"somepath(//tensorflow/tools/pip_package:build_pip_package, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out

TF_CAPI_EXPORT extern void TFE_ContextUpdateServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status);

// This API is for experimental usage and may be subject to change.
TF_CAPI_EXPORT extern void TFE_ContextSetServerDefWithTimeout(
    TFE_Context* ctx, int keep_alive_secs, const void* proto, size_t proto_len,
    int64_t init_timeout_in_ms, TF_Status* status,

`tensorflow` pip package has a new, optional installation method for Linux that installs necessary Nvidia CUDA libraries through pip. As long as the Nvidia driver is already installed on the system, you may now run `pip install tensorflow[and-cuda]` to install TensorFlow's Nvidia CUDA library dependencies in the Python environment. Aside from the Nvidia driver, no other pre-existing Nvidia CUDA packages are necessary.

*   Enable JIT-compiled i64-indexed kernels on GPU for large tensors with more...

    disallowed_deps = if_static(
        [],
        otherwise = [
            "@local_xla//xla/stream_executor/cuda:all_runtime",
            "@local_xla//xla/stream_executor/cuda:cuda_driver",
            "@local_xla//xla/stream_executor/cuda:cuda_platform",
            "@local_xla//xla/stream_executor/cuda:cudnn_plugin",
            "@local_xla//xla/stream_executor/cuda:cufft_plugin",

    sdkmanager 'cmake;3.6.4111459'; \
    sdkmanager 'emulator'; \
    sdkmanager 'system-images;android-27;google_apis;x86'; \
    sdkmanager 'sources;android-27'; \
    sdkmanager 'extras;google;Android_Emulator_Hypervisor_Driver'; \
    sdkmanager 'extras;google;auto'; \
    sdkmanager 'extras;google;google_play_services'; \
    sdkmanager 'extras;google;instantapps'; \
    sdkmanager 'extras;google;m2repository'; \

    --@local_config_cuda//:enable_cuda \
    "somepath(//tensorflow/tools/pip_package:wheel, " \
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cudart + "\
    "@local_config_cuda//cuda:cuda_driver + "\
    "@local_config_cuda//cuda:cudnn + "\
    "@local_config_cuda//cuda:curand + "\
    "@local_config_cuda//cuda:cusolver + "\
    "@local_config_tensorrt//:tensorrt)" --keep_going > $BATS_TEST_TMPDIR/out

template <typename Gradient, typename BackwardFunction, typename TapeTensor>
class GradientTape {
 public:
  // If `persistent` is true, GradientTape will not eagerly delete backward
  // functions (and hence the tensors they keep alive). Instead, everything
  // is deleted in ~GradientTape. Persistent GradientTapes are useful when
  // users want to compute multiple gradients over the same tape.

// Return a new execution session with the associated graph, or NULL on
// error. Does not take ownership of any input parameters.
//
// *`graph` must be a valid graph (not deleted or nullptr). `graph` will be
// kept alive for the lifetime of the returned TF_Session. New nodes can still
// be added to `graph` after this call.
TF_CAPI_EXPORT extern TF_Session* TF_NewSession(TF_Graph* graph,