开源软件名称(OpenSource Name):tensorflow/serving开源软件地址(OpenSource Url):https://github.com/tensorflow/serving开源编程语言(OpenSource Language):C++ 88.0%开源软件介绍(OpenSource Introduction):TensorFlow ServingTensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. It deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, providing clients with versioned access via a high-performance, reference-counted lookup table. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and data. To note a few features:
Serve a Tensorflow model in 60 seconds# Download the TensorFlow Serving Docker image and repo
docker pull tensorflow/serving
git clone https://github.com/tensorflow/serving
# Location of demo models
TESTDATA="$(pwd)/serving/tensorflow_serving/servables/tensorflow/testdata"
# Start TensorFlow Serving container and open the REST API port
docker run -t --rm -p 8501:8501 \
-v "$TESTDATA/saved_model_half_plus_two_cpu:/models/half_plus_two" \
-e MODEL_NAME=half_plus_two \
tensorflow/serving &
# Query the model using the predict API
curl -d '{"instances": [1.0, 2.0, 5.0]}' \
-X POST http://localhost:8501/v1/models/half_plus_two:predict
# Returns => { "predictions": [2.5, 3.0, 4.5] } End-to-End Training & Serving TutorialRefer to the official Tensorflow documentations site for a complete tutorial to train and serve a Tensorflow Model. DocumentationSet upThe easiest and most straight-forward way of using TensorFlow Serving is with Docker images. We highly recommend this route unless you have specific needs that are not addressed by running in a container.
UseExport your Tensorflow modelIn order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. SavedModel is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models. Please refer to Tensorflow documentation for detailed instructions on how to export SavedModels. Configure and Use Tensorflow Serving
ExtendTensorflow Serving's architecture is highly modular. You can use some parts individually (e.g. batch scheduling) and/or extend it to serve new use cases.
ContributeIf you'd like to contribute to TensorFlow Serving, be sure to review the contribution guidelines. For more informationPlease refer to the official TensorFlow website for more information. |
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