diff --git a/.github/CODEOWNERS b/.github/CODEOWNERS new file mode 100644 index 00000000..9a363541 --- /dev/null +++ b/.github/CODEOWNERS @@ -0,0 +1,2 @@ +* @fbeneventi +/.github/ @fbeneventi \ No newline at end of file diff --git a/.gitignore b/.gitignore index 81570459..31698e7c 100644 --- a/.gitignore +++ b/.gitignore @@ -4,4 +4,5 @@ .ipynb_* examon-cache/ examon-cache/* - +build +site diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 00000000..e75a06c4 --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,88 @@ +# Contributing to ExaMon + +First off, thank you for considering contributing to our project! + +## How Can I Contribute? + +### Reporting Bugs + +Before creating bug reports, please check the issue list as you might find out that you don't need to create one. When you are creating a bug report, please include as many details as possible: + +* Use a clear and descriptive title +* Describe the exact steps which reproduce the problem +* Provide specific examples to demonstrate the steps +* Describe the behavior you observed after following the steps +* Explain which behavior you expected to see instead and why +* Include screenshots if possible + +### Suggesting Enhancements + +If you have a suggestion for the project, we'd love to hear about it. Please include: + +* A clear and detailed explanation of the feature +* The motivation behind this feature +* Any alternative solutions you've considered +* If applicable, examples from other projects + +### Pull Request Process + +1. Fork the repository and create your branch from `master` +2. If you've added code that should be tested, add tests +3. Ensure the test suite passes +4. Update the documentation if needed +5. Issue that pull request! + +#### Pull Request Guidelines + +* Follow our coding standards (see below) +* Include relevant issue numbers in your PR description +* Update the README.md with details of changes if applicable +* The PR must pass all CI/CD checks [TBD] +* Wait for review from maintainers + +### Development Setup + +1. Fork and clone the repo +3. Create a branch: `git checkout -b my-branch-name` + +### Coding Standards + +* Use consistent code formatting +* Write clear commit messages following [Conventional Commits](https://www.conventionalcommits.org/) +* Comment your code where necessary +* Write tests for new features +* Keep the code simple and maintainable + +### Commit Messages + +We follow a basic specification: + +``` +type(scope): description +[optional body] +[optional footer] +``` + +The type should be one of the following: + +| Type | Description | +|------|-------------| +| add | Introduces a new feature or functionality | +| fix | Patches a bug or resolves an issue | +| change | Modifies existing functionality or behavior | +| remove | Deletes or deprecates functionality | +| merge | Combines branches or resolves conflicts | +| doc | Updates documentation or comments | + + +### First Time Contributors + +Looking for work? Check out our issues labeled `good first issue` or `help wanted`. + +## License + +By contributing, you agree that your contributions will be licensed under the same license that covers the project. + +## Questions? + +Don't hesitate to contact the project maintainers if you have any questions! diff --git a/README.md b/README.md index 5925985f..53cf43dc 100644 --- a/README.md +++ b/README.md @@ -35,7 +35,7 @@ git clone https://github.com/ExamonHPC/examon.git Once you have the above setup, you need to create the Docker services: ```bash -docker-compose up -d +docker compose up -d ``` This will build the Docker images and fetch some prebuilt images and then start the services. You can refer to the `docker-compose.yml` file to see the full configuration. @@ -62,14 +62,14 @@ Fill out the form with the following settings: ### Collecting data using the dummy "examon_pub" plugin Once all Docker services are running (can be started either by `docker-compose up -d` or `docker-compose start`), the MQTT broker is available at `TEST_SERVER` port `1883` where `TEST_SERVER` is the address of the server where the services run. -To test the installation we can use the `examon_pub` plugin available in the `publishers/examon_pub` folder of this project. +To test the installation we can use the `examon_pub.py` plugin available in the `publishers/examon_pub` folder of this project. It is highly recommended to follow the tutorial described in the Jupyter notebook `README-notebook.ipynb` to understand how an Examon plugin works. After having installed and configured it on one or more test nodes we can start the data collection running for example: ```bash -[root@testnode00]$ ./examon_pub -b TEST_SERVER -p 1883 -t org/myorg -s 1 run +[root@testnode00]$ python ./examon_pub.py -b TEST_SERVER -p 1883 -s 1 run ``` If everything went well, the data are available both through the Grafana interface and using the `examon-client`. diff --git a/docs/About.md b/docs/About.md new file mode 100644 index 00000000..34446608 --- /dev/null +++ b/docs/About.md @@ -0,0 +1,3 @@ +# About + +ExaMon is an open source framework developed by Francesco Beneventi at [DEI - Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi"](https://dei.unibo.it/en/index.html) of the University of Bologna under the supervision of Profs. Luca Benini, Andrea Bartolini and Andrea Borghesi and in collaboration with [CINECA](https://www.hpc.cineca.it/) and [E4](https://www.e4company.com/en/). diff --git a/docs/Administrators/Getting_started.md b/docs/Administrators/Getting_started.md new file mode 100644 index 00000000..2ac5fa47 --- /dev/null +++ b/docs/Administrators/Getting_started.md @@ -0,0 +1,78 @@ +# ExaMon Docker Setup +This setup will install all server-side components of the ExaMon framework: + + - MQTT broker and Db connector + - Grafana + - KairosDB + - Cassandra + +## Prerequisites +Since Cassandra is the component that requires the majority of resources, you can find more details about the suggested hardware configuration of the system that will host the services here: + +[Hardware Configuration](https://cassandra.apache.org/doc/latest/operating/hardware.html#:~:text=While%20Cassandra%20can%20be%20made,at%20least%2032GB%20of%20RAM) + +To install all the services needed by ExaMon we will use Docker and Docker Compose: + +[Install Docker and Docker Compose](https://docs.docker.com/engine/installation/). + + +## Setup + +### Clone the Git repository + +First you will need to clone the Git repository: + +```bash +git clone https://github.com/ExamonHPC/examon.git +``` + +### Create Docker Services + +Once you have the above setup, you need to create the Docker services: + +```bash +docker compose up -d +``` + +This will build the Docker images and fetch some prebuilt images and then start the services. You can refer to the `docker-compose.yml` file to see the full configuration. + +### Configure Grafana + +Log in to the Grafana server using your browser and the default credentials: + +http://localhost:3000 + +Follow the normal procedure for adding a new data source (KairosDB): + +[Add a Datasource](https://grafana.com/docs/grafana/latest/datasources/add-a-data-source/) + +Fill out the form with the following settings: + + - Type: `KairosDB` + - Name: `kairosdb` + - Url: http://kairosdb:8083 + - Access: `Server` + +## Usage Examples + +### Collecting data using the dummy "examon_pub" plugin +Once all Docker services are running (can be started either by `docker compose up -d` or `docker compose start`), the MQTT broker is available at `TEST_SERVER` port `1883` where `TEST_SERVER` is the address of the server where the services run. + +To test the installation we can use the `examon_pub.py` plugin available in the `publishers/examon_pub` folder of this project. + +It is highly recommended to follow the tutorial described in the Jupyter notebook `README-notebook.ipynb` to understand how an Examon plugin works. + +After having installed and configured it on one or more test nodes we can start the data collection running for example: + +```bash +[root@testnode00]$ python ./examon_pub.py -b TEST_SERVER -p 1883 -s 1 run +``` +If everything went well, the data are available both through the Grafana interface and using the [examon-client](../Users/Demo_ExamonQL.ipynb). + + +## Where to go next + +- Write your first plugin: [Example plugin](../Plugins/examon_pub.ipynb) +- Write your first query: [Example query](../Users/Demo_ExamonQL.ipynb) + + diff --git a/docs/Introduction.md b/docs/Introduction.md new file mode 100644 index 00000000..44c83ec7 --- /dev/null +++ b/docs/Introduction.md @@ -0,0 +1,23 @@ +
+ ![](images/image1.png){ width="300" } +
+ +ExaMon (Exascale Monitoring) is a data collection and analysis platform designed to manage large amounts of data. Its main prerogatives are to easily manage heterogeneous data, both in streaming and batch mode and to provide access to this data through a common interface. This simplifies the use of data to support applications such as real-time anomaly detection, predictive maintenance, and efficient resource and energy management leveraging machine learning and artificial intelligence techniques. Due to its scalable and distributed nature, it is easily applicable to HPC systems, especially exascale-sized ones, the primary use case for which it was designed. + +The key feature of the framework is its data model, designed to be schema-less and scalable. In this way, it allows to collect a huge amount of heterogeneous data under a single interface. This data lake, which makes all the data available online to any user at any time, is proposed as a solution to break down internal data silos in organizations. The main benefit of this approach is that it enables capturing the full value of the data by making it immediately usable. In addition, having all the data in one place makes it easier to create complete and immediate executive reports, enabling faster and more informed decisions. + +Another key aspect of the framework's design is making industry data easily available for research purposes. Indeed, researchers only need to manage a single data source to have a complete picture of complex industrial systems, and the benefits can be many. The ease of access to a huge variety and quantity of real-world data will enable them to create innovative solutions with results that may have real-world impact. + +
+ ![](images/image13.png){ width="80%" } +
+ +Furthermore, access to a wide variety of heterogeneous data with very low latency enables the realization of accurate digital twins. In this regard, the framework can provide both historical data for building accurate models, and fresh data for quickly making inferences on the same models. Moreover, the availability of up-to-date data in near real-time allows the construction of visual models that enable the rapid acquisition of knowledge about the state of any complex system. In fact, by exploiting the language of visual communication, it is possible to extend collaboration by bringing together a wide range of experts focused on problem-solving or optimization of the system itself. + +
+ ![](images/image3.png){ width="80%" } +
+ +The architecture of the framework is based on established protocols and technologies rather than specific tools and implementations. The communication layer is based on the publish-subscribe model that finds various implementations, such as in the MQTT protocol. The need to interact with different data sources, ranging from complex room cooling systems to internal CPU sensors, requires a simple, scalable, low-latency communication protocol that is resilient to network conditions and natively designed to enable machine-to-machine (M2M) communication in complex environments. Moreover, data persistence is handled by a NoSQL-type database, an industry-proven technology, designed to be horizontally scalable and built to efficiently handle large amounts of data. On top of these two pillars, the other components are primarily dedicated to handling the two main categories of data that characterize the ExaMon framework. The first is the time series data type, which represents the majority of the data sources managed by ExaMon and is suitable for managing all the sensors and logs available today in a data center. The second is the generic tabular data type, suitable for managing metadata and any other data that does not fall into the first category. ExaMon provides the tools and interfaces to coordinate these two categories and interface them with the user in the most seamless way. + +As a data platform, one of ExaMon's priorities is data sharing. To maximize its effectiveness, it offers both domain-specific interfaces (DSLs), which allow more experienced users to take full advantage of the data source's capabilities, and more high-level, standard interfaces such as the ANSI SQL language. Again, ExaMon promotes tools that are state of the art for time series data visualization, such as Grafana. Although more experienced users can interface with ExaMon using tools such as Jupyter notebooks (via a dedicated client), more user-friendly BI solutions such as Apache Superset, which uses web visualization technologies and the ANSI SQL language, are also provided to streamline the user experience. There is also compatibility with tools such as Apache Spark and Dask for large-scale data analysis in both streaming and batch modes. Finally, CLI-type tools are also available to provide access to the data and typical features directly from the user's shell. \ No newline at end of file diff --git a/docs/Marconi100/Metrics_reference.md b/docs/Marconi100/Metrics_reference.md new file mode 100644 index 00000000..62109af6 --- /dev/null +++ b/docs/Marconi100/Metrics_reference.md @@ -0,0 +1,434 @@ +# Marconi 100 - CINECA + + +
+ ![](../images/Marconi100.jpg){ width="300" } +
+ + +- Model: IBM Power AC922 (Whiterspoon) +- Racks: 55 total (49 compute) +- Nodes: 980 +- Processors: 2x16 cores IBM POWER9 AC922 at 2.6(3.1) GHz +- Accelerators: 4 x NVIDIA Volta V100 GPUs/node, Nvlink 2.0, 16GB +- Cores: 32 cores/node, Hyperthreading x4 +- RAM: 256 GB/node (242 usable) +- Peak Performance: about 32 Pflop/s, 32 TFlops per node +- Internal Network: Mellanox IB EDR DragonFly++ 100Gb/s +- Disk Space: 8PB raw GPFS storage + +## Metrics + +This Section is a brief description of some of the metrics collected by ExaMon from the Marconi100 cluster. It is intended only as an example and is therefore not exhaustive. The Marconi, Galileo and Galileo 100 clusters have similar metrics. + +## IPMI + +The following table describes the metrics collected by the ipmi_pub plugin. + +| | | | +|------------------|----------------------------------------------------------------------------|------| +| Metric Name | Description | Unit | +| pX_coreY_temp | Temperature of core n. Y in the CPU socket n. X. X=0..1, Y=0..23 | °C | +| dimmX_temp | Temperature of DIMM module n. X. X=0..15 | °C | +| gpuX_core_temp | Temperature of the core for the GPU id X. X=0,1,3,4 | °C | +| gpuX_mem_temp | Temperature of the memory for the GPU id X. X=0,1,3,4 | °C | +| fanX_Y | Speed of the Fan Y in module X. X=0..3, Y=0,1 | RPM | +| pX_vdd_temp | Temperature of the voltage regulator for the CPU socket n. X. X=0..1 | °C | +| fan_disk_power | Power consumption of the disk fan | W | +| pX_io_power | Power consumption for the I/O subsystem for the CPU socket n. X. X=0..1 | W | +| pX_mem_power | Power consumption for the memory subsystem for the CPU socket n. X. X=0..1 | W | +| pX_power | Power consumption for the CPU socket n. X. X=0..1 | W | +| psX_input_power | Power consumption at the input of power supply n. X. X=0..1 | W | +| total_power | Total node power consumption | W | +| psX_input_voltag | Voltage at the input of power supply n. X. X=0..1 | V | +| psX_output_volta | Voltage at the output of power supply n. X. X=0..1 | V | +| psX_output_curre | Current at the output of power supply n. X. X=0..1 | A | +| pcie | Temperature at the PCIExpress slots | °C | +| ambient | Temperature at the node inlet | °C | + +## Ganglia + +The following table describes the metrics collected by the ganglia_pub plugin. The data are extracted from a Ganglia^([\[6\]](#ftnt6)) instance that CINECA runs on Marconi100. + +| | | | | +|----------------------------------|-----------|-------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| Metric name | Type | Unit | Description | +| gexec | core | | gexec available | +| cpu_aidle | cpu | % | Percent of time since boot idle CPU | +| cpu_idle | cpu | % | Percentage of time that the CPU or CPUs were idle and the system did not have an outstanding disk I/O request | +| cpu_nice | cpu | % | Percentage of CPU utilization that occurred while executing at the user level with nice priority | +| cpu_speed | cpu | MHz | CPU Speed in terms of MHz | +| cpu_steal | cpu | % | cpu_steal | +| cpu_system | cpu | % | Percentage of CPU utilization that occurred while executing at the system level | +| cpu_user | cpu | % | Percentage of CPU utilization that occurred while executing at the user level | +| cpu_wio | cpu | % | Percentage of time that the CPU or CPUs were idle during which the system had an outstanding disk I/O request | +| cpu_num | | | | +| disk_free | disk | GB | Total free disk space | +| disk_total | disk | GB | Total available disk space | +| part_max_used | disk | % | Maximum percent used for all partitions | +| load_fifteen | load | | Fifteen minute load average | +| load_five | load | | Five minute load average | +| load_one | load | | One minute load average | +| mem_buffers | memory | KB | Amount of buffered memory | +| mem_cached | memory | KB | Amount of cached memory | +| mem_free | memory | KB | Amount of available memory | +| mem_shared | memory | KB | Amount of shared memory | +| mem_total | memory | KB | Total amount of memory displayed in KBs | +| swap_free | memory | KB | Amount of available swap memory | +| swap_total | memory | KB | Total amount of swap space displayed in KBs | +| bytes_in | network | bytes/sec | Number of bytes in per second | +| bytes_out | network | bytes/sec | Number of bytes out per second | +| pkts_in | network | packets/sec | Packets in per second | +| pkts_out | network | packets/sec | Packets out per second | +| proc_run | process | | Total number of running processes | +| proc_total | process | | Total number of processes | +| boottime | system | s | The last time that the system was started | +| machine_type | system | | System architecture | +| os_name | system | | Operating system name | +| os_release | system | | Operating system release date | +| cpu_ctxt | cpu | ctxs/sec | Context Switches | +| cpu_intr | cpu | % | cpu_intr | +| cpu_sintr | cpu | % | cpu_sintr | +| multicpu_idle0 | cpu | % | Percentage of CPU utilization that occurred while executing at the idle level | +| procs_blocked | cpu | processes | Processes blocked | +| procs_created | cpu | proc/sec | Number of processes and threads created | +| disk_free_absolute_developers | disk | GB | Disk space available (GB) on /developers | +| disk_free_percent_developers | disk | % | Disk space available (%) on /developers | +| diskstat_sda_io_time | diskstat | s | The time in seconds spent in I/O operations | +| diskstat_sda_percent_io_time | diskstat | percent | The percent of disk time spent on I/O operations | +| diskstat_sda_read_bytes_per_sec | diskstat | bytes/sec | The number of bytes read per second | +| diskstat_sda_reads_merged | diskstat | reads | The number of reads merged. Reads which are adjacent to each other may be merged for efficiency. Multiple reads may become one before it is handed to the disk, and it will be counted (and queued) as only one I/O. | +| diskstat_sda_reads | diskstat | reads | The number of reads completed | +| diskstat_sda_read_time | diskstat | s | The time in seconds spent reading | +| diskstat_sda_weighted_io_time | diskstat | s | The weighted time in seconds spend in I/O operations. This measures each I/O start, I/O completion, I/O merge, or read of these stats by the number of I/O operations in progress times the number of seconds spent doing I/O. | +| diskstat_sda_write_bytes_per_sec | diskstat | bytes/sec | The number of bytes written per second | +| diskstat_sda_writes_merged | diskstat | writes | The number of writes merged. Writes which are adjacent to each other may be merged for efficiency. Multiple writes may become one before it is handed to the disk, and it will be counted (and queued) as only one I/O. | +| diskstat_sda_writes | diskstat | writes | The number of writes completed | +| diskstat_sda_write_time | diskstat | s | The time in seconds spent writing | +| ipmi_ambient_temp | ipmi | C | IPMI data | +| ipmi_avg_power | ipmi | Watts | IPMI data | +| ipmi_cpu1_temp | ipmi | C | IPMI data | +| ipmi_cpu2_temp | ipmi | C | IPMI data | +| ipmi_gpu_outlet_temp | ipmi | C | IPMI data | +| ipmi_hdd_inlet_temp | ipmi | C | IPMI data | +| ipmi_pch_temp | ipmi | C | IPMI data | +| ipmi_pci_riser_1\_temp | ipmi | C | IPMI data | +| ipmi_pci_riser_2\_temp | ipmi | C | IPMI data | +| ipmi_pib_ambient_temp | ipmi | C | IPMI data | +| mem_anonpages | memory | Bytes | AnonPages | +| mem_dirty | memory | Bytes | The total amount of memory waiting to be written back to the disk. | +| mem_hardware_corrupted | memory | Bytes | HardwareCorrupted | +| mem_mapped | memory | Bytes | Mapped | +| mem_writeback | memory | Bytes | The total amount of memory actively being written back to the disk. | +| vm_pgmajfault | memory_vm | ops/s | pgmajfault | +| vm_pgpgin | memory_vm | ops/s | pgpgin | +| vm_pgpgout | memory_vm | ops/s | pgpgout | +| vm_vmeff | memory_vm | pct | VM efficiency | +| rx_bytes_eth0 | network | bytes/sec | received bytes per sec | +| rx_drops_eth0 | network | pkts/sec | receive packets dropped per sec | +| rx_errs_eth0 | network | pkts/sec | received error packets per sec | +| rx_pkts_eth0 | network | pkts/sec | received packets per sec | +| tx_bytes_eth0 | network | bytes/sec | transmitted bytes per sec | +| tx_drops_eth0 | network | pkts/sec | transmitted dropped packets per sec | +| tx_errs_eth0 | network | pkts/sec | transmitted error packets per sec | +| tx_pkts_eth0 | network | pkts/sec | transmitted packets per sec | +| procstat_gmond_cpu | procstat | percent | The total percent CPU utilization | +| procstat_gmond_mem | procstat | B | The total memory utilization | +| softirq_blockiopoll | softirq | ops/s | Soft Interrupts | +| softirq_block | softirq | ops/s | Soft Interrupts | +| softirq_hi | softirq | ops/s | Soft Interrupts | +| softirq_hrtimer | softirq | ops/s | Soft Interrupts | +| softirq_netrx | softirq | ops/s | Soft Interrupts | +| softirq_nettx | softirq | ops/s | Soft Interrupts | +| softirq_rcu | softirq | ops/s | Soft Interrupts | +| softirq_sched | softirq | ops/s | Soft Interrupts | +| softirq_tasklet | softirq | ops/s | Soft Interrupts | +| softirq_timer | softirq | ops/s | Soft Interrupts | +| entropy_avail | ssl | bits | Entropy Available | +| tcpext_listendrops | tcpext | count/s | listendrops | +| tcpext_tcploss_percentage | tcpext | pct | TCP percentage loss, tcploss / insegs + outsegs | +| tcp_attemptfails | tcp | count/s | attempt fails | +| tcp_insegs | tcp | count/s | insegs | +| tcp_outsegs | tcp | count/s | outsegs | +| tcp_retrans_percentage | tcp | pct | TCP retrans percentage, retranssegs / insegs + outsegs | +| udp_indatagrams | udp | count/s | indatagrams | +| udp_inerrors | udp | count/s | inerrors | +| udp_outdatagrams | udp | count/s | outdatagrams | +| multicpu_idle16 | cpu | % | Percentage of CPU utilization that occurred while executing at the idle level | +| multicpu_steal16 | cpu | % | Percentage of CPU preempted by the hypervisor | +| multicpu_system16 | cpu | % | Percentage of CPU utilization that occurred while executing at the system level | +| multicpu_user16 | cpu | % | Percentage of CPU utilization that occurred while executing at the user level | +| multicpu_wio16 | cpu | % | Percentage of CPU utilization that occurred while executing at the wio level | +| diskstat_sdb_io_time | diskstat | s | The time in seconds spent in I/O operations | +| diskstat_sdb_percent_io_time | diskstat | percent | The percent of disk time spent on I/O operations | +| diskstat_sdb_read_bytes_per_sec | diskstat | bytes/sec | The number of bytes read per second | +| diskstat_sdb_reads_merged | diskstat | reads | The number of reads merged. Reads which are adjacent to each other may be merged for efficiency. Multiple reads may become one before it is handed to the disk, and it will be counted (and queued) as only one I/O. | +| diskstat_sdb_reads | diskstat | reads | The number of reads completed | +| diskstat_sdb_read_time | diskstat | s | The time in seconds spent reading | +| diskstat_sdb_weighted_io_time | diskstat | s | The weighted time in seconds spend in I/O operations. This measures each I/O start, I/O completion, I/O merge, or read of these stats by the number of I/O operations in progress times the number of seconds spent doing I/O. | +| diskstat_sdb_write_bytes_per_sec | diskstat | bytes/sec | The number of bytes written per second | +| diskstat_sdb_writes_merged | diskstat | writes | The number of writes merged. Writes which are adjacent to each other may be merged for efficiency. Multiple writes may become one before it is handed to the disk, and it will be counted (and queued) as only one I/O. | +| diskstat_sdb_writes | diskstat | writes | The number of writes completed | +| diskstat_sdb_write_time | diskstat | s | The time in seconds spent writing | +| GpuX_dec_utilization | gpu | % | X=0,..,3 | +| GpuX_enc_utilization | gpu | % | X=0,..,3 | +| GpuX_enforced_power_limit | gpu | Watts | X=0,..,3 | +| GpuX_gpu_temp | gpu | Celsius | X=0,..,3 | +| GpuX_low_util_violation | gpu | | X=0,..,3 | +| GpuX_mem_copy_utilization | gpu | % | X=0,..,3 | +| GpuX_mem_util_samples | gpu | | X=0,..,3 | +| GpuX_memory_clock | gpu | Mhz | X=0,..,3 | +| GpuX_memory_temp | gpu | Celsius | X=0,..,3 | +| GpuX_power_management_limit | gpu | Watts | X=0,..,3 | +| GpuX_power_usage | gpu | Watts | X=0,..,3 | +| GpuX_pstate | gpu | | X=0,..,3 | +| GpuX_reliability_violation | gpu | | X=0,..,3 | +| GpuX_sm_clock | gpu | Mhz | X=0,..,3 | + +## Nagios + +This is a description of the metrics collected by the ExaMon "nagios_pub" plugin. The data reflect those monitored by the Nagios^([\[7\]](#ftnt7)) tool that currently runs in the CINECA clusters. Specifically, the plugin interfaces with a Nagios extension developed by CINECA called "Hnagios"^([\[8\]](#ftnt8)). Although the monitored services and metrics are similar between all clusters, here we will specifically discuss those of Marconi100. + +### Metrics + +Currently, this plugin collects three metrics + +| | | +|-----|------------------------------| +| | name | +| 0 | hostscheduleddowtimecomments | +| 1 | plugin_output | +| 2 | state | + +#### Hostscheduleddowtimecomments + +This metric is obtained from the "Hnagios" output and reports comments made by system administrators about the maintenance status of the specific monitored resource + +| | | | | +|-----|------------------------------|---------------|----------------------------------------------------| +| | name | tag key | tag values | +| 0 | hostscheduleddowtimecomments | node | \[ems02, login03, login08, master01, master02, ... | +| 1 | hostscheduleddowtimecomments | slot | \[01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 1... | +| 2 | hostscheduleddowtimecomments | description | \[afs::blocked_conn::status, afs::bosserver::st... | +| 3 | hostscheduleddowtimecomments | plugin | \[nagios_pub\] | +| 4 | hostscheduleddowtimecomments | chnl | \[data\] | +| 5 | hostscheduleddowtimecomments | host_group | \[compute, compute,cincompute, efgwcompute, efg... | +| 6 | hostscheduleddowtimecomments | cluster | \[galileo, marconi, marconi100\] | +| 7 | hostscheduleddowtimecomments | state | \[0, 1, 2, 3\] | +| 8 | hostscheduleddowtimecomments | nagiosdrained | \[0, 1\] | +| 9 | hostscheduleddowtimecomments | org | \[cineca\] | +| 10 | hostscheduleddowtimecomments | state_type | \[0, 1\] | +| 11 | hostscheduleddowtimecomments | rack | \[205, 206, 207, 208, 209, 210, 211, 212, 213, ... | + +#### Plugin_output + +This metric collects the outbound messages from Nagios agents responsible for monitoring services. + +| | | | | +|-----|---------------|---------------|----------------------------------------------------| +| | name | tag key | tag values | +| 0 | plugin_output | node | \[ems02, ethcore01-mgt, ethcore02-mgt, gss03, g... | +| 1 | plugin_output | slot | \[01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 1... | +| 2 | plugin_output | description | \[EFGW_cluster::status::availability, EFGW_clus... | +| 3 | plugin_output | plugin | \[nagios_pub\] | +| 4 | plugin_output | chnl | \[data\] | +| 5 | plugin_output | host_group | \[compute, compute,cincompute, containers, cumu... | +| 6 | plugin_output | cluster | \[galileo, marconi, marconi100\] | +| 7 | plugin_output | state | \[0, 1, 2, 3\] | +| 8 | plugin_output | nagiosdrained | \[0, 1\] | +| 9 | plugin_output | org | \[cineca\] | +| 10 | plugin_output | state_type | \[0, 1\] | +| 11 | plugin_output | rack | \[202, 205, 206, 207, 208, 209, 210, 211, 212, ... | + +#### State + +This metric collects the equivalent numerical value of the actual state of the service monitored by Nagios. + +| | | | | +|-----|-------|---------------|----------------------------------------------------| +| | name | tag key | tag values | +| 0 | state | node | \[ems02, ethcore01-mgt, ethcore02-mgt, gss03, g... | +| 1 | state | slot | \[01, 02, 03, 04, 05, 06, 07, 08, 09, 10, 11, 1... | +| 2 | state | description | \[EFGW_cluster::status::availability, EFGW_clus... | +| 3 | state | plugin | \[nagios_pub\] | +| 4 | state | chnl | \[data\] | +| 5 | state | host_group | \[compute, compute,cincompute, containers, cumu... | +| 6 | state | cluster | \[galileo, marconi, marconi100\] | +| 7 | state | nagiosdrained | \[0, 1\] | +| 8 | state | org | \[cineca\] | +| 9 | state | state_type | \[0, 1\] | +| 10 | state | rack | \[202, 205, 206, 207, 208, 209, 210, 211, 212, ... | + +### Resources monitored in Marconi100 + +The name and type of the services/resources monitored by Nagios and corresponding to the metrics just described above are collected in the "description" tag. + +#### Nagios checks for Marconi100 + +In the following table is collected a brief description of the services  monitored by Nagios in the Marconi100 cluster. + +| | | +|-----------------------|-----------------------------------| +| Service/resource | Description | +| alive::ping | Ping command output | +| backup::local::status | Backup service | +| batchs::... | Batch scheduler services | +| bmc::events | Events from the node BMC | +| cluster::... | Cluster availability | +| container::... | Status of the container system | +| dev::... | Node devices | +| file::integrity | Files integrity | +| filesys::... | Filesystem elements | +| galera::... | Status of the database components | +| globus::... | Status of the FTP system | +| memory::phys::total | Physical memory size | +| monitoring::health | Monitoring subsystem | +| net::ib::status | Infiniband | +| nfs::rpc::status | NFS | +| nvidia::... | GPUs | +| service::... | Misc. services | +| ssh::... | SSH server | +| sys::... | Misc. systems (GPFS,...) | + +### Nagios state encoding + +This table describes the numerical encoding of the state metric values and the state_type tag, as defined by Nagios. + +[TABLE] + +## + +## Nvidia + +The following table describes the metrics collected by the nvidia_pub plugin. + +PLEASE NOTE This plugin has collected data only for a short period (January/February 2020) and is currently not enabled due to CINECA policy. + +| | | | +|--------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------|------| +| Metric name | Description | Unit | +| clock.sm | Current frequency of SM (Streaming Multiprocessor) clock. | MHz | +| clocks.gr | Current frequency of graphics (shader) clock. | MHz | +| clocks.mem | Current frequency of memory clock. | MHz | +| clocks_throttle_reasons.active | Bitmask of active clock throttle reasons. See nvml.h for more details | | +| power.draw | The last measured power draw for the entire board, in watts. Only available if power management is supported. This reading is accurate to within +/- 5 watts. | W | +| temperature.gpu | Core GPU temperature. in degrees C. | °C | + +## Slurm + +Currently the job scheduler data is collected as per-job data in plain Cassandra tables. + +This is a description of the data currently stored (where available) for each executed job: + +| | | +|-----------------------|--------------------------------------------------------------------------------------| +| Table fields | Description | +| account | charge to specified account | +| accrue_time | time job is eligible for running | +| admin_comment | administrator's arbitrary comment | +| alloc_node | local node and system id making the resource allocation | +| alloc_sid | local sid making resource alloc | +| array_job_id | job_id of a job array or 0 if N/A | +| array_max_tasks | Maximum number of running tasks | +| array_task_id | task_id of a job array | +| array_task_str | string expression of task IDs in this record | +| assoc_id | association id for job | +| batch_features | features required for batch script's node | +| batch_flag | 1 if batch: queued job with script | +| batch_host | name of host running batch script | +| billable_tres | billable TRES cache. updated upon resize | +| bitflags | Various job flags | +| boards_per_node | boards per node required by job | +| burst_buffer | burst buffer specifications | +| burst_buffer_state | burst buffer state info | +| command | command to be executed, built from submitted  job's argv and NULL for salloc command | +| comment | arbitrary comment | +| contiguous | 1 if job requires contiguous nodes | +| core_spec | specialized core count | +| cores_per_socket | cores per socket required by job | +| cpu_freq_gov | cpu frequency governor | +| cpu_freq_max | Maximum cpu frequency | +| cpu_freq_min | Minimum cpu frequency | +| cpus_alloc_layout | map: list of cpu allocated per node | +| cpus_allocated | map: number of cpu allocated per node | +| cpus_per_task | number of processors required for each task | +| cpus_per_tres | semicolon delimited list of TRES=# values | +| dependency | synchronize job execution with other jobs | +| derived_ec | highest exit code of all job steps | +| eligible_time | time job is eligible for running | +| end_time | time of termination, actual or expected | +| exc_nodes | comma separated list of excluded nodes | +| exit_code | exit code for job (status from wait call) | +| features | comma separated list of required features | +| group_id | group job submitted as | +| job_id | job ID | +| job_state | state of the job, see enum job_states | +| last_sched_eval | last time job was evaluated for scheduling | +| licenses | licenses required by the job | +| max_cpus | maximum number of cpus usable by job | +| max_nodes | maximum number of nodes usable by job | +| mem_per_cpu | boolean | +| mem_per_node | boolean | +| mem_per_tres | semicolon delimited list of TRES=# values | +| min_memory_cpu | minimum real memory required per allocated CPU | +| min_memory_node | minimum real memory required per node | +| name | name of the job | +| network | network specification | +| nice | requested priority change | +| nodes | list of nodes allocated to job | +| ntasks_per_board | number of tasks to invoke on each board | +| ntasks_per_core | number of tasks to invoke on each core | +| ntasks_per_core_str | number of tasks to invoke on each core  as string | +| ntasks_per_node | number of tasks to invoke on each node | +| ntasks_per_socket | number of tasks to invoke on each socket | +| ntasks_per_socket_str | number of tasks to invoke on each socket as string | +| num_cpus | minimum number of cpus required by job | +| num_nodes | minimum number of nodes required by job | +| partition | name of assigned partition | +| pn_min_cpus | minimum \# CPUs per node, default=0 | +| pn_min_memory | minimum real memory per node, default=0 | +| pn_min_tmp_disk | minimum tmp disk per node, default=0 | +| power_flags | power management flags,  see SLURM_POWERFLAGS | +| pre_sus_time | time job ran prior to last suspend | +| preempt_time | preemption signal time | +| priority | relative priority of the job, 0=held, 1=required nodes DOWN/DRAINED | +| profile | Level of acct_gather_profile {all / none} | +| qos | Quality of Service | +| reboot | node reboot requested before start | +| req_nodes | comma separated list of required nodes | +| req_switch | Minimum number of switches | +| requeue | enable or disable job requeue option | +| resize_time | time of latest size change | +| restart_cnt | count of job restarts | +| resv_name | reservation name | +| run_time | job run time (seconds) | +| run_time_str | job run time (seconds) as string | +| sched_nodes | list of nodes scheduled to be used for job | +| shared | 1 if job can share nodes with other jobs | +| show_flags | conveys level of details requested | +| sockets_per_board | sockets per board required by job | +| sockets_per_node | sockets per node required by job | +| start_time | time execution begins, actual or expected | +| state_reason | reason job still pending or failed, see slurm.h:enum job_state_reason | +| std_err | pathname of job's stderr file | +| std_in | pathname of job's stdin file | +| std_out | pathname of job's stdout file | +| submit_time | time of job submission | +| suspend_time | time job last suspended or resumed | +| system_comment | slurmctld's arbitrary comment | +| threads_per_core | threads per core required by job | +| time_limit | maximum run time in minutes or INFINITE | +| time_limit_str | maximum run time in minutes or INFINITE as string | +| time_min | minimum run time in minutes or INFINITE | +| tres_alloc_str | tres used in the job as string | +| tres_bind | Task to TRES binding directives | +| tres_freq | TRES frequency directives | +| tres_per_job | semicolon delimited list of TRES=# values | +| tres_per_node | semicolon delimited list of TRES=# values | +| tres_per_socket | semicolon delimited list of TRES=# values | +| tres_per_task | semicolon delimited list of TRES=# values | +| tres_req_str | tres requested in the job as string | +| user_id | user the job runs as | +| wait4switch | Maximum time to wait for minimum switches | +| wckey | wckey for job | +| work_dir | pathname of working directory | diff --git a/docs/MonteCimone/Examon_Monte_Cimone.ipynb b/docs/MonteCimone/Examon_Monte_Cimone.ipynb new file mode 100644 index 00000000..e6b2d876 --- /dev/null +++ b/docs/MonteCimone/Examon_Monte_Cimone.ipynb @@ -0,0 +1,1674 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Introductory notebook for getting started with ExaMon and the Monte Cimone RISC-V cluster.\n", + "\n", + "## Prerequisites\n", + "\n", + "- Ability to connect with an account (ssh) to MonteCimone\n", + "- Web browser\n", + "\n", + "## To access the Grafana instance via the browser\n", + "\n", + "- On your laptop/workstation, create a tunnel with your MC user using the following command:\n", + " ```bash\n", + " ssh -L 3000:localhost:3000 -L 5000:localhost:5000 -p 2223 @137.204.56.52\n", + " ```\n", + "- Open your web browser and go to the following page:\n", + " - [http://localhost:3000/](http://localhost:3000/)\n", + "- Enter the following credentials to access the dashboard:\n", + " ```bash\n", + " User: ext_student\n", + " Password: ext_student\n", + " ```\n", + "- Once logged in, you will be in the HOME page. From there, you can open the example dashboard by visiting the following link:\n", + " - http://localhost:3000/d/PaU3WSt7z/montecimone-overview?orgId=1\n", + "\n", + "## To access the same data via script/notebook\n", + "\n", + "- Prerequisites:\n", + " - In addition to the previous prerequisites, the ability to run a jupyter server (py3) on your laptop/workstation\n", + "\n", + "- On your laptop, start:\n", + " - a tunnel as in the previous step\n", + " - a python 3 jupyter server.\n", + "- To access the db, the examon-client is required\n", + " - it is installed directly in the notebook by executing, once only, in a cell:\n", + " ```bash\n", + " - ! pip install https://github.com/fbeneventi/releases/releases/latest/download/examon-client.zip\n", + " ```\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + } + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "\n", + "# ssh -L 3000:192.168.1.201:3000 -L 5000:192.168.1.201:5000 -p 2223 @137.204.56.52\n", + "\n", + "\n", + "import os\n", + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "from examon.examon import Client, ExamonQL\n", + "\n", + "# Connect\n", + "USER = 'ext_student'\n", + "PWD = 'ext_student'\n", + "ex = Client('127.0.0.1', port='3000', user=USER, password=PWD, verbose=False, proxy=True)\n", + "sq = ExamonQL(ex)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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8load_avg.5m
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11memory_usage.free
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18procs.new
19procs.run
20system.csw
21system.int
22temperature.average
23temperature.cpu_temp
24temperature.mb_temp
25temperature.nvme_temp
26temperature.total
27total_cpu_usage.idl
28total_cpu_usage.stl
29total_cpu_usage.sys
30total_cpu_usage.usr
31total_cpu_usage.wai
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nametag keytag values
0INSTRUCTIONSnode[mcimone-node-1, mcimone-node-2, mcimone-node-...
1INSTRUCTIONScore[0, 1, 2, 3]
2INSTRUCTIONSplugin[pmu_pub]
3INSTRUCTIONSchnl[data]
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" + ], + "text/plain": [ + " name tag key tag values\n", + "0 INSTRUCTIONS node [mcimone-node-1, mcimone-node-2, mcimone-node-...\n", + "1 INSTRUCTIONS core [0, 1, 2, 3]\n", + "2 INSTRUCTIONS plugin [pmu_pub]\n", + "3 INSTRUCTIONS chnl [data]\n", + "4 INSTRUCTIONS cluster [hifive]\n", + "5 INSTRUCTIONS org [unibo]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = sq.DESCRIBE(metric='INSTRUCTIONS') \\\n", + " .execute()\n", + " \n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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7hifive0INSTRUCTIONSmcimone-node-12023-06-27 19:20:55.500000+02:001.794780e+11
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9hifive0INSTRUCTIONSmcimone-node-12023-06-27 19:20:56.500000+02:001.794791e+11
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" + ], + "text/plain": [ + " cluster core name node timestamp \\\n", + "0 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:52+02:00 \n", + "1 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:52.500000+02:00 \n", + "2 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:53+02:00 \n", + "3 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:53.500000+02:00 \n", + "4 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:54+02:00 \n", + "5 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:54.500000+02:00 \n", + "6 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:55+02:00 \n", + "7 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:55.500000+02:00 \n", + "8 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:56+02:00 \n", + "9 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-27 19:20:56.500000+02:00 \n", + "\n", + " value \n", + "0 1.794506e+11 \n", + "1 1.794748e+11 \n", + "2 1.794753e+11 \n", + "3 1.794758e+11 \n", + "4 1.794764e+11 \n", + "5 1.794769e+11 \n", + "6 1.794775e+11 \n", + "7 1.794780e+11 \n", + "8 1.794786e+11 \n", + "9 1.794791e+11 " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = sq.SELECT('node','cluster','core') \\\n", + " .FROM('INSTRUCTIONS') \\\n", + " .TSTART(30, 'minutes') \\\n", + " .execute()\n", + " \n", + "data.df_table.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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end_timejob_idjob_statenamenodesnum_cpusnum_nodesstart_timeuser_idwork_dir
02023-06-27T18:18:13.000Z2825CANCELLEDbashmcimone-node-1112023-06-27T18:00:08.000Z2001/home/abartolini/HPL/src/hpl-2.3
12023-06-28T11:11:47.000Z2868COMPLETEDtestmcimone-node-1412023-06-28T11:11:47.000Z6010/home/userdeiphd10
22023-06-27T14:48:34.000Z2821FAILEDhplmcimone-node-1212023-06-27T14:48:32.000Z2001/home/abartolini
32023-06-28T11:15:55.000Z2872COMPLETEDtestmcimone-node-1412023-06-28T11:15:53.000Z6008/home/userdeiphd08
42023-06-28T07:05:00.000Z2860COMPLETEDbashmcimone-node-1112023-06-28T07:04:54.000Z6001/home/userdeiphd01
52023-06-27T18:19:49.000Z2831FAILEDhplmcimone-node-1212023-06-27T18:19:46.000Z2001/home/abartolini
62023-06-28T11:32:38.000Z2887COMPLETEDsleepmcimone-node-1112023-06-28T11:32:08.000Z6005/home/userdeiphd05
72023-06-28T11:27:32.000Z2877COMPLETEDbashmcimone-node-1112023-06-28T11:27:26.000Z6008/home/userdeiphd08/02_ex
82023-06-28T11:19:44.000Z2874COMPLETEDtestmcimone-node-1412023-06-28T11:19:33.000Z6010/home/userdeiphd10
92023-06-27T21:11:24.000Z2845COMPLETEDstreammcimone-node-1412023-06-27T21:11:05.000Z2001/home/abartolini
102023-06-28T07:34:06.000Z2864FAILEDhplmcimone-node-1212023-06-28T07:34:03.000Z2001/home/abartolini
112023-06-28T11:31:14.000Z2882COMPLETEDsleepmcimone-node-1112023-06-28T11:31:03.000Z6011/home/userdeiphd11
122023-06-28T11:13:29.000Z2869COMPLETEDtestmcimone-node-1412023-06-28T11:13:28.000Z6008/home/userdeiphd08
132023-06-28T11:09:07.000Z2867COMPLETEDtestmcimone-node-1412023-06-28T11:09:06.000Z6008/home/userdeiphd08
142023-06-28T11:30:23.000Z2878COMPLETED07smc.shmcimone-node-1112023-06-28T11:30:22.000Z6007/home/userdeiphd07
152023-06-28T11:36:17.000Z2901COMPLETEDtest.shmcimone-node-1112023-06-28T11:36:16.000Z6001/home/userdeiphd01
162023-06-28T11:38:31.000Z2903COMPLETED07j.shmcimone-node-1112023-06-28T11:38:29.000Z6007/home/userdeiphd07
172023-06-28T11:33:56.000Z2893COMPLETEDechomcimone-node-1112023-06-28T11:33:56.000Z6011/home/userdeiphd11
182023-06-27T22:03:38.000Z2855COMPLETEDhplmcimone-node-1412023-06-27T22:03:33.000Z2001/home/abartolini
192023-06-28T11:33:42.000Z2892COMPLETEDechomcimone-node-1112023-06-28T11:33:41.000Z6003/home/userdeiphd03
202023-06-28T11:33:41.000Z2890COMPLETEDechomcimone-node-1112023-06-28T11:33:40.000Z6003/home/userdeiphd03
212023-06-28T11:32:08.000Z2885COMPLETEDechomcimone-node-1112023-06-28T11:32:07.000Z6005/home/userdeiphd05
222023-06-28T11:34:27.000Z2895COMPLETEDsleepmcimone-node-1112023-06-28T11:33:57.000Z6011/home/userdeiphd11
232023-06-28T07:30:40.000Z2861COMPLETEDbashmcimone-node-1112023-06-28T07:05:12.000Z2001/home/abartolini
242023-06-28T08:44:09.000Z2866CANCELLEDhplmcimone-node-1412023-06-28T07:38:27.000Z2001/home/abartolini
252023-06-27T21:41:14.000Z2854COMPLETEDstreammcimone-node-1112023-06-27T21:40:57.000Z2001/home/abartolini
262023-06-28T07:36:11.000Z2865COMPLETEDhplmcimone-node-1412023-06-28T07:36:06.000Z2001/home/abartolini
272023-06-28T11:31:03.000Z2880COMPLETEDechomcimone-node-1112023-06-28T11:31:02.000Z6011/home/userdeiphd11
282023-06-28T11:30:49.000Z2879FAILEDbashmcimone-node-1112023-06-28T11:30:23.000Z6008/home/userdeiphd08/02_ex
292023-06-28T07:04:37.000Z2859COMPLETEDbashmcimone-node-1112023-06-28T07:04:26.000Z2001/home/abartolini
302023-06-27T14:48:11.000Z2820FAILEDrun_hpl.shmcimone-node-1112023-06-27T14:48:11.000Z2001/home/abartolini
312023-06-27T21:38:29.000Z2852COMPLETEDstreammcimone-node-1112023-06-27T21:38:11.000Z2001/home/abartolini
322023-06-28T11:19:09.000Z2873COMPLETEDtestmcimone-node-1412023-06-28T11:19:07.000Z6010/home/userdeiphd10
332023-06-27T21:13:04.000Z2846COMPLETEDstreammcimone-node-1412023-06-27T21:12:47.000Z2001/home/abartolini
342023-06-27T21:18:39.000Z2851COMPLETEDstreammcimone-node-1112023-06-27T21:18:22.000Z2001/home/abartolini
352023-06-28T11:44:48.000Z2905FAILEDTest1mcimone-node-1112023-06-28T11:44:47.000Z6004/home/userdeiphd04
362023-06-28T11:14:08.000Z2871COMPLETEDtestmcimone-node-1412023-06-28T11:14:07.000Z6008/home/userdeiphd08
372023-06-27T14:50:37.000Z2822FAILEDhplmcimone-node-1212023-06-27T14:50:34.000Z2001/home/abartolini
\n", + "
" + ], + "text/plain": [ + " end_time job_id job_state name nodes \\\n", + "0 2023-06-27T18:18:13.000Z 2825 CANCELLED bash mcimone-node-1 \n", + "1 2023-06-28T11:11:47.000Z 2868 COMPLETED test mcimone-node-1 \n", + "2 2023-06-27T14:48:34.000Z 2821 FAILED hpl mcimone-node-1 \n", + "3 2023-06-28T11:15:55.000Z 2872 COMPLETED test mcimone-node-1 \n", + "4 2023-06-28T07:05:00.000Z 2860 COMPLETED bash mcimone-node-1 \n", + "5 2023-06-27T18:19:49.000Z 2831 FAILED hpl mcimone-node-1 \n", + "6 2023-06-28T11:32:38.000Z 2887 COMPLETED sleep mcimone-node-1 \n", + "7 2023-06-28T11:27:32.000Z 2877 COMPLETED bash mcimone-node-1 \n", + "8 2023-06-28T11:19:44.000Z 2874 COMPLETED test mcimone-node-1 \n", + "9 2023-06-27T21:11:24.000Z 2845 COMPLETED stream mcimone-node-1 \n", + "10 2023-06-28T07:34:06.000Z 2864 FAILED hpl mcimone-node-1 \n", + "11 2023-06-28T11:31:14.000Z 2882 COMPLETED sleep mcimone-node-1 \n", + "12 2023-06-28T11:13:29.000Z 2869 COMPLETED test mcimone-node-1 \n", + "13 2023-06-28T11:09:07.000Z 2867 COMPLETED test mcimone-node-1 \n", + "14 2023-06-28T11:30:23.000Z 2878 COMPLETED 07smc.sh mcimone-node-1 \n", + "15 2023-06-28T11:36:17.000Z 2901 COMPLETED test.sh mcimone-node-1 \n", + "16 2023-06-28T11:38:31.000Z 2903 COMPLETED 07j.sh mcimone-node-1 \n", + "17 2023-06-28T11:33:56.000Z 2893 COMPLETED echo mcimone-node-1 \n", + "18 2023-06-27T22:03:38.000Z 2855 COMPLETED hpl mcimone-node-1 \n", + "19 2023-06-28T11:33:42.000Z 2892 COMPLETED echo mcimone-node-1 \n", + "20 2023-06-28T11:33:41.000Z 2890 COMPLETED echo mcimone-node-1 \n", + "21 2023-06-28T11:32:08.000Z 2885 COMPLETED echo mcimone-node-1 \n", + "22 2023-06-28T11:34:27.000Z 2895 COMPLETED sleep mcimone-node-1 \n", + "23 2023-06-28T07:30:40.000Z 2861 COMPLETED bash mcimone-node-1 \n", + "24 2023-06-28T08:44:09.000Z 2866 CANCELLED hpl mcimone-node-1 \n", + "25 2023-06-27T21:41:14.000Z 2854 COMPLETED stream mcimone-node-1 \n", + "26 2023-06-28T07:36:11.000Z 2865 COMPLETED hpl mcimone-node-1 \n", + "27 2023-06-28T11:31:03.000Z 2880 COMPLETED echo mcimone-node-1 \n", + "28 2023-06-28T11:30:49.000Z 2879 FAILED bash mcimone-node-1 \n", + "29 2023-06-28T07:04:37.000Z 2859 COMPLETED bash mcimone-node-1 \n", + "30 2023-06-27T14:48:11.000Z 2820 FAILED run_hpl.sh mcimone-node-1 \n", + "31 2023-06-27T21:38:29.000Z 2852 COMPLETED stream mcimone-node-1 \n", + "32 2023-06-28T11:19:09.000Z 2873 COMPLETED test mcimone-node-1 \n", + "33 2023-06-27T21:13:04.000Z 2846 COMPLETED stream mcimone-node-1 \n", + "34 2023-06-27T21:18:39.000Z 2851 COMPLETED stream mcimone-node-1 \n", + "35 2023-06-28T11:44:48.000Z 2905 FAILED Test1 mcimone-node-1 \n", + "36 2023-06-28T11:14:08.000Z 2871 COMPLETED test mcimone-node-1 \n", + "37 2023-06-27T14:50:37.000Z 2822 FAILED hpl mcimone-node-1 \n", + "\n", + " num_cpus num_nodes start_time user_id \\\n", + "0 1 1 2023-06-27T18:00:08.000Z 2001 \n", + "1 4 1 2023-06-28T11:11:47.000Z 6010 \n", + "2 2 1 2023-06-27T14:48:32.000Z 2001 \n", + "3 4 1 2023-06-28T11:15:53.000Z 6008 \n", + "4 1 1 2023-06-28T07:04:54.000Z 6001 \n", + "5 2 1 2023-06-27T18:19:46.000Z 2001 \n", + "6 1 1 2023-06-28T11:32:08.000Z 6005 \n", + "7 1 1 2023-06-28T11:27:26.000Z 6008 \n", + "8 4 1 2023-06-28T11:19:33.000Z 6010 \n", + "9 4 1 2023-06-27T21:11:05.000Z 2001 \n", + "10 2 1 2023-06-28T07:34:03.000Z 2001 \n", + "11 1 1 2023-06-28T11:31:03.000Z 6011 \n", + "12 4 1 2023-06-28T11:13:28.000Z 6008 \n", + "13 4 1 2023-06-28T11:09:06.000Z 6008 \n", + "14 1 1 2023-06-28T11:30:22.000Z 6007 \n", + "15 1 1 2023-06-28T11:36:16.000Z 6001 \n", + "16 1 1 2023-06-28T11:38:29.000Z 6007 \n", + "17 1 1 2023-06-28T11:33:56.000Z 6011 \n", + "18 4 1 2023-06-27T22:03:33.000Z 2001 \n", + "19 1 1 2023-06-28T11:33:41.000Z 6003 \n", + "20 1 1 2023-06-28T11:33:40.000Z 6003 \n", + "21 1 1 2023-06-28T11:32:07.000Z 6005 \n", + "22 1 1 2023-06-28T11:33:57.000Z 6011 \n", + "23 1 1 2023-06-28T07:05:12.000Z 2001 \n", + "24 4 1 2023-06-28T07:38:27.000Z 2001 \n", + "25 1 1 2023-06-27T21:40:57.000Z 2001 \n", + "26 4 1 2023-06-28T07:36:06.000Z 2001 \n", + "27 1 1 2023-06-28T11:31:02.000Z 6011 \n", + "28 1 1 2023-06-28T11:30:23.000Z 6008 \n", + "29 1 1 2023-06-28T07:04:26.000Z 2001 \n", + "30 1 1 2023-06-27T14:48:11.000Z 2001 \n", + "31 1 1 2023-06-27T21:38:11.000Z 2001 \n", + "32 4 1 2023-06-28T11:19:07.000Z 6010 \n", + "33 4 1 2023-06-27T21:12:47.000Z 2001 \n", + "34 1 1 2023-06-27T21:18:22.000Z 2001 \n", + "35 1 1 2023-06-28T11:44:47.000Z 6004 \n", + "36 4 1 2023-06-28T11:14:07.000Z 6008 \n", + "37 2 1 2023-06-27T14:50:34.000Z 2001 \n", + "\n", + " work_dir \n", + "0 /home/abartolini/HPL/src/hpl-2.3 \n", + "1 /home/userdeiphd10 \n", + "2 /home/abartolini \n", + "3 /home/userdeiphd08 \n", + "4 /home/userdeiphd01 \n", + "5 /home/abartolini \n", + "6 /home/userdeiphd05 \n", + "7 /home/userdeiphd08/02_ex \n", + "8 /home/userdeiphd10 \n", + "9 /home/abartolini \n", + "10 /home/abartolini \n", + "11 /home/userdeiphd11 \n", + "12 /home/userdeiphd08 \n", + "13 /home/userdeiphd08 \n", + "14 /home/userdeiphd07 \n", + "15 /home/userdeiphd01 \n", + "16 /home/userdeiphd07 \n", + "17 /home/userdeiphd11 \n", + "18 /home/abartolini \n", + "19 /home/userdeiphd03 \n", + "20 /home/userdeiphd03 \n", + "21 /home/userdeiphd05 \n", + "22 /home/userdeiphd11 \n", + "23 /home/abartolini \n", + "24 /home/abartolini \n", + "25 /home/abartolini \n", + "26 /home/abartolini \n", + "27 /home/userdeiphd11 \n", + "28 /home/userdeiphd08/02_ex \n", + "29 /home/abartolini \n", + "30 /home/abartolini \n", + "31 /home/abartolini \n", + "32 /home/userdeiphd10 \n", + "33 /home/abartolini \n", + "34 /home/abartolini \n", + "35 /home/userdeiphd04 \n", + "36 /home/userdeiphd08 \n", + "37 /home/abartolini " + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "# Setup \n", + "sq.jc.JOB_TABLES.extend(['job_info_hifive'])\n", + "\n", + "data = sq.SELECT('name','user_id','job_id','job_state','start_time','end_time','nodes','num_nodes','num_cpus','work_dir') \\\n", + " .FROM('job_info_hifive') \\\n", + " .WHERE(node='mcimone-node-1') \\\n", + " .TSTART('27-06-2023 08:09:00') \\\n", + " .TSTOP('28-06-2023 23:09:00') \\\n", + " .execute() \n", + "\n", + "df = pd.DataFrame(json.loads(data))\n", + "df.head(50)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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end_timejob_idjob_statenamenodesnum_cpusnum_nodesstart_timeuser_idwork_dir
02023-06-28T08:44:09.000Z2866CANCELLEDhplmcimone-node-1412023-06-28T07:38:27.000Z2001/home/abartolini
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" + ], + "text/plain": [ + " end_time job_id job_state name nodes num_cpus \\\n", + "0 2023-06-28T08:44:09.000Z 2866 CANCELLED hpl mcimone-node-1 4 \n", + "\n", + " num_nodes start_time user_id work_dir \n", + "0 1 2023-06-28T07:38:27.000Z 2001 /home/abartolini " + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import json\n", + "\n", + "\n", + "data = sq.SELECT('name','user_id','job_id','job_state','start_time','end_time','nodes','num_nodes','num_cpus','work_dir') \\\n", + " .FROM('job_info_hifive') \\\n", + " .WHERE(job_id='2866') \\\n", + " .TSTART('27-06-2023 08:09:00') \\\n", + " .execute() \n", + "\n", + "df = pd.DataFrame(json.loads(data))\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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clustercorenamenodetimestampvalue
0hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:27+02:003.100586e+11
1hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:27.500000+02:003.100629e+11
2hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:28+02:003.100923e+11
3hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:28.500000+02:003.100927e+11
4hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:29+02:003.100957e+11
5hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:29.500000+02:003.101099e+11
6hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:30+02:003.101479e+11
7hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:30.500000+02:003.103973e+11
8hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:31+02:003.108438e+11
9hifive0INSTRUCTIONSmcimone-node-12023-06-28 07:38:31.500000+02:003.113999e+11
\n", + "
" + ], + "text/plain": [ + " cluster core name node timestamp \\\n", + "0 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:27+02:00 \n", + "1 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:27.500000+02:00 \n", + "2 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:28+02:00 \n", + "3 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:28.500000+02:00 \n", + "4 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:29+02:00 \n", + "5 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:29.500000+02:00 \n", + "6 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:30+02:00 \n", + "7 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:30.500000+02:00 \n", + "8 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:31+02:00 \n", + "9 hifive 0 INSTRUCTIONS mcimone-node-1 2023-06-28 07:38:31.500000+02:00 \n", + "\n", + " value \n", + "0 3.100586e+11 \n", + "1 3.100629e+11 \n", + "2 3.100923e+11 \n", + "3 3.100927e+11 \n", + "4 3.100957e+11 \n", + "5 3.101099e+11 \n", + "6 3.101479e+11 \n", + "7 3.103973e+11 \n", + "8 3.108438e+11 \n", + "9 3.113999e+11 " + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = sq.SELECT('node','cluster','core') \\\n", + " .FROM('INSTRUCTIONS') \\\n", + " .WHERE(node='mcimone-node-1') \\\n", + " .TSTART('28-06-2023 07:38:27') \\\n", + " .TSTOP('28-06-2023 08:44:09') \\\n", + " .execute()\n", + " \n", + "data.df_table.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 252320 entries, 0 to 252319\n", + "Data columns (total 6 columns):\n", + "cluster 252320 non-null object\n", + "core 252320 non-null object\n", + "name 252320 non-null object\n", + "node 252320 non-null object\n", + "timestamp 252320 non-null datetime64[ns, Europe/Rome]\n", + "value 252320 non-null float64\n", + "dtypes: datetime64[ns, Europe/Rome](1), float64(1), object(4)\n", + "memory usage: 11.6+ MB\n" + ] + } + ], + "source": [ + "data.df_table.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " cluster core node instructions_per_second\n", + "23656 hifive 3 mcimone-node-1 38523114.0\n", + "1 hifive 0 mcimone-node-1 8620240.0\n", + "7886 hifive 1 mcimone-node-1 14623376.0\n", + "15771 hifive 2 mcimone-node-1 14995134.0\n", + "7887 hifive 1 mcimone-node-1 83411746.0\n", + "23657 hifive 3 mcimone-node-1 11415616.0\n", + "2 hifive 0 mcimone-node-1 58787754.0\n", + "15772 hifive 2 mcimone-node-1 14676376.0\n", + "23658 hifive 3 mcimone-node-1 1645138.0\n", + "7888 hifive 1 mcimone-node-1 350340.0\n", + "3 hifive 0 mcimone-node-1 803444.0\n", + "15773 hifive 2 mcimone-node-1 1136978.0\n", + "23659 hifive 3 mcimone-node-1 2783320.0\n", + "7889 hifive 1 mcimone-node-1 5273074.0\n", + "15774 hifive 2 mcimone-node-1 2275020.0\n", + "4 hifive 0 mcimone-node-1 6114222.0\n", + "23660 hifive 3 mcimone-node-1 376575758.0\n", + "5 hifive 0 mcimone-node-1 28421320.0\n", + "15775 hifive 2 mcimone-node-1 21301446.0\n", + "7890 hifive 1 mcimone-node-1 140078054.0\n", + "15776 hifive 2 mcimone-node-1 11004830.0\n", + "6 hifive 0 mcimone-node-1 76035596.0\n", + "23661 hifive 3 mcimone-node-1 188484324.0\n", + "7891 hifive 1 mcimone-node-1 271213162.0\n", + "7892 hifive 1 mcimone-node-1 475208026.0\n", + "15777 hifive 2 mcimone-node-1 483062156.0\n", + "23662 hifive 3 mcimone-node-1 486469840.0\n", + "7 hifive 0 mcimone-node-1 498794364.0\n", + "7893 hifive 1 mcimone-node-1 885180146.0\n", + "23663 hifive 3 mcimone-node-1 894075140.0\n", + "... ... ... ... ...\n", + "23647 hifive 2 mcimone-node-1 233339268.0\n", + "15762 hifive 1 mcimone-node-1 228270452.0\n", + "23648 hifive 2 mcimone-node-1 217027384.0\n", + "7878 hifive 0 mcimone-node-1 213077746.0\n", + "31533 hifive 3 mcimone-node-1 218002042.0\n", + "15763 hifive 1 mcimone-node-1 221205918.0\n", + "15764 hifive 1 mcimone-node-1 221965008.0\n", + "31534 hifive 3 mcimone-node-1 233791354.0\n", + "23649 hifive 2 mcimone-node-1 223936646.0\n", + "7879 hifive 0 mcimone-node-1 201658966.0\n", + "31535 hifive 3 mcimone-node-1 209675462.0\n", + "23650 hifive 2 mcimone-node-1 217535866.0\n", + "15765 hifive 1 mcimone-node-1 221215852.0\n", + "7880 hifive 0 mcimone-node-1 209611952.0\n", + "23651 hifive 2 mcimone-node-1 266259612.0\n", + "15766 hifive 1 mcimone-node-1 241839476.0\n", + "7881 hifive 0 mcimone-node-1 251117610.0\n", + "31536 hifive 3 mcimone-node-1 233599118.0\n", + "23652 hifive 2 mcimone-node-1 226341798.0\n", + "31537 hifive 3 mcimone-node-1 224304384.0\n", + "7882 hifive 0 mcimone-node-1 207978252.0\n", + "15767 hifive 1 mcimone-node-1 224403098.0\n", + "23653 hifive 2 mcimone-node-1 223050652.0\n", + "31538 hifive 3 mcimone-node-1 223850414.0\n", + "15768 hifive 1 mcimone-node-1 219587870.0\n", + "7883 hifive 0 mcimone-node-1 223695594.0\n", + "15769 hifive 1 mcimone-node-1 225851464.0\n", + "7884 hifive 0 mcimone-node-1 207533258.0\n", + "23654 hifive 2 mcimone-node-1 220584236.0\n", + "31539 hifive 3 mcimone-node-1 224012882.0\n", + "\n", + "[31536 rows x 4 columns]\n" + ] + } + ], + "source": [ + "df = data.df_table\n", + "\n", + "# Sort the DataFrame by 'timestamp'\n", + "df = df.sort_values('timestamp')\n", + "\n", + "# Calculate the time difference between consecutive rows for each core and node\n", + "df['time_diff'] = df.groupby(['core', 'node'])['timestamp'].diff()\n", + "\n", + "# Calculate the Instructions per second for each core and node\n", + "df['instructions_per_second'] = df.groupby(['core', 'node'])['value'].diff() / df['time_diff'].dt.total_seconds()\n", + "\n", + "# Drop rows with NaN values (first row for each core and node)\n", + "df = df.dropna()\n", + "\n", + "# Print the resulting DataFrame\n", + "print(df[['cluster', 'core', 'node', 'instructions_per_second']])" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[['timestamp','node','core','instructions_per_second']]\\\n", + ".pivot_table(index='timestamp', columns=['node','core'], dropna=True, aggfunc='first')\\\n", + ".plot(figsize=[15,30], subplots=True);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/MonteCimone/MonteCimone.md b/docs/MonteCimone/MonteCimone.md new file mode 100644 index 00000000..f996f28c --- /dev/null +++ b/docs/MonteCimone/MonteCimone.md @@ -0,0 +1,56 @@ +# Monte Cimone - UniBO + +
+ ![](../images/monte-cimone.jpg){ width="300" } +
+ +## Configuration of the Monte Cimone RISC-V cluster: + +- Manufacturer: E4 +- Form factor: 1U +- Nodes: 8 +- Blade configuration: Four blades with dual boards +- Motherboard: HiFive Unmatched developed by SiFive +- SoC: Freedom U740 +- Cores: Four U74 cores at 1.4GHz and one S7 core with Mix+Match technology +- Cache: 2MB L2 cache +- Memory: 16GB DDR4-1866 +- Storage: 1TB NVMe SSD + + +## Metrics + +| Metric | Description | Unit of Measurement | +|-----------------------|--------------------------------------|---------------------| +| CYCLES | Number of cycles | cycles | +| INSTRUCTIONS | Number of instructions executed | count | +| dsk_total.read | Total disk read operations | operations | +| dsk_total.writ | Total disk write operations | operations | +| io_total.read | Total input/output read operations | operations | +| io_total.writ | Total input/output write operations | operations | +| load_avg.15m | Load average over 15 minutes | load average | +| load_avg.1m | Load average over 1 minute | load average | +| load_avg.5m | Load average over 5 minutes | load average | +| memory_usage.buff | Memory used for buffering | bytes | +| memory_usage.cach | Memory used for caching | bytes | +| memory_usage.free | Free memory available | bytes | +| memory_usage.used | Memory currently in use | bytes | +| net_total.recv | Total network data received | bytes | +| net_total.send | Total network data sent | bytes | +| paging.in | Paging operations in | operations | +| paging.out | Paging operations out | operations | +| procs.blk | Number of processes blocked | count | +| procs.new | Number of new processes | count | +| procs.run | Number of running processes | count | +| system.csw | Number of context switches | count | +| system.int | Number of interrupts | count | +| temperature.average | Average system temperature | Celsius | +| temperature.cpu_temp | CPU temperature | Celsius | +| temperature.mb_temp | Motherboard temperature | Celsius | +| temperature.nvme_temp | NVMe device temperature | Celsius | +| temperature.total | Total system temperature | Celsius | +| total_cpu_usage.idl | CPU idle time | percentage | +| total_cpu_usage.stl | CPU steal time | percentage | +| total_cpu_usage.sys | CPU system time | percentage | +| total_cpu_usage.usr | CPU user time | percentage | +| total_cpu_usage.wai | CPU wait time | percentage | diff --git a/docs/Plugins/examon_pub.ipynb b/docs/Plugins/examon_pub.ipynb new file mode 100644 index 00000000..28681d2f --- /dev/null +++ b/docs/Plugins/examon_pub.ipynb @@ -0,0 +1,596 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a2df3f41", + "metadata": {}, + "source": [ + "# Example plugin\n", + "This notebook shows how to create a simple Examon publisher using Python (v3)\n", + "\n", + "## Install \n", + "Install the publisher library.\n", + "\n", + "NOTE: This is a development release so the final API may be different in future versions." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f94c1faa", + "metadata": { + "scrolled": false + }, + "outputs": [], + "source": [ + "! python -m pip install --upgrade https://github.com/fbeneventi/releases/releases/latest/download/examon-common-py3.zip" + ] + }, + { + "cell_type": "markdown", + "id": "3a5ab405", + "metadata": {}, + "source": [ + "## Configure\n", + "The below cell will create the examon_pub.conf file that should be edited according to the server configuration" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8e723e11", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting examon_pub.conf\n" + ] + } + ], + "source": [ + "%%file examon_pub.conf\n", + "; Sample examon publisher config file.\n", + ";\n", + "\n", + "; The below section collects all the settings related to the\n", + "; MQTT transport layer\n", + "[MQTT]\n", + "; MQTT broker IP address and port\n", + "MQTT_BROKER = 127.0.0.1\n", + "MQTT_PORT = 1883\n", + "; MQTT output topic (optional). This setting is used only with\n", + "; the 'json' and 'bulk' MQTT output formats\n", + "MQTT_TOPIC =\n", + "; To be used when password authentication is enabled (optional)\n", + "MQTT_USER =\n", + "MQTT_PASSWORD =\n", + "\n", + "; The below section collects all the settings related to the\n", + "; KairosDB database \n", + "[KairosDB]\n", + "; KairosDB server IP address and port\n", + "K_SERVERS =\n", + "K_PORT =\n", + "; To be used when password authentication is enabled (optional)\n", + "K_USER = \n", + "K_PASSWORD =\n", + "\n", + "; The below section collects all the settings related to the\n", + "; ExaMon collector \n", + "[Daemon]\n", + "; Default sampling interval in seconds (float)\n", + "TS = 2\n", + "; Path to the log file\n", + "LOG_FILENAME = examon_pub.log\n", + "; Path to the pid file\n", + "PID_FILENAME = examon_pub.pid" + ] + }, + { + "cell_type": "markdown", + "id": "450e6ecd", + "metadata": {}, + "source": [ + "## Example\n", + "This is the main file where the publisher is defined. In this example, a dummy Sensor class creates some random data to be published." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "cad27c46", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting examon_pub.py\n" + ] + } + ], + "source": [ + "%%file examon_pub.py\n", + "\n", + "import json\n", + "import time\n", + "import random\n", + "\n", + "from examon.plugin.examonapp import ExamonApp\n", + "from examon.plugin.sensorreader import SensorReader\n", + "\n", + "\n", + "class Sensor:\n", + " def __init__(self, sensor_name='random_sensor', range_min=0, range_max=100.0):\n", + " self.sensor_name = sensor_name\n", + " self.range_min = range_min\n", + " self.range_max = range_max\n", + " \n", + " def get_sensor_data(self):\n", + " return {\n", + " 'sensor_name': self.sensor_name,\n", + " 'value': random.uniform(self.range_min, self.range_max)\n", + " }\n", + " \n", + " def read_data(self):\n", + " pass\n", + " \n", + "\n", + "def read_data(sr):\n", + " \n", + " # get timestamp and data \n", + " timestamp = int(time.time()*1000)\n", + " raw_data = sr.sensor.get_sensor_data()\n", + " \n", + " # build the examon metric\n", + " metric = {}\n", + " metric['name'] = raw_data['sensor_name']\n", + " metric['value'] = raw_data['value']\n", + " metric['timestamp'] = timestamp\n", + " metric['tags'] = sr.get_tags()\n", + " \n", + " # return format:\n", + " # * list of metrics\n", + " examon_data = [metric]\n", + " # * worker id (string) useful for debug/log\n", + " worker_id = sr.sensor.sensor_name\n", + " \n", + " return (worker_id, examon_data,)\n", + " \n", + " \n", + "def worker(conf, tags):\n", + " \"\"\"\n", + " Worker process code\n", + " \"\"\"\n", + " # sensor instance \n", + " sensor = Sensor()\n", + " \n", + " # SensorReader app\n", + " sr = SensorReader(conf, sensor)\n", + " \n", + " # add read_data callback\n", + " sr.read_data = read_data \n", + " \n", + " # set the default tags\n", + " sr.add_tags(tags)\n", + " \n", + " # run the worker loop\n", + " sr.run()\n", + "\n", + " \n", + "if __name__ == '__main__':\n", + "\n", + " # start creating an Examon app\n", + " app = ExamonApp()\n", + "\n", + " app.parse_opt()\n", + " # for checking\n", + " print(\"Config:\")\n", + " print(json.dumps(app.conf, indent=4))\n", + "\n", + " # set default metrics tags\n", + " tags = app.examon_tags()\n", + " tags['org'] = 'examon'\n", + " tags['plugin'] = 'examon_pub'\n", + " tags['chnl'] = 'data'\n", + " \n", + " # add a worker\n", + " app.add_worker(worker, app.conf, tags)\n", + " \n", + " # run!\n", + " app.run() \n" + ] + }, + { + "cell_type": "markdown", + "id": "551d5c0c", + "metadata": {}, + "source": [ + "## Execution\n", + "The Examon publisher created above can be executed from the shell and the default configuration can be changed using the command line parameters." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "91c9eff1", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "usage: examon_pub.py [-h] [-b MQTT_BROKER] [-p MQTT_PORT] [-t MQTT_TOPIC]\r\n", + " [-s TS] [-x PID_FILENAME] [-l LOG_FILENAME]\r\n", + " [-d {mqtt,kairosdb}] [-f {csv,json,bulk}] [--compress]\r\n", + " [--kairosdb-server K_SERVERS] [--kairosdb-port K_PORT]\r\n", + " [--kairosdb-user K_USER] [--kairosdb-password K_PASSWORD]\r\n", + " [--logfile-size LOGFILE_SIZE_B]\r\n", + " [--loglevel {DEBUG,INFO,WARNING,ERROR,CRITICAL}]\r\n", + " [--dry-run] [--mqtt-user MQTT_USER]\r\n", + " [--mqtt-password MQTT_PASSWORD]\r\n", + " {run,start,restart,stop}\r\n", + "\r\n", + "positional arguments:\r\n", + " {run,start,restart,stop}\r\n", + " Run mode\r\n", + "\r\n", + "optional arguments:\r\n", + " -h, --help show this help message and exit\r\n", + " -b MQTT_BROKER IP address of the MQTT broker\r\n", + " -p MQTT_PORT Port of the MQTT broker\r\n", + " -t MQTT_TOPIC MQTT topic\r\n", + " -s TS Sampling time (seconds)\r\n", + " -x PID_FILENAME pid filename\r\n", + " -l LOG_FILENAME log filename\r\n", + " -d {mqtt,kairosdb} select where to send data (default: mqtt)\r\n", + " -f {csv,json,bulk} MQTT payload format (default: csv)\r\n", + " --compress enable payload compression (default: False)\r\n", + " --kairosdb-server K_SERVERS\r\n", + " kairosdb servers\r\n", + " --kairosdb-port K_PORT\r\n", + " kairosdb port\r\n", + " --kairosdb-user K_USER\r\n", + " kairosdb username\r\n", + " --kairosdb-password K_PASSWORD\r\n", + " kairosdb password\r\n", + " --logfile-size LOGFILE_SIZE_B\r\n", + " log file size (max) in bytes\r\n", + " --loglevel {DEBUG,INFO,WARNING,ERROR,CRITICAL}\r\n", + " log level\r\n", + " --dry-run Data is not sent to the broker if True (default:\r\n", + " False)\r\n", + " --mqtt-user MQTT_USER\r\n", + " MQTT username\r\n", + " --mqtt-password MQTT_PASSWORD\r\n", + " MQTT password\r\n" + ] + } + ], + "source": [ + "! python examon_pub.py -h" + ] + }, + { + "cell_type": "markdown", + "id": "374a56da", + "metadata": {}, + "source": [ + "### Dry Run\n", + "Before the actual execution can be useful a \"dry run\" to check the final payload. The MQTT packet (topic, payload) is printed in the lines that have the tag \"[MqttPub]\"." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ec10b31d", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config:\n", + "{\n", + " \"MQTT_BROKER\": \"127.0.0.1\",\n", + " \"MQTT_PORT\": \"1883\",\n", + " \"MQTT_TOPIC\": \"\",\n", + " \"MQTT_USER\": \"\",\n", + " \"MQTT_PASSWORD\": \"\",\n", + " \"K_SERVERS\": \"\",\n", + " \"K_PORT\": \"\",\n", + " \"K_USER\": \"\",\n", + " \"K_PASSWORD\": \"\",\n", + " \"TS\": \"2\",\n", + " \"LOG_FILENAME\": \"examon_pub.log\",\n", + " \"PID_FILENAME\": \"examon_pub.pid\",\n", + " \"runmode\": \"run\",\n", + " \"OUT_PROTOCOL\": \"mqtt\",\n", + " \"MQTT_FORMAT\": \"csv\",\n", + " \"COMPRESS\": false,\n", + " \"LOGFILE_SIZE_B\": 5242880,\n", + " \"LOG_LEVEL\": \"DEBUG\",\n", + " \"DRY_RUN\": true\n", + "}\n", + "Starting jobs...\n", + "INFO - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - Connecting to MQTT server: 127.0.0.1:1883\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - MQTT logs: Sending CONNECT (u0, p0, wr0, wq0, wf0, c1, k60) client_id=b''\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - Connect rc: 0\n", + "INFO - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - MQTT started\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - MQTT logs: Received CONNACK (0, 0)\n", + "INFO - 04/01/2022 06:31:27 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000117 seconds\n", + "INFO - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - Connected with result code 0\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 78.60698026998385;1648830687.054\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.004744 sec, insert_rate: 210.779637 sens/sec\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:27 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:28 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:28 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000082 seconds\n", + "DEBUG - 04/01/2022 06:31:28 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 7.620401543358602;1648830688.004\n", + "DEBUG - 04/01/2022 06:31:28 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002280 sec, insert_rate: 438.551234 sens/sec\n", + "DEBUG - 04/01/2022 06:31:28 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:28 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:30 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:30 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000079 seconds\n", + "DEBUG - 04/01/2022 06:31:30 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 40.6563350814879;1648830690.006\n", + "DEBUG - 04/01/2022 06:31:30 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002456 sec, insert_rate: 407.213981 sens/sec\n", + "DEBUG - 04/01/2022 06:31:30 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:30 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:32 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:32 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000079 seconds\n", + "DEBUG - 04/01/2022 06:31:32 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 1.0554041222391009;1648830692.005\n", + "DEBUG - 04/01/2022 06:31:32 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002471 sec, insert_rate: 404.621262 sens/sec\n", + "DEBUG - 04/01/2022 06:31:32 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:32 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:34 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:34 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000082 seconds\n", + "DEBUG - 04/01/2022 06:31:34 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 76.4034034158195;1648830694.007\n", + "DEBUG - 04/01/2022 06:31:34 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002533 sec, insert_rate: 394.795181 sens/sec\n", + "DEBUG - 04/01/2022 06:31:34 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:34 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:36 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:36 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000081 seconds\n", + "DEBUG - 04/01/2022 06:31:36 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 79.65416560731711;1648830696.006\n", + "DEBUG - 04/01/2022 06:31:36 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002764 sec, insert_rate: 361.765051 sens/sec\n", + "DEBUG - 04/01/2022 06:31:36 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:36 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "DEBUG - 04/01/2022 06:31:38 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start timeout timer\n", + "INFO - 04/01/2022 06:31:38 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Retrieved and processed 1 metrics in 0.000080 seconds\n", + "DEBUG - 04/01/2022 06:31:38 PM - [Process-1] - [mqtt.py] - examon.transport.mqtt - [MqttPub] Topic: org/examon/plugin/examon_pub/chnl/data/random_sensor - Payload: 56.91540569779116;1648830698.005\n", + "DEBUG - 04/01/2022 06:31:38 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Worker [random_sensor] - Insert: 1 sensors, time: 0.002786 sec, insert_rate: 358.886284 sens/sec\n", + "DEBUG - 04/01/2022 06:31:38 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Cancel timeout timer\n", + "DEBUG - 04/01/2022 06:31:38 PM - [Process-1] - [sensorreader.py] - examon.plugin.sensorreader - Start new loop\n", + "^C\n", + "Interrupted..\n", + "Process Process-1:\n" + ] + } + ], + "source": [ + "! python examon_pub.py run --dry-run --loglevel=DEBUG" + ] + }, + { + "cell_type": "markdown", + "id": "c45ce9c9", + "metadata": {}, + "source": [ + "### Run \n", + "Actual execution" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4548c646", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config:\n", + "{\n", + " \"MQTT_BROKER\": \"127.0.0.1\",\n", + " \"MQTT_PORT\": \"1883\",\n", + " \"MQTT_TOPIC\": \"\",\n", + " \"MQTT_USER\": \"\",\n", + " \"MQTT_PASSWORD\": \"\",\n", + " \"K_SERVERS\": \"\",\n", + " \"K_PORT\": \"\",\n", + " \"K_USER\": \"\",\n", + " \"K_PASSWORD\": \"\",\n", + " \"TS\": \"2\",\n", + " \"LOG_FILENAME\": \"examon_pub.log\",\n", + " \"PID_FILENAME\": \"examon_pub.pid\",\n", + " \"runmode\": \"run\",\n", + " \"OUT_PROTOCOL\": \"mqtt\",\n", + " \"MQTT_FORMAT\": \"csv\",\n", + " \"COMPRESS\": false,\n", + " \"LOGFILE_SIZE_B\": 5242880,\n", + " \"LOG_LEVEL\": \"WARNING\",\n", + " \"DRY_RUN\": false\n", + "}\n", + "Starting jobs...\n", + "^C\n", + "Interrupted..\n", + "Process Process-1:\n" + ] + } + ], + "source": [ + "! python examon_pub.py run --loglevel=WARNING" + ] + }, + { + "cell_type": "markdown", + "id": "b9277a8c", + "metadata": {}, + "source": [ + "### Run as a service (daemon mode)\n", + "In this example, the publisher is executed in daemon mode\n", + "```console\n", + "$ python examon_pub.py start --loglevel=WARNING\n", + "```\n", + "To stop it:\n", + "```console\n", + "$ python examon_pub.py stop\n", + "```\n", + " " + ] + }, + { + "cell_type": "markdown", + "id": "1b10cd9e", + "metadata": {}, + "source": [ + "## Multiple sensors example\n", + "\n", + "This example shows how to handle multiple sensors and dynamic tags." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "e865aa64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting examon_pub.py\n" + ] + } + ], + "source": [ + "%%file examon_pub.py\n", + "\n", + "import json\n", + "import time\n", + "import random\n", + "\n", + "from examon.plugin.examonapp import ExamonApp\n", + "from examon.plugin.sensorreader import SensorReader\n", + "\n", + "\n", + "class Sensor:\n", + " def __init__(self, sensor_name='random_sensor', range_min=0, range_max=100.0):\n", + " self.sensor_name = sensor_name\n", + " self.range_min = range_min\n", + " self.range_max = range_max\n", + " \n", + " def get_sensor_data(self, num_sensors=10):\n", + " payload = []\n", + " \n", + " for s in range(0, num_sensors):\n", + " payload.append({\n", + " 'sensor_name': self.sensor_name,\n", + " 'id': str(s),\n", + " 'value': random.uniform(self.range_min, self.range_max)\n", + " })\n", + " \n", + " return payload\n", + " \n", + " def read_data(self):\n", + " pass\n", + " \n", + "\n", + "def read_data(sr):\n", + " \n", + " # get timestamp and data \n", + " timestamp = int(time.time()*1000)\n", + " raw_packet = sr.sensor.get_sensor_data()\n", + " \n", + " # build the examon metric\n", + " examon_data = []\n", + " for raw_data in raw_packet:\n", + " metric = {}\n", + " metric['name'] = raw_data['sensor_name']\n", + " metric['value'] = raw_data['value']\n", + " metric['timestamp'] = timestamp\n", + " metric['tags'] = sr.get_tags()\n", + " # dynamically add new custom tags\n", + " metric['tags']['id'] = str(raw_data['id'])\n", + " # build the final packet\n", + " examon_data.append(metric)\n", + " \n", + " # worker id (string) useful for debug/log\n", + " worker_id = sr.sensor.sensor_name\n", + " \n", + " return (worker_id, examon_data,)\n", + " \n", + " \n", + "def worker(conf, tags):\n", + " \"\"\"\n", + " Worker process code\n", + " \"\"\"\n", + " # sensor instance \n", + " sensor = Sensor()\n", + " \n", + " # SensorReader app\n", + " sr = SensorReader(conf, sensor)\n", + " \n", + " # add read_data callback\n", + " sr.read_data = read_data \n", + " \n", + " # set the default tags\n", + " sr.add_tags(tags)\n", + " \n", + " # run the worker loop\n", + " sr.run()\n", + "\n", + " \n", + "if __name__ == '__main__':\n", + "\n", + " # start creating an Examon app\n", + " app = ExamonApp()\n", + "\n", + " app.parse_opt()\n", + " # for checking\n", + " print(\"Config:\")\n", + " print(json.dumps(app.conf, indent=4))\n", + "\n", + " # set default metrics tags\n", + " tags = app.examon_tags()\n", + " tags['org'] = 'examon'\n", + " tags['plugin'] = 'examon_pub'\n", + " tags['chnl'] = 'data'\n", + " \n", + " # add a worker\n", + " app.add_worker(worker, app.conf, tags)\n", + " \n", + " # run!\n", + " app.run() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/Users/Demo_ExamonQL.ipynb b/docs/Users/Demo_ExamonQL.ipynb new file mode 100644 index 00000000..a1749f37 --- /dev/null +++ b/docs/Users/Demo_ExamonQL.ipynb @@ -0,0 +1,6021 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "view-in-github" + }, + "source": [ + "\"Open" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "X2bCTV-Fpbdz" + }, + "source": [ + "# Exploring the ExaMon database\n", + "\n", + "This introductory notebook is a tutorial that will help you take your first steps with the `examon-client`, a tool that allows you to interact with the ExaMon database.\n", + "\n", + "The tutorial will show you the basics of how to obtain information about the data stored in the database (metadata) and how to make real queries and obtain a dataframe as a result.\n", + "\n", + "The tutorial will use the ExaMon instance running at CINECA as an example and also the notebook execution can take place directly on Google Colab. In this case it is recommended to mount your Drive account. Alternatively, you can download the notebook (top right button) and run it locally.\n", + "\n", + "If you are interested in working on the CINECA ExaMon instance, to obtain the ExaMon credentials please contact:\n", + "\n", + "- [Andrea Bartolini](mailto://a.bartolini@unibo.it)\n", + "- [Francesco Beneventi](mailto://francesco.beneventi@unibo.it)\n", + "\n", + "**Please note:** by using your ExaMon account you are able to access data owned by CINECA and are therefore subject to the same privacy regulations that every CINECA user is required to follow." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JHVb5XXYpnS1", + "outputId": "739cfd0f-3d81-4af8-8fba-6b5513844266" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n", + "/content/drive/MyDrive/examon_workdir\n", + "Collecting 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in Google Colab\n", + "\n", + "# (optional)\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')\n", + "# Create and change to the Examon workspace folder (optional)\n", + "! mkdir -p /content/drive/MyDrive/examon_workdir\n", + "%cd /content/drive/MyDrive/examon_workdir\n", + "\n", + "# Install (required)\n", + "! pip install https://github.com/fbeneventi/releases/releases/latest/download/examon-client.zip" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lGwAUSLJpbd7" + }, + "source": [ + "### Examon setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rX2qBc4ypbd4", + "outputId": "e826a56b-cf2e-4117-8403-210fb9c5ba4a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "username:admin\n", + "password:\n", + "··········\n", + "Creating the local metadata cache (one-time task). Please wait ...\n" + ] + } + ], + "source": [ + "# Init steps\n", + "\n", + "import os\n", + "import getpass\n", + "import numpy as np\n", + "import pandas as pd\n", + "from examon.examon import Client, ExamonQL\n", + "\n", + "# Connect\n", + "USER = input('username:')\n", + "print('password:')\n", + "PWD = getpass.getpass()\n", + "ex = Client('examon.cineca.it', port='3002', user=USER, password=PWD, verbose=False, proxy=True)\n", + "print('Creating the local metadata cache (one-time task). Please wait ...')\n", + "sq = ExamonQL(ex)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "14GR9TWNpbd8" + }, + "source": [ + "### Metric list\n", + "To start with Examon, it is recommended that you first get a list of the sensors contained in the database. The initial object (ExamonQL) instantiation will do a full db scan checking for all the metrics tags. This will happen only the first time since the client uses caches where possible to save the database bandwith." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "Qr72HO3Tpbd8", + "outputId": "840f6ebf-711e-45eb-edd2-2809ccc6fb6b", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\n" + ], + "text/plain": [ + " name tag keys\n", + "0 0_0 [chnl, cluster, node, org, plugin, rack, slot,...\n", + "1 1U_Stg_HDD1_Pres [chnl, cluster, health, node, org, plugin, type]\n", + "2 12V [chnl, cluster, health, node, org, plugin, typ...\n", + "3 1U_Stg_HDD0_Pres [chnl, cluster, health, node, org, plugin, type]\n", + "4 1U_Stg_HDD3_Pres [chnl, cluster, health, node, org, plugin, type]\n", + "... ... ...\n", + "2317 vm_pgmajfault [chnl, cluster, gcluster, group, node, org, pl...\n", + "2318 state [chnl, cluster, description, host_group, nagio...\n", + "2319 swap_total [chnl, cluster, gcluster, group, node, org, pl...\n", + "2320 swap_free [chnl, cluster, gcluster, group, node, org, pl...\n", + "2321 PS1_Temperature [chnl, cluster, health, node, org, part, plugi...\n", + "\n", + "[2322 rows x 2 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = sq.DESCRIBE() \\\n", + " .execute()\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iqJ_07BDpbd_" + }, + "source": [ + "The database contains this number of valid metric names:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pWgFocqCpbd_", + "outputId": "ea072c37-b585-46df-bf24-5336ced9e369" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "2322" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-vsfHlnTpbeA" + }, + "source": [ + "To get an entry from the table:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "7Oot9kRapbeB", + "outputId": "06ba532e-10a9-4839-9cfa-ea9705fa232b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['chnl', 'cluster', 'health', 'node', 'org', 'part', 'plugin', 'type', 'units']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df.name == 'Ambient_Temp']['tag keys'].values[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "j9htruL4pbeB" + }, + "source": [ + "### Tag values\n", + "It is possible to obtain all the possible values of all the tag keys of a given metric:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 300 + }, + "id": "4MMgVjUHpbeC", + "outputId": "8c5dfb49-c10f-4455-d757-c726a89e835b" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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nametag keytag values
0CPU_Utilizationchnl[data]
1CPU_Utilizationcluster[galileo, marconi]
2CPU_Utilizationhealth[ok]
3CPU_Utilizationnode[node001, node002, node003, node004, node005, ...
4CPU_Utilizationorg[cineca]
5CPU_Utilizationpart[knl, skylake]
6CPU_Utilizationplugin[confluent_pub, ipmi_pub]
7CPU_Utilizationtype[Other]
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\n" + ], + "text/plain": [ + " name tag key tag values\n", + "0 CPU_Utilization chnl [data]\n", + "1 CPU_Utilization cluster [galileo, marconi]\n", + "2 CPU_Utilization health [ok]\n", + "3 CPU_Utilization node [node001, node002, node003, node004, node005, ...\n", + "4 CPU_Utilization org [cineca]\n", + "5 CPU_Utilization part [knl, skylake]\n", + "6 CPU_Utilization plugin [confluent_pub, ipmi_pub]\n", + "7 CPU_Utilization type [Other]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = sq.DESCRIBE(metric='CPU_Utilization') \\\n", + " .execute()\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "2otQWFAgpbeC" + }, + "source": [ + "### All the possible values of a given tag key\n", + "In this example we will search all the plugin names currently available in the Examon database." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 488 + }, + "id": "e9gfBDTZpbeC", + "outputId": "3bebedfd-43c6-454f-e9bf-f59f502a015d", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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tag values
0ipmi_pub
1confluent_pub
2vertiv_pub
3schneider_pub
4pmu_pub
5logics_pub
6predictive_maintenance_pub
7ganglia_pub
8slurm_pub
9nvidia_pub
10weather_pub
11dstat_pub
12examon-ai_pub
13nagios_pub
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name
012V
11U_Stg_HDD0_Pres
21U_Stg_HDD1_Pres
31U_Stg_HDD2_Pres
41U_Stg_HDD3_Pres
......
380Vcpu2
381Voltage_Fault
382XCC_Corrupted
383XCC_SWitchover
384XCC_Switchover
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385 rows × 1 columns

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\n" + ], + "text/plain": [ + " name\n", + "0 12V\n", + "1 1U_Stg_HDD0_Pres\n", + "2 1U_Stg_HDD1_Pres\n", + "3 1U_Stg_HDD2_Pres\n", + "4 1U_Stg_HDD3_Pres\n", + ".. ...\n", + "380 Vcpu2\n", + "381 Voltage_Fault\n", + "382 XCC_Corrupted\n", + "383 XCC_SWitchover\n", + "384 XCC_Switchover\n", + "\n", + "[385 rows x 1 columns]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df = sq.DESCRIBE(tag_key = 'plugin', tag_value='confluent_pub') \\\n", + " .execute()\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uRHw4Y0ypbeE" + }, + "source": [ + "#### Metrics valid only for Marconi skaylake nodes\n", + "Some metrics are valid (exist) only for a subset of the monitored resources. In this example we will search for the metrics collected by the 'confluent_pub' plugin and for the 'marconi' cluster and for only the 'skylake' partition. The 'JOIN' command let you 'intersect' ('inner' join) the results of each DESCRIBE command." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 423 + }, + "id": "GtyxzJnvpbeE", + "outputId": "f01ea484-bc6e-4899-b2e6-79c6db61c6b6", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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name
0All_CPUs
1All_DIMMs
2All_PCI_Error
3Ambient_Temp
4Aux_Log
......
150TPM_TCM_Lock
151TXT_ACM_Module
152XCC_Corrupted
153XCC_SWitchover
154XCC_Switchover
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155 rows × 1 columns

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name
0hostscheduleddowtimecomments
1plugin_output
2state
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nametag keytag values
0plugin_outputchnl[data]
1plugin_outputcluster[galileo, marconi, marconi100]
2plugin_outputdescription[EFGW_cluster::status::availability, EFGW_clus...
3plugin_outputhost_group[compute, compute,cincompute, containers, cumu...
4plugin_outputnagiosdrained[0, 1]
5plugin_outputnode[aggregation-mgt, comlab01, deepops, dgx01, dg...
6plugin_outputorg[cineca]
7plugin_outputplugin[nagios_pub]
8plugin_outputrack[201, 202, 205, 206, 207, 208, 209, 210, 211, ...
9plugin_outputslot[01, 02, 03, 04, 05, 06, 07, 08, 09, 1, 10, 11...
10plugin_outputstate[0, 1, 2, 3]
11plugin_outputstate_type[0, 1]
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Lets check it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TnZN4NKrpbeG", + "outputId": "93531659-d96a-4f68-8f2a-20fdb526c025", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['EFGW_cluster::status::availability',\n", + " 'EFGW_cluster::status::criticality',\n", + " 'EFGW_cluster::status::internal',\n", + " 'GALILEO_cluster::status::availability',\n", + " 'GALILEO_cluster::status::criticality',\n", + " 'GALILEO_cluster::status::internal',\n", + " 'afs::blocked_conn::status',\n", + " 'afs::bosserver::status',\n", + " 'afs::ptserver::status',\n", + " 'afs::space::status',\n", + " 'afs::vlserver::status',\n", + " 'alive::ping',\n", + " 'backup::afs::status',\n", + " 'backup::eufus_gw::status',\n", + " 'backup::local::status',\n", + " 'backup::masters::status',\n", + " 'backup::shared::status',\n", + " 'batchs::JobsH',\n", + " 'batchs::client',\n", + " 'batchs::client::serverrespond',\n", + " 'batchs::client::state',\n", + " 'batchs::manager',\n", + " 'batchs::manager::state',\n", + " 'bmc::events',\n", + " 'cluster::status::availability',\n", + " 'cluster::status::criticality',\n", + " 'cluster::status::internal',\n", + " 'cluster::status::wattage',\n", + " 'cluster::us::availability',\n", + " 'cluster::us::criticality',\n", + " 'container::check::health',\n", + " 'container::check::internal',\n", + " 'container::check::mounts',\n", + " 'core::total',\n", + " 'crm::resources::m100',\n", + " 'crm::status::m100',\n", + " 'dev::ipmi::events',\n", + " 'dev::raid::status',\n", + " 'dev::swc::bntfru',\n", + " 'dev::swc::bnthealth',\n", + " 'dev::swc::bnttemp',\n", + " 'dev::swc::confcheck',\n", + " 'dev::swc::confcheckself',\n", + " 'dev::swc::cumulushealth',\n", + " 'dev::swc::cumulussensors',\n", + " 'dev::swc::isl',\n", + " 'dev::swc::isleth',\n", + " 'dev::swc::mlxhealth',\n", + " 'dev::swc::mlxsensors',\n", + " 'file::integrity',\n", + " 'filesys::dres::mount',\n", + " 'filesys::eurofusion::mount',\n", + " 'filesys::local::avail',\n", + " 'filesys::local::mount',\n", + " 'filesys::shared::mount',\n", + " 'firewalld::status',\n", + " 'galera::status::Integrity',\n", + " 'galera::status::NodeStatus',\n", + " 'galera::status::ReplicaStatus',\n", + " 'globus::gridftp',\n", + " 'globus::gsissh',\n", + " 'gss::rg::encl',\n", + " 'gss::rg::pdisks',\n", + " 'gss::rg::peer',\n", + " 'gss::rg::vdisks',\n", + " 'memory::phys::total',\n", + " 'monitoring::health',\n", + " 'net::ib::status',\n", + " 'net::opa',\n", + " 'net::opa::edge_director_links_status',\n", + " 'net::opa::edge_link_err_rate',\n", + " 'net::opa::edge_link_quality',\n", + " 'net::opa::edge_status',\n", + " 'net::opa::pciwidth',\n", + " 'nfs::rpc::status',\n", + " 'nvidia::configuration',\n", + " 'nvidia::memory::replace',\n", + " 'nvidia::memory::retirement',\n", + " 'service::MedeA',\n", + " 'service::cert',\n", + " 'service::galera',\n", + " 'service::galera::mysql',\n", + " 'service::galera:arbiter',\n", + " 'service::galera:mysql',\n", + " 'service::ganglia',\n", + " 'service::nxserver',\n", + " 'service::nxserver::sessions',\n", + " 'service::unicore::tsi',\n", + " 'service::unicore::uftpd',\n", + " 'ssh::daemon',\n", + " 'sys::arcldap::status',\n", + " 'sys::corosync::rings',\n", + " 'sys::cpus::freq',\n", + " 'sys::glusterfs::dgx',\n", + " 'sys::glusterfs::examon',\n", + " 'sys::glusterfs::home',\n", + " 'sys::glusterfs::install',\n", + " 'sys::glusterfs::install8',\n", + " 'sys::glusterfs::scratch',\n", + " 'sys::glusterfs::secinv',\n", + " 'sys::glusterfs::slurm',\n", + " 'sys::glusterfs::slurmstate',\n", + " 'sys::glusterfs::status',\n", + " 'sys::gpfs::status',\n", + " 'sys::ldap_srv::status',\n", + " 'sys::orphaned_cgroups::count',\n", + " 'sys::pacemaker::crm',\n", + " 'sys::rvitals',\n", + " 'sys::sssd::events',\n", + " 'sys::xcatpod::sync',\n", + " 'unicore::tsi',\n", + " 'unicore::uftpd',\n", + " 'vm::virsh::state']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['tag key'] == 'description']['tag values'].values[0]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B1n8cm81pbeG" + }, + "source": [ + "Lets see if there are services in a 'critical' state (2) and which node affect:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qXOWlAKKpbeH" + }, + "outputs": [], + "source": [ + "data = sq.SELECT('node','cluster','description','state') \\\n", + " .FROM('plugin_output') \\\n", + " .WHERE(plugin='nagios_pub', state='2') \\\n", + " .TSTART(30, 'minutes') \\\n", + " .execute()\n", + "\n", + "display(data.df_table.head(10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ba_6j2OXpbeH", + "outputId": "21798cbb-656b-459f-c397-c09574e4ce1f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(8548, 7)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.df_table.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5-N1MoiDpbeH" + }, + "source": [ + "## Query Examples\n", + "### 1) Marconi Skylake Power Consumption\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "k3vG7rP2pbeH", + "scrolled": true + }, + "outputs": [], + "source": [ + "data = sq.SELECT('cluster','part','node') \\\n", + " .FROM('Sys_Power') \\\n", + " .WHERE(cluster='marconi', part='skylake') \\\n", + " .TSTART(30, 'minutes') \\\n", + " .AGGRBY('avg', sampling_value=1, sampling_unit='minutes') \\\n", + " .execute()\n", + "\n", + "display(data.df_table.head())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gtdESZVPpbeH", + "outputId": "6aaa363c-e5ab-4769-922e-a7eeb0d76a37" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(46503, 6)" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.df_table.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "r99dN9RzpbeI" + }, + "source": [ + "Check the number of nodes ('node' tag):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 138 + }, + "id": "5rL0IIKbpbeJ", + "outputId": "220d6e4f-10eb-49f5-80a3-20119b516923" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "timestamp 88\n", + "value 37\n", + "name 1\n", + "cluster 1\n", + "part 1\n", + "node 3121\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data.df_table.nunique())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9m1YcTtLpbeJ" + }, + "source": [ + "#### Time Series Format\n", + "Reshape the 'df_table' to a time series table: first column (index) = timestamp, remaining columns = nodes power vectors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 373 + }, + "id": "WEp4HFUcpbeJ", + "outputId": "65d23a33-cce3-49db-fd6f-67dd6b41d5de" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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2023-09-22 17:49:00.061000+02:00120.0120.0120.0120.0120.0120.0120.0134.996730282.488555262.508175...270.001292259.998708319.988375309.997417299.997417329.989667290.0249.998708260.005167329.997417
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2023-09-22 17:50:00.031000+02:00120.0120.0120.0120.0120.0120.0120.0130.000292265.001021274.999271...272.500031257.499969297.499719304.999938294.999938309.999750290.0247.499969270.000125324.999938
2023-09-22 17:51:00.027000+02:00120.0120.0120.0120.0120.0120.0120.0125.001687247.505905287.495782...274.999854255.000146275.001313300.000292290.000292290.001167290.0245.000146279.999417320.000292
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name
0All_DIMMs
1All_PCI_Error
2Ambient_Temp
3CMOS_Battery
4CPU_1_DTS
......
61PSU2_Failure
62PSU2_IN_Failure
63Power_Supply_1
64Power_Supply_2
65SysBrd_Vol_Fault
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66 rows × 1 columns

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nametag keytag values
0CPU_1_Overtempchnl[data]
1CPU_1_Overtempcluster[galileo, marconi]
2CPU_1_Overtemphealth[critical, failed, ok, warning]
3CPU_1_Overtempnode[r054c02s01, r054c02s02, r054c02s03, r054c02s0...
4CPU_1_Overtemporg[cineca]
5CPU_1_Overtemppart[knl, skylake]
6CPU_1_Overtempplugin[confluent_pub]
7CPU_1_Overtemptype[Temperature]
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\n" + ], + "text/plain": [ + " name tag key tag values\n", + "0 CPU_1_Overtemp chnl [data]\n", + "1 CPU_1_Overtemp cluster [galileo, marconi]\n", + "2 CPU_1_Overtemp health [critical, failed, ok, warning]\n", + "3 CPU_1_Overtemp node [r054c02s01, r054c02s02, r054c02s03, r054c02s0...\n", + "4 CPU_1_Overtemp org [cineca]\n", + "5 CPU_1_Overtemp part [knl, skylake]\n", + "6 CPU_1_Overtemp plugin [confluent_pub]\n", + "7 CPU_1_Overtemp type [Temperature]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# show the tags to filter\n", + "df = sq.DESCRIBE(metric='CPU_1_Overtemp') \\\n", + " .execute()\n", + "\n", + "display(df)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "YA5bGJKQpbeM", + "outputId": "86fc3718-e834-4587-d1c4-f80f8285c6ae", + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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timestampvaluenamechnlclusterhealthnodeorgpartplugintype
02022-12-13 14:05:00.032000+01:00criticalCPU_1_Overtempdatamarconicriticalr135c11s01cinecaskylakeconfluent_pubTemperature
12023-09-14 11:53:00.038000+02:00criticalCPU_1_Overtempdatamarconicriticalr137c11s03cinecaskylakeconfluent_pubTemperature
22023-05-26 11:47:00.151000+02:00criticalCPU_1_Overtempdatamarconicriticalr138c02s02cinecaskylakeconfluent_pubTemperature
32023-05-26 23:47:00.034000+02:00criticalCPU_1_Overtempdatamarconicriticalr138c02s02cinecaskylakeconfluent_pubTemperature
42023-05-30 13:16:00.034000+02:00criticalCPU_1_Overtempdatamarconicriticalr138c02s02cinecaskylakeconfluent_pubTemperature
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timestampvaluenamechnlclusterhealthorgpartplugintype
node
r137c11s032023-09-14 11:53:00.038000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r143c04s032023-06-22 15:27:00.217000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r143c11s012023-06-13 19:40:00.031000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r138c13s022023-06-01 15:51:00.037000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r138c02s022023-05-26 11:47:00.151000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
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timestampvaluenamechnlclusterhealthorgpartplugintype
node
r138c13s022023-09-17 22:29:00.175000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r137c11s032023-09-14 11:53:00.038000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r143c02s042023-09-02 08:09:00.031000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r143c04s032023-06-22 15:47:00.162000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
r143c11s012023-06-14 06:02:00.027000+02:00criticalCPU_1_Overtempdatamarconicriticalcinecaskylakeconfluent_pubTemperature
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\n" + ], + "text/plain": [ + " timestamp value name chnl \\\n", + "node \n", + "r138c13s02 2023-09-17 22:29:00.175000+02:00 critical CPU_1_Overtemp data \n", + "r137c11s03 2023-09-14 11:53:00.038000+02:00 critical CPU_1_Overtemp data \n", + "r143c02s04 2023-09-02 08:09:00.031000+02:00 critical CPU_1_Overtemp data \n", + "r143c04s03 2023-06-22 15:47:00.162000+02:00 critical CPU_1_Overtemp data \n", + "r143c11s01 2023-06-14 06:02:00.027000+02:00 critical CPU_1_Overtemp data \n", + "\n", + " cluster health org part plugin type \n", + "node \n", + "r138c13s02 marconi critical cineca skylake confluent_pub Temperature \n", + "r137c11s03 marconi critical cineca skylake confluent_pub Temperature \n", + "r143c02s04 marconi critical cineca skylake confluent_pub Temperature \n", + "r143c04s03 marconi critical cineca skylake confluent_pub Temperature \n", + "r143c11s01 marconi critical cineca skylake confluent_pub Temperature " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(data \\\n", + " .df_table \\\n", + " .groupby('node') \\\n", + " .last() \\\n", + " .sort_values(by=['timestamp'],ascending=False) \\\n", + " .head())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zmp7QPZypbeN" + }, + "source": [ + "For example, node 'r145c10s04' showed a crtical status for the CPU1 temperature starting from 2019-09-23 16:54 to 2019-09-26 04:27. Lets check it plotting that range plus 1 hour before and after:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 867 + }, + "id": "OkOvKV-bpbeN", + "outputId": "a9785576-87ab-4484-94a1-3dd164c71209" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data = sq.SELECT('*') \\\n", + " .FROM('CPU_1_Temp') \\\n", + " .WHERE(node='r145c10s04') \\\n", + " .TSTART('23-09-2019 15:54:00') \\\n", + " .TSTOP('26-09-2019 05:27:00') \\\n", + " .execute()\n", + "\n", + "data.to_series(flat_index=True, interp='time', dropna=True, columns=['node']).df_ts.plot(figsize=[15,12])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "60hfvFhwpbeN" + }, + "source": [ + "Where we can see values greater than 90 °C for the CPU1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "n44qfjpfpbeO" + }, + "source": [ + "## Job scheduler data\n", + "
\n", + "NOTE This is an experimental feature and is subject to change in future versions.\n", + "
\n", + "\n", + "Currently the job scheduler data is collected as per-job data in plain Cassandra tables. The available tables in the database are\n", + "* **job_info_galileo**: Galileo jobs data\n", + "* **job_info_marconi**: Marconi jobs data\n", + "\n", + "This is a description of the data currently stored (where available) for each executed job:\n", + "\n", + "| Table fields | Description |\n", + "|-----------------------|--------------------------------------------------------------------------------------|\n", + "| account | charge to specified account |\n", + "| accrue_time | time job is eligible for running |\n", + "| admin_comment | administrator's arbitrary comment |\n", + "| alloc_node | local node and system id making the resource allocation |\n", + "| alloc_sid | local sid making resource alloc |\n", + "| array_job_id | job_id of a job array or 0 if N/A |\n", + "| array_max_tasks | Maximum number of running tasks |\n", + "| array_task_id | task_id of a job array |\n", + "| array_task_str | string expression of task IDs in this record |\n", + "| assoc_id | association id for job |\n", + "| batch_features | features required for batch script's node |\n", + "| batch_flag | 1 if batch: queued job with script |\n", + "| batch_host | name of host running batch script |\n", + "| billable_tres | billable TRES cache. updated upon resize |\n", + "| bitflags | Various job flags |\n", + "| boards_per_node | boards per node required by job |\n", + "| burst_buffer | burst buffer specifications |\n", + "| burst_buffer_state | burst buffer state info |\n", + "| command | command to be executed, built from submitted job's argv and NULL for salloc command |\n", + "| comment | arbitrary comment |\n", + "| contiguous | 1 if job requires contiguous nodes |\n", + "| core_spec | specialized core count |\n", + "| cores_per_socket | cores per socket required by job |\n", + "| cpu_freq_gov | cpu frequency governor |\n", + "| cpu_freq_max | Maximum cpu frequency |\n", + "| cpu_freq_min | Minimum cpu frequency |\n", + "| cpus_alloc_layout | map: list of cpu allocated per node |\n", + "| cpus_allocated | map: number of cpu allocated per node |\n", + "| cpus_per_task | number of processors required for each task |\n", + "| cpus_per_tres | semicolon delimited list of TRES=# values |\n", + "| dependency | synchronize job execution with other jobs |\n", + "| derived_ec | highest exit code of all job steps |\n", + "| eligible_time | time job is eligible for running |\n", + "| end_time | time of termination, actual or expected |\n", + "| exc_nodes | comma separated list of excluded nodes |\n", + "| exit_code | exit code for job (status from wait call) |\n", + "| features | comma separated list of required features |\n", + "| group_id | group job submitted as |\n", + "| job_id | job ID |\n", + "| job_state | state of the job, see enum job_states |\n", + "| last_sched_eval | last time job was evaluated for scheduling |\n", + "| licenses | licenses required by the job |\n", + "| max_cpus | maximum number of cpus usable by job |\n", + "| max_nodes | maximum number of nodes usable by job |\n", + "| mem_per_cpu | boolean |\n", + "| mem_per_node | boolean |\n", + "| mem_per_tres | semicolon delimited list of TRES=# values |\n", + "| min_memory_cpu | minimum real memory required per allocated CPU |\n", + "| min_memory_node | minimum real memory required per node |\n", + "| name | name of the job |\n", + "| network | network specification |\n", + "| nice | requested priority change |\n", + "| nodes | list of nodes allocated to job |\n", + "| ntasks_per_board | number of tasks to invoke on each board |\n", + "| ntasks_per_core | number of tasks to invoke on each core |\n", + "| ntasks_per_core_str | number of tasks to invoke on each core as string |\n", + "| ntasks_per_node | number of tasks to invoke on each node |\n", + "| ntasks_per_socket | number of tasks to invoke on each socket |\n", + "| ntasks_per_socket_str | number of tasks to invoke on each socket as string |\n", + "| num_cpus | minimum number of cpus required by job |\n", + "| num_nodes | minimum number of nodes required by job |\n", + "| partition | name of assigned partition |\n", + "| pn_min_cpus | minimum # CPUs per node, default=0 |\n", + "| pn_min_memory | minimum real memory per node, default=0 |\n", + "| pn_min_tmp_disk | minimum tmp disk per node, default=0 |\n", + "| power_flags | power management flags, see SLURM_POWER_FLAGS_ |\n", + "| pre_sus_time | time job ran prior to last suspend |\n", + "| preempt_time | preemption signal time |\n", + "| priority | relative priority of the job, 0=held, 1=required nodes DOWN/DRAINED |\n", + "| profile | Level of acct_gather_profile {all / none} |\n", + "| qos | Quality of Service |\n", + "| reboot | node reboot requested before start |\n", + "| req_nodes | comma separated list of required nodes |\n", + "| req_switch | Minimum number of switches |\n", + "| requeue | enable or disable job requeue option |\n", + "| resize_time | time of latest size change |\n", + "| restart_cnt | count of job restarts |\n", + "| resv_name | reservation name |\n", + "| run_time | job run time (seconds) |\n", + "| run_time_str | job run time (seconds) as string |\n", + "| sched_nodes | list of nodes scheduled to be used for job |\n", + "| shared | 1 if job can share nodes with other jobs |\n", + "| show_flags | conveys level of details requested |\n", + "| sockets_per_board | sockets per board required by job |\n", + "| sockets_per_node | sockets per node required by job |\n", + "| start_time | time execution begins, actual or expected |\n", + "| state_reason | reason job still pending or failed, see slurm.h:enum job_state_reason |\n", + "| std_err | pathname of job's stderr file |\n", + "| std_in | pathname of job's stdin file |\n", + "| std_out | pathname of job's stdout file |\n", + "| submit_time | time of job submission |\n", + "| suspend_time | time job last suspended or resumed |\n", + "| system_comment | slurmctld's arbitrary comment |\n", + "| threads_per_core | threads per core required by job |\n", + "| time_limit | maximum run time in minutes or INFINITE |\n", + "| time_limit_str | maximum run time in minutes or INFINITE as string |\n", + "| time_min | minimum run time in minutes or INFINITE |\n", + "| tres_alloc_str | tres used in the job as string |\n", + "| tres_bind | Task to TRES binding directives |\n", + "| tres_freq | TRES frequency directives |\n", + "| tres_per_job | semicolon delimited list of TRES=# values |\n", + "| tres_per_node | semicolon delimited list of TRES=# values |\n", + "| tres_per_socket | semicolon delimited list of TRES=# values |\n", + "| tres_per_task | semicolon delimited list of TRES=# values |\n", + "| tres_req_str | tres reqeusted in the job as string |\n", + "| user_id | user the job runs as |\n", + "| wait4switch | Maximum time to wait for minimum switches |\n", + "| wckey | wckey for job |\n", + "| work_dir | pathname of working directory |" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6f0G_itspbeO" + }, + "source": [ + "### Query examples\n", + "Queries can be executed as usual but paying attention to the following limitations:\n", + "\n", + "* both TSTART and TSTOP statements must be specified\n", + "* the date currently is supported only in the string format\n", + "* pushdown filters (executed on the datastore) are available only for a subset of table columns:\n", + " * job_id\n", + " * job_state\n", + " * account\n", + " * user_id\n", + " * node (keys of __cpus_alloc_layout__ table column)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "a50PC-dMpbeP" + }, + "outputs": [], + "source": [ + "# Ask for all galileo jobs executed between '28-09-2019 08:09:00' and '30-09-2019 08:09:00'\n", + "\n", + "import json\n", + "\n", + "# Setup\n", + "sq.jc.JOB_TABLES.add('job_info_galileo')\n", + "\n", + "data = sq.SELECT('*') \\\n", + " .FROM('job_info_galileo') \\\n", + " .TSTART('28-09-2019 08:09:00') \\\n", + " .TSTOP('30-09-2019 08:09:00') \\\n", + " .execute()\n", + "\n", + "df = pd.DataFrame(json.loads(data))\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "AON79M0hpbeP" + }, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P3Ku7Bh0pbeQ" + }, + "outputs": [], + "source": [ + "# Ask for all galileo jobs executed between '28-09-2019 08:09:00' and '30-09-2019 08:09:00',\n", + "# allocated on node \"r038c04s03\"\n", + "\n", + "data = sq.SELECT('*') \\\n", + " .FROM('job_info_galileo') \\\n", + " .WHERE(node='r038c04s03') \\\n", + " .TSTART('28-09-2019 08:09:00') \\\n", + " .TSTOP('30-09-2019 08:09:00') \\\n", + " .execute()\n", + "\n", + "df = pd.DataFrame(json.loads(data))\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "SRrwgGUWpbeQ" + }, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kBWGccYspbeR" + }, + "outputs": [], + "source": [ + "# Ask for all galileo jobs executed between '28-09-2019 08:09:00' and '30-09-2019 08:09:00',\n", + "# allocated on node \"r038c04s03\" and job_state = 'FAILED'\n", + "\n", + "data = sq.SELECT('*') \\\n", + " .FROM('job_info_galileo') \\\n", + " .WHERE(node='r038c04s03', job_state='FAILED') \\\n", + " .TSTART('28-09-2019 08:09:00') \\\n", + " .TSTOP('30-09-2019 08:09:00') \\\n", + " .execute()\n", + "\n", + "df = pd.DataFrame(json.loads(data))\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jT32A5nGpbeR" + }, + "outputs": [], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "txlU4T8epbeR" + }, + "outputs": [], + "source": [ + "# Marconi100 jobs\n", + "\n", + "# Setup for Marconi100\n", + "sq.jc.JOB_TABLES.add('job_info_marconi100')\n", + "\n", + "data = sq.SELECT('*') \\\n", + " .FROM('job_info_marconi100') \\\n", + " .TSTART('28-09-2020 08:09:00') \\\n", + " .TSTOP('30-09-2020 08:09:00') \\\n", + " .execute()\n", + "\n", + "df = pd.read_json(data)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4sbZJdswpbeS", + "outputId": "a8972f69-908e-4780-a714-59227dd8d06b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(11614, 110)" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-TKj0WfKpbeS" + }, + "source": [ + "#### Asynchronous queries\n", + "\n", + "In case of big queries it can be useful to use the asynchronous mode (available from client version v0.4.0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7ehRvCBOpbeS" + }, + "outputs": [], + "source": [ + "import time\n", + "\n", + "# One month of data\n", + "tstart = '01-04-2021 00:00:00'\n", + "tstop = '30-04-2021 00:00:00'\n", + "\n", + "t0 = time.time()\n", + "data = sq.SELECT('*') \\\n", + " .FROM('job_info_marconi100') \\\n", + " .TSTART(tstart) \\\n", + " .TSTOP(tstop) \\\n", + " .execute_async()\n", + "\n", + "print('Elapsed Time: %f seconds' % (time.time() - t0))\n", + "\n", + "df = pd.read_json(data)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "sUivp1pGpbeT", + "outputId": "0f7842d1-4650-4d98-8766-9efc5e689b38" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(109608, 110)" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + } + ], + "metadata": { + "colab": { + "include_colab_link": true, + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/docs/Users/Getting_started.md b/docs/Users/Getting_started.md new file mode 100644 index 00000000..8e4e930f --- /dev/null +++ b/docs/Users/Getting_started.md @@ -0,0 +1,3 @@ +# Getting Started + +- [Introductory notebook](Demo_ExamonQL.ipynb) \ No newline at end of file diff --git a/docs/blog/.authors.yml b/docs/blog/.authors.yml new file mode 100644 index 00000000..d6532568 --- /dev/null +++ b/docs/blog/.authors.yml @@ -0,0 +1,5 @@ +authors: + francesco: + name: Francesco Beneventi + description: Creator + avatar: https://avatars.githubusercontent.com/u/8551300 \ No newline at end of file diff --git a/docs/blog/.meta.yml b/docs/blog/.meta.yml new file mode 100644 index 00000000..1efd8d91 --- /dev/null +++ b/docs/blog/.meta.yml @@ -0,0 +1,6 @@ +authors: + - francesco +categories: + - Hello + - World + - Test \ No newline at end of file diff --git a/docs/blog/index.md b/docs/blog/index.md new file mode 100644 index 00000000..d93b3439 --- /dev/null +++ b/docs/blog/index.md @@ -0,0 +1 @@ +# Blog diff --git a/docs/blog/posts/first_post.md b/docs/blog/posts/first_post.md new file mode 100644 index 00000000..dd2ce3f4 --- /dev/null +++ b/docs/blog/posts/first_post.md @@ -0,0 +1,14 @@ +--- +date: 2023-08-31 +authors: [francesco] +description: > + Firts blog entry +categories: + - Test +links: + - Getting started with Insiders: insiders/getting-started.md#requirements + - setup/setting-up-a-blog.md#built-in-blog-plugin +--- + +# First Post +Hello World! diff --git a/docs/contactus.md b/docs/contactus.md new file mode 100644 index 00000000..024ad430 --- /dev/null +++ b/docs/contactus.md @@ -0,0 +1,6 @@ +# Contact Us + +* [Andrea Bartolini - (PI)](mailto:a.bartolini@unibo.it) +* [Andrea Borghesi - (PI)](mailto:andrea.borghesi3@unibo.it) +* [Francesco Beneventi - (Developer)](mailto:francesco.beneventi@e4company.com) +* [Luca Benini - (PI)](mailto:luca.benini@unibo.it) diff --git a/docs/credits.md b/docs/credits.md new file mode 100644 index 00000000..d8bf5f66 --- /dev/null +++ b/docs/credits.md @@ -0,0 +1,9 @@ +# Credits + +This work is supported by the EU FETHPC projects: + +- [MULTITHERMAN (g.a. 291125)](https://cordis.europa.eu/project/id/291125) +- [ANTAREX (g.a. 671623)](https://antarex.fe.up.pt/) +- [IOTWINS (g.a. 857191)](https://www.iotwins.eu/) +- [REGALE (g.a. 956560)](https://regale-project.eu/) +- [GRAPH MASSIVIZER (g.a. 101093202)](https://graph-massivizer.eu/) diff --git a/docs/getting_started.md b/docs/getting_started.md new file mode 100644 index 00000000..306dc7a8 --- /dev/null +++ b/docs/getting_started.md @@ -0,0 +1,33 @@ +# Welcome to the ExaMon Documentation + +ExaMon is a powerful monitoring and analytics framework that helps you collect and analyze data from a variety of sources. It employs data engeenering best practices and the latest open-source tools to deliver a fully featured data analysis environment. The following documentation provides instructions for installing framework components and accessing and analyzing data. See the [introductory section](Introduction.md) for a more complete overview of the framework's purpose and capabilities. + +## Admin Documentation + +If you're an administrator of ExaMon, our admin documentation provides detailed information on how to install and configure ExaMon. Our admin documentation covers topics such as: + +- Installing and configuring the ExaMon server +- Setting up data sources +- Managing users and permissions +- Configuring alerting and notifications +- Managing dashboards and reports + +[Click here to access the Admin Documentation](Administrators/Getting_started.md) + +## User Documentation + +If you're a user of ExaMon, our user documentation provides detailed information on how to access and analyze data using ExaMon. Our user documentation covers topics such as: + +- Setting up the ExaMon client +- Accessing and analyzing data +- Visualizing data using charts and graphs +- Setting up alerts and notifications +- Managing your data using dashboards + +[Click here to access the User Documentation](Users/Getting_started.md) + +## Community + +We hope this documentation helps you get the most out of ExaMon. If you have any questions or feedback, please don't hesitate to contact us at: + +- [ExaMon Forum](https://github.com/orgs/ExamonHPC/discussions) \ No newline at end of file diff --git a/docs/images/Marconi100.jpg b/docs/images/Marconi100.jpg new file mode 100644 index 00000000..29b8b058 Binary files /dev/null and b/docs/images/Marconi100.jpg differ diff --git a/docs/images/image1.png b/docs/images/image1.png new file mode 100644 index 00000000..48d7a3ea Binary files /dev/null and b/docs/images/image1.png differ diff --git a/docs/images/image10.png b/docs/images/image10.png new file mode 100644 index 00000000..fde46ce4 Binary files /dev/null and b/docs/images/image10.png differ diff --git a/docs/images/image11.png b/docs/images/image11.png new file mode 100644 index 00000000..e4006746 Binary files /dev/null and b/docs/images/image11.png differ diff --git a/docs/images/image12.png b/docs/images/image12.png new file mode 100644 index 00000000..012b795e Binary files /dev/null and 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+
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+ + +
+

A holistic solution for monitoring HPC resources

+

{{ config.site_description }}

+ + Quick start + + + Go to GitHub + +
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+ +

+ + Collect +

+

Collect and store data from any source using a flexible data model and scalable data store.

+
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+ + Analyze +

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Use data immediately: From zero-code dashboards to advanced BI tools using ANSI-SQL or even Python in a popular Jupyter notebook.

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+ + Improve +

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Enable teams with different areas of expertise to work together, maximizing the extraction of valuable insights.

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Breaking Down Data Silos

+

ExaMon provides a centralized repository that breaks down data silos, consolidates information, and offers downstream users a single place to look for all data sources. This accelerates knowledge discovery and results in faster decision-making and improved performance.

+
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+ sp1 +

+
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+ sp1 +

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Empowering Technology Transfer

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Enables smooth technology transfer between academia and industry, encompassing data collection, analysis, collaboration, and continuous improvement, fostering innovation and real-world impact.

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+

Fueling Digital Twins

+

With a unified interface that smoothly integrates different data sources, creating and operating a digital twin becomes easier. ExaMon streamlines the process of gathering, analyzing, and presenting various data on a 3D digital representation of the assets, which allows for thorough insights, quick collaboration, and effective overall optimization.

+
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+ sp1 +

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+ + Quick start + +
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+

ExaMon is a project by DEI - Department of Electrical, Electronic, and Information Engineering "Guglielmo Marconi" of the University of Bologna.

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Partners

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European Projects

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+ + Logo 0 + Logo 1 + Logo 2 + + + Logo 3 + Logo 4 + +
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+ + {% endblock %} + + + {% block content %}{% endblock %} + + + {% block footer %} + + {% endblock %} \ No newline at end of file diff --git a/docs/overrides/main.html b/docs/overrides/main.html new file mode 100644 index 00000000..3b255fe5 --- /dev/null +++ b/docs/overrides/main.html @@ -0,0 +1,91 @@ +{#- + This file was automatically generated - do not edit + -#} + {% extends "base.html" %} + + {% block outdated %} + You're not viewing the latest version. + + Click here to go to latest. + + {% endblock %} + + + + {% block extrahead %} + + + {% set title = config.site_name %} + {% if page and page.title and not page.is_homepage %} + {% set title = config.site_name ~ " - " ~ page.title | striptags %} + {% endif %} + + + {% set image = config.site_url ~ 'assets/images/banner.png' %} + + + + + + + + + + + + + + + + + + + + + + + + {% endblock %} + + + + + {% block content %} + + + {% if page.nb_url %} + + {% include ".icons/material/download.svg" %} + + {% endif %} + + + {{ super() }} +
+ +
+{% endblock %} + + + + + {% block analytics %} + {{ super() }} + + + + {% endblock %} \ No newline at end of file diff --git a/docs/publications.md b/docs/publications.md new file mode 100644 index 00000000..aab74638 --- /dev/null +++ b/docs/publications.md @@ -0,0 +1,28 @@ +# Publications + + +Bartolini, A., Beneventi, F., Borghesi, A., Cesarini, D., Libri, A., Benini, L., & Cavazzoni, C. (2019). Paving the way toward energy-aware and automated datacentre. *Workshop Proceedings of the 48th International Conference on Parallel Processing*, 1–8. (1) +{ .annotate } + +1. `@inproceedings{bartolini2019paving, + title={Paving the way toward energy-aware and automated datacentre}, + author={Bartolini, Andrea and Beneventi, Francesco and Borghesi, Andrea and Cesarini, Daniele and Libri, Antonio and Benini, Luca and Cavazzoni, Carlo}, + booktitle={Workshop Proceedings of the 48th International Conference on Parallel Processing}, + pages={1--8}, + year={2019} +}`[:fontawesome-brands-google-scholar:](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/388104/ICPP_Paving_Bartolini.pdf?sequence=4) + +Beneventi, F., Bartolini, A., Cavazzoni, C., & Benini, L. (2017). Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools. *Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017*, 1038–1043. IEEE. (1) +{ .annotate } + +1. `@inproceedings{beneventi2017continuous, + title={Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools}, + author={Beneventi, Francesco and Bartolini, Andrea and Cavazzoni, Carlo and Benini, Luca}, + booktitle={Design, Automation \& Test in Europe Conference \& Exhibition (DATE), 2017}, + pages={1038--1043}, + year={2017}, + organization={IEEE} +}`[:fontawesome-brands-google-scholar:](https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/192078/2016_DATE_Beneventi_FP.pdf?sequence=4) + + + diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 00000000..b1df4865 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,5 @@ +.md-grid { + max-width: 1680px; + } + + diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..f0638760 --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,81 @@ +site_name: ExaMon +site_url: 'https://fbeneventi.github.io/' +repo_url: https://github.com/ExamonHPC/examon +repo_name: ExamonHPC/examon +site_description: Gain deeper insights into your data with advanced monitoring, analytics and visualization tools. +# copyright: Copyright © 2023 Francesco Beneventi + +theme: + name: material + custom_dir: docs/overrides/ + features: + - toc.follow + - search.suggest + - search.highlight + - search.share + - navigation.footer + - announce.dismiss + logo: assets/images/favicon.png + plugins: + - search: + lang: en + prebuild_index: true + icon: + annotation: material/arrow-right-circle + +plugins: + - blog + - search: + separator: '[\s\-,:!=\[\]()"`/]+|\.(?!\d)|&[lg]t;|(?!\b)(?=[A-Z][a-z])' + - mkdocs-jupyter: + include_source: True + +extra_css: + - stylesheets/extra.css + +markdown_extensions: + - toc + - extra + - codehilite + - pymdownx.highlight + - pymdownx.superfences + - pymdownx.inlinehilite + - pymdownx.tabbed + - admonition + - pymdownx.details + - pymdownx.superfences + - attr_list + - md_in_html + - pymdownx.superfences + - pymdownx.emoji: + emoji_index: !!python/name:material.extensions.emoji.twemoji + emoji_generator: !!python/name:material.extensions.emoji.to_svg + +nav: + - Home: "index.md" + - Introduction: "Introduction.md" + - Getting started: "getting_started.md" + - Administrators: + - Getting started: Administrators/Getting_started.md + - Users: + - Getting started: Users/Getting_started.md + - Plugins: + - Example plugin: 'Plugins/examon_pub.ipynb' + - Clusters: + - Marconi 100: "Marconi100/Metrics_reference.md" + - MonteCimone: + - Introduction: "MonteCimone/MonteCimone.md" + - Notebooks: + - Getting started: "MonteCimone/Examon_Monte_Cimone.ipynb" + - About: "About.md" + - Credits: "credits.md" + - Contact Us: "contactus.md" + - Publications: "publications.md" + - Blog: + - blog/index.md + +extra: + version: + provider: mike + alias: true + default: latest diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 00000000..14e16317 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +mike==2.1.3 +mkdocs==1.6.1 +mkdocs-get-deps==0.2.0 +mkdocs-jupyter==0.25.1 +mkdocs-material==9.5.44 +mkdocs-material-extensions==1.3.1 +