Serving Models  |  TFX  |  TensorFlow

IntroductionTensorFlow Serving is a flexible, high-performance serving system for machine learning models, designed for production environments. TensorFlow Serving makes it easy to deploy new algorithms and experiments, while keeping the same server architecture and APIs. TensorFlow Serving provides out-of-the-box integration with TensorFlow models, but can be easily extended to serve other types of models and … Read more Serving Models  |  TFX  |  TensorFlow


 
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Home – Keras Documentation

Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Use Keras if you need … Read more Home – Keras Documentation


 
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Docker  |  TensorFlow | GPU

GPU support Docker is the easiest way to run TensorFlow on a GPU since the host machine only requires the NVIDIA® driver (the NVIDIA® CUDA® Toolkit is not required). Install the Nvidia Container Toolkit to add NVIDIA® GPU support to Docker. nvidia-container-runtime is only available for Linux. See the nvidia-container-runtime platform support FAQ for details. … Read more Docker  |  TensorFlow | GPU


 
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Docker  |  TensorFlow

Docker Contents TensorFlow Docker requirements Download a TensorFlow Docker image Start a TensorFlow Docker container Examples using CPU-only images GPU support Examples using GPU-enabled images Docker uses containers to create virtual environments that isolate a TensorFlow installation from the rest of the system. TensorFlow programs are run within this virtual environment that can share resources … Read more Docker  |  TensorFlow


 
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