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tensorflow/c/eager/c_api_experimental.h
TF_Status* status); // ----------------------------------------------------------------------------- // Eager Executor APIs. typedef struct TFE_Executor TFE_Executor; // Creates a new eager Executor. Nodes in one executor are guaranteed to be // executed in sequence. Assigning nodes to different executors allows executing // nodes in parallel. // in_flight_nodes_limit: when is_async is true, this value controls the
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Feb 21 22:37:46 GMT 2024 - 39.5K bytes - Viewed (0) -
tensorflow/c/eager/c_api.h
// that shares the underlying buffer. Otherwise, it currently requires at least // one of the source or destination devices to be CPU (i.e., for the source or // destination tensor to be placed in host memory). // If async execution is enabled, the copy may be enqueued and the call will // return "non-ready" handle. Else, this function returns after the copy has // been done. TF_CAPI_EXPORT extern TFE_TensorHandle* TFE_TensorHandleCopyToDevice(
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Apr 27 21:07:00 GMT 2023 - 22.8K bytes - Viewed (1) -
tensorflow/c/eager/tape.h
} return absl::OkStatus(); } // TODO(agarwal): use an automatic mechanism for handling None arguments to // gradient functions. // // Some gradient functions can accept None arguments for gradients. The // following maps the operation name to the indices at which the corresponding // gradient function can accept None values. e.g. FusedBatchNorm outputs 5 // values and hence receives 5 gradient values during backprop. However the
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Apr 02 12:40:29 GMT 2024 - 47.2K bytes - Viewed (1) -
tensorflow/c/eager/parallel_device/parallel_device_lib.h
const std::vector<std::string> underlying_devices_; // A sequence of thread wrappers, one per device, for executing operations in // parallel. // // Conceptually this is a thread pool with one thread per device. It requires // less synchronization than a thread pool would for this task, since Execute // acquires each thread in order (and so only one Execute will schedule // blocking collective operations at a time), and avoids some dynamic
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tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache.h
/// the file contents do not extend past `offset` and thus nothing was /// placed in `out`. /// 4) `TF_OK` otherwise (i.e. the read succeeded, and at least one byte was /// placed /// in `buffer`). /// /// Caller is responsible for allocating memory for `buffer`. /// `buffer` will be left unchanged in case of errors.
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