Metadata-Version: 2.1
Name: google_cloud_mlflow
Version: 0.0.3
Summary: MLflow Google Cloud Vertex AI integration package
Home-page: https://github.com/Ark-kun/google_cloud_mlflow
Author: Alexey Volkov
Author-email: alexey.volkov@ark-kun.com
License: Apache License 2.0
Project-URL: Source, https://github.com/pypa/sampleproject/
Project-URL: Issues, https://github.com/Ark-kun/google_cloud_mlflow/issues
Description: # MLflow plugin for Google Cloud Vertex AI
        
        ## Installation
        
        ```shell
        python3 -m pip install google_cloud_mlflow
        ```
        
        ## Deployment plugin usage
        
        ### Command-line
        
        Create deployment
        
        ```shell
        mlflow deployments create --target google_cloud --name "deployment name" --model-uri "models:/mymodel/mymodelversion" --config destination_image_uri="gcr.io/<repo>/<path>"
        ```
        
        List deployments
        
        ```shell
        mlflow deployments list --target google_cloud
        ```
        
        Get deployment
        
        ```shell
        mlflow deployments get --target google_cloud --name "deployment name"
        ```
        
        Delete deployment
        
        ```shell
        mlflow deployments delete --target google_cloud --name "deployment name"
        ```
        
        Update deployment
        
        ```shell
        mlflow deployments update --target google_cloud --name "deployment name" --model-uri "models:/mymodel/mymodelversion" --config destination_image_uri="gcr.io/<repo>/<path>"
        ```
        
        Predict
        
        ```shell
        mlflow deployments predict --target google_cloud --name "deployment name" --input-path "inputs.json" --output-path "outputs.json
        ```
        
        Get help
        
        ```shell
        mlflow deployments help --target google_cloud
        ```
        
        ### Python
        
        ```python
        from mlflow import deployments
        client = deployments.get_deploy_client("google_cloud")
        
        # Create deployment
        model_uri = "models:/mymodel/mymodelversion"
        deployment = client.create_deployment(
            name="deployment name",
            model_uri=model_uri,
            # Config is optional
            config=dict(
                # Deployed model config
                machine_type="n1-standard-2",
                min_replica_count=None,
                max_replica_count=None,
                accelerator_type=None,
                accelerator_count=None,
                service_account=None,
                explanation_metadata=None, # JSON string
                explanation_parameters=None, # JSON string
        
                # Model container image building config
                destination_image_uri=None,
                timeout=None,
        
                # Model deployment config
                sync="true",
        
                # Endpoint config
                description=None,
        
                # Vertex AI config
                project=None,
                location=None,
                experiment=None,
                experiment_description=None,
                staging_bucket=None,
        
        # List deployments
        deployments = client.list_deployments()
        
        # Get deployment
        deployments = client.get_deployment(name="deployment name")
        
        # Delete deployment
        deployment = client.delete_deployment(name="deployment name")
        
        # Update deployment
        deployment = client.create_deployment(
            name="deployment name",
            model_uri=model_uri,
            # Config is optional
            config=dict(...),
        )
        
        # Predict
        import pandas
        df = pandas.DataFrame([
            {"a": 1,"b": 2,"c": 3},
            {"a": 4,"b": 5,"c": 6}
        ])
        predictions = client.predict("deployment name", df)
        ```
        
        ## Model Registry plugin usage
        
        Set the MLflow Model Registry URI to a directory in some Google Cloud Storage bucket, then log models using `mlflow.log_model` as usual.
        
        ```python
        mlflow.set_registry_uri("gs://<bucket>/models/")
        ```
        
Keywords: mlflow,Google Cloud,Vertex AI
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.6
Description-Content-Type: text/markdown
