### Hardware attacks

Physical GPUs or TPUs can also be the target of attacks. [Published
research](https://scholar.google.com/scholar?q=gpu+side+channel) shows that it
might be possible to use side channel attacks on the GPU to leak data from other
running models or processes in the same system. GPUs can also have
implementation bugs that might allow attackers to leave malicious code running

                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    /* Given upstream grad U and a BiasAdd: A + bias, the gradients are:
     *
     *    dA = U
     *    dbias = reduceSum(U, dims = channel_dim)
     */

    AbstractTensorHandle* upstream_grad = grad_outputs[0];
    DCHECK(upstream_grad);

    // Recover data format from forward pass for gradient.
    std::string data_format;

**Here's why we have this policy:** We want to focus on the work that benefits
the whole community, e.g., fixing bugs and adding features. Individual support
should be sought on Stack Overflow or other non-GitHub channels. It helps us to

        "//tensorflow/core/distributed_runtime/eager:eager_client",
        "//tensorflow/core/distributed_runtime/eager:remote_mgr",
        "//tensorflow/core/distributed_runtime/rpc:grpc_channel",
        "//tensorflow/core/distributed_runtime/rpc:grpc_server_lib",
        "//tensorflow/core/distributed_runtime/rpc:grpc_worker_cache",
        "//tensorflow/core/distributed_runtime/rpc:grpc_worker_service",

*   [Learn ML with TensorFlow](https://www.tensorflow.org/resources/learn-ml)
*   [TensorFlow Twitter](https://twitter.com/tensorflow)
*   [TensorFlow YouTube](https://www.youtube.com/channel/UC0rqucBdTuFTjJiefW5t-IQ)
*   [TensorFlow model optimization roadmap](https://www.tensorflow.org/model_optimization/guide/roadmap)
*   [TensorFlow White Papers](https://www.tensorflow.org/about/bib)

      per-channel scales for dynamic range quantization use case (`float32`
      inputs / outputs and `int8` weights). The change enables new quantization
      schema globally in the converter and inference engine. The new behaviour
      can be disabled via experimental
      flag `converter._experimental_disable_per_channel_quantization_for_dense_layers = True`.
    * C API:

# Install needed gcloud components
RUN Add-Type -AssemblyName "System.IO.Compression.FileSystem"; \
    $pkg_url = \"https://dl.google.com/dl/cloudsdk/channels/rapid/downloads/google-cloud-cli-396.0.0-windows-x86_64.zip\"; \
    $pkg_zip = \"c:/temp/gcloud.zip\"; \
    $pkg_extracted_path = \"c:/google-cloud-sdk\"; \
    (New-Object Net.WebClient).DownloadFile($pkg_url, $pkg_zip); \