155565, + 0.022023023288798942, + 0.02207133114644232, + 0.022119639004085697, + 0.022167946861729074, + 0.022216254719372455, + 0.022264562577015833, + 0.02231287043465921, + 0.022361178292302587, + 0.022409486149945965, + 0.022457794007589342, + 0.02250610186523272, + 0.022554409722876097, + 0.022602717580519474, + 0.02265102543816285, + 0.02269933329580623, + 0.02274764115344961, + 0.022795949011092987, + 0.022844256868736364, + 0.022892564726379742, + 0.02294087258402312, + 0.0229891804416665, + 0.023037488299309877, + 0.023085796156953255, + 0.023134104014596632, + 0.02318241187224001, + 0.023230719729883387, + 0.023279027587526764, + 0.02332733544517014, + 0.02337564330281352, + 0.023423951160456896, + 0.023472259018100274, + 0.023520566875743654, + 0.02356887473338703, + 0.02361718259103041, + 0.023665490448673786, + 0.023713798306317164, + 0.023762106163960545, + 0.023810414021603922, + 0.0238587218792473, + 0.023907029736890677, + 0.023955337594534054, + 0.02400364545217743, + 0.02405195330982081, + 0.024100261167464186, + 0.024148569025107564, + 0.02419687688275094, + 0.024245184740394318, + 0.0242934925980377, + 0.024341800455681076, + 0.024390108313324454, + 0.02443841617096783, + 0.02448672402861121, + 0.02453503188625459, + 0.024583339743897967, + 0.024631647601541344, + 0.02467995545918472, + 0.0247282633168281, + 0.024776571174471476, + 0.024824879032114854, + 0.02487318688975823, + 0.024921494747401608, + 0.024969802605044986, + 0.025018110462688363, + 0.025066418320331744, + 0.02511472617797512, + 0.0251630340356185, + 0.025211341893261876, + 0.025259649750905253, + 0.025307957608548634, + 0.02535626546619201, + 0.02540457332383539, + 0.025452881181478766, + 0.025501189039122144, + 0.02554949689676552, + 0.025597804754408898, + 0.025646112612052276, + 0.025694420469695653, + 0.02574272832733903, + 0.025791036184982408, + 0.02583934404262579, + 0.025887651900269166, + 0.025935959757912543, + 0.02598426761555592, + 0.026032575473199298, + 0.02608088333084268, + 0.026129191188486056, + 0.026177499046129434, + 0.02622580690377281, + 0.026274114761416188, + 0.026322422619059566, + 0.026370730476702943, + 0.02641903833434632, + 0.026467346191989698, + 0.026515654049633075, + 0.026563961907276452, + 0.026612269764919833, + 0.02666057762256321, + 0.026708885480206588, + 0.026757193337849965, + 0.026805501195493343, + 0.026853809053136724, + 0.0269021169107801, + 0.026950424768423478, + 0.026998732626066856, + 0.027047040483710233, + 0.02709534834135361, + 0.027143656198996988, + 0.027191964056640365, + 0.027240271914283742, + 0.02728857977192712, + 0.027336887629570497, + 0.027385195487213878, + 0.027433503344857255, + 0.027481811202500633, + 0.02753011906014401, + 0.027578426917787387, + 0.027626734775430768, + 0.027675042633074146, + 0.027723350490717523, + 0.0277716583483609, + 0.027819966206004278, + 0.027868274063647655, + 0.027916581921291032, + 0.02796488977893441, + 0.028013197636577787, + 0.028061505494221164, + 0.028109813351864542, + 0.028158121209507923, + 0.0282064290671513, + 0.028254736924794677, + 0.028303044782438055, + 0.028351352640081432, + 0.028399660497724813, + 0.02844796835536819, + 0.028496276213011568, + 0.028544584070654945, + 0.028592891928298322, + 0.0286411997859417, + 0.028689507643585077, + 0.028737815501228454, + 0.028786123358871832, + 0.02883443121651521, + 0.028882739074158587, + 0.028931046931801967, + 0.028979354789445345, + 0.029027662647088722, + 0.0290759705047321, + 0.029124278362375477, + 0.029172586220018858, + 0.029220894077662235, + 0.029269201935305612, + 0.02931750979294899, + 0.029365817650592367, + 0.029414125508235744, + 0.029462433365879122, + 0.0295107412235225, + 0.029559049081165877, + 0.029607356938809254, + 0.02965566479645263, + 0.029703972654096012, + 0.02975228051173939, + 0.029800588369382767, + 0.029848896227026144, + 0.02989720408466952, + 0.029945511942312902, + 0.02999381979995628, + 0.030042127657599657, + 0.030090435515243034, + 0.030138743372886412, + 0.03018705123052979, + 0.030235359088173167, + 0.030283666945816544, + 0.03033197480345992, + 0.0303802826611033, + 0.03042859051874668, + 0.030476898376390057, + 0.030525206234033434, + 0.03057351409167681, + 0.03062182194932019, + 0.030670129806963566, + 0.030718437664606944, + 0.030766745522250324, + 0.030815053379893702, + 0.03086336123753708, + 0.030911669095180457, + 0.030959976952823834, + 0.03100828481046721, + 0.03105659266811059, + 0.031104900525753966, + 0.031153208383397343, + 0.031201516241040724, + 0.0312498240986841, + 0.03129813195632748, + 0.03134643981397085, + 0.03139474767161424, + 0.03144305552925761, + 0.03149136338690099, + 0.03153967124454437, + 0.031587979102187747, + 0.031636286959831124, + 0.0316845948174745, + 0.03173290267511788, + 0.031781210532761256, + 0.03182951839040463, + 0.03187782624804801, + 0.03192613410569139, + 0.031974441963334765, + 0.03202274982097815, + 0.03207105767862152, + 0.032119365536264904, + 0.032167673393908275, + 0.03221598125155166, + 0.03226428910919503, + 0.032312596966838414, + 0.03236090482448179, + 0.03240921268212517, + 0.032457520539768546, + 0.03250582839741192, + 0.0325541362550553, + 0.03260244411269868, + 0.032650751970342055, + 0.03269905982798543, + 0.03274736768562882, + 0.03279567554327219, + 0.03284398340091557, + 0.03289229125855894, + 0.032940599116202327, + 0.0329889069738457, + 0.03303721483148908, + 0.03308552268913246, + 0.033133830546775836, + 0.03318213840441921, + 0.03323044626206259, + 0.03327875411970597, + 0.033327061977349345, + 0.03337536983499272, + 0.0334236776926361, + 0.03347198555027948, + 0.033520293407922855, + 0.03356860126556624, + 0.03361690912320961, + 0.033665216980852994, + 0.033713524838496364, + 0.03376183269613975, + 0.03381014055378312, + 0.0338584484114265, + 0.03390675626906988, + 0.03395506412671326, + 0.034003371984356635, + 0.03405167984200001, + 0.03409998769964339, + 0.03414829555728677, + 0.034196603414930145, + 0.03424491127257352, + 0.034293219130216906, + 0.03434152698786028, + 0.03438983484550366, + 0.03443814270314703, + 0.034486450560790416, + 0.034534758418433786, + 0.03458306627607717, + 0.03463137413372055, + 0.034679681991363925, + 0.0347279898490073, + 0.03477629770665068, + 0.03482460556429406, + 0.034872913421937435, + 0.03492122127958081, + 0.03496952913722419, + 0.03501783699486757, + 0.035066144852510944, + 0.03511445271015433, + 0.0351627605677977, + 0.03521106842544108, + 0.035259376283084454, + 0.03530768414072784, + 0.03535599199837121, + 0.03540429985601459, + 0.03545260771365797, + 0.03550091557130135, + 0.035549223428944725, + 0.0355975312865881, + 0.03564583914423148, + 0.03569414700187486, + 0.035742454859518234, + 0.03579076271716161, + 0.035839070574804996, + 0.035887378432448366, + 0.03593568629009175, + 0.03598399414773512, + 0.036032302005378505, + 0.036080609863021876, + 0.03612891772066526, + 0.03617722557830864, + 0.036225533435952015, + 0.03627384129359539, + 0.03632214915123877, + 0.03637045700888215, + 0.036418764866525524, + 0.0364670727241689, + 0.03651538058181228, + 0.036563688439455656, + 0.036611996297099034, + 0.03666030415474242, + 0.03670861201238579, + 0.03675691987002917, + 0.03680522772767254, + 0.03685353558531593, + 0.0369018434429593, + 0.03695015130060268, + 0.03699845915824606, + 0.03704676701588944, + 0.037095074873532814, + 0.03714338273117619, + 0.03719169058881957, + 0.037239998446462946, + 0.037288306304106324, + 0.0373366141617497, + 0.037384922019393085, + 0.037433229877036456, + 0.03748153773467984, + 0.03752984559232321, + 0.037578153449966595, + 0.037626461307609965, + 0.03767476916525335, + 0.03772307702289673, + 0.037771384880540104, + 0.03781969273818348, + 0.03786800059582686, + 0.037916308453470236, + 0.037964616311113614, + 0.03801292416875699, + 0.03806123202640037, + 0.038109539884043746, + 0.03815784774168712, + 0.03820615559933051, + 0.03825446345697388, + 0.03830277131461726, + 0.03835107917226063, + 0.03839938702990402, + 0.03844769488754739, + 0.03849600274519077, + 0.03854431060283415, + 0.038592618460477526, + 0.038640926318120904, + 0.03868923417576428, + 0.03873754203340766, + 0.038785849891051036, + 0.03883415774869441, + 0.03888246560633779, + 0.038930773463981175, + 0.038979081321624545, + 0.03902738917926793, + 0.0390756970369113, + 0.039124004894554684, + 0.039172312752198055, + 0.03922062060984144, + 0.039268928467484816, + 0.039317236325128194, + 0.03936554418277157, + 0.03941385204041495, + 0.039462159898058326, + 0.0395104677557017, + 0.03955877561334508, + 0.03960708347098846, + 0.039655391328631835, + 0.03970369918627521, + 0.0397520070439186, + 0.03980031490156197, + 0.03984862275920535, + 0.03989693061684872, + 0.039945238474492106, + 0.03999354633213548, + 0.04004185418977886, + 0.04009016204742224, + 0.040138469905065616, + 0.04018677776270899, + 0.04023508562035237, + 0.04028339347799575, + 0.040331701335639125, + 0.0403800091932825, + 0.04042831705092588, + 0.040476624908569264, + 0.040524932766212635, + 0.04057324062385602, + 0.04062154848149939, + 0.040669856339142774, + 0.040718164196786144, + 0.04076647205442953, + 0.040814779912072906, + 0.04086308776971628, + 0.04091139562735966, + 0.04095970348500304, + 0.041008011342646415, + 0.04105631920028979, + 0.04110462705793317, + 0.04115293491557655, + 0.041201242773219925, + 0.0412495506308633, + 0.041297858488506686, + 0.04134616634615006, + 0.04139447420379344, + 0.04144278206143681, + 0.041491089919080196, + 0.041539397776723566, + 0.04158770563436695, + 0.04163601349201033, + 0.041684321349653705, + 0.04173262920729708, + 0.04178093706494046, + 0.04182924492258384, + 0.041877552780227215, + 0.04192586063787059, + 0.04197416849551397, + 0.042022476353157354, + 0.042070784210800724, + 0.04211909206844411, + 0.04216739992608748, + 0.04221570778373086, + 0.042264015641374234, + 0.04231232349901762, + 0.042360631356660995, + 0.04240893921430437, + 0.04245724707194775, + 0.04250555492959113, + 0.042553862787234505, + 0.04260217064487788, + 0.04265047850252126, + 0.04269878636016464, + 0.042747094217808014, + 0.04279540207545139, + 0.042843709933094776, + 0.042892017790738146, + 0.04294032564838153, + 0.0429886335060249, + 0.043036941363668285, + 0.043085249221311656, + 0.04313355707895504, + 0.04318186493659842, + 0.043230172794241795, + 0.04327848065188517, + 0.04332678850952855, + 0.04337509636717193, + 0.043423404224815304, + 0.04347171208245868, + 0.04352001994010206, + 0.04356832779774544, + 0.043616635655388813, + 0.0436649435130322, + 0.04371325137067557, + 0.04376155922831895, + 0.04380986708596232, + 0.04385817494360571, + 0.043906482801249085, + 0.04395479065889246, + 0.04400309851653584, + 0.04405140637417922, + 0.044099714231822594, + 0.04414802208946597, + 0.04419632994710935, + 0.044244637804752726, + 0.044292945662396103, + 0.04434125352003948, + 0.044389561377682865, + 0.044437869235326236, + 0.04448617709296962, + 0.04453448495061299, + 0.044582792808256375, + 0.044631100665899745, + 0.04467940852354313, + 0.04472771638118651, + 0.044776024238829884, + 0.04482433209647326, + 0.04487263995411664, + 0.044920947811760016, + 0.044969255669403393, + 0.04501756352704677, + 0.04506587138469015, + 0.04511417924233353, + 0.0451624870999769, + 0.04521079495762029, + 0.04525910281526366, + 0.04530741067290704, + 0.04535571853055041, + 0.0454040263881938, + 0.045452334245837174, + 0.04550064210348055, + 0.04554894996112393, + 0.045597257818767306, + 0.04564556567641068, + 0.04569387353405406, + 0.04574218139169744, + 0.045790489249340816, + 0.04583879710698419, + 0.04588710496462757, + 0.045935412822270955, + 0.045983720679914325, + 0.0460320285375577, + 0.04608033639520109, + 0.046128644252844464, + 0.04617695211048784, + 0.04622525996813122, + 0.046273567825774596, + 0.04632187568341797, + 0.04637018354106135, + 0.04641849139870473, + 0.046466799256348106, + 0.04651510711399148, + 0.04656341497163486, + 0.04661172282927824, + 0.046660030686921615, + 0.04670833854456499, + 0.04675664640220837, + 0.046804954259851754, + 0.04685326211749513, + 0.04690156997513851, + 0.046949877832781886, + 0.04699818569042526, + 0.04704649354806864, + 0.04709480140571202, + 0.047143109263355396, + 0.04719141712099877, + 0.04723972497864215, + 0.04728803283628553, + 0.047336340693928905, + 0.04738464855157228, + 0.04743295640921566, + 0.04748126426685904, + 0.047529572124502414, + 0.04757787998214579, + 0.047626187839789176, + 0.04767449569743255, + 0.04772280355507593, + 0.04777111141271931, + 0.047819419270362686, + 0.04786772712800606, + 0.04791603498564944, + 0.04796434284329282, + 0.048012650700936195, + 0.04806095855857957, + 0.04810926641622295, + 0.04815757427386633, + 0.048205882131509704, + 0.04825418998915308, + 0.04830249784679646, + 0.04835080570443984, + 0.04839911356208322, + 0.0484474214197266, + 0.048495729277369976, + 0.04854403713501335, + 0.04859234499265673, + 0.04864065285030011, + 0.048688960707943485, + 0.04873726856558686, + 0.04878557642323024, + 0.04883388428087362, + 0.048882192138516994, + 0.04893049999616037, + 0.04897880785380375, + 0.049027115711447126, + 0.049075423569090504, + 0.04912373142673388, + 0.049172039284377266, + 0.04922034714202064, + 0.04926865499966402, + 0.0493169628573074, + 0.049365270714950775, + 0.04941357857259415, + 0.04946188643023753, + 0.04951019428788091, + 0.049558502145524284, + 0.04960681000316766, + 0.04965511786081104, + 0.049703425718454416, + 0.049751733576097794, + 0.04980004143374117, + 0.04984834929138455, + 0.04989665714902793, + 0.04994496500667131, + 0.04999327286431469, + 0.050041580721958065, + 0.05008988857960144, + 0.05013819643724482, + 0.0501865042948882, + 0.050234812152531574, + 0.05028312001017495, + 0.05033142786781833, + 0.050379735725461706, + 0.050428043583105084, + 0.05047635144074846, + 0.05052465929839184, + 0.050572967156035216, + 0.05062127501367859, + 0.05066958287132197, + 0.050717890728965355, + 0.05076619858660873, + 0.05081450644425211, + 0.05086281430189549, + 0.050911122159538864, + 0.05095943001718224, + 0.05100773787482562, + 0.051056045732468996, + 0.051104353590112374, + 0.05115266144775575, + 0.05120096930539913, + 0.051249277163042506, + 0.05129758502068588, + 0.05134589287832926, + 0.05139420073597264, + 0.05144250859361602, + 0.0514908164512594, + 0.05153912430890278, + 0.051587432166546154, + 0.05163574002418953, + 0.05168404788183291, + 0.051732355739476286, + 0.051780663597119664, + 0.05182897145476304, + 0.05187727931240642, + 0.051925587170049796, + 0.05197389502769317, + 0.05202220288533655, + 0.05207051074297993, + 0.052118818600623305, + 0.05216712645826668, + 0.05221543431591006, + 0.052263742173553444, + 0.05231205003119682, + 0.0523603578888402, + 0.052408665746483576, + 0.052456973604126954, + 0.05250528146177033, + 0.05255358931941371, + 0.052601897177057086, + 0.05265020503470046, + 0.05269851289234384, + 0.05274682074998722, + 0.052795128607630595, + 0.05284343646527397, + 0.05289174432291735, + 0.05294005218056073, + 0.05298836003820411, + 0.05303666789584749, + 0.053084975753490866, + 0.053133283611134244, + 0.05318159146877762, + 0.053229899326421, + 0.053278207184064376, + 0.05332651504170775, + 0.05337482289935113, + 0.05342313075699451, + 0.053471438614637885, + 0.05351974647228126, + 0.05356805432992464, + 0.05361636218756802, + 0.053664670045211395, + 0.05371297790285477, + 0.05376128576049815, + 0.053809593618141534, + 0.05385790147578491, + 0.05390620933342829, + 0.053954517191071666, + 0.05400282504871504, + 0.05405113290635842, + 0.0540994407640018, + 0.054147748621645175, + 0.05419605647928855, + 0.05424436433693193, + 0.05429267219457531, + 0.054340980052218685, + 0.05438928790986206, + 0.05443759576750544, + 0.05448590362514882, + 0.0545342114827922, + 0.05458251934043558, + 0.054630827198078956, + 0.05467913505572233, + 0.05472744291336571, + 0.05477575077100909, + 0.054824058628652465, + 0.05487236648629584, + 0.05492067434393922, + 0.0549689822015826, + 0.055017290059225975, + 0.05506559791686935, + 0.05511390577451273, + 0.05516221363215611, + 0.055210521489799484, + 0.05525882934744286, + 0.05530713720508624, + 0.05535544506272962, + 0.055403752920373, + 0.05545206077801638, + 0.055500368635659755, + 0.05554867649330313, + 0.05559698435094651, + 0.05564529220858989, + 0.055693600066233265, + 0.05574190792387664, + 0.05579021578152002, + 0.0558385236391634, + 0.055886831496806774, + 0.05593513935445015, + 0.05598344721209353, + 0.056031755069736906, + 0.05608006292738029, + 0.05612837078502367, + 0.056176678642667045, + 0.05622498650031042, + 0.0562732943579538, + 0.05632160221559718, + 0.056369910073240555, + 0.05641821793088393, + 0.05646652578852731, + 0.05651483364617069, + 0.056563141503814064, + 0.05661144936145744, + 0.05665975721910082, + 0.056708065076744196, + 0.056756372934387574, + 0.05680468079203095, + 0.05685298864967433, + 0.05690129650731771, + 0.05694960436496109, + 0.05699791222260447, + 0.057046220080247845, + 0.05709452793789122, + 0.0571428357955346, + 0.05719114365317798, + 0.057239451510821354, + 0.05728775936846473, + 0.05733606722610811, + 0.057384375083751486, + 0.057432682941394864, + 0.05748099079903824, + 0.05752929865668162, + 0.057577606514324996, + 0.05762591437196838, + 0.05767422222961176, + 0.057722530087255135, + 0.05777083794489851, + 0.05781914580254189, + 0.05786745366018527, + 0.057915761517828644, + 0.05796406937547202, + 0.0580123772331154, + 0.058060685090758776, + 0.058108992948402154, + 0.05815730080604553 + ], + "y": [ + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012, + 1869.0950000000012 + ] + } + ], + "layout": { + "autosize": true, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "choropleth": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "choropleth" + } + ], + "contour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "contour" + } + ], + "contourcarpet": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "contourcarpet" + } + ], + "heatmap": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmap" + } + ], + "heatmapgl": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "heatmapgl" + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "histogram2d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2d" + } + ], + "histogram2dcontour": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "histogram2dcontour" + } + ], + "mesh3d": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "type": "mesh3d" + } + ], + "parcoords": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "parcoords" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "scatter3d": [ + { + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatter3d" + } + ], + "scattercarpet": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattercarpet" + } + ], + "scattergeo": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergeo" + } + ], + "scattergl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattergl" + } + ], + "scattermapbox": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scattermapbox" + } + ], + "scatterpolar": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolar" + } + ], + "scatterpolargl": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterpolargl" + } + ], + "scatterternary": [ + { + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "type": "scatterternary" + } + ], + "surface": [ + { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "autotypenumbers": "strict", + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "autorange": true, + "range": [ + 0.012137214323792684, + 0.06074862326690174 + ], + "title": { + "text": "Loss" + }, + "type": "linear" + }, + "yaxis": { + "autorange": true, + "range": [ + 103.53610754413774, + 4466.793892455864 + ], + "title": { + "text": "User CPU" + }, + "type": "linear" + } + } + }, + "image/png": "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", + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "x_col = 'generated.loss'\n", "y_col = 'telemetry_diff.cpu.times_avg.user'\n", "color_col = 'generated.responsible_ai_metrics.params'\n", - "analytics.scatter2d_with_colors(df,\n", + "flow_plot.scatter2d_with_colors(df,\n", " x_col='generated.loss',\n", " y_col='telemetry_diff.cpu.times_avg.user',\n", " color_col='generated.responsible_ai_metrics.params',\n", @@ -458,17 +8220,96 @@ " yaxis_title='User CPU',\n", " plot_horizon_line=True,\n", " horizon_quantile=0.5,\n", - " plot_pareto=True)" + " plot_pareto=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "cf639f68-00e7-4f1f-924e-e22f08c61dd9", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
task_idgenerated.losstelemetry_diff.cpu.times_avg.usergenerated.responsible_ai_metrics.params
04c31962e-4b36-4179-887a-605ac636cf3d0.014729397.45162990.0
1e30b1584-015d-4736-ad3a-c300cb57f6020.040326411.42359840.0
2c4b82fb9-62f4-4d9e-ad18-dee40433356c0.0581571179.7642184840.0
34fccd55d-29f4-48b9-b029-e6382c45e74e0.0182421862.611890690.0
\n", + "
" + ], + "text/plain": [ + " task_id generated.loss \\\n", + "0 4c31962e-4b36-4179-887a-605ac636cf3d 0.014729 \n", + "1 e30b1584-015d-4736-ad3a-c300cb57f602 0.040326 \n", + "2 c4b82fb9-62f4-4d9e-ad18-dee40433356c 0.058157 \n", + "3 4fccd55d-29f4-48b9-b029-e6382c45e74e 0.018242 \n", + "\n", + " telemetry_diff.cpu.times_avg.user generated.responsible_ai_metrics.params \n", + "0 397.45 162990.0 \n", + "1 411.42 359840.0 \n", + "2 1179.76 42184840.0 \n", + "3 1862.61 1890690.0 " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "clauses = [\n", " (y_col, \"<=\", 0.5),\n", @@ -499,12 +8340,134 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "6ff83981-3a4c-4d26-a3ca-5a9c3649917c", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
task_idsubmitted_atactivity_idworkflow_idutc_timestampadapter_idusercampaign_idsys_namenode_name...telemetry_diff.network.netio_per_interface.bridge100.bytes_senttelemetry_diff.network.netio_per_interface.bridge100.bytes_recvtelemetry_diff.network.netio_per_interface.bridge100.packets_senttelemetry_diff.network.netio_per_interface.bridge100.packets_recvtelemetry_diff.network.netio_per_interface.bridge100.errintelemetry_diff.network.netio_per_interface.bridge100.errouttelemetry_diff.network.netio_per_interface.bridge100.dropintelemetry_diff.network.netio_per_interface.bridge100.dropoutstatuselapsed_time
04c31962e-4b36-4179-887a-605ac636cf3d2024-02-09 01:05:28.202881024wrappere02f8776-3777-4856-952b-5b0cfbed21652024-02-09 01:06:27.422988032daskrootsuper_campaignDarwinMAC132633...0.00.02.00.00.00.00.00.0FINISHED59.133646
\n", + "

1 rows × 334 columns

\n", + "
" + ], + "text/plain": [ + " task_id submitted_at \\\n", + "0 4c31962e-4b36-4179-887a-605ac636cf3d 2024-02-09 01:05:28.202881024 \n", + "\n", + " activity_id workflow_id \\\n", + "0 wrapper e02f8776-3777-4856-952b-5b0cfbed2165 \n", + "\n", + " utc_timestamp adapter_id user campaign_id sys_name \\\n", + "0 2024-02-09 01:06:27.422988032 dask root super_campaign Darwin \n", + "\n", + " node_name ... \\\n", + "0 MAC132633 ... \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_sent \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.bytes_recv \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_sent \\\n", + "0 2.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.packets_recv \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.errin \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.errout \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.dropin \\\n", + "0 0.0 \n", + "\n", + " telemetry_diff.network.netio_per_interface.bridge100.dropout status \\\n", + "0 0.0 FINISHED \n", + "\n", + " elapsed_time \n", + "0 59.133646 \n", + "\n", + "[1 rows x 334 columns]" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "df.query(f\"task_id == '{df.head(1)['task_id'].values[0]}'\") " ] diff --git a/resources/sample_settings.yaml b/resources/sample_settings.yaml index 3ebcdadc..1f210289 100644 --- a/resources/sample_settings.yaml +++ b/resources/sample_settings.yaml @@ -47,13 +47,7 @@ web_server: port: 5000 sys_metadata: - place_holder: "" -# sys_name: 0 -# node_name: 0 -# login_name: 0 -# public_ip: 0 -# private_ip: 0 -# + environment_id: "frontier" extra_metadata: place_holder: "" diff --git a/tests/adapters/test_dask.py b/tests/adapters/test_dask.py index c90315b4..0a5fefd7 100644 --- a/tests/adapters/test_dask.py +++ b/tests/adapters/test_dask.py @@ -5,9 +5,12 @@ from dask.distributed import Client, LocalCluster -from flowcept import FlowceptConsumerAPI, TaskQueryAPI +from flowcept import FlowceptConsumerAPI, TaskQueryAPI, DBAPI from flowcept.commons.flowcept_logger import FlowceptLogger -from flowcept.commons.utils import assert_by_querying_tasks_until +from flowcept.commons.utils import ( + assert_by_querying_tasks_until, + evaluate_until, +) from tests.adapters.dask_test_utils import ( setup_local_dask_cluster, close_dask, @@ -45,6 +48,7 @@ class TestDask(unittest.TestCase): def __init__(self, *args, **kwargs): super(TestDask, self).__init__(*args, **kwargs) self.query_api = TaskQueryAPI() + self.db_api = DBAPI() self.logger = FlowceptLogger() @classmethod @@ -63,7 +67,7 @@ def atest_pure_workflow(self): self.logger.debug(o2.result()) self.logger.debug(o2.key) sleep(3) - return o2.key + return wf_id, o2.key def test_dummyfunc(self): i1 = np.random.random() @@ -130,14 +134,19 @@ def error_task_submission(self): return o1.key def test_observer_and_consumption(self): - o2_task_id = self.atest_pure_workflow() + wf_id, o2_task_id = self.atest_pure_workflow() print("Task_id=" + o2_task_id) + print("wf_id=" + wf_id) print("Done workflow!") assert assert_by_querying_tasks_until( {"task_id": o2_task_id}, condition_to_evaluate=lambda docs: "telemetry_at_end" in docs[0], ) - print("Query condition met!") + assert evaluate_until( + lambda: self.db_api.get_workflow(workflow_id=wf_id) is not None, + msg="Checking if workflow object was saved in db", + ) + print("All conditions met!") def test_observer_and_consumption_varying_args(self): o2_task_id = self.varying_args() diff --git a/tests/api/dbapi_test.py b/tests/api/dbapi_test.py index e026d117..0439cbe0 100644 --- a/tests/api/dbapi_test.py +++ b/tests/api/dbapi_test.py @@ -29,7 +29,8 @@ def test_wf_dao(self): wf2_id = str(uuid4()) print(wf2_id) - wf2 = WorkflowObject(workflow_id=wf2_id) + wf2 = WorkflowObject() + wf2.workflow_id = wf2_id tel = TelemetryCapture() assert dbapi.insert_or_update_workflow(wf2) diff --git a/tests/decorator_tests/ml_tests/dl_trainer.py b/tests/decorator_tests/ml_tests/dl_trainer.py index c5948723..506f32ab 100644 --- a/tests/decorator_tests/ml_tests/dl_trainer.py +++ b/tests/decorator_tests/ml_tests/dl_trainer.py @@ -6,8 +6,6 @@ import flowcept.commons import flowcept.instrumentation.decorators from flowcept import ( - model_explainer, - model_profiler, FlowceptConsumerAPI, ) from flowcept.instrumentation.decorators.flowcept_task import flowcept_task @@ -16,6 +14,10 @@ register_modules, register_module_as_workflow, ) +from flowcept.instrumentation.decorators.responsible_ai import ( + model_explainer, + model_profiler, +) class TestNet(nn.Module): diff --git a/tests/decorator_tests/ml_tests/llm_tests/llm_decorator_test.py b/tests/decorator_tests/ml_tests/llm_tests/llm_decorator_test.py index 673c474b..b13435f5 100644 --- a/tests/decorator_tests/ml_tests/llm_tests/llm_decorator_test.py +++ b/tests/decorator_tests/ml_tests/llm_tests/llm_decorator_test.py @@ -2,11 +2,9 @@ import torch -from flowcept import model_profiler - import unittest - +from flowcept.instrumentation.decorators.responsible_ai import model_profiler from tests.decorator_tests.ml_tests.llm_tests.llm_trainer import ( model_train, get_wiki_text, diff --git a/tests/decorator_tests/ml_tests/llm_tests/llm_trainer.py b/tests/decorator_tests/ml_tests/llm_tests/llm_trainer.py index 1913982d..6c6ecc02 100644 --- a/tests/decorator_tests/ml_tests/llm_tests/llm_trainer.py +++ b/tests/decorator_tests/ml_tests/llm_tests/llm_trainer.py @@ -9,13 +9,14 @@ from datasets import load_dataset import flowcept -from flowcept import model_profiler, FlowceptConsumerAPI +from flowcept import FlowceptConsumerAPI from flowcept.instrumentation.decorators.flowcept_task import flowcept_task from flowcept.instrumentation.decorators.flowcept_torch import ( register_modules, register_module_as_workflow, torch_args_handler, ) +from flowcept.instrumentation.decorators.responsible_ai import model_profiler tokenizer = get_tokenizer("basic_english") diff --git a/tests/telemetry_test.py b/tests/telemetry_test.py index e0433ef5..6326ab59 100644 --- a/tests/telemetry_test.py +++ b/tests/telemetry_test.py @@ -1,5 +1,4 @@ import unittest -import json from flowcept.flowceptor.telemetry_capture import TelemetryCapture @@ -10,5 +9,4 @@ def test_telemetry(self): tele_capture.init_gpu_telemetry() telemetry = tele_capture.capture() assert telemetry.to_dict() - print(json.dumps(telemetry.to_dict(), indent=True)) tele_capture.shutdown_gpu_telemetry() From d16b3f22d946ae1071f47a99dc7a17fccca58d2f Mon Sep 17 00:00:00 2001 From: Renan Souza Date: Sat, 9 Mar 2024 00:10:28 -0500 Subject: [PATCH 03/87] Minor changes to return adapter_id and campaign_id to tasks --- flowcept/configs.py | 1 + .../flowceptor/adapters/base_interceptor.py | 19 +++++++++++++------ .../decorators/flowcept_torch.py | 2 +- resources/sample_settings.yaml | 2 +- 4 files changed, 16 insertions(+), 8 deletions(-) diff --git a/flowcept/configs.py b/flowcept/configs.py index d9e8d5a6..dc1e5cfb 100644 --- a/flowcept/configs.py +++ b/flowcept/configs.py @@ -155,6 +155,7 @@ PRIVATE_IP = None SYS_NAME = None NODE_NAME = None +ENVIRONMENT_ID = None sys_metadata = settings.get("sys_metadata", None) if sys_metadata is not None: diff --git a/flowcept/flowceptor/adapters/base_interceptor.py b/flowcept/flowceptor/adapters/base_interceptor.py index 6dfc03aa..8c9b5b80 100644 --- a/flowcept/flowceptor/adapters/base_interceptor.py +++ b/flowcept/flowceptor/adapters/base_interceptor.py @@ -55,11 +55,14 @@ def __init__(self, plugin_key): self._generated_workflow_id = False # self._registered_workflow = False - @staticmethod - def _enrich_workflow_message(workflow_obj: WorkflowObject): + def _enrich_workflow_message(self, workflow_obj: WorkflowObject): workflow_obj.utc_timestamp = get_utc_now() workflow_obj.flowcept_settings = settings + if self.settings is not None: + # TODO :base-interceptor-refactor: :code-reorg: :usability: revisit all times we assume settings is not none + workflow_obj.adapter_id = self.settings.key + if workflow_obj.user is None: workflow_obj.user = FLOWCEPT_USER @@ -82,6 +85,13 @@ def _enrich_task_message(self, task_msg: TaskObject): if task_msg.utc_timestamp is None: task_msg.utc_timestamp = get_utc_now() + if self.settings is not None: + # TODO :base-interceptor-refactor: :code-reorg: :usability: revisit all times we assume settings is not none + task_msg.adapter_id = self.settings.key + + if task_msg.campaign_id is None: + task_msg.campaign_id = CAMPAIGN_ID + if task_msg.node_name is None and NODE_NAME is not None: task_msg.node_name = NODE_NAME @@ -192,10 +202,7 @@ def send_workflow_message(self, workflow_obj: WorkflowObject): ] = machine_info if ENRICH_MESSAGES: - if self.settings is not None: - # TODO :base-interceptor-refactor: :code-reorg: :usability: revisit all times we assume settings is not none - workflow_obj.adapter_id = self.settings.key - BaseInterceptor._enrich_workflow_message(workflow_obj) + self._enrich_workflow_message(workflow_obj) _msg = workflow_obj.to_dict() self.logger.debug( f"Going to send to Redis an WORKFLOW message:" diff --git a/flowcept/instrumentation/decorators/flowcept_torch.py b/flowcept/instrumentation/decorators/flowcept_torch.py index 059ee4f1..dd51d277 100644 --- a/flowcept/instrumentation/decorators/flowcept_torch.py +++ b/flowcept/instrumentation/decorators/flowcept_torch.py @@ -111,5 +111,5 @@ def register_module_as_workflow(module: nn.Module, parent_workflow_id=None): workflow_obj = WorkflowObject() workflow_obj.parent_workflow_id = parent_workflow_id workflow_obj.name = module.__class__.__name__ - DBAPI().insert_or_update_workflow(workflow_obj) + DBAPI().insert_or_update_workflow(workflow_obj) # TODO :refactor: we should be using workflow message intercept instead return workflow_obj.workflow_id diff --git a/resources/sample_settings.yaml b/resources/sample_settings.yaml index 1f210289..ef55b451 100644 --- a/resources/sample_settings.yaml +++ b/resources/sample_settings.yaml @@ -22,7 +22,7 @@ log: experiment: user: root - experiment_id: flowcept_experiment + campaign_id: super_campaign main_redis: host: localhost From 7da75c90f2309d1c2f01ee153596ff1deab85c1d Mon Sep 17 00:00:00 2001 From: Renan Souza Date: Sat, 9 Mar 2024 00:14:21 -0500 Subject: [PATCH 04/87] Code format --- flowcept/instrumentation/decorators/flowcept_torch.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/flowcept/instrumentation/decorators/flowcept_torch.py b/flowcept/instrumentation/decorators/flowcept_torch.py index dd51d277..89d89997 100644 --- a/flowcept/instrumentation/decorators/flowcept_torch.py +++ b/flowcept/instrumentation/decorators/flowcept_torch.py @@ -111,5 +111,7 @@ def register_module_as_workflow(module: nn.Module, parent_workflow_id=None): workflow_obj = WorkflowObject() workflow_obj.parent_workflow_id = parent_workflow_id workflow_obj.name = module.__class__.__name__ - DBAPI().insert_or_update_workflow(workflow_obj) # TODO :refactor: we should be using workflow message intercept instead + DBAPI().insert_or_update_workflow( + workflow_obj + ) # TODO :refactor: we should be using workflow message intercept instead return workflow_obj.workflow_id From 91b7e41c3e0e3fca7514976d4ce86467edefdd23 Mon Sep 17 00:00:00 2001 From: Renan Souza Date: Sat, 9 Mar 2024 00:50:09 -0500 Subject: [PATCH 05/87] Analaytics.ipynb --- notebooks/analytics.ipynb | 389 +++++++++++++++++++++++++++++--------- 1 file changed, 297 insertions(+), 92 deletions(-) diff --git a/notebooks/analytics.ipynb b/notebooks/analytics.ipynb index 62c4289b..f99fe4ab 100644 --- a/notebooks/analytics.ipynb +++ b/notebooks/analytics.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "id": "222b4132-fc10-4503-a108-592d5e742515", "metadata": { "tags": [] @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 2, "id": "c7b11fbf-ec74-46e7-9824-4685a9288c55", "metadata": { "tags": [] @@ -54,7 +54,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 3, "id": "176f01c5-5e59-44e3-ad65-409fcfdc2f9b", "metadata": { "tags": [] @@ -63,35 +63,35 @@ { "data": { "text/plain": [ - "'e02f8776-3777-4856-952b-5b0cfbed2165'" + "'fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb'" ] }, - "execution_count": 35, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Need to run only if this is the first time.\n", - "#wf_id = ingest_mock_data()\n", - "#wf_id" + "wf_id = ingest_mock_data()\n", + "wf_id" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "id": "96442d46-7ebb-470d-962b-11b65e7aca12", "metadata": { "tags": [] }, "outputs": [], "source": [ - "wf_id = '100faab4-ff4c-4f78-92a7-6f20ec1fad83'" + "#wf_id = '100faab4-ff4c-4f78-92a7-6f20ec1fad83'" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "id": "e41fe652-d7e8-4e3d-a780-dfec4e5142b0", "metadata": { "tags": [] @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "2c3cd6d6-fc22-4155-80e0-da7ffc9f8e0e", "metadata": { "tags": [] @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "id": "d2c04cbe-4b78-49ee-b74d-5e7680a4478f", "metadata": { "tags": [] @@ -179,10 +179,10 @@ " \n", " \n", " 0\n", - " 6b1209fe-e078-4572-b082-db14d0de025e\n", + " e192beb7-91e9-495f-a7eb-6110fce50d88\n", " 2024-02-09 01:05:28.202881024\n", " wrapper\n", - " 100faab4-ff4c-4f78-92a7-6f20ec1fad83\n", + " fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb\n", " 2024-02-09 01:06:27.422988032\n", " dask\n", " root\n", @@ -203,10 +203,10 @@ " \n", " \n", " 1\n", - " 8646acc7-bdd7-4504-bfb4-3768b97912d6\n", + " fb503919-a4d5-40ce-9409-654dea015de5\n", " 2024-02-09 01:05:28.206701056\n", " wrapper\n", - " 100faab4-ff4c-4f78-92a7-6f20ec1fad83\n", + " fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb\n", " 2024-02-09 01:06:29.350380800\n", " dask\n", " root\n", @@ -227,10 +227,10 @@ " \n", " \n", " 2\n", - " d36a4538-7e52-49df-b8c9-332506160d5b\n", + " c0711786-5ae9-4bb0-bd92-0f8babd2dbd4\n", " 2024-02-09 01:05:28.210365952\n", " wrapper\n", - " 100faab4-ff4c-4f78-92a7-6f20ec1fad83\n", + " fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb\n", " 2024-02-09 01:08:17.270892032\n", " dask\n", " root\n", @@ -256,14 +256,14 @@ ], "text/plain": [ " task_id submitted_at \\\n", - "0 6b1209fe-e078-4572-b082-db14d0de025e 2024-02-09 01:05:28.202881024 \n", - "1 8646acc7-bdd7-4504-bfb4-3768b97912d6 2024-02-09 01:05:28.206701056 \n", - "2 d36a4538-7e52-49df-b8c9-332506160d5b 2024-02-09 01:05:28.210365952 \n", + "0 e192beb7-91e9-495f-a7eb-6110fce50d88 2024-02-09 01:05:28.202881024 \n", + "1 fb503919-a4d5-40ce-9409-654dea015de5 2024-02-09 01:05:28.206701056 \n", + "2 c0711786-5ae9-4bb0-bd92-0f8babd2dbd4 2024-02-09 01:05:28.210365952 \n", "\n", " activity_id workflow_id \\\n", - "0 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", - "1 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", - "2 wrapper 100faab4-ff4c-4f78-92a7-6f20ec1fad83 \n", + "0 wrapper fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb \n", + "1 wrapper fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb \n", + "2 wrapper fa41fbe3-1d6b-44ac-ae8c-68190bd7a8bb \n", "\n", " utc_timestamp adapter_id user campaign_id sys_name \\\n", "0 2024-02-09 01:06:27.422988032 dask root super_campaign Darwin \n", @@ -323,7 +323,7 @@ "[3 rows x 334 columns]" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "8a8c1dd7-9647-4e7a-82e3-f7db7752f824", "metadata": { "tags": [] @@ -621,7 +621,7 @@ "[5 rows x 39 columns]" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -640,7 +640,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 9, "id": "5497b4c8-ba90-4ae4-82d7-0ef821fe2f4f", "metadata": { "tags": [] @@ -813,7 +813,7 @@ "3 Sequential(\\n (0): Linear(in_features=60, out... " ] }, - "execution_count": 40, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -846,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 10, "id": "c669ac40-60b4-49e0-ae62-a2cda2c5815a", "metadata": { "tags": [] @@ -1118,7 +1118,7 @@ "[4 rows x 58 columns]" ] }, - "execution_count": 41, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1164,7 +1164,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "id": "066717e4-2110-4d62-aedd-005c1198cefa", "metadata": { "tags": [] @@ -3876,7 +3876,7 @@ "[58 rows x 58 columns]" ] }, - "execution_count": 42, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3897,7 +3897,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 12, "id": "e03dab0b-1a03-46a7-bbb2-4d16339abfe1", "metadata": { "tags": [] @@ -3929,7 +3929,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 13, "id": "89923e60-b251-45aa-8723-d42c548f8ea1", "metadata": { "tags": [] @@ -4063,7 +4063,7 @@ "[630 rows x 3 columns]" ] }, - "execution_count": 44, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -4084,7 +4084,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 14, "id": "fe702218-c80c-4e78-a642-8a91b5571b1d", "metadata": { "tags": [] @@ -4218,7 +4218,7 @@ "[181 rows x 3 columns]" ] }, - "execution_count": 45, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4229,7 +4229,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 15, "id": "f68a5db8-fcbe-4762-83fe-255a19a3ccc8", "metadata": { "tags": [] @@ -4416,7 +4416,7 @@ "85 1.00 " ] }, - "execution_count": 46, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -4427,7 +4427,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 16, "id": "43601683-a091-412a-bbc6-e66f78546fc9", "metadata": { "tags": [] @@ -4542,7 +4542,7 @@ "28 1.00 " ] }, - "execution_count": 47, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -4553,7 +4553,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 17, "id": "01ea1a46-7fe7-4334-b546-b23943af98e4", "metadata": { "tags": [] @@ -4980,7 +4980,7 @@ "174 0.87 " ] }, - "execution_count": 48, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -4991,7 +4991,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 18, "id": "5b3c1356-6209-4d92-b87f-d84f00b20041", "metadata": { "tags": [] @@ -5170,7 +5170,7 @@ "31 telemetry_diff.process.activity 0.87 " ] }, - "execution_count": 49, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -5181,7 +5181,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 19, "id": "bae58142-3f70-4df8-a57a-4f75eef0cca8", "metadata": { "tags": [] @@ -5200,7 +5200,7 @@ " 'max': '0.06'}" ] }, - "execution_count": 50, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -5211,7 +5211,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 20, "id": "f9a5bdd4-b5ed-441d-9b96-bdd57fe89bd6", "metadata": {}, "outputs": [ @@ -5279,7 +5279,7 @@ "1 #Params 13.47M 18.57M 162.99K 1.51M 5.29M 17.06M 43.93M" ] }, - "execution_count": 51, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -5300,7 +5300,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 21, "id": "c369915d-b12d-4bf7-b0f4-02e5b5be8a9b", "metadata": { "tags": [] @@ -5324,7 +5324,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 22, "id": "e765a93d-a005-4d77-9d42-4acde84bb72a", "metadata": { "tags": [] @@ -5355,7 +5355,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 23, "id": "4613ace3-ab5a-4553-9629-ac94daa30c0f", "metadata": { "tags": [] @@ -5367,12 +5367,41 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 24, "id": "6bb073fa-6e17-403e-8c9f-3884b86119f5", "metadata": { "tags": [] }, "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -8151,8 +8180,8 @@ "xaxis": { "autorange": true, "range": [ - 0.012137214323792684, - 0.06074862326690174 + 0.012171395586224147, + 0.06071444200447028 ], "title": { "text": "Loss" @@ -8172,11 +8201,11 @@ } } }, - "image/png": "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", + "image/png": "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", "text/html": [ - "