The OCI MLflow plugin enables OCI users to use OCI resources to manage their machine learning usecase life cycle. This table below provides the mapping between the MLflow features and the OCI resources that are used.
| MLflow Use Case | OCI Resource |
|---|---|
| User running machine learning experiments on notebook, logs model artifacts, model performance etc | Data Science Jobs, Object Storage, MySQL |
| Batch workloads using spark | Data Flow, Object Storage, MySQL |
| Model Deployment | Data Science Model Deployment |
| User running machine learning experiments on notebook, logs model artifacts, model performance etc | Object Storage, MySQL |
To install the oci-mlflow plugin call -
python3 -m pip install oci-mlflowTo test the oci-mlflow plugin call -
mlflow deployments help -t oci-datascience- OCI MLflow Documentation
- Getting started with Oracle Accelerated Data Science SDK
- Getting started with OCI Data Science Jobs
- Getting started with Data Science Environments
- Getting started with Custom Conda Environments
- Oracle AI & Data Science Blog
- OCI Documentation
export MLFLOW_TRACKING_URI=<tracking server url>
mlflow run . --experiment-name My-Experiment --backend oci-datascience --backend-config ./oci-datascience-config.jsonmlflow deployments help -t oci-datascience
export MLFLOW_TRACKING_URI=<tracking server url>
mlflow deployments create --name <model deployment name> -m models:/<registered model name>/<model version> -t oci-datascience --config deploy-config-file=deployment_specification.yamlThis project welcomes contributions from the community. Before submitting a pull request, pleasereview our contribution guide
Find Getting Started instructions for developers in README-development.md
Consult the security guide SECURITY.md for our responsible security vulnerability disclosure process.
Copyright (c) 2023 Oracle and/or its affiliates. Licensed under the Universal Permissive License v1.0