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tensorflow/c/eager/c_api.h
// containing any run metadata information accumulated so far and clears this // information. // If async mode is enabled, this call blocks till all currently pending ops are // done. TF_CAPI_EXPORT extern void TFE_ContextExportRunMetadata(TFE_Context* ctx, TF_Buffer* buf,
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
~DeviceThread(); // Requests that the worker thread execute the specified operation. Blocks // until the previously pending operation (a StartExecute without a Join) has // finished, if any. // // `cancellation_manager` must live until after `Join` finishes and pending // `is_async` operations finish. In addition to allowing the caller to cancel // the operation, its `StartCancel` method will be called if op execution
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tensorflow/c/eager/parallel_device/parallel_device_lib.h
// // If `is_async=false` (constructor argument), `cancellation_manager` must // live until `Join` finishes. If `is_async=true` it must live until `Join` is // followed by `TFE_ContextAsyncWait` to clear pending operations. It will be // used to cancel all other operations if any fails. // // Set step_id to configure the step id used for rendezvous creation. step id
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ci/official/README.md
2. Running the CI scripts locally, as explained below 3. **Google employees only**: Google employees can use an internal-only tool called "MLCI" that makes testing more convenient: it can execute any full CI job against a pending change. Search for "MLCI" internally to find it. You may invoke a CI script of your choice by following these instructions: ```bash cd tensorflow-git-dir
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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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SECURITY.md
TensorFlow in a multitenant design mixes the risks described above with the inherent ones from multitenant configurations. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks. ### Tenant isolation Since any tenants or users providing models, graphs or checkpoints can execute code in context of the TensorFlow service, it is important to design isolation
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tensorflow/c/eager/immediate_execution_context.h
// will take ownership and maintain devices' lifetime. virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0; // Block until all pending nodes are finished. virtual Status AsyncWait() = 0; // Add a function (serialized FunctionDef protocol buffer) so that it can // be executed as an op. Return error if the function with the same name
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