https://github.com/oracle/oci-mlflow
The Oracle Cloud Infrastructure (OCI) MLflow plugin empowers users of OCI by providing seamless integration with OCI resources, allowing them to effectively manage the entire life cycle of their machine learning use cases.
https://github.com/oracle/oci-mlflow
accelerated-data-science cloud machine-learning mlflow mlflow-tracking-server mlops oci oracle oracle-ads
Last synced: 8 months ago
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The Oracle Cloud Infrastructure (OCI) MLflow plugin empowers users of OCI by providing seamless integration with OCI resources, allowing them to effectively manage the entire life cycle of their machine learning use cases.
- Host: GitHub
- URL: https://github.com/oracle/oci-mlflow
- Owner: oracle
- License: upl-1.0
- Created: 2023-05-31T17:40:18.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2025-03-18T19:13:50.000Z (over 1 year ago)
- Last Synced: 2025-03-18T20:26:36.120Z (over 1 year ago)
- Topics: accelerated-data-science, cloud, machine-learning, mlflow, mlflow-tracking-server, mlops, oci, oracle, oracle-ads
- Language: Python
- Homepage: https://oci-mlflow.readthedocs.io
- Size: 233 KB
- Stars: 22
- Watchers: 5
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README-development.md
- Contributing: CONTRIBUTING.md
- License: LICENSE.txt
- Security: SECURITY.md
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README
# Development
The target audience for this README is developers wanting to contribute to `OCI MLflow` plugins. If you want to use the OCI MLflow plugins with your own programs, see `README.md`.
## Setting Up Dependencies
```
python3 -m pip install -r dev-requirements.txt
```
# Generating the wheel
The OCI MLflow plugins are packaged as a wheel. To generate the wheel, you can run:
```
make dist
```
Alternatively you can run -
```
python -m build
```
This wheel can then be installed using pip.
## Setting Up Tracking Server
Create a file called `.env` in the root folder of the project with following contents -
```
# Defaults to resource_principal if not provided
OCIFS_IAM_TYPE=api_key
# Artifacts location. Can be local folder or OCI Object Storage bucket
MLFLOW_ARTIFACTS_DESTINATION=oci://bucket@namespace/
MLFLOW_DEFAULT_ARTIFACT_ROOT=oci://bucket@namespace/
# Backend provider. Default is sqllite
BACKEND_PROVIDER=sqllite
# ------MySQL-----------------------
# BACKEND_PROVIDER=mysql
# The database credentials can be stored in the Vault service, or they can be provided in the config.
# See more details how to save the credentials to the Vault -
# https://accelerated-data-science.readthedocs.io/en/latest/user_guide/secrets/mysql.html
# DB_SECRET_OCID=ocid1.vaultsecret.oc1.iad..
# ----OR------------------------------
# MLFLOW_BACKEND_STORE_URI=mysql+mysqlconnector://{username}:{password}@{host}:{db_port}/{db_name}
# ------------------------------------
MLFLOW_SERVE_ARTIFACTS=1
MLFLOW_GUNICORN_OPTS=--log-level debug
MLFLOW_WORKERS=4
MLFLOW_HOST=0.0.0.0
```
### Building And Running Tracking Server
To build an `oci-mlflow` container image run -
```
make clean build-image
```
Alternatively you can run -
```
docker build --network host --build-arg RND=1 -t oci-mlflow:latest -f container-image/Dockerfile .
```
To build and launch tracking server run -
```
make clean launch
```
Alternatively you can run -
```
docker build --network host --build-arg RND=1 -t oci-mlflow:latest -f container-image/Dockerfile .
docker run --rm -it --net host -v ~/.oci:/root/.oci --env-file .env --name oci-mlflow:latest
```
To build `oci-mlflow` wheel file and then rebuild and launch the container image, run -
```
make clean wheel launch
```
Alternatively you can run -
```
python -m build
cp dist/*.whl container-image/run/
docker build --network host --build-arg RND=1 -t oci-mlflow:latest -f container-image/Dockerfile .
docker run --rm -it --net host -v ~/.oci:/root/.oci --env-file .env --name oci-mlflow oci-mlflow:latest
```
To build and start a shell prompt within `oci-mlflow` container image, run -
```
make clean wheel launch-shell
```
Alternatively you can run -
```
python -m build
cp dist/*.whl container-image/run/
docker build --network host --build-arg RND=1 -t oci-mlflow:latest -f container-image/Dockerfile .
docker run --rm -it --net host -v ~/.oci:/root/.oci --env-file .env --entrypoint bash --name oci-mlflow-shell oci-mlflow:latest
```
# Running Tests
The SDK uses pytest as its test framework. To run tests use:
```
python3 -m pytest tests/*
```
# Generating Documentation
Sphinx is used for documentation. You can generate HTML locally with the following:
```
python3 -m pip install -r dev-requirements.txt
cd docs
make html
```