# CuDNN: https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#ubuntu-network-installation
libcudnn8=8.9.6.50-1+cuda12.2
libcudnn8-dev=8.9.6.50-1+cuda12.2

# This can be removed once NVIDIA publishes a cuda-12.3.2 Docker image.
# For now it ensures that we install at least version 12.3.107 of PTXAS,
# since 12.3.103 has a bug.

      -
        name: Set up Docker Buildx
        uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325 # v2.2.1
      -
        name: Login to GCR
        if: contains(github.event.pull_request.labels.*.name, 'build and push to gcr.io for staging')
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0
        with:

#   container and start fresh, run "docker rm -f tf". Removing the container
#   destroys some temporary bazel data and causes longer builds.
#
#   You will need the NVIDIA Container Toolkit for GPU testing:
#   https://github.com/NVIDIA/nvidia-container-toolkit
#
#   Note: if you interrupt a bazel command on docker (ctrl-c), you
#   will need to run `docker exec tf pkill bazel` to quit bazel.
#

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See

typedef struct TFE_MonitoringBuckets TFE_MonitoringBuckets;
TF_CAPI_EXPORT extern TFE_MonitoringBuckets*
TFE_MonitoringNewExponentialBuckets(double scale, double growth_factor,
                                    int bucket_count);
TF_CAPI_EXPORT extern void TFE_MonitoringDeleteBuckets(
    TFE_MonitoringBuckets* buckets);

// APIs for Sampler without label.
typedef struct TFE_MonitoringSampler0 TFE_MonitoringSampler0;

  std::string bucket, object;
  ParseGCSPath(path, true, &bucket, &object, status);
  if (TF_GetCode(status) != TF_OK) return;

  auto gcs_file = static_cast<GCSFile*>(filesystem->plugin_filesystem);
  if (object.empty()) {
    auto bucket_metadata =
        gcs_file->gcs_client.GetBucketMetadata(bucket, gcs::Fields(""));
    TF_SetStatusFromGCSStatus(bucket_metadata.status(), status);

        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash

                                                           int bucket_count) {
  return new TFE_MonitoringBuckets([scale, growth_factor, bucket_count]() {
    return tensorflow::monitoring::Buckets::Exponential(scale, growth_factor,
                                                        bucket_count);
  });
}

void TFE_MonitoringDeleteBuckets(TFE_MonitoringBuckets* buckets) {

        to `tf.string_strip`), `tf.strings.substr`, `tf.strings.to_hash_bucket`
        (corresponds to `tf.string_to_hash_bucket`),
        `tf.strings.to_hash_bucket_fast` (corresponds to
        `tf.string_to_hash_bucket_fast`), `tf.strings.to_hash_bucket_strong`
        (corresponds to `tf.string_to_hash_bucket_strong`).

## Thanks to our Contributors

      -
        name: Set up Docker Buildx
        uses: docker/setup-buildx-action@8c0edbc76e98fa90f69d9a2c020dcb50019dc325 # v2.2.1
      -
        name: Login to DockerHub
        uses: docker/login-action@f4ef78c080cd8ba55a85445d5b36e214a81df20a # v2.1.0
        with:
          username: ${{ secrets.DOCKERHUB_USERNAME }}
          password: ${{ secrets.DOCKERHUB_TOKEN }}