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tensorflow/c/eager/c_api_unified_experimental.h
// Creates a new TensorFlow function. A Function is an execution context, and as // such it can trace operations through TF_ExecuteOperation. After completing // tracing, a function can be obtained by TF_FinalizeFunction. TF_ExecutionContext* TF_CreateFunction(const char* fn_name, TF_Status* status); // Creates a context for eager execution of operations. TF_ExecutionContext* TF_NewEagerExecutionContext(TFE_ContextOptions*,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sun Oct 24 11:27:00 GMT 2021 - 7K bytes - Viewed (0) -
tensorflow/c/eager/c_api_experimental.h
// Registers a custom device for use with eager execution. // // Eager operations may be placed on this device, e.g. `with // tf.device("CUSTOM"):` from Python if `device_name` for this call is // "/job:localhost/replica:0/task:0/device:CUSTOM:0". // // The custom device defines copy operations for moving TensorHandles on and // off, and an execution operation for named operations. Often execution will
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tensorflow/c/eager/c_api_unified_experimental_internal.h
}; namespace internal { struct TracingOperationDeleter { void operator()(TracingOperation* p) const { if (p != nullptr) { p->Release(); } } }; } // namespace internal using TracingOperationPtr = std::unique_ptr<TracingOperation, internal::TracingOperationDeleter>; // This holds the context for the execution: dispatching operations either to an // MLIR implementation or to a graph implementation.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Nov 13 22:20:40 GMT 2020 - 5.2K bytes - Viewed (0) -
tensorflow/c/eager/immediate_execution_context.h
: AbstractContext(kind) {} ~ImmediateExecutionContext() override {} }; namespace internal { struct ImmediateExecutionContextDeleter { void operator()(ImmediateExecutionContext* p) const { if (p != nullptr) { p->Release(); } } }; } // namespace internal using ImmediateContextPtr = std::unique_ptr<ImmediateExecutionContext,
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Jul 06 08:34:00 GMT 2023 - 12.3K bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/modular_filesystem_registration.cc
strings::StrCat("Plugin ABI (", pluginABI, ") for ", where, " operations doesn't match expected core ABI (", coreABI, "). Plugin cannot be loaded.")); return OkStatus(); } // Checks if the plugin and core ABI numbers match, for all operations. // // If the numbers don't match, plugin cannot be loaded. // // Uses the simpler `CheckABI(int, int, StringPiece)`.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Tue Mar 07 22:08:43 GMT 2023 - 12.8K bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/modular_filesystem.h
read_only_memory_region_ops_; std::function<void*(size_t)> plugin_memory_allocate_; std::function<void(void*)> plugin_memory_free_; ModularFileSystem(const ModularFileSystem&) = delete; void operator=(const ModularFileSystem&) = delete; }; class ModularRandomAccessFile final : public RandomAccessFile { public: ModularRandomAccessFile(const std::string& filename,
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tensorflow/c/experimental/filesystem/filesystem_interface.h
/// that plugins provide (some are mandatory, some are optional, with or without /// a default implementation). /// /// Each plugin implements the operations that are supported and TensorFlow will /// properly handle the cases when an operation is not supported (i.e., return /// the corresponding `Status` value). ///
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tensorflow/c/eager/abstract_tensor_handle.h
AbstractTensorHandleKind getKind() const { return kind_; } private: const AbstractTensorHandleKind kind_; }; namespace internal { struct AbstractTensorHandleDeleter { void operator()(AbstractTensorHandle* p) const { if (p != nullptr) { p->Unref(); } } }; } // namespace internal // TODO(b/185908092): Make AbstractTensorHandlePtr an IntrusivePtr.
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri Mar 03 00:30:36 GMT 2023 - 3K bytes - Viewed (0) -
tensorflow/c/eager/gradients.h
// maintains the map from `op_id` to a `OpTapeEntry` which stores the `op_type`, // inputs and outputs and the gradient function These data structures combined // allow us to trace the data dependencies between operations and hence compute // gradients. // // `ZerosLike` is not expected to be called and returns a nullptr. The creation // of default zeros grads is handled by the `DefaultGradientFunction` registered // for each op.
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SECURITY.md
variables as part of those interactions (ex: using a string variable to build a filesystem path), a maliciously created checkpoint might be able to change the targets of those operations, which could result in arbitrary read/write/executions. ### Running a TensorFlow server TensorFlow is a platform for distributed computing, and as such there is a
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