name: OSV-Scanner Scheduled Scan

on:
  schedule:
    - cron: 0 4 * * 1

permissions:
  # Require writing security events to upload SARIF file to security tab
  security-events: write
  # Only need to read contents
  contents: read

jobs:
  scan-scheduled:
    if: github.repository == 'tensorflow/tensorflow'

      - name: Upload pip wheel to PyPI
        if: github.event_name == 'schedule' || (github.event_name == 'push' && startsWith(github.ref, 'refs/tags/v2')) # only if it is a scheduled nightly or tagged
        shell: bash

# limitations under the License.
# ==============================================================================

name: ARM CI Extended C++

on:
  push:
    tags:
      - v2.**
  schedule:
    - cron: '0 2 * * *'

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:

********************************************************************************

## TensorFlow's Official CI and Build/Test Scripts

TensorFlow's official CI jobs run the scripts in this folder. Our internal CI
system, Kokoro, schedules our CI jobs by combining a build script with a file
from the `envs` directory that is filled with configuration options:

-   Nightly jobs (Run nightly on the `nightly` branch)

# limitations under the License.
# ==============================================================================

name: ARM CI Extended

on:
  push:
    tags:
      - v2.**
  schedule:
    - cron: '0 4 * * *'

permissions:
  contents: read

jobs:
  build:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    runs-on: [self-hosted, linux, ARM64]
    strategy:

# install it manually. We use Twine in nightly builds to upload Python packages
# to PyPI.
if [[ "$TFCI_MACOS_TWINE_INSTALL_ENABLE" == 1 ]]; then
  pip install twine==3.6.0
fi

# Scheduled nightly and release builds upload build artifacts (Pip packages,
# Libtensorflow archives) to GCS buckets. TFCI Mac VMs need to authenticate as
# a service account that has the right permissions to be able to do so.
set +x

*   Added warmup capabilities to `tf.keras.optimizers.schedules.CosineDecay` learning rate scheduler. You can now specify an initial and target learning rate, and our scheduler will perform a linear interpolation between the two after which it will begin a decay phase.

  // threadpool, so that we avoid the possibility of running the runner_ in the
  // threadpool of GPU event mgr, as that can trigger more callbacks to be
  // scheduled on that same threadpool, causing a deadlock in cases where the
  // caller of event_mgr->ThenExecute() blocks on the completion of the callback
  // (as in the case of ConstOp kernel creation on GPU, which involves copying a