Xkool Aliyun OSS store plugin for MLflow

Forked and modified from SeaOfOcean at https://github.com/SeaOfOcean/mlflow-aliyunstore

This repository provides a MLflow plugin that allows users to use Aliyun OSS as the artifact store for MLflow.

Usage

Pip install the package on both your client and the server

pip install mlflow_oss_artifact

Configure environment variables in your OS for Aliyun OSS authentication

Note: checkout this post on stackoverflow to make them permanent if necessary

export MLFLOW_OSS_ENDPOINT_URL=<oss-xx-cityname.aliyuncs.com>
export MLFLOW_OSS_KEY_ID=<your_oss_key_id>
export MLFLOW_OSS_KEY_SECRET=<your_oss_key_secret>
export MLFLOW_OSS_BUCKET_NAME=<your_bucket_name>

To use To use Aliyun OSS as an artifact store, an OSS URI of the form oss://<path> must be provided, as shown in the example below:

import mlflow
import mlflow.pyfunc

class Mod(mlflow.pyfunc.PythonModel):
    def predict(self, ctx, inp):
        return 7

exp_name = "myexp"
mlflow.create_experiment(exp_name, artifact_location="oss://mlflow-test/")
mlflow.set_experiment(exp_name)
mlflow.pyfunc.log_model('model_test', python_model=Mod())

In the example provided above, the log_model operation creates three entries in the OSS storage oss://mlflow-test/$RUN_ID/artifacts/model_test/, the MLmodel file and the conda.yaml file associated with the model.