The Pusher component in TensorFlow Extended (TFX) is a fundamental part of the TFX pipeline that handles the deployment of trained models to various target environments. The deployment targets for the Pusher component in TFX are diverse and flexible, allowing users to deploy their models to different platforms depending on their specific requirements. In this answer, we will explore some of the common deployment targets for the Pusher component and provide a comprehensive explanation of each.
1. Local Deployment:
The Pusher component supports local deployment, which allows users to deploy their trained models on the local machine. This is useful for testing and development purposes, where the model can be deployed and evaluated without the need for a distributed system or external infrastructure. Local deployment is achieved by simply specifying the local path where the model artifacts are stored.
Example:
python pusher = Pusher( model=trainer.outputs['model'], model_blessing=evaluator.outputs['blessing'], push_destination=pusher_pb2.PushDestination( filesystem=pusher_pb2.PushDestination.Filesystem( base_directory='/path/to/local/deployment' ) ) )
2. Google Cloud AI Platform:
The Pusher component also supports deployment to Google Cloud AI Platform, a managed service that provides a serverless environment for running machine learning models. This allows users to easily deploy their models to the cloud and take advantage of the scalability and reliability offered by Google Cloud. To deploy to Google Cloud AI Platform, users need to provide the project ID, model name, and version name.
Example:
python pusher = Pusher( model=trainer.outputs['model'], model_blessing=evaluator.outputs['blessing'], push_destination=pusher_pb2.PushDestination( ai_platform_push=pusher_pb2.PushDestination.AIPlatformPush( project_id='my-project', model_id='my-model', version_id='v1' ) ) )
3. TensorFlow Serving:
TensorFlow Serving is an open-source serving system for deploying machine learning models. The Pusher component in TFX supports deployment to TensorFlow Serving, allowing users to deploy their models to a distributed serving infrastructure. This enables high-performance and scalable model serving, making it suitable for production deployments. To deploy to TensorFlow Serving, users need to provide the TensorFlow Serving model server's address and port.
Example:
python pusher = Pusher( model=trainer.outputs['model'], model_blessing=evaluator.outputs['blessing'], push_destination=pusher_pb2.PushDestination( tensorflow_serving=pusher_pb2.PushDestination.TensorFlowServing( tags=['serve'], server='localhost:8500' ) ) )
4. Other Custom Deployment Targets:
The Pusher component in TFX is designed to be extensible, allowing users to define their own custom deployment targets. This gives users the flexibility to deploy their models to any environment or system that can consume TensorFlow models. Users can implement their own custom `PushDestination` subclass and register it with the Pusher component to enable deployment to their target environment.
Example:
python class MyCustomPushDestination(pusher_pb2.PushDestination): def __init__(self, my_custom_arg): self.my_custom_arg = my_custom_arg pusher = Pusher( model=trainer.outputs['model'], model_blessing=evaluator.outputs['blessing'], push_destination=MyCustomPushDestination(my_custom_arg='custom_value') )
The Pusher component in TFX supports various deployment targets, including local deployment, Google Cloud AI Platform, TensorFlow Serving, and custom deployment targets. This flexibility allows users to deploy their trained models to different environments depending on their specific needs and infrastructure setup.
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