# 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 [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push"
else

# 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

    *   Support for TensorRT is otherwise unaffected as long as TensorRT is
        already installed on the system.
*   Using .post1 instead of a full minor release allowed us to push this release
    out quickly. However, please note the following caveat:
    *   For users wishing to pin their Python dependency in a requirements file
        or other situation, under Python's version specification rules,

# to build TensorFlow. Look at ci/official/envs to find which types of jobs
# push to the cache.  For macOS, use --config=tf_public_macos_cache
build:tf_public_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/january2024" --remote_upload_local_results=false
# Cache pushes are limited to TF's CI system.
build:tf_public_cache_push --config=tf_public_cache --remote_upload_local_results=true --google_default_credentials

# 2. The internal version numbers get changed
TFCI_NIGHTLY_UPDATE_VERSION_ENABLE=1

# 3. Push results to public build cache (unique cache for MacOS)
if [[ $(uname -s) == "Darwin" ]]; then
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_macos_cache_push"
else
  TFCI_BAZEL_COMMON_ARGS="$TFCI_BAZEL_COMMON_ARGS --config tf_public_cache_push"

[tools](https://www.tensorflow.org/resources/tools),
[libraries](https://www.tensorflow.org/resources/libraries-extensions), and
[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within