Removing the deprecated log_xxx_metadata calls (#28)

* removing the deprecated calls

* correcting the ref

* fixing the review comments

* fixing the steps

* new way to fetch artifacts

* fixed the errors

* fixed imports

.github/workflows/ci.yml

@@ -55,5 +55,5 @@ jobs:
         with:
           stack-name: ${{ matrix.stack-name }}
           python-version: ${{ matrix.python-version }}
-          ref-zenml: ${{ inputs.ref-zenml || 'develop' }}
+          ref-zenml: ${{ inputs.ref-zenml || 'feature/followup-run-metadata' }}
           ref-template: ${{ inputs.ref-template || github.ref }}

template/steps/data_preprocessor.py

@@ -7,7 +7,7 @@
 from sklearn.preprocessing import MinMaxScaler
 from typing_extensions import Annotated
 from utils.preprocess import ColumnsDropper, DataFrameCaster, NADropper
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 
 
 @step
@@ -67,8 +67,9 @@ def data_preprocessor(
     dataset_tst = preprocess_pipeline.transform(dataset_tst)
 
     # Log metadata so we can load it in the inference pipeline
-    log_artifact_metadata(
-        artifact_name="preprocess_pipeline",
+    log_metadata(
         metadata={"random_state": random_state, "target": target},
+        artifact_name="preprocess_pipeline",
+        infer_artifact=True,
     )
     return dataset_trn, dataset_tst, preprocess_pipeline

template/steps/model_evaluator.py

@@ -4,20 +4,22 @@
 
 import pandas as pd
 from sklearn.base import ClassifierMixin
-from zenml import log_artifact_metadata, step
+
+from zenml import log_metadata, step
+from zenml.client import Client
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
 
 
 @step
 def model_evaluator(
-    model: ClassifierMixin,
-    dataset_trn: pd.DataFrame,
-    dataset_tst: pd.DataFrame,
-    min_train_accuracy: float = 0.0,
-    min_test_accuracy: float = 0.0,
-    target: Optional[str] = "target",
+        model: ClassifierMixin,
+        dataset_trn: pd.DataFrame,
+        dataset_tst: pd.DataFrame,
+        min_train_accuracy: float = 0.0,
+        min_test_accuracy: float = 0.0,
+        target: Optional[str] = "target",
 ) -> float:
     """Evaluate a trained model.
 
@@ -63,24 +65,31 @@ def model_evaluator(
         dataset_tst.drop(columns=[target]),
         dataset_tst[target],
     )
-    logger.info(f"Train accuracy={trn_acc*100:.2f}%")
-    logger.info(f"Test accuracy={tst_acc*100:.2f}%")
+    logger.info(f"Train accuracy={trn_acc * 100:.2f}%")
+    logger.info(f"Test accuracy={tst_acc * 100:.2f}%")
 
     messages = []
     if trn_acc < min_train_accuracy:
         messages.append(
-            f"Train accuracy {trn_acc*100:.2f}% is below {min_train_accuracy*100:.2f}% !"
+            f"Train accuracy {trn_acc * 100:.2f}% is below {min_train_accuracy * 100:.2f}% !"
         )
     if tst_acc < min_test_accuracy:
         messages.append(
-            f"Test accuracy {tst_acc*100:.2f}% is below {min_test_accuracy*100:.2f}% !"
+            f"Test accuracy {tst_acc * 100:.2f}% is below {min_test_accuracy * 100:.2f}% !"
         )
     else:
         for message in messages:
             logger.warning(message)
 
-    log_artifact_metadata(
-        metadata={"train_accuracy": float(trn_acc), "test_accuracy": float(tst_acc)},
-        artifact_name="sklearn_classifier",
+    client = Client()
+    latest_classifier = client.get_artifact_version("sklearn_classifier")
+
+    log_metadata(
+        metadata={
+            "train_accuracy": float(trn_acc),
+            "test_accuracy": float(tst_acc)
+        },
+        artifact_version_id=latest_classifier.id
     )
+
     return float(tst_acc)