shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
      - name: Clean repository
        shell: bash

cat <<EOF
IMPORTANT: These tests ran under docker. This script does not clean up the
container for you! You can delete the container with:

$ docker rm -f tf

You can also execute more commands within the container with e.g.:

$ docker exec tf bazel clean
$ docker exec -it tf bash
EOF

    execution time and the sharding
    [could create an overhead on the test execution](https://github.com/bazelbuild/bazel/issues/2113#issuecomment-264054799).

2.  Using [Docker](https://www.docker.com) and TensorFlow's CI scripts.

    ```bash
    # Install Docker first, then this will build and run cpu tests
    tensorflow/tools/ci_build/ci_build.sh CPU bazel test //tensorflow/...
    ```

    See

	// io.CopyN done shortly afterwards to trigger any IO errors.
	var seekSkipped int64 // Number of bytes skipped via Seek
	if sr, ok := r.(io.Seeker); ok && n > 1 {
		// Not all io.Seeker can actually Seek. For example, os.Stdin implements
		// io.Seeker, but calling Seek always returns an error and performs
		// no action. Thus, we try an innocent seek to the current position
		// to see if Seek is really supported.

        shell: bash
        run: |
          running_containers=$(docker ps -q) && \
          if [[ $running_containers == "" ]]; then
            echo "No running containers";
          else
            echo "Running container(s) found" && \
            docker stop $running_containers;
          fi
          docker container prune -f
          docker image prune -af
      - name: Clean repository
        shell: bash

# $TFCI_OUTPUT_DIR/script.log
exec > >(tee "$TFCI_OUTPUT_DIR/script.log") 2>&1

# Setup tfrun, a helper function for executing steps that can either be run
# locally or run under Docker. setup_docker.sh, below, redefines it as "docker
# exec".
# Important: "tfrun foo | bar" is "( tfrun foo ) | bar", not "tfrun (foo | bar)".
# Therefore, "tfrun" commands cannot include pipes -- which is

# Packages to be installed for the new Docker image.

# Packages needed to build devtoolset
file
flex
g++
make
patch
rpm2cpio
unar
wget
xz-utils
cpio

# Other build-related tools
apt-transport-https
autoconf
automake
build-essential
ca-certificates
llvm-18
clang-18
clang-tidy-18
lld-18
clang-format-12
curl
git
parallel
sudo
swig
unzip
zip
openjdk-21-jdk
vim

dist: xenial

sudo: required
services:
  - docker

language: java

jdk:
  - openjdk11

cache:
  directories:
    - $HOME/.m2/repository

before_script:
  - java -version

script:

Files in this directory are data for Go's API checker ("go tool api", in src/cmd/api).

Each file is a list of API features, one per line.

go1.txt (and similarly named files) are frozen once a version has been
shipped. Each file adds new lines but does not remove any.

except.txt lists features that may disappear without breaking true
compatibility.

Starting with go1.19.txt, each API feature line must end in "#nnnnn"

WIP ML Build Docker container for ML repositories (Tensorflow, JAX and XLA).

This container branches off from
/tensorflow/tools/tf_sig_build_dockerfiles/. However, since
hermetic CUDA and hermetic Python is now available for Tensorflow, a lot of the
requirements installed on the original container can be removed to reduce the
footprint of the container and make it more reusable across different ML