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the application developer, whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In...

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# Using TensorFlow Securely

This document discusses the TensorFlow security model. It describes the security
risks to consider when using models, checkpoints or input data for training or
serving. We also provide guidelines on what constitutes a vulnerability in
TensorFlow and how to report them.

This document applies to other repositories in the TensorFlow organization,
covering security practices for the entirety of the TensorFlow ecosystem.

    [`tf.distribute.Strategy`](https://www.tensorflow.org/beta/guide/distribute_strategy)
    API to distribute training with minimal code changes, yielding great
    out-of-the-box performance. It supports distributed training with Keras
    model.fit, as well as with custom training loops. Multi-GPU support is
    available, along with experimental support for multi worker and Cloud TPUs.
    Check out the

 [CertificatePinningKotlin]: https://github.com/square/okhttp/blob/master/samples/guide/src/main/java/okhttp3/recipes/kt/CertificatePinning.kt
 [provider_installer]: https://developer.android.com/training/articles/security-gms-provider

   *     not support creating temporary directories securely
   * @deprecated For Android users, see the <a
   *     href="https://developer.android.com/training/data-storage" target="_blank">Data and File
   *     Storage overview</a> to select an appropriate temporary directory (perhaps {@code
   *     context.getCacheDir()}), and create your own directory under that. (For example, you might

// --------------------------------------------------------------------------
// In-process TensorFlow server functionality, for use in distributed training.
// A Server instance encapsulates a set of devices and a Session target that
// can participate in distributed training. A server belongs to a cluster
// (specified by a ClusterSpec), and corresponds to a particular task in a

   *     not support creating temporary directories securely
   * @deprecated For Android users, see the <a
   *     href="https://developer.android.com/training/data-storage" target="_blank">Data and File
   *     Storage overview</a> to select an appropriate temporary directory (perhaps {@code
   *     context.getCacheDir()}), and create your own directory under that. (For example, you might