name: Create Release

on:
  push:
    tags:
      - 'v*.*.*'

permissions:
  contents: write
  pull-requests: read

jobs:
  create_release:
    name: Create Release
    runs-on: ubuntu-latest

    steps:
      - name: Checkout code
        uses: actions/checkout@v4

      - name: Generate Release Notes and Publish
        id: generate_release_notes
        uses: release-drafter/release-drafter@v6

        required: false
        default: "false"

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  job:
    if: github.repository_owner == 'fastapi'
    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"

        required: false
        default: "false"

env:
  UV_SYSTEM_PYTHON: 1

jobs:
  job:
    if: github.repository_owner == 'fastapi'
    runs-on: ubuntu-latest
    permissions:
      contents: write
    steps:
      - name: Dump GitHub context
        env:
          GITHUB_CONTEXT: ${{ toJson(github) }}
        run: echo "$GITHUB_CONTEXT"

    paths:
      - CITATION.cff

permissions:
  contents: read

jobs:
  validate:
    if: github.repository == 'tensorflow/tensorflow' # Don't do this in forks
    name: "validate"
    runs-on: ubuntu-latest
    steps:
      - name: Check out a copy of the repository
        uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0

      - name: Check whether the citation metadata from CITATION.cff is valid

It was found that the directory staging committer was the fastest among the three, S3A connector should be configured with the following parameters for optimal results:

```
cat ${HADOOP_CONF_DIR}/core-site.xml | kv-pairify | grep "s3a"

fs.s3a.access.key=minio

# despite lacking Nvidia CUDA support.
TFCI_BUILD_PIP_PACKAGE_WHEEL_NAME_ARG="--repo_env=WHEEL_NAME=tensorflow"
TFCI_DOCKER_ENABLE=1
TFCI_DOCKER_IMAGE=us-docker.pkg.dev/ml-oss-artifacts-published/ml-public-container/ml-build-arm64:latest
TFCI_DOCKER_PULL_ENABLE=1
TFCI_DOCKER_REBUILD_ARGS="--target=tf ci/official/containers/ml_build_arm64"
TFCI_INDEX_HTML_ENABLE=1
TFCI_LIB_SUFFIX="-cpu-linux-arm64"
TFCI_OUTPUT_DIR=build_output
TFCI_WHL_AUDIT_ENABLE=1

typing_extensions ~= 4.14.1
gast == 0.4.0
termcolor == 2.3.0
wrapt == 1.16.0
tblib == 2.0.0
ml_dtypes >= 0.5.1, < 0.6.0
auditwheel >= 6.1.0
# Install tensorboard, and keras
# Note that here we want the latest version that matches TF major.minor version
# Note that we must use nightly here as these are used in nightly jobs
# For release jobs, we will pin these on the release branch
keras-nightly ~= 3.10.0.dev
tb-nightly ~= 2.20.0.a

      contents: read  # for actions/checkout to fetch code
      security-events: write  # for github/codeql-action/analyze to upload SARIF results
    runs-on: ubuntu-latest

    strategy:
      fail-fast: false
      matrix:
        # Override automatic language detection by changing the below list
        # Supported options are ['csharp', 'cpp', 'go', 'java', 'javascript', 'python']

## Pydantic `Settings`

Glücklicherweise bietet Pydantic ein großartiges Werkzeug zur Verarbeitung dieser Einstellungen, die von Umgebungsvariablen stammen, mit <a href="https://docs.pydantic.dev/latest/concepts/pydantic_settings/" class="external-link" target="_blank">Pydantic: Settings Management</a>.

### `pydantic-settings` installieren

Installieren Sie zunächst das Package `pydantic-settings`:

### Installing xl-meta

To install, [Go](https://golang.org/dl/) must be installed. Once installed, execute this to install the binary:

```bash
go install github.com/minio/minio/docs/debugging/xl-meta@latest
```

### Using xl-meta