Removing the deprecated log_xxx_metadata calls (#48)

* removing deprecated metadata calls

* fixing the call

.github/workflows/ci.yml

@@ -57,5 +57,5 @@ 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 }}

README.md

@@ -125,13 +125,13 @@ To create parallel processing of computationally expensive operations we use a l
   <summary>Code snippet 💻</summary>
 
 ```python
-from zenml import log_artifact_metadata
+from zenml import log_metadata
 
 score = accuracy_score(y_tst, y_pred)
 # log score along with output artifact as metadata
-log_artifact_metadata(
-    output_name="hp_result",
-    metric=float(score),
+log_metadata(
+    metadata={"metric": float(score)},
+    artifact_name="hp_result",
 )
 ```
 </details>

template/steps/training/model_trainer.py

@@ -5,7 +5,7 @@
 import mlflow
 import pandas as pd
 from sklearn.base import ClassifierMixin
-from zenml import ArtifactConfig, log_artifact_metadata, step, get_step_context
+from zenml import ArtifactConfig, step, get_step_context
 from zenml.client import Client
 from zenml.integrations.mlflow.experiment_trackers import MLFlowExperimentTracker
 from zenml.integrations.mlflow.steps.mlflow_registry import mlflow_register_model_step

template/steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_single_search.py

@@ -9,7 +9,7 @@
 from sklearn.metrics import accuracy_score
 from sklearn.model_selection import RandomizedSearchCV
 from utils import get_model_from_config
-from zenml import log_artifact_metadata, step
+from zenml import log_metadata, step
 from zenml.logger import get_logger
 
 logger = get_logger(__name__)
@@ -79,9 +79,10 @@ def hp_tuning_single_search(
     y_pred = cv.predict(X_tst)
     score = accuracy_score(y_tst, y_pred)
     # log score along with output artifact as metadata
-    log_artifact_metadata(
+    log_metadata(
         metadata={"metric": float(score)},
         artifact_name="hp_result",
+        infer_artifact=True,
     )
     ### YOUR CODE ENDS HERE ###
     return cv.best_estimator_