Updated CLI registermodel function

docs/source/images/modelling.drawio

@@ -1 +1 @@
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name="Calibrating">7Vtde6I4FP41XnYeIPLhZWttbbfudMbu7LQ380SIwBSIG0LV/voNmghIqrajwOjc+JCTD8g55815kxNboBvOrgmceAPsoKClKc6sBS5bmqZ1dPabCuZLgaWBpcAlvrMUqZlg6L8iLlS4NPEdFBcaUowD6k+KQhtHEbJpQQYJwdNiszEOim+dQBeVBEMbBmXpv75DPTELM5P3ke964s2q0VnWhFA05jOJPejgaU4Eei3QJRjT5VM466Ig1Z3Qy6X5/KLfJndW9JJM+p1oeOkOzpaDXb2ny2oKBEX0w0Mno9fpt6d7JVG+aM/ew1M/vLriXZQXGCRcX3yudC4USHASOSgdRG2Bi6nnUzScQDutnTKPYTKPhgGvHuOIXsHQD1JvOSc+DLiQO4baZmVIbF7UWcmBsbcafseZco28IELRLGdnPvNrhENEyZw1EbVKm0+Vu7Gm8PI0cwrd4DIv5xCmMD/kjuiuBs+UzR64vt+h+7KqkcNclxcxoR52cQSDXia9KBoja3OH8YQLfyJK51y/MKH4AwZ6nxVinBAbbZgoXzAoJC6iG9pZy3apFjbalKAAUv+lCPC9mweUoPEVTpngElJYAUjGfhB0cYDJ4gXAgcga20weU4KfUa7GsC00Gh8UO9YadqwydlRNgp32oaBjngp0rB2hozYLO1YJOw8opicJHq1O8JjtL3//DGb9v/ArNl/Cqd1DqB7woJlPv+eeH9mz8knTefEyVZciCnNRiNh8v+cL+W5pOeu3KImOe0Yh86mmw1Bqaas5ljZ3tfQnvWDrhhoagH0betH1nBA4zzWYYD+icW7k+1SQX13WaC3orG0CtnUwrS0dwOYO7GH50ZmXrmb/8fgh7JALIPcEnU0ItlEcs8W7+hCiI8tpy0KIpY2AYVTJv4BRN/9SzZJ9ToWRqbtSMqGShlAytczJcpAqWS/24CR9FPVbQTWC9rO7sOjnhAZ+hA5no19gX5VCRxqTyytbHcjZMyjEJLaDot0ogmSclO6NRi1I4rvlCxJTsKb82TTyZavdrnDZeug4Or65JeFN4t2QPr19hESyoz8G6EjovnT2zQrlWjmGdGHgjwikiIl7MfVDSCUs+UiDuiE5y6+WD2t6I+BRBx8WUWX7lllvFoiMksl66fOJYKi9tq03aj/TB0pJ10cKGSDy4M1lyxu/WxJ3fBy1NCOgqb8zzBhu+nQeuJj41AurJ2/j8VizpeTNMUaGXulxTaXh6enH19fhsPt58HA1077NekB/7J2V7XYE5A1I0srS2SvNApEkr4xcP6aI4UZZ8bh0+8Pv4hxnAFoncRbYESXGwQJQuw5UiHQJT3zwhImo2ZItUYt5sW1psVqCnb4rP9x7SuXXvKFM6Tk/lMa6AXupb5fPUfcf6SqjhGaVZ6XSbfcxHM+BXfdHam1kT6r7em4JvJHwrzjfv7PNtE6z1qzynlbc511yDP6OSqk4Uh0dmTIq3jFMAA9Jxa3GJU6BLHHKY4kIJUKQ6rVgK+O/BIuKs3ih8XPWgGlullWKUVY7s8gVA7IvXo5ZfA8T59695h5M07ToA0UzRjjlkwWbcxF7vxuxYoDG6Qip1XwbBudcHPqOs1g0ZC6XOWWK7sIN4XpjZOkqpIS1tiUOtY9LxNIMFqh3mc5dxarhuo5Yfn+3gxTx3Xlu+eaB40ldbdG1ujln2TS/FwndlL9qcIp45v+gwFUG8fXg+m70z8XFTf91l3+nHK8t6tsPb/psWc4xPas60fWrlIqsMo2y6bTzzxbgDQt1DrgHYMXs74DLu7DZfypB738=</diagram><diagram id="wosXcDLAJT1C6PcZYE3D" name="Predicting">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</diagram></mxfile>

setup.py

@@ -65,7 +65,7 @@
     ],
     include_package_data=True,
 
-    python_requires='>=3.6',
+    python_requires='>=3.6,<3.9',
     extras_require=extras,
 
     entry_points={

src/myautoml/cli.py

@@ -1,34 +1,52 @@
+import logging
+
 import click
+from dotenv import load_dotenv
 import mlflow
 
-from myautoml.utils.mlflow import track_model_from_file, track_model_data, register_model
+from myautoml.utils.mlflow.tracking import track_sk_model_from_file
+
+_logger = logging.getLogger(__name__)
 
 
 @click.command()
 @click.option('--experiment_name', '-e', help='Experiment name in MLflow')
 @click.option('--model_path', '-m', help='Path to the trained model file')
 @click.option('--model_name', '-n', help='Name to register the model with')
-def registermodel(experiment_name, model_path, model_name):
+@click.option('--log_level', help='Logging level of messages to log to console', default='INFO')
+@click.option('--dotenv', help='Path to the dotenv file with environment variables')
+def registermodel(experiment_name, model_path, model_name, log_level, dotenv):
+    if dotenv:
+        load_dotenv(dotenv)
+
+    logging.basicConfig(
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+        level=log_level
+    )
+
+    logging.getLogger('azure').setLevel('WARNING')
+    logging.getLogger('git').setLevel('INFO')
+    logging.getLogger('matplotlib').setLevel('INFO')
+    logging.getLogger('urllib3').setLevel('INFO')
+
     if experiment_name is None:
         experiment_name = "Default"
     if model_path is not None:
-        click.secho("Uploading model to MLflow Server", fg="green", bold=True)
-        run_info = track_model_from_file(local_path=model_path,
-                                         experiment_name=experiment_name)
-        track_model_data(run_info.run_id, tags={"cli_upload": True})
+        run_info = track_sk_model_from_file(
+            local_path=model_path,
+            experiment_name=experiment_name,
+            run_name=None,
+            model_artifact_path='model',
+            registered_model_name=model_name,
+            params=None,
+            tags={"cli_upload": True},
+            metrics=None,
+            artifacts=None
+        )
         experiment_run_url = f"{mlflow.get_tracking_uri()}/#/experiments/{run_info.experiment_id}/runs/{run_info.run_id}"
-
-        if model_name is not None:
-            click.secho("Registering model with MLflow Server", fg="green", bold=True)
-            model_version = register_model(run_id=run_info.run_id,
-                                           model_name=model_name)
-            registered_model_url = f"{mlflow.get_tracking_uri()}/#/models/{model_name}/versions/{model_version.version}"
-        else:
-            click.secho(f"No name specified to register the model!", fg="white", bold=True)
-            registered_model_url = ""
-
         click.echo(f"Experiment run URL: {experiment_run_url}")
-        click.echo(f"Registered model URL: {registered_model_url}")
+        # registered_model_url = f"{mlflow.get_tracking_uri()}/#/models/{model_name}/versions/{model_version.version}"
+        # click.echo(f"Registered model URL: {registered_model_url}")
 
     else:
         click.secho(f"No model specified to upload!", fg="red", bold=True)

src/myautoml/utils/mlflow/__init__.py

@@ -1,2 +1,2 @@
-from .tracking import get_model, track_model, track_model_from_file, track_model_data, log_sk_model
+from .tracking import get_model, track_model, track_model_data, log_sk_model
 from .models import get_registered_model, register_model