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tensorflow/c/eager/unified_api_testutil.h
absl::Span<AbstractTensorHandle* const>, absl::Span<AbstractTensorHandle*>)>; // Runs `model` maybe wrapped in a function call op. This can be thought as // being equivalent to the following python code. // // if use_function: // outputs = tf.function(model)(inputs) // else: // outputs = model(inputs)
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tensorflow/c/eager/immediate_execution_distributed_manager.h
int keep_alive_secs) = 0; // Set up a multi-client distributed execution environment. Must be called // on all tasks in the cluster. This call internally coordinates with other // tasks to initialize the eager context and TF server for multi-client // execution. virtual Status EnableCollectiveOps(const ServerDef& server_def) = 0;
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tensorflow/c/c_api.cc
const void** buf, size_t* len); // TODO(josh11b,mrry): Change Session to be able to use a Graph* // directly, instead of requiring us to serialize to a GraphDef and // call Session::Extend(). bool ExtendSessionGraphHelper(TF_Session* session, TF_Status* status) { if (session->graph != nullptr) { // Take the graph lock before the session lock to avoid deadlock. This is
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 102.3K bytes - Viewed (0) -
tensorflow/c/eager/c_api_experimental.h
// // This call may not block for execution of ops enqueued concurrently with this // call. TF_CAPI_EXPORT extern void TFE_ExecutorWaitForAllPendingNodes( TFE_Executor*, TF_Status* status); // When an error happens, any pending operations are discarded, and newly issued // ops return an error. This call clears the error state and re-enables // execution of newly issued ops. //
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ci/official/README.md
all scripts can use them. - `utilities/setup_docker.sh` creates a container called `tf` with all `TFCI_` variables shared to it. 3. Top-level scripts (`wheel.sh`, etc.) reference `env` variables and call `utilities/` scripts. - The `tfrun` function makes a command run correctly in Docker if Docker
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tensorflow/c/eager/dlpack.cc
auto tf_dlm_type = GetDlDataType(data_type, status); if (!status->status.ok()) { return nullptr; } TensorReference tensor_ref(*tensor); // This will call buf_->Ref() auto* tf_dlm_tensor_ctx = new TfDlManagedTensorCtx(tensor_ref); tf_dlm_tensor_ctx->reference = tensor_ref; DLManagedTensor* dlm_tensor = &tf_dlm_tensor_ctx->tensor;
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tensorflow/c/eager/gradients.cc
output_gradients, result, /*build_default_zeros_grads*/ false)); return absl::OkStatus(); } // Helper functions which delegate to `AbstractOperation`, update // the state of the ForwardOperation and call the tape as appropriate. // These APIs are mainly to facilitate testing and are subject to change. namespace internal { Status Reset(AbstractOperation* op_, const char* op,
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RELEASE.md
* Add `tf.contrib.distributions.bijectors.Reshape`. * Support shape inference (i.e., shapes containing -1) in the Reshape bijector. * Add `streaming_precision_recall_at_equal_thresholds,` a method for computing streaming precision and recall with `O(num_thresholds + size of predictions)` time and space complexity. * Change `RunConfig` default behavior to not set a random seed, making random
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tensorflow/c/eager/c_api.cc
} // Note: this function looks up a thread local policy. So it should be called in // the appropriate client thread. In particular, in async mode, it may not be // safe to call this function from the async EagerExecutor threads. extern TFE_ContextDevicePlacementPolicy TFE_ContextGetDevicePlacementPolicy( TFE_Context* ctx) { return static_cast<TFE_ContextDevicePlacementPolicy>(
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tensorflow/c/eager/tape.h
new GradientTape<Gradient, BackwardFunction, TapeTensor>(false)); AccumulatorCallState& call_state = call_state_.top(); call_state.backward_tape = tape.get(); auto pop_backward_tape = gtl::MakeCleanup([&call_state] { call_state.backward_tape = nullptr; }); std::vector<Gradient*> forwardprop_aids; std::vector<int64_t> sources; std::unordered_set<int64_t> sources_set;
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