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ci/official/envs/rbe
# inside the container can authenticate. Note: TF's CI does not have any credential # stored here.
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. # # Pass --config=pip to run the same suite of tests. If you want to run just one
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ci/official/README.md
# # Note: if you interrupt a bazel command on docker (ctrl-c), you # will need to run `docker exec tf pkill bazel` to quit bazel. # # Note: new files created from the container are owned by "root". # You can run e.g. `docker exec tf chown -R $(id -u):$(id -g) build_output` # to transfer ownership to your user. # # Docker is enabled by default on Linux. You may disable it if you prefer:
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WORKSPACE
"python_repository", ) python_repository(name = "python_version_repo") load("@python_version_repo//:py_version.bzl", "TF_PYTHON_VERSION") python_register_toolchains( name = "python", ignore_root_user_error = True, python_version = TF_PYTHON_VERSION, ) load("@python//:defs.bzl", "interpreter") load("@rules_python//python:pip.bzl", "package_annotation", "pip_parse") NUMPY_ANNOTATIONS = {
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ci/official/requirements_updater/README.md
dependencies, it will need to be updated now, for the script to run successfully. 6) A new `requirements_lock_3_11.txt` file should appear under the root of the
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CONTRIBUTING.md
longer supported for) development. Use TF SIG Build Dockerfiles in development to avoid installing the packages directly on your system (in which case remember to change the directory from `/root` to `/tensorflow` once you get into the running container so `bazel` can find the `tensorflow` workspace). you can do this by using the following command. As an example- ```bash
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tensorflow/BUILD
# copybara:comment_begin(oss-only) copy_file( name = "root_init_gen", src = select({ "api_version_2": "_api/v2/v2.py", "//conditions:default": "_api/v1/v1.py", }), out = "__init__.py", ) genrule( name = "virtual_root_init_gen", srcs = select({ "api_version_2": [":virtual_root_template_v2.__init__.py"],
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ci/official/requirements_updater/WORKSPACE
) updater_config_repository(name = "updater_config_repository") python_register_multi_toolchains( name = "python", default_version = default_python_version, ignore_root_user_error = True, python_versions = [ "3.9", "3.10", "3.11", "3.12", ], register_coverage_tool = True, )
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ci/official/containers/linux_arm64/devel.usertools/aarch64_clang.bazelrc
# use the wheel's TensorFlow installation instead of the one made available # through bazel. This must be done in a different root directory, //bazel_pip/..., # because "import tensorflow" run from the root directory would instead import # the folder instead of the venv package. # # Pass --config=pip to run the same suite of tests. If you want to run just one
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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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