jf pip

Run pip install with Artifactory integration and optional build-info collection.

This topic covers the following tasks:

• Configure pip for Artifactory (jf pip-config)
• Run pip commands (jf pip)

When to Use

Use jf pip if your Python project uses pip with requirements.txt for dependency management. For Pipenv-based projects (with Pipfile), use jf pipenv. For Poetry-based projects (with pyproject.toml), use jf poetry.

Prerequisites

• pip must be installed (Python 3.6+).
• Run jf pip-config in the project directory before the first install.
• Configure a server with jf config add or jf c add.
• Authentication to Artifactory is required.
• Using a Python virtual environment is recommended.

Configuration: jf pip-config

Generate pip build configuration for dependency resolution and deployment. Run this once per project before your first install.

To configure pip for Artifactory:

Synopsis

jf pip-config [options]

Aliases: pipc

Configuration Options

Configuration Examples

View Help

jf pip-config --help

Non-Interactive Configuration with Flags

jf pip-config --server-id-resolve=<server-id> --repo-resolve=<repo-name> --server-id-deploy=<server-id> --repo-deploy=<repo-name>

Where:

• <server-id>: The server ID configured using jf config add
• <repo-name>: The name of the repository in Artifactory

For example:

jf pip-config --server-id-resolve=my-server --repo-resolve=pypi-virtual --server-id-deploy=my-server --repo-deploy=pypi-local

Why Run Config First?

You must run jf pip-config before jf pip install. The config command creates .jfrog/projects/pip.yaml which directs pip to resolve from and deploy to Artifactory repositories. Without it, jf pip does not know where to fetch packages.

Shortcut: In CI/CD, pass all flags non-interactively so the config step is fully automated and reproducible.

Configuration Notes

• Covers twine too: This config also applies to jf twine upload for publishing Python packages.
• Run once per project: Re-run when changing repository assignments.
• Resolution and deployment: Use --repo-resolve for installing packages and --repo-deploy for publishing.

Expected Output

$ jf pip-config --server-id-resolve=my-server --repo-resolve=pypi-virtual --server-id-deploy=my-server --repo-deploy=pypi-local
pip build configuration saved successfully.

How to Verify

After running, confirm the configuration exists:

cat .jfrog/projects/pip.yaml

Build: jf pip

Run pip install with Artifactory integration and optional build-info collection.

To run pip with Artifactory integration:

Synopsis

jf pip <pip-arguments> [options]

Aliases: none

Arguments

Build Options

Build Examples

View Help

jf pip --help

Install Dependencies with Build-Info

jf pip install -r requirements.txt --build-name=<build-name> --build-number=<build-number>

Where:

• <build-name> is a name for the build (e.g., my-python-app)
• <build-number> is a number or identifier for the build run (e.g., 1)

For example:

jf pip install -r requirements.txt --build-name=my-python-app --build-number=1

Install a Single Package

jf pip install <package-name>

For example:

jf pip install requests

Recording All Dependencies

JFrog CLI records installed packages as build-info dependencies. The recorded dependencies are packages installed during the jf pip install command execution. When running inside a Python environment that already has some packages installed, those packages will not be included in the build-info because they were not originally installed by JFrog CLI. A warning message will appear in the log.

How to include all packages in the build-info:

The details of all installed packages are cached by jf pip install in .jfrog/projects/deps.cache.json, located under the root of the project. JFrog CLI uses this cache for including previously installed packages in the build-info.

If the Python environment had packages installed before the first execution of the install command, those packages will be missing from the cache. Running the install command with both the --no-cache-dir and --force-reinstall pip options will re-download and install these packages, adding them to the build-info and cache. It is also recommended to run the command from inside a virtual environment.

Important Notes

• pip commands: All standard pip arguments work (install, install -r requirements.txt, freeze, and others).
• Build-info: Use --build-name and --build-number to collect installed package info, then publish with jf rt build-publish.
• Publishing: jf pip is for installing packages. To publish Python packages to Artifactory, use jf twine.

Platform Notes

• Windows: The .pypirc configuration file is located at %USERPROFILE%\.pypirc. On macOS/Linux it is at ~/.pypirc.
• Virtual environments: On Windows, activate with .\venv\Scripts\activate. On macOS/Linux, use source venv/bin/activate.
• pip vs pip3: On systems where Python 2 and 3 coexist, use pip3 to ensure the correct version is used.

CI/CD Example (GitHub Actions)

# .github/workflows/build.yml
steps:
  - uses: actions/checkout@v4
  - name: Setup JFrog CLI
    uses: jfrog/setup-jfrog-cli@v4
    env:
      JF_URL: ${{ vars.JF_URL }}
      JF_ACCESS_TOKEN: ${{ secrets.JF_ACCESS_TOKEN }}
  - name: Setup Python
    uses: actions/setup-python@v5
    with:
      python-version: '3.12'
  - name: Configure pip
    run: jf pip-config --server-id-resolve=setup-jfrog-cli-server --repo-resolve=pypi-virtual
  - name: Install dependencies
    run: jf pip install -r requirements.txt --build-name=my-python-app --build-number=${{ github.run_number }}
  - name: Publish build info
    run: jf rt build-publish my-python-app ${{ github.run_number }}

Troubleshooting

Enable debug logging: export JFROG_CLI_LOG_LEVEL=DEBUG

Related Topics

• Build Tools Overview — Capabilities matrix and tool reference
• Native Mode — Supported packages with Native Mode