Additional changes

Author: AlexejPenner

native-experiment-tracking/README.md

@@ -0,0 +1,35 @@
+# :: Track experiments in ZenML natively
+
+Although ZenML plugs into many [experiment trackers](https://www.zenml.io/vs/zenml-vs-experiment-trackers), a lot of 
+the functionality of experiment trackers is already covered by ZenML's native metadata and artifact tracking.
+This project aims to show these capabilities.
+
+## :running: Run locally
+
+
+#### Option 1 - Interactively explore the quickstart using Jupyter Notebook:
+```bash
+pip install notebook
+jupyter notebook
+# open quickstart.ipynb
+```
+
+#### Option 2 - Execute the whole ML pipeline from a Python script:
+```bash
+# Pip install all requirements
+pip install -r requirements.txt
+
+# Install required zenml integrations
+zenml integration install sklearn pandas -y
+
+# Initialize ZenML
+zenml init
+```
+
+## Explore your experiments
+
+...
+
+
+
+

native-experiment-tracking/requirements.txt

@@ -1,4 +1,4 @@
-zenml[server]>=0.50.0
+zenml[server]>=0.67.0
 notebook
 scikit-learn
 pyarrow

native-experiment-tracking/run.py

@@ -18,7 +18,6 @@
 
 import click
 
-from zenml.client import Client
 from zenml.logger import get_logger
 
 from pipelines import training
@@ -46,8 +45,6 @@ def main(
     Args:
         no_cache: If `True` cache will be disabled.
     """
-    client = Client()
-
     config_folder = os.path.join(
         os.path.dirname(os.path.realpath(__file__)),
         "configs",
@@ -61,7 +58,7 @@ def main(
     )
     training.with_options(**pipeline_args)()
     training.with_options(**pipeline_args)()
-    logger.info("Training pipeline with SGD finished successfully!\n\n")
+    logger.info("Training pipeline finished successfully!\n\n")
 
 if __name__ == "__main__":
     main()

native-experiment-tracking/steps/model_evaluator.py

@@ -109,11 +109,8 @@ def model_evaluator(
 
     client = Client()
     mv = get_step_context().model
-    print("got mv")
     m = client.list_model_versions(model_name_or_id=mv.model_id, size=1)
-    print("got m")
     number_of_versions = m.total
-    print(f"num mv {number_of_versions}")
 
     # Initialize a list to store test accuracies
     versions = [str(mv.number)]
@@ -124,7 +121,6 @@ def model_evaluator(
 
     # Fetch test accuracies until we have 10 versions or reach the start
     while len(versions) < 15 and index > 0:
-        print(f"getting index: {index}")
         zenml_model_version = client.get_model_version("breast_cancer_classifier", index, hydrate=False)
         if zenml_model_version.run_metadata:
             test_accuracy = zenml_model_version.run_metadata['test_accuracy'].value