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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image
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tensorflow/c/eager/parallel_device/parallel_device_test.cc
TFE_NewContextOptions(), TFE_DeleteContextOptions); std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config( TF_CreateConfig( /*enable_xla_compilation=*/false, /*gpu_memory_allow_growth=*/true, /*num_cpu_devices=*/2), TF_DeleteBuffer); TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length, status.get());
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false # Change the value of CACHEBUSTER when upgrading the toolchain, or when testing # different compilation methods. E.g. for a PR to test a new CUDA version, set # the CACHEBUSTER to the PR number. build --action_env=CACHEBUSTER=20220325 # Use Python 3.X as installed in container image
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tensorflow/c/c_api_experimental.h
// // a) ConfigProto.optimizer_options.global_jit_level is set to ON_1 if // `enable_xla_compilation` is non-zero, and OFF otherwise. // b) ConfigProto.gpu_options.allow_growth is set to `gpu_memory_allow_growth`. // c) ConfigProto.device_count is set to `num_cpu_devices`. TF_CAPI_EXPORT extern TF_Buffer* TF_CreateConfig( unsigned char enable_xla_compilation, unsigned char gpu_memory_allow_growth, unsigned int num_cpu_devices);
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tensorflow/c/eager/parallel_device/parallel_device_lib.h
// A non-blocking version of `Execute`. After each call, `Join` must be called // before `StartExecute` is called again. Using `StartExecute` with `Join` // allows the caller to schedule computation on multiple ParallelDevices // without sequencing those operations (first call `StartExecute` on each // parallel device, then call `Join` on each; even if some of the `Join`s
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tensorflow/c/c_api_experimental.cc
unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) { tensorflow::BuildXlaOpsPassFlags* flags = tensorflow::GetBuildXlaOpsPassFlags(); bool original = flags->tf_xla_enable_lazy_compilation; flags->tf_xla_enable_lazy_compilation = enable; return original; } unsigned char TF_SetTfXlaCpuGlobalJit(unsigned char enable) { tensorflow::MarkForCompilationPassFlags* flags = tensorflow::GetMarkForCompilationPassFlags();
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tensorflow/c/c_api.h
// - export functions to set Config fields // -------------------------------------------------------------------------- // The new graph construction API, still under development. // Represents a computation graph. Graphs may be shared between sessions. // Graphs are thread-safe when used as directed below. typedef struct TF_Graph TF_Graph; // Return a new graph object. TF_CAPI_EXPORT extern TF_Graph* TF_NewGraph(void);
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tensorflow/c/eager/parallel_device/parallel_device_lib_test.cc
TFE_NewContextOptions(), TFE_DeleteContextOptions); std::unique_ptr<TF_Buffer, decltype(&TF_DeleteBuffer)> config( TF_CreateConfig( /*enable_xla_compilation=*/false, /*gpu_memory_allow_growth=*/true, /*num_cpu_devices=*/2), TF_DeleteBuffer); TFE_ContextOptionsSetConfig(opts.get(), config->data, config->length, status.get());
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configure.py
True, ('Would you like to override eigen strong inline for some C++ ' 'compilation to reduce the compilation time?'), 'Eigen strong inline overridden.', 'Not overriding eigen strong inline, ' 'some compilations could take more than 20 mins.'): # Due to a known MSVC compiler issue # https://github.com/tensorflow/tensorflow/issues/10521
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SECURITY.md
should be used with caution when working with untrusted models. ### Saved graphs and checkpoints When loading untrusted serialized computation graphs (in form of a `GraphDef`, `SavedModel`, or equivalent on-disk format), the set of computation primitives available to TensorFlow is powerful enough that you should assume that the TensorFlow process effectively executes arbitrary code.
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