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tensorflow/c/eager/c_api_experimental.h
// Causes the calling thread to block till all ops dispatched in this executor // have been executed. Note that "execution" here refers to kernel execution / // scheduling of copies, etc. Similar to sync execution, it doesn't guarantee // that lower level device queues (like GPU streams) have been flushed. // // This call may not block for execution of ops enqueued concurrently with this // call.
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requirements_lock_3_10.txt
dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> 1712896733 +0000
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requirements_lock_3_11.txt
dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> 1712896733 +0000
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tensorflow/c/eager/c_api_experimental.cc
"Coordination service is not enabled."); return; } absl::Duration timeout; if (timeout_in_ms > 0) { timeout = absl::Milliseconds(timeout_in_ms); } else { // Block until the key-value is set or the worker shuts down. timeout = absl::InfiniteDuration(); } auto status_or_value = coord_agent->GetKeyValue(key, timeout); status->status = status_or_value.status();
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tensorflow/c/eager/c_api.cc
buf->Unref(); return tensorflow::wrap(tensorflow::TensorHandle::CreateLocalHandle( std::move(t), device, device, context)); } // This function will block till the operation that produces `h` has // completed. This is only valid on local TFE_TensorHandles. Returns the size in // bytes of the memory pointed to by the device pointer returned above.
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tensorflow/c/c_api_experimental.cc
static std::unordered_map<std::string, void*>* loaded_libs = new std::unordered_map<std::string, void*>(); tensorflow::Env* env = tensorflow::Env::Default(); { tensorflow::mutex_lock lock(mu); auto it = loaded_libs->find(library_filename); if (it != loaded_libs->end()) { lib_handle->lib_handle = it->second; } else { status->status =
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tensorflow/c/eager/tape.h
// (also known as adjoint function) to compute, given downstream gradients, // upstream gradients. // // TODO(apassos) provide concrete template instantiations for TFE_TensorHandle // specialization, which is blocked by quite a few things needing to loop back // into python now. template <typename Gradient, typename BackwardFunction, typename TapeTensor> class VSpace { public: virtual ~VSpace() {}
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requirements_lock_3_12.txt
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requirements_lock_3_9.txt
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