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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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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/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/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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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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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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tensorflow/c/eager/c_api_unified_experimental_test.cc
TF_ExecutionContextGetTFEContext(graph_ctx, status.get()); ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get())); TF_DeleteExecutionContext(graph_ctx); } // The above tests are run for a combination of: // - graphdef and MLIR tracing engine INSTANTIATE_TEST_SUITE_P(Tracing, UnifiedCAPI, ::testing::Combine(::testing::Values("graphdef",
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tensorflow/c/c_api_test.cc
TF_Operation* plusB = Add(plus2, b, graph, s, "plusB"); ASSERT_EQ(TF_OK, TF_GetCode(s)) << TF_Message(s); // Setup a session and a partial run handle. The partial run will allow // computation of A + 2 + B in two phases (calls to TF_SessionPRun): // 1. Feed A and get (A+2) // 2. Feed B and get (A+2)+B TF_SessionOptions* opts = TF_NewSessionOptions(); TF_Session* sess = TF_NewSession(graph, opts, s);
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tensorflow/BUILD
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.bazelrc
# not on sm_90 GPUs. compute_80 kernels though can also run on sm_90 GPUs. build:cuda_clang --repo_env=TF_CUDA_COMPUTE_CAPABILITIES="sm_60,sm_70,sm_80,sm_89,compute_90" # Set up compilation CUDA version and paths and use the CUDA Clang toolchain. build:cuda_clang_official --config=cuda_clang build:cuda_clang_official --action_env=TF_CUDA_VERSION="12" build:cuda_clang_official --action_env=TF_CUDNN_VERSION="8"
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