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tensorflow/c/eager/c_api.h
size_t proto_len, TF_Status* status); // A handle to a tensor on a device. // // Like a TF_Tensor, a TFE_TensorHandle refers to a tensor with a value, shape, // type etc. Unlike a TF_Tensor, a TFE_TensorHandle may refer to such tensors // placed in the memory of different devices or remote address spaces.
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ci/official/README.md
# # Important: because the container is persistent, you cannot change TFCI # variables in between script executions. To forcibly remove the # container and start fresh, run "docker rm -f tf". Removing the container # destroys some temporary bazel data and causes longer builds. # # You will need the NVIDIA Container Toolkit for GPU testing:
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tensorflow/c/eager/c_api_experimental.h
TF_CAPI_EXPORT extern bool TFE_ExecutorIsAsync(TFE_Executor*); // 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. //
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tensorflow/c/c_api_test.cc
// Import it in a fresh graph. TF_DeleteGraph(graph); graph = TF_NewGraph(); TF_ImportGraphDefOptions* opts = TF_NewImportGraphDefOptions(); TF_GraphImportGraphDef(graph, graph_def, opts, s); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); TF_Operation* scalar = TF_GraphOperationByName(graph, "scalar"); // Import it in a fresh graph with an unused input mapping.
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tensorflow/c/c_api.h
TF_ImportGraphDefOptions* opts, unsigned char uniquify_prefix); // Set any imported nodes with input `src_name:src_index` to have that input // replaced with `dst`. `src_name` refers to a node in the graph to be imported, // `dst` references a node already existing in the graph being imported into. // `src_name` is copied and has no lifetime requirements.
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.bazelrc
build:mkl_aarch64_threadpool --define=build_with_mkl_aarch64=true build:mkl_aarch64_threadpool -c opt # CUDA: This config refers to building CUDA op kernels with nvcc. build:cuda --repo_env TF_NEED_CUDA=1 build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain build:cuda --@local_config_cuda//:enable_cuda # CUDA: This config refers to building CUDA op kernels with clang. build:cuda_clang --config=cuda
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configure.py
no_reply=no_reply, bazel_config_name='cuda_clang', ) def set_tf_download_clang(environ_cp): """Set TF_DOWNLOAD_CLANG action_env.""" question = 'Do you wish to download a fresh release of clang? (Experimental)' yes_reply = 'Clang will be downloaded and used to compile tensorflow.' no_reply = 'Clang will not be downloaded.' set_action_env_var( environ_cp, 'TF_DOWNLOAD_CLANG',
Python - Registered: Tue Apr 23 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (0) -
RELEASE.md
prediction-flavored outputs, in addition to the classification-flavored one. Building signatures from these allows TF Serving to honor requests using the different APIs (Classify, Regress, and Predict). Furthermore, `serving_input_receiver_fn()` may now specify alternative subsets of nodes that may act as inputs. This allows, for instance, producing a prediction
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