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tensorflow/c/c_api_internal.h
// ExtendSessionGraphHelper manually. std::atomic<bool> extend_before_run; }; struct TF_ImportGraphDefOptions { tensorflow::ImportGraphDefOptions opts; // Backing memory for TensorId fields in opts. // TODO(skyewm): it'd be better if ImportGraphDefOptions owned this. std::vector<tensorflow::string> tensor_id_data; }; struct TF_ImportGraphDefResults {
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Sat May 13 00:49:12 GMT 2023 - 7.6K bytes - Viewed (0) -
tensorflow/c/eager/c_api_test.cc
// .backing_device of shape is CPU since the tensor is backed by CPU backing_device_name = TFE_TensorHandleBackingDeviceName(retvals[0], status.get()); ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); ASSERT_TRUE(absl::StrContains(backing_device_name, "CPU:0")) << backing_device_name; TFE_DeleteOp(shape_op);
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Aug 03 20:50:20 GMT 2023 - 94.6K bytes - Viewed (1) -
tensorflow/c/experimental/filesystem/plugins/gcs/ram_file_block_cache_test.cc
// The cache should now be empty. EXPECT_EQ(cache.CacheSize(), 0); } TEST(RamFileBlockCacheTest, ParallelReads) { // This fetcher won't respond until either `callers` threads are calling it // concurrently (at which point it will respond with success to all callers), // or 10 seconds have elapsed (at which point it will respond with an error). const int callers = 4; BlockingCounter counter(callers);
C++ - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Fri Oct 15 03:16:57 GMT 2021 - 23.2K bytes - Viewed (0) -
ci/official/envs/linux_arm64
TFCI_BAZEL_TARGET_SELECTING_CONFIG_PREFIX=linux_arm64 # Note: this is not set to "--cpu", because that changes the package name # to tensorflow_cpu. These ARM builds are supposed to have the name "tensorflow" # despite lacking Nvidia CUDA support. TFCI_BUILD_PIP_PACKAGE_ARGS="--repo_env=WHEEL_NAME=tensorflow" TFCI_DOCKER_ENABLE=1 TFCI_DOCKER_IMAGE=gcr.io/tensorflow-sigs/build-arm64:tf-2-16-multi-python TFCI_DOCKER_PULL_ENABLE=1
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tensorflow/c/eager/c_api.h
// Some TF ops need a step container to be set to limit the lifetime of some // resources (mostly TensorArray and Stack, used in while loop gradients in // graph mode). Calling this on a context tells it to start a step. TF_CAPI_EXPORT extern void TFE_ContextStartStep(TFE_Context* ctx); // Ends a step. When there is no active step (that is, every started step has
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/c_api_experimental.h
// TFE_ExecutorWaitForAllPendingNodes before calling this API if you want to // make sure all nodes are finished. TF_CAPI_EXPORT extern void TFE_DeleteExecutor(TFE_Executor*); // Returns true if the executor is in async mode. TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*); // Causes the calling thread to block till all ops dispatched in this executor
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/dlpack.cc
#include "tensorflow/core/platform/logging.h" namespace tensorflow { namespace { // Managing context for the DLManagedTensor, will manage the lifetime of // DLManagedTensor. When calling DLManagedTensor::deleter, it will notify the // original framework of destruction, and this context will be deleted also. struct TfDlManagedTensorCtx { TensorReference reference; std::vector<int64_t> shape;
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Feb 15 09:49:45 GMT 2024 - 12.8K bytes - Viewed (0) -
tensorflow/c/experimental/filesystem/filesystem_interface.h
void (*sync)(const TF_WritableFile* file, TF_Status* status); /// Closes `*file`. /// /// Flushes all buffers and deallocates all resources. /// /// Calling `close` must not result in calling `cleanup`. /// /// Core TensorFlow will never call `close` twice. void (*close)(const TF_WritableFile* file, TF_Status* status); } TF_WritableFileOps;
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Fri May 27 17:36:54 GMT 2022 - 53.1K bytes - Viewed (0) -
SECURITY.md
## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate formats that can be processed by TensorFlow. These modifications and conversions are handled by a variety of libraries that
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RELEASE.md
* Improvements and fixes in Keras loss masking: * Whether you represent a ragged tensor as a `tf.RaggedTensor` or using [keras masking](https://www.tensorflow.org/guide/keras/masking_and_padding), the returned loss values should be the identical to each other. In previous versions Keras may have silently ignored the mask.
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