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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.
C++ - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 03:35:10 GMT 2024 - 96.9K bytes - Viewed (3) -
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
Plain Text - Registered: Tue May 07 12:40:20 GMT 2024 - Last Modified: Mon Apr 29 19:17:57 GMT 2024 - 727.7K bytes - Viewed (8) -
.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 30 12:39:09 GMT 2024 - Last Modified: Mon Apr 15 18:25:36 GMT 2024 - 53.8K bytes - Viewed (1)