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distributed_tensorflow's Introduction

Distributed TensorFlow

Run in standalone

python -m trainer.task

Run in distributed

Start the ps server.

CUDA_VISIBLE_DEVICES='' TF_CONFIG='{"cluster": {"ps": ["127.0.0.1:3001"], "worker": ["127.0.0.1:3002"], "master": ["127.0.0.1:3003"]}, "task": {"index": 0, "type": "ps"}}' python -m trainer.task

Start the worker.

CUDA_VISIBLE_DEVICES='' TF_CONFIG='{"cluster": {"ps": ["127.0.0.1:3001"], "worker": ["127.0.0.1:3002"], "master": ["127.0.0.1:3003"]}, "task": {"index": 0, "type": "worker"}}' python -m trainer.task

Start the master.

CUDA_VISIBLE_DEVICES='' TF_CONFIG='{"cluster": {"ps": ["127.0.0.1:3001"], "worker": ["127.0.0.1:3002"], "master": ["127.0.0.1:3003"]}, "task": {"index": 0, "type": "master"}}' python -m trainer.task

Serving

Run with simple_tensorflow_serving.

simple_tensorflow_serving --port=8500 --model_base_path="./saved_model"

Predict with curl or other clients.

curl -H "Content-Type: application/json" -X POST -d '{"keys": [[11.0], [2.0]], "features": [[1], [2]]}' http://127.0.0.1:8500

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