Manage Project - PDM - Python Development Master

PDM can act as a PEP 517 build backend, to enable that, write the following lines in your pyproject.toml. If you used pdm init to create it for you, it should be done already.

requires = ["pdm-pep517"]
build-backend = "pdm.pep517.api"

pip will read the backend settings to install or build a package.

Choose a Python interpreter#

If you have used pdm init, you must have already seen how PDM detects and selects the Python interpreter. After initialized, you can also change the settings by pdm use <python_version_or_path>. The argument can be either a version specifier of any length, or a relative or absolute path to the python interpreter, but remember the Python interpreter must conform with the python_requires constraint in the project file.

How requires-python controls the project#

PDM respects the value of requires-python in the way that it tries to pick package candidates that can work on all python versions that requires-python contains. For example, if requires-python is >=2.7, PDM will try to find the latest version of foo, whose requires-python version range is a superset of >=2.7.

So, make sure you write requires-python properly if you don't want any outdated packages to be locked.

Build distribution artifacts#

$ pdm build
- Building sdist...
- Built pdm-test-0.0.0.tar.gz
- Building wheel...
- Built pdm_test-0.0.0-py3-none-any.whl

The artifacts can then be uploaded to PyPI by twine. Available options can be found by typing pdm build --help.

Show the current Python environment#

$ pdm info
Python Interpreter: D:/Programs/Python/Python38/python.exe (3.8.0)
Project Root:       D:/Workspace/pdm
$ pdm info --env
  "implementation_name": "cpython",
  "implementation_version": "3.8.0",
  "os_name": "nt",
  "platform_machine": "AMD64",
  "platform_release": "10",
  "platform_system": "Windows",
  "platform_version": "10.0.18362",
  "python_full_version": "3.8.0",
  "platform_python_implementaiton": "CPython",
  "python_version": "3.8",
  "sys_platform": "win32"

Configure the project#

PDM's config command works just like git config, except that --list isn't needed to show configurations.

Show the current configurations:

Get one single configuration:

Change a configuration value and store in home configuration:

$ pdm config pypi.url ""

By default, the configuration are changed globally, if you want to make the config seen by this project only, add a --local flag:

$ pdm config --local pypi.url ""

Any local configurations will be stored in .pdm.toml under the project root directory.

The configuration files are searched in the following order:

  1. <PROJECT_ROOT>/.pdm.toml - The project configuration
  2. ~/.pdm/config.toml - The home configuration

If -g/--global option is used, the first item will be replaced by ~/.pdm/global-project/.pdm.toml.

You can find all available configuration items in Configuration Page.

Cache the installation of wheels#

If a package is required by many projects on the system, each project has to keep its own copy. This may become a waste of disk space especially for data science and machine learning libraries.

PDM supports caching the installations of the same wheel by installing it into a centralized package repository and linking to that installation in different projects. To enabled it, run:

$ pdm config feature.install_cache on

It can be enabled on a project basis, by adding --local option to the command.

The caches are located under $(pdm config cache_dir)/packages. One can view the cache usage by pdm cache info. But be noted the cached installations are managed automatically -- They get deleted when not linked from any projects. Manually deleting the caches from the disk may break some projects on the system.


Only the installation of named requirements resolved from PyPI can be cached.

Manage global project#

Sometimes users may want to keep track of the dependencies of global Python interpreter as well. It is easy to do so with PDM, via -g/--global option which is supported by most subcommands.

If the option is passed, ~/.pdm/global-project will be used as the project directory, which is almost the same as normal project except that pyproject.toml will be created automatically for you and it doesn't support build features. The idea is taken from Haskell's stack.

However, unlike stack, by default, PDM won't use global project automatically if a local project is not found. Users should pass -g/--global explicitly to activate it, since it is not very pleasing if packages go to a wrong place. But PDM also leave the decision to users, just set the config auto_global to true.

If you want global project to track another project file other than ~/.pdm/global-project, you can provide the project path via -p/--project <path> option.


Be careful with remove and sync --clean commands when global project is used, because it may remove packages installed in your system Python.

Working with a virtualenv#

Although PDM enforces PEP 582 by default, it also allows users to install packages into the virtualenv. It is controlled by the configuration item use_venv. When it is set to True PDM will use the virtualenv if:

Besides, when use-venv is on and the interpreter path given is a venv-like path, PDM will reuse that venv directory as well.

For enhanced virtualenv support such as virtualenv management and auto-creation, please go for pdm-venv, which can be installed as a plugin.

Import project metadata from existing project files#

If you are already other package manager tools like Pipenv or Poetry, it is easy to migrate to PDM. PDM provides import command so that you don't have to initialize the project manually, it now supports:

  1. Pipenv's Pipfile
  2. Poetry's section in pyproject.toml
  3. Flit's section in pyproject.toml
  4. requirements.txt format used by Pip

Also, when you are executing pdm init or pdm install, PDM can auto-detect possible files to import if your PDM project has not been initialized yet.

Export locked packages to alternative formats#

You can also export pdm.lock to other formats, to ease the CI flow or image building process. Currently, only requirements.txt and format is supported:

$ pdm export -o requirements.txt
$ pdm export -f setuppy -o

Hide the credentials from pyproject.toml#

There are many times when we need to use sensitive information, such as login credentials for the PyPI server and username passwords for VCS repositories. We do not want to expose this information in pyproject.toml and upload it to git.

PDM provides several methods to achieve this:

  1. User can give the auth information with environment variables which are encoded in the URL directly:
url = "http://${INDEX_USER}:${INDEX_PASSWD}"
name = "test"
verify_ssl = false

dependencies = [
  "mypackage @ git+http://${VCS_USER}:${VCS_PASSWD}"

Environment variables must be encoded in the form ${ENV_NAME}, other forms are not supported. Besides, only auth part will be expanded.

  1. If the credentials are not provided in the URL and a 401 response is received from the server, PDM will prompt for username and password when -v/--verbose is passed as command line argument, otherwise PDM will fail with an error telling users what happens. Users can then choose to store the credentials in the keyring after a confirmation question.

  2. A VCS repository applies the first method only, and an index server applies both methods.

Run Scripts in Isolated Environment#

With PDM, you can run arbitrary scripts or commands with local packages loaded:

$ pdm run flask run -p 54321

PDM also supports custom script shortcuts in the optional [tool.pdm.scripts] section of pyproject.toml.

You can then run pdm run <shortcut_name> to invoke the script in the context of your PDM project. For example:

start_server = "flask run -p 54321"

And then in your terminal:

$ pdm run start_server
Flask server started at

Any extra arguments will be appended to the command:

$ pdm run start_server -h
Flask server started at

PDM supports 3 types of scripts:

Normal command#

Plain text scripts are regarded as normal command, or you can explicitly specify it:

start_server = {cmd = "flask run -p 54321"}

In some cases, such as when wanting to add comments between parameters, it might be more convenient to specify the command as an array instead of a string:

start_server = {cmd = [
    # Important comment here about always using port 54321
    "-p", "54321"

Shell script#

Shell scripts can be used to run more shell-specific tasks, such as pipeline and output redirecting. This is basically run via subprocess.Popen() with shell=True:

filter_error = {shell = "cat error.log|grep CRITICAL > critical.log"}

Call a Python function#

The script can be also defined as calling a python function in the form <module_name>:<func_name>:

foobar = {call = "foo_package.bar_module:main"}

The function can be supplied with literal arguments:

foobar = {call = "foo_package.bar_module:main('dev')"}

Environment variables support#

All environment variables set in the current shell can be seen by pdm run and will be expanded when executed. Besides, you can also define some fixed environment variables in your pyproject.toml:

start_server.cmd = "flask run -p 54321"
start_server.env = {FOO = "bar", FLASK_ENV = "development"}

Note how we use TOML's syntax to define a compound dictionary.

A dotenv file is also supported via env_file = "<file_path>" setting.

For environment variables and/or dotenv file shared by all scripts, you can define env and env_file settings under a special key named _ of tool.pdm.scripts table:

_.env_file = ".env"
start_server = "flask run -p 54321"
migrate_db = "flask db upgrade"

Besides, PDM also injects the root path of the project via PDM_PROJECT_ROOT environment variable.

Show the list of scripts shortcuts#

Use pdm run --list/-l to show the list of available script shortcuts:

$ pdm run --list
Name        Type  Script           Description
----------- ----- ---------------- ----------------------
test_cmd    cmd   flask db upgrade
test_script call  test_script:main call a python function
test_shell  shell echo $FOO        shell command

You can add an help option with the description of the script, and it will be displayed in the Description column in the above output.

Manage caches#

PDM provides a convenient command group to manage the cache, there are four kinds of caches:

  1. wheels/ stores the built results of non-wheel distributions and files.
  2. http/ stores the HTTP response content.
  3. metadata/ stores package metadata retrieved by the resolver.
  4. hashes/ stores the file hashes fetched from the package index or calculated locally.
  5. packages/ The centrialized repository for installed wheels.

See the current cache usage by typing pdm cache info. Besides, you can use add, remove and list subcommands to manage the cache content. Find the usage by the --help option of each command.

How we make PEP 582 packages available to the Python interpreter#

Thanks to the site packages loading on Python startup. It is possible to patch the sys.path by executing the shipped with PDM. The interpreter can search the directories for the nearest __pypackage__ folder and append it to the sys.path variable.

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