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Making Deployments Easy with TF Serving | TF Ev...
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Rishit Dagli
May 11, 2021
Programming
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Making Deployments Easy with TF Serving | TF Everywhere India
My talk at TensorFlow Everywhere India
Rishit Dagli
May 11, 2021
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Transcript
Making Deployments Easy with TF Serving Rishit Dagli High School
TEDx, TED-Ed Speaker rishit_dagli Rishit-dagli
“Most models don’t get deployed.”
of models don’t get deployed. 90%
Source: Laurence Moroney
Source: Laurence Moroney
• High School Student • TEDx and Ted-Ed Speaker •
♡ Hackathons and competitions • ♡ Research • My coordinates - www.rishit.tech $whoami rishit_dagli Rishit-dagli
• Devs who have worked on Deep Learning Models (Keras)
• Devs looking for ways to put their model into production ready manner Ideal Audience
Why care about ML deployments? Source: memegenerator.net
None
• Package the model What things to take care of?
• Package the model • Post the model on Server
What things to take care of?
• Package the model • Post the model on Server
• Maintain the server What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale Global availability What things to take care of?
• Package the model • Post the model on Server
• Maintain the server Auto-scale Global availability Latency What things to take care of?
• Package the model • Post the model on Server
• Maintain the server • API What things to take care of?
• Package the model • Post the model on Server
• Maintain the server • API • Model Versioning What things to take care of?
Simple Deployments Why are they inefficient?
None
Simple Deployments Why are they inefficient? • No consistent API
• No model versioning • No mini-batching • Inefficient for large models Source: Hannes Hapke
TensorFlow Serving
TensorFlow Serving TensorFlow Data validation TensorFlow Transform TensorFlow Model Analysis
TensorFlow Serving TensorFlow Extended
• Part of TensorFlow Extended TensorFlow Serving
• Part of TensorFlow Extended • Used Internally at Google
TensorFlow Serving
• Part of TensorFlow Extended • Used Internally at Google
• Makes deployment a lot easier TensorFlow Serving
The Process
• The SavedModel format • Graph definitions as protocol buffer
Export Model
SavedModel Directory
auxiliary files e.g. vocabularies SavedModel Directory
auxiliary files e.g. vocabularies SavedModel Directory Variables
auxiliary files e.g. vocabularies SavedModel Directory Variables Graph definitions
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving Also supports gRPC
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving
TensorFlow Serving
Inference
• Consistent APIs • Supports simultaneously gRPC: 8500 REST: 8501
• No lists but lists of lists Inference
• No lists but lists of lists Inference
• JSON response • Can specify a particular version Inference
with REST Default URL http://{HOST}:8501/v1/ models/test Model Version http://{HOST}:8501/v1/ models/test/versions/ {MODEL_VERSION}: predict
• JSON response • Can specify a particular version Inference
with REST Default URL http://{HOST}:8501/v1/ models/test Model Version http://{HOST}:8501/v1/ models/test/versions/ {MODEL_VERSION}: predict Port Model name
Inference with REST
• Better connections • Data converted to protocol buffer •
Request types have designated type • Payload converted to base64 • Use gRPC stubs Inference with gRPC
Model Meta Information
• You have an API to get meta info •
Useful for model tracking in telementry systems • Provides model input/ outputs, signatures Model Meta Information
Model Meta Information http://{HOST}:8501/ v1/models/{MODEL_NAME} /versions/{MODEL_VERSION} /metadata
Batch Inferences
• Use hardware efficiently • Save costs and compute resources
• Take multiple requests process them together • Super cool😎 for large models Batch inferences
• max_batch_size • batch_timeout_micros • num_batch_threads • max_enqueued_batches • file_system_poll_wait
_seconds • tensorflow_session _paralellism • tensorflow_intra_op _parallelism Batch Inference Highly customizable
• Load configuration file on startup • Change parameters according
to use cases Batch Inference
Also take a look at...
• Kubeflow deployments • Data pre-processing on server🚅 • AI
Platform Predictions • Deployment on edge devices • Federated learning Also take a look at...
bit.ly/tf-everywhere-ind Demos!
bit.ly/serving-deck Slides
Thank You rishit_dagli Rishit-dagli