output_units=1,
metrics=['accuracy'],
epochs=20))
# Add an evaluator
training_pipeline.add_evaluator(
TFMAEvaluator(slices=[['has_diabetes']],
metrics={
transformed_label_name('has_diabetes'):
['binary_crossentropy', 'binary_accuracy']
}))
# Add the deployer
training_pipeline.add_deployment(
GCAIPDeployer(
project_id=GCP_PROJECT,
model_name=MODEL_NAME,
))
# Run the pipeline
training_pipeline.run()
# Another way to do is is to create a DeploymentPipeline.
# Uncomment to create the model via this pipeline
# from zenml.core.pipelines.deploy_pipeline import DeploymentPipeline
# model_uri = training_pipeline.get_model_uri()
# deploy_pipeline = DeploymentPipeline(model_uri=model_uri)
# deploy_pipeline.add_deployment(
# GCAIPDeployer(
# model_name=MODEL_NAME + '_v2',
# project_id=GCP_PROJECT
training_pipeline.add_evaluator(
TFMAEvaluator(
slices=[['has_diabetes']],
metrics={'has_diabetes': ['binary_crossentropy', 'binary_accuracy']}))
# Add cortex deployer
api_config = {
"name": CORTEX_MODEL_NAME,
"kind": "RealtimeAPI",
"predictor": {
"type": "tensorflow",
# Set signature key of the model as we are using Tensorflow Trainer
"models": {
"signature_key": "serving_default"
}
}
}
training_pipeline.add_deployment(
CortexDeployer(
env=CORTEX_ENV,
api_config=api_config,
predictor=TensorFlowPredictor,
))
# Define the artifact store
artifact_store = ArtifactStore(
os.path.join(GCP_BUCKET, 'cortex/artifact_store'))
# Run the pipeline
training_pipeline.run(artifact_store=artifact_store)
