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Machine Learning Operations Toolkit

Project description

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⏳ Tempo: The MLOps Software Development Kit

Vision

Enable data scientists to see a productionised machine learning model within moments, not months. Easy to work with locally and also in kubernetes, whatever your preferred data science tools

Overview

Tempo provides a unified interface to multiple MLOps projects that enable data scientists to deploy and productionise machine learning systems.

Motivating Example

Tempo allows you to interact with scalable orchestration engines like Seldon Core and KFServing, and leverage a broad range of machine learning services like TFserving, Triton, MLFlow, etc.

sklearn_model = Model(
        name="test-iris-sklearn",
        platform=ModelFramework.SKLearn,
        uri="gs://seldon-models/sklearn/iris")

sklearn_model = Model(
        name="test-iris-sklearn",
        platform=ModelFramework.SKLearn,
        uri="gs://seldon-models/sklearn/iris")

@pipeline(name="mypipeline",
          uri="gs://seldon-models/custom",
          models=[sklearn_model, xgboost_model])
class MyPipeline(object):

    @predictmethod
    def predict(self, payload: np.ndarray) -> np.ndarray:
        res1 = sklearn_model(payload)
        if res1[0][0] > 0.7:
            return res1
        else:
            return xgboost_model(payload)

my_pipeline = MyPipeline()

# Deploy only the models into kubernetes
my_pipeline.deploy_models()
my_pipeline.wait_ready()

# Run the request using the local pipeline function but reaching to remote models
my_pipeline.predict(np.array([[4.9, 3.1, 1.5, 0.2]]))

Productionisation Workflows

Declarative Interface

Even though Tempo provides a dynamic imperative interface, it is possible to convert into a declarative representation of components.

yaml = my_pipeline.to_k8s_yaml()

print(yaml)

Environment Packaging

You can also manage the environments of your pipelines to introduce reproducibility of local and production environments.

@pipeline(name="mypipeline",
          uri="gs://seldon-models/custom",
          conda_env="tempo",
          models=[sklearn_model, xgboost_model])
class MyPipeline(object):
    # ...

my_pipeline = MyPipeline()

# Save the full conda environment of the pipeline
my_pipeline.save()
# Upload the full conda environment
my_pipeline.upload()

# Deploy the full pipeline remotely
my_pipeline.deploy()
my_pipeline.wait_ready()

# Run the request to the remote deployed pipeline
my_pipeline.remote(np.array([[4.9, 3.1, 1.5, 0.2]]))

Examples

Project details


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