#
# setup.python.sh: Install a specific Python version and packages for it.
# Usage: setup.python.sh <pyversion> <requirements.txt>
set -xe

source ~/.bashrc
VERSION=$1
REQUIREMENTS=$2

# Install Python packages for this container's version
if [[ ${VERSION} == "python3.13-nogil" || ${VERSION} == "python3.14-nogil" ]]; then
  cat >pythons.txt <<EOF
$VERSION
EOF

  cat >pythons.txt <<EOF
$VERSION
$VERSION-dev
$VERSION-venv
EOF
else
  cat >pythons.txt <<EOF
$VERSION
$VERSION-dev
$VERSION-venv
EOF
fi

if [[ ${VERSION} == "python3.14" ]]; then
  if [[ ! -d Python-3.14.0rc1 ]]; then
    apt update && apt install -y libssl-dev zlib1g-dev libbz2-dev libreadline-dev libncurses5-dev libffi-dev liblzma-dev
    wget https://www.python.org/ftp/python/3.14.0/Python-3.14.0rc1.tar.xz

# Python Veri Tiplerine Giriş

Python isteğe bağlı olarak "tip belirteçlerini" destekler.

 **"Tip belirteçleri"** bir değişkenin <abbr title="örneğin: str, int, float, bool">tipinin</abbr> belirtilmesine olanak sağlayan özel bir sözdizimidir.

Değişkenlerin tiplerini belirterek editör ve araçlardan daha fazla destek alabilirsiniz.

따라서, 다음과 같이 Pydantic 객체 `user_in`을 생성할 수 있습니다:

```Python
user_in = UserIn(username="john", password="secret", email="******@****.***")
```

그 다음, 다음과 같이 호출합니다:

```Python
user_dict = user_in.dict()
```

이제 변수 `user_dict`에 데이터가 포함된 `dict`를 가지게 됩니다(이는 Pydantic 모델 객체가 아닌 `dict`입니다).

그리고 다음과 같이 호출하면:

```Python
print(user_dict)
```

Python의 `dict`가 다음과 같이 출력됩니다:

```Python
{

# Setup Python
COPY setup.python.sh /setup.python.sh
COPY builder.requirements.txt /builder.requirements.txt
RUN /setup.python.sh python3.9 /builder.requirements.txt
RUN /setup.python.sh python3.10 /builder.requirements.txt
RUN /setup.python.sh python3.11 /builder.requirements.txt
RUN /setup.python.sh python3.13 /builder.requirements.txt
RUN /setup.python.sh python3.13-nogil /builder.requirements.txt

RUN /setup.python.sh python3.9 requirements.txt
RUN /setup.python.sh python3.10 requirements.txt
RUN /setup.python.sh python3.11 requirements.txt
RUN /setup.python.sh python3.12 requirements.txt
RUN /setup.python.sh python3.13 requirements.txt
# python3.13-nogil is a free-threaded build of python3.13.
RUN /setup.python.sh python3.13-nogil requirements.txt
RUN /setup.python.sh python3.14 requirements.txt

venv_dir=$(mktemp -d)
if [[ $(uname -s) != MSYS_NT* ]]; then
  "python${TFCI_PYTHON_VERSION}" -m venv "$venv_dir"
  python="$venv_dir/bin/python3"
else
  # When using the Linux-like path, venv creation quietly fails, which is
  # why it's converted here.
  venv_dir=$(cygpath -m $venv_dir)
  "/c/python${TFCI_PYTHON_VERSION}/python.exe" -m venv "$venv_dir"
  python="$venv_dir/Scripts/python.exe"
fi

자세한 내용은 <a href="https://docs.python.org/3/library/__main__.html" class="external-link" target="_blank">공식 Python 문서</a>를 확인하세요

///

## 디버거로 코드 실행

코드에서 직접 Uvicorn 서버를 실행하고 있기 때문에 디버거에서 직접 Python 프로그램(FastAPI 애플리케이션)을 호출할 수 있습니다.

---

예를 들어 Visual Studio Code에서 다음을 수행할 수 있습니다.

* "Debug" 패널로 이동합니다.
* "Add configuration...".
* "Python"을 선택합니다.
* "`Python: Current File (Integrated Terminal)`" 옵션으로 디버거를 실행합니다.

if [[ "$TFCI_NIGHTLY_UPDATE_VERSION_ENABLE" == 1 ]]; then
  python3 tensorflow/tools/ci_build/update_version.py --nightly
fi

# This generates a pure python wheel of the format "*-py3-none-any.whl"
bazel run --HERMETIC_PYTHON_VERSION=3.13 //tensorflow/tools/pip_package:setup_py_binary -- bdist_wheel --dist-dir "$TFCI_OUTPUT_DIR"

# Get the name of the pure python wheel that was built. This should
# resolve to either

  # Python 3.12 removed the module `imp` which is needed by gcloud CLI so we set
  # `CLOUDSDK_PYTHON` to Python 3.11 which is the system Python on TFCI Mac
  # VMs.
  pyenv install -s "3.11"
  if [[ $? -eq 0 ]]; then
    pyenv local "3.11"
    export CLOUDSDK_PYTHON=$(pyenv which python3.11)
  else
    echo "Python 3.11 not found, falling back to system python"
    export CLOUDSDK_PYTHON=$(which python3)