TensorFlower Gardener <******@****.***> 1695063637 -0700

      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(

package(default_visibility = ["//visibility:public"])

filegroup(
    name = "gcc",
    srcs = glob(["bin/*-gcc"]),
)

filegroup(
    name = "ar",
    srcs = glob(["bin/*-ar"]),
)

filegroup(
    name = "ld",
    srcs = glob(["bin/*-ld"]),
)

filegroup(
    name = "nm",
    srcs = glob(["bin/*-nm"]),
)

filegroup(
    name = "objcopy",
    srcs = glob(["bin/*-objcopy"]),
)

filegroup(
    name = "objdump",

    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;

    ```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"
    ```

# 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

     * 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.

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

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