# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

# ==============================================================================
# Sourcing this enables Bazel remote cache (read and write)
# Note that "_push" cache configs write to GCS buckets and require
# authentication. If you are not a Googler, source "public_cache" to enable the
# public read-only cache.
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then

RUN wget --retry-connrefused --waitretry=1 --read-timeout=20 --timeout=15 --tries=5 https://github.com/bazelbuild/bazelisk/releases/download/v1.12.0/bazelisk-linux-arm64 -O /usr/local/bin/bazel && chmod +x /usr/local/bin/bazel

# 3. Add modifiers. Some modifiers for local execution are:
#      Ex. disk_cache -- Use a local cache
#      Ex. public_cache -- Use TF's public cache (read-only)
#      Ex. public_cache_push -- Use TF's public cache (read and write, Googlers only)
#      Ex. rbe        -- Use RBE for faster builds (Googlers only; see below)
#      Ex. no_docker  -- Disable docker on enabled platforms

# Contributing guidelines

## Pull Request Checklist

Before sending your pull requests, make sure you do the following:

-   Read the [contributing guidelines](CONTRIBUTING.md).
-   Read the [Code of Conduct](CODE_OF_CONDUCT.md).
-   Ensure you have signed the
    [Contributor License Agreement (CLA)](https://cla.developers.google.com/).
-   Check if your changes are consistent with the

        `"LOCAL"` (case insensitive). If `"AUTO"`, tf.data service runtime
        decides which workers to read from. If `"ANY"`, TF workers read from any
        tf.data service workers. If `"LOCAL"`, TF workers will only read from
        local in-processs tf.data service workers. `"AUTO"` works well for most
        cases, while users can specify other targets. For example, `"LOCAL"`

variables as part of those interactions (ex: using a string variable to build a
filesystem path), a maliciously created checkpoint might be able to change the
targets of those operations, which could result in arbitrary
read/write/executions.

### Running a TensorFlow server

TensorFlow is a platform for distributed computing, and as such there is a
TensorFlow server (`tf.train.Server`). The TensorFlow server is intended for

# Convenient cache configurations
# Use a cache directory mounted to /tf/cache. Very useful!
build:sigbuild_local_cache --disk_cache=/tf/cache
# Use the public-access TF DevInfra cache (read only)
build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false

    # bazel query $(paste -sd "+" $BATS_FILE_TMPDIR/changed_files) --keep_going
    # Only shows Added, Changed, Modified, Renamed, and Type-changed files
    if [[ "$(git rev-parse --abbrev-ref HEAD)" == "pull_branch" ]]; then
        # TF's CI runs 'git fetch origin "pull/PR#/merge:pull_branch"'
        # To get the as-merged branch during the CI tests

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
# Sourcing this enables Bazel remote cache (public, read-only)
# The cache configs are different for MacOS and Linux
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache"
else