mlflow-algorithmia 0.1.0

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mlflow-algorithmia

Deploy MLflow models to Algorithmia

Install

pip install git+git://github.com/algorithmia/mlflow-algorithmia.git

pip install mlflow-algorithmia

Usage

This is based on the mlflow tutorial we reproduce some steps here but for more details look at the official mlflow docs.

python examples/sklearn_elasticnet_wine/train.py

This will create an mlruns directory that contains the trained model, you can view the UI running mlflow ui and start the mlflow server running:

$ mlflow models serve -m mlruns/0/<run-id>/artifacts/model -p 1234

# Make a test query

$ curl -X POST -H "Content-Type:application/json; format=pandas-split" --data '{"columns":["alcohol", "chlorides", "citric acid", "density", "fixed acidity", "free sulfur dioxide", "pH", "residual sugar", "sulphates", "total sulfur dioxide", "volatile acidity"],"data":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}' http://127.0.0.1:1234/invocations

[5.120775719594933]

Now let's deploy the same endpoint in Algorithmia. You will need:

1. An Algorithmia API key with Read + Write data access
2. The path to the model (under mlruns) you want to deploy, for example: mlruns/0/<run-id>/artifacts/model

# Set your Algorithmia API key
export ALGORITHMIA_USERNAME=<username>
export ALGORITHMIA_API_KEY=<api-key>

# Create a deployment
mlflow deployments create -t algorithmia --name mlflow_sklearn_demo -m <path-to-model-dir>

Query the model in Algorithmia:

• You need the ALGORITHMIA_USERNAME and ALGORITHMIA_API_KEY variables from before and the <version> you want to query
• Note that if there are no published versions need to use a build hash as <version>

curl -X POST -d '{"columns":["alcohol", "chlorides", "citric acid", "density", "fixed acidity", "free sulfur dioxide", "pH", "residual sugar", "sulphates", "total sulfur dioxide", "volatile acidity"],"data":[[12.8, 0.029, 0.48, 0.98, 6.2, 29, 3.33, 1.2, 0.39, 75, 0.66]]}' -H 'Content-Type: application/json' -H 'Authorization: Simple '${ALGORITHMIA_API_KEY} https://api.algorithmia.com/v1/algo/${ALGORITHMIA_USERNAME}/mlflow_sklearn_demo/<version>

You can also use mlflow deployments predict to query the model, on this case it will always query the latest published version of the model.

echo '{"alcohol":{"0":12.8},"chlorides":{"0":0.029},"citric acid":{"0":0.48},"density":{"0":0.98},"fixed acidity":{"0":6.2},"free sulfur dioxide":{"0":29},"pH":{"0":3.33},"residual sugar":{"0":1.2},"sulphates":{"0":0.39},"total sulfur dioxide":{"0":75},"volatile acidity":{"0":0.66}}' > predict_input.json

mlflow deployments predict -t algorithmia --name mlflow_sklearn_demo -I predict_input.json

To update deployment for example after training a new model

mlflow deployments update -t algorithmia --name mlflow_sklearn_demo -m <path-to-new-model-dir>

To delete the deployment

mlflow deployments delete -t algorithmia --name mlflow_sklearn_demo