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RELEASE.md
optimizations. Setting the variable to 0 or unsetting it will disable the optimizations. * These optimizations can yield slightly different numerical results from when they are off due to floating-point round-off errors from different computation approaches and orders. * To verify that the optimizations are on, look for a message with
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CONTRIBUTING.md
#### Debug builds When [building Tensorflow](https://www.tensorflow.org/install/source), passing `--config=dbg` to Bazel will build with debugging information and without optimizations, allowing you to use GDB or other debuggers to debug C++ code. For example, you can build the pip package with debugging information by running: ```bash
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tensorflow/c/c_api_experimental.h
// as if passed in XLA_FLAGS. This has global effect. TF_CAPI_EXPORT void TF_SetXlaAutoJitMode(const char* mode); // Returns whether the single GPU or general XLA auto jit optimizations are // enabled through MarkForCompilationPassFlags. TF_CAPI_EXPORT unsigned char TF_GetXlaAutoJitEnabled(); // Sets XLA's minimum cluster size. This has global effect.
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tensorflow/c/c_api.cc
// -------------------------------------------------------------------------- TF_SessionOptions* TF_NewSessionOptions() { TF_SessionOptions* out = new TF_SessionOptions; // Disable optimizations for static graph to allow calls to Session::Extend. out->options.config.mutable_experimental() ->set_disable_optimize_for_static_graph(true); return out; }
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tensorflow/c/eager/c_api_test.cc
// Add a config_proto attr, to trigger grappler graph rewrites in the current // eager runtime. if (enable_grappler) { tensorflow::ConfigProto config; // Do not skip grappler optimization even for small graphs. config.mutable_graph_options() ->mutable_rewrite_options() ->set_min_graph_nodes(-1); string serialized_config;
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.bazelrc
build:cuda --@local_config_cuda//:enable_cuda # CUDA: This config refers to building CUDA op kernels with clang. build:cuda_clang --config=cuda # Enable TensorRT optimizations https://developer.nvidia.com/tensorrt build:cuda_clang --config=tensorrt build:cuda_clang --action_env=TF_CUDA_CLANG="1" build:cuda_clang --@local_config_cuda//:cuda_compiler=clang
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ci/official/containers/linux_arm64/builder.devtoolset/stringop_trunc.patch
- | ((const unsigned char *) (const char *) (src))[idx + 2]) << 8 \ - | ((const unsigned char *) (const char *) (src))[idx + 3]) -# endif #else /* These are a few types we need for the optimizations if we cannot use unaligned memory accesses. */ @@ -94,148 +72,11 @@ __STRING2_COPY_TYPE (8); /* Set N bytes of S to C. */ #if !defined _HAVE_STRING_ARCH_memset -# if !__GNUC_PREREQ (3, 0)
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.github/bot_config.yml
* All you need is a good internet connection and you are all set. * Try to build TF from sources by changing CPU optimization flags. *Please let us know if this helps.* windows_comment: > From the stack trace it looks like you are hitting windows path length limit. * Try to disable path length limit on Windows 10.
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tensorflow/c/eager/gradients.cc
}; // Returns the number of elements in the gradient tensor. int64_t TapeVSpace::NumElements(AbstractTensorHandle* tensor) const { // TODO(srbs): It seems like this is used only for performance optimization // and not for correctness. The only downside of keeping this 1 seems to be // that the gradient accumulation is unbounded and we will never // aggressively aggregate accumulated gradients to recover memory.
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tensorflow/c/eager/c_api_experimental.h
#include "tensorflow/c/c_api_macros.h" #include "tensorflow/c/eager/c_api.h" #ifdef __cplusplus extern "C" { #endif // Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This // is for performance optimization by reusing an exiting unused op rather than // creating a new op every time. If `raw_device_name` is `NULL` or empty, it // does not set the device name. If it's not `NULL`, then it attempts to parse
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