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ci/official/containers/linux_arm64/ld.so.conf
TensorFlower Gardener <******@****.***> 1695063637 -0700
Plain Text - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Mon Sep 18 19:00:37 GMT 2023 - 790 bytes - Viewed (0) -
configure.py
write_to_bazelrc('build --linkopt="-fuse-ld=gold"') # Get the linker version ld_version = run_shell([gcc_env, '-Wl,-version']).split() ld_version_int = convert_version_to_int(ld_version[3]) if ld_version_int is None: ld_version_int = convert_version_to_int(ld_version[4]) # Enable if 'ld' version >= 2.35 if ld_version_int >= 2035000: write_to_bazelrc(
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tensorflow/c/c_test.c
fprintf(stderr, "TF_GetLocalTempDirectories returned no results\n"); return 1; } char file_name[100]; time_t t = time(NULL); snprintf(file_name, sizeof(file_name), "test-%d-%ld.txt", getpid(), t); size_t length = 2 + strlen(path) + strlen(file_name); char* full_path = malloc(length); snprintf(full_path, length, "%s/%s", path, file_name); TF_WritableFileHandle* h;
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Wed Apr 24 20:50:35 GMT 2024 - 2.8K bytes - Viewed (0) -
CONTRIBUTING.md
```bash export flags="--config=opt -k" ``` If the tests are to be run on the GPU, add CUDA paths to LD_LIBRARY_PATH and add the `cuda` option flag ```bash export LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH" export flags="--config=opt --config=cuda -k" ```
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ci/official/containers/linux_arm64/Dockerfile
# likely to change daily. ARG CACHEBUSTER=0 RUN echo $CACHEBUSTER # Setup build and environment COPY devel.usertools /usertools COPY devel.bashrc /root/.bashrc COPY ld.so.conf /dt10/etc/ # Setup JAX Python environment. FROM devel as jax RUN /setup.packages.sh /cuda.packages.txt COPY jax.requirements.txt /devel.requirements.txt COPY setup.python.sh /setup.python.sh
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.github/bot_config.yml
* If you have above configuration and using _**Ubuntu/Linux**_ platform - * Try adding the CUDA, CUPTI, and cuDNN installation directories to the $LD_LIBRARY_PATH environment variable. * Refer [linux setup guide](https://www.tensorflow.org/install/gpu#linux_setup). * If error still persists then, apparently your CPU model does not support AVX instruction sets.
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
build --python_path="/usr/local/bin/python3" # Build TensorFlow v2 build --define=tf_api_version=2 --action_env=TF2_BEHAVIOR=1 # Use lld as the linker build --linkopt="-fuse-ld=lld" build --linkopt="-lm" build --linkopt="-Wl,--undefined-version" # Prevent double-compilation of some TF code, ref. b/183279666 (internal) # > TF's gen_api_init_files has a genrule to run the core TensorFlow code
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.bazelrc
build:cuda_clang_official --action_env=GCC_HOST_COMPILER_PATH="/dt9/usr/bin/gcc" build:cuda_clang_official --action_env=CLANG_CUDA_COMPILER_PATH="/usr/lib/llvm-17/bin/clang" build:cuda_clang_official --action_env=LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64" build:cuda_clang_official --crosstool_top="@sigbuild-r2.17-clang_config_cuda//crosstool:toolchain" # Build with nvcc for CUDA and clang for host
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