unidep 0.42.0

Unified Conda and Pip requirements management.

🚀 UniDep - Unified Conda and Pip Dependency Management 🚀

Managing dependencies in Python projects can be a complex task, especially when dealing with both Pip and Conda (non Python) packages. This often leads to confusion and inefficiency, as developers juggle between multiple dependency files.

• 📝 Use a Single File (requirements.yaml or pyproject.toml): Manage both Conda and Pip dependencies, reducing complexity.
• ⚙️ Integrates with pyproject.toml and setup.py: Specified dependencies are automatically included during pip install.
• 🏢 Monorepo Support: Render (multiple) requirements.yaml or pyproject.toml files into one Conda environment.yaml file and maintain fully consistent global and per sub package conda-lock files.
• 💻 One-Command Installation: unidep install handles Conda, Pip, and local dependencies effortlessly.
• 🌍 Platform-Specific Support: Specify dependencies for different operating systems or architectures.
• 🔧 pip-compile Integration: Enables generation of fully pinned requirements.txt files from requirements.yaml or pyproject.toml files using pip-compile.

unidep is designed to make dependency management in Python projects as simple and efficient as possible. Try it now and streamline your development process!

[!TIP] Check out the example requirements.yaml and pyproject.toml below.

:books: Table of Contents

• :package: Installation
• :memo: requirements.yaml and pyproject.toml structure
  • Example
    • Example requirements.yaml
    • Example pyproject.toml
  • Key Points
  • Supported Version Pinnings
  • Conflict Resolution
    • How It Works
  • Platform Selectors
    • Supported Selectors
    • Usage
    • Implementation
• :jigsaw: Build System Integration
  • Example packages
  • Setuptools Integration
  • Hatchling Integration
• :desktop_computer: As a CLI
  • unidep merge
  • unidep install
  • unidep install-all
  • unidep conda-lock
  • unidep pip-compile
  • unidep pip
  • unidep conda
• :hammer_and_wrench: Troubleshooting
  • pip install fails with FileNotFoundError
• :warning: Limitations

:package: Installation

To install unidep, run the following command:

pip install "unidep[all]"

or

conda install -c conda-forge unidep

:memo: requirements.yaml and pyproject.toml structure

unidep allows either using a

1. requirements.yaml file with a specific format (similar but not the same as a Conda environment.yaml file) or
2. pyproject.toml file with a [tool.unidep] section.

Both files contain the following keys:

• name (Optional): For documentation, not used in the output.
• channels: List of conda channels for packages, such as conda-forge.
• dependencies: Mix of Conda and Pip packages.
• local_dependencies (Optional): List of paths to other requirements.yaml or pyproject.toml files to include.
• platforms (Optional): List of platforms that are supported (used in conda-lock).

Whether you use a requirements.yaml or pyproject.toml file, the same information can be specified in either. Choose the format that works best for your project.

Example

Example requirements.yaml

Example of a requirements.yaml file:

name: example_environment
channels:
  - conda-forge
dependencies:
  - numpy  # same name on conda and pip
  - conda: python-graphviz  # When names differ between Conda and Pip
    pip: graphviz
  - pip: slurm-usage >=1.1.0,<2  # pip-only
  - conda: mumps  # conda-only
  # Use platform selectors; below only on linux64
  - conda: cuda-toolkit =11.8 # [linux64]
local_dependencies:
  - ../other-project-using-unidep  # include other projects that use unidep
  - ../common-requirements.yaml  # include other requirements.yaml files
platforms:  # (Optional) specify platforms that are supported (used in conda-lock)
  - linux-64
  - osx-arm64

[!IMPORTANT] unidep can process this during pip install and create a Conda installable environment.yaml or conda-lock.yml file, and more!

[!NOTE] For a more in-depth example containing multiple installable projects, see the example directory.

Example pyproject.toml

Alternatively, one can fully configure the dependencies in the pyproject.toml file in the [tool.unidep] section:

[tool.unidep]
channels = ["conda-forge"]
dependencies = [
    "numpy",                                         # same name on conda and pip
    { conda = "python-graphviz", pip = "graphviz" }, # When names differ between Conda and Pip
    { pip = "slurm-usage >=1.1.0,<2" },              # pip-only
    { conda = "mumps" },                             # conda-only
    { conda = "cuda-toolkit =11.8:linux64" }         # Use platform selectors by appending `:linux64`
]
local_dependencies = [
    "../other-project-using-unidep", # include other projects that use unidep
    "../common-requirements.yaml"    # include other requirements.yaml files
]
platforms = [ # (Optional) specify platforms that are supported (used in conda-lock)
    "linux-64",
    "osx-arm64"
]

This data structure is identical to the requirements.yaml format, with the exception of the name field and the platform selectors. In the requirements.yaml file, one can use e.g., # [linux64], which in the pyproject.toml file is :linux64 at the end of the package name.

See Build System Integration for more information on how to set up unidep with different build systems (Setuptools or Hatchling).

[!IMPORTANT] In these docs, we often mention the requirements.yaml format for simplicity, but the same information can be specified in pyproject.toml as well. Everything that is possible in requirements.yaml is also possible in pyproject.toml!

Key Points

• Standard names (e.g., - numpy) are assumed to be the same for Conda and Pip.
• Use a dictionary with conda: <package> and pip: <package> to specify different names across platforms.
• Use pip: to specify packages that are only available through Pip.
• Use conda: to specify packages that are only available through Conda.
• Use # [selector] (YAML only) or package:selector to specify platform-specific dependencies.
• Use platforms: to specify the platforms that are supported.
• Use local_dependencies: to include other requirements.yaml or pyproject.toml files and merge them into one.

We use the YAML notation here, but the same information can be specified in pyproject.toml as well.

Supported Version Pinnings

UniDep supports a range of version pinning operators (the same as Conda):

• Standard Version Constraints: Specify exact versions or ranges with standard operators like =, >, <, >=, <=.

  • Example: =1.0.0, >1.0.0, <2.0.0.

• Version Exclusions: Exclude specific versions using !=.

  • Example: !=1.5.0.

• Redundant Pinning Resolution: Automatically resolves redundant version specifications.

  • Example: >1.0.0, >0.5.0 simplifies to >1.0.0.

• Contradictory Version Detection: Errors are raised for contradictory pinnings to maintain dependency integrity. See the Conflict Resolution section for more information.

  • Example: Specifying >2.0.0, <1.5.0 triggers a VersionConflictError.

• Invalid Pinning Detection: Detects and raises errors for unrecognized or improperly formatted version specifications.

• Conda Build Pinning: UniDep also supports Conda's build pinning, allowing you to specify builds in your pinning patterns.

  • Example: Conda supports pinning builds like qsimcirq * cuda* or vtk * *egl*.
  • Limitation: While UniDep allows such build pinning, it requires that there be a single pin per package. UniDep cannot resolve conflicts where multiple build pinnings are specified for the same package.
    • Example: UniDep can handle qsimcirq * cuda*, but it cannot resolve a scenario with both qsimcirq * cuda* and qsimcirq * cpu*.

• Other Special Cases: In addition to Conda build pins, UniDep supports all special pinning formats, such as VCS (Version Control System) URLs or local file paths. This includes formats like package @ git+https://git/repo/here or package @ file:///path/to/package. However, UniDep has a limitation: it can handle only one special pin per package. These special pins can be combined with an unpinned version specification, but not with multiple special pin formats for the same package.

  • Example: UniDep can manage dependencies specified as package @ git+https://git/repo/here and package in the same requirements.yaml. However, it cannot resolve scenarios where both package @ git+https://git/repo/here and package @ file:///path/to/package are specified for the same package.

[!WARNING] Pinning Validation and Combination: UniDep actively validates and/or combines pinnings only when multiple different pinnings are specified for the same package. This means if your requirements.yaml files include multiple pinnings for a single package, UniDep will attempt to resolve them into a single, coherent specification. However, if the pinnings are contradictory or incompatible, UniDep will raise an error to alert you of the conflict.

Conflict Resolution

unidep features a conflict resolution mechanism to manage version conflicts and platform-specific dependencies in requirements.yaml or pyproject.toml files.

How It Works

• Version Pinning Priority: unidep gives priority to version-pinned packages when the same package is specified multiple times. For instance, if both foo and foo <1 are listed, foo <1 is selected due to its specific version pin.

• Platform-Specific Version Pinning: unidep resolves platform-specific dependency conflicts by preferring the version with the narrowest platform scope. For instance, given foo <3 # [linux64] and foo >1, it installs foo >1,<3 exclusively on Linux-64 and foo >1 on all other platforms.

• Intractable Conflicts: When conflicts are irreconcilable (e.g., foo >1 vs. foo <1), unidep raises an exception.

Platform Selectors

This tool supports a range of platform selectors that allow for specific handling of dependencies based on the user's operating system and architecture. This feature is particularly useful for managing conditional dependencies in diverse environments.

Supported Selectors

The following selectors are supported:

• linux: For all Linux-based systems.
• linux64: Specifically for 64-bit Linux systems.
• aarch64: For Linux systems on ARM64 architectures.
• ppc64le: For Linux on PowerPC 64-bit Little Endian architectures.
• osx: For all macOS systems.
• osx64: Specifically for 64-bit macOS systems.
• arm64: For macOS systems on ARM64 architectures (Apple Silicon).
• macos: An alternative to osx for macOS systems.
• unix: A general selector for all UNIX-like systems (includes Linux and macOS).
• win: For all Windows systems.
• win64: Specifically for 64-bit Windows systems.

Usage

Selectors are used in requirements.yaml files to conditionally include dependencies based on the platform:

dependencies:
  - some-package >=1  # [unix]
  - another-package  # [win]
  - special-package  # [osx64]
  - pip: cirq  # [macos win]
    conda: cirq  # [linux]

Or when using pyproject.toml instead of requirements.yaml:

[tool.unidep]
dependencies = [
    "some-package >=1:unix",
    "another-package:win",
    "special-package:osx64",
    { pip = "cirq:macos win", conda = "cirq:linux" },
]

In this example:

• some-package is included only in UNIX-like environments (Linux and macOS).
• another-package is specific to Windows.
• special-package is included only for 64-bit macOS systems.
• cirq is managed by pip on macOS and Windows, and by conda on Linux. This demonstrates how you can specify different package managers for the same package based on the platform.

Note that the package-name:unix syntax can also be used in the requirements.yaml file, but the package-name # [unix] syntax is not supported in pyproject.toml.

Implementation

unidep parses these selectors and filters dependencies according to the platform where it's being installed. It is also used for creating environment and lock files that are portable across different platforms, ensuring that each environment has the appropriate dependencies installed.

:jigsaw: Build System Integration

[!TIP] See example/ for working examples of using unidep with different build systems.

unidep seamlessly integrates with popular Python build systems to simplify dependency management in your projects.

Example packages

Explore these installable example packages to understand how unidep integrates with different build tools and configurations:

Project	Build Tool	pyproject.toml	requirements.yaml	setup.py
setup_py_project	setuptools	✅	✅	✅
setuptools_project	setuptools	✅	✅	❌
pyproject_toml_project	setuptools	✅	❌	❌
hatch_project	hatch	✅	✅	❌
hatch2_project	hatch	✅	❌	❌

Setuptools Integration

For projects using setuptools, configure unidep in pyproject.toml and either specify dependencies in a requirements.yaml file or include them in pyproject.toml too.

• Using pyproject.toml only: The [project.dependencies] field in pyproject.toml gets automatically populated from requirements.yaml or from the [tool.unidep] section in pyproject.toml.
• Using setup.py: The install_requires field in setup.py automatically reflects dependencies specified in requirements.yaml or pyproject.toml.

Example pyproject.toml Configuration:

[build-system]
build-backend = "setuptools.build_meta"
requires = ["setuptools", "unidep"]

[project]
dynamic = ["dependencies"]

Hatchling Integration

For projects managed with Hatch, unidep can be configured in pyproject.toml to automatically process the dependencies from requirements.yaml or from the [tool.unidep] section in pyproject.toml.

Example Configuration for Hatch:

[build-system]
requires = ["hatchling", "unidep"]
build-backend = "hatchling.build"

[project]
dynamic = ["dependencies"]
# Additional project configurations

[tool.hatch]
# Additional Hatch configurations

[tool.hatch.metadata.hooks.unidep]

:desktop_computer: As a CLI

See example for more information or check the output of unidep -h for the available sub commands:

usage: unidep [-h]
              {merge,install,install-all,conda-lock,pip-compile,pip,conda,version}
              ...

Unified Conda and Pip requirements management.

positional arguments:
  {merge,install,install-all,conda-lock,pip-compile,pip,conda,version}
                        Subcommands
    merge               Combine multiple (or a single) `requirements.yaml` or
                        `pyproject.toml` files into a single Conda installable
                        `environment.yaml` file.
    install             Automatically install all dependencies from one or
                        more `requirements.yaml` or `pyproject.toml` files.
                        This command first installs dependencies with Conda,
                        then with Pip. Finally, it installs local packages
                        (those containing the `requirements.yaml` or
                        `pyproject.toml` files) using `pip install [-e]
                        ./project`.
    install-all         Install dependencies from all `requirements.yaml` or
                        `pyproject.toml` files found in the current directory
                        or specified directory. This command first installs
                        dependencies using Conda, then Pip, and finally the
                        local packages.
    conda-lock          Generate a global `conda-lock.yml` file for a
                        collection of `requirements.yaml` or `pyproject.toml`
                        files. Additionally, create individual `conda-
                        lock.yml` files for each `requirements.yaml` or
                        `pyproject.toml` file consistent with the global lock
                        file.
    pip-compile         Generate a fully pinned `requirements.txt` file from
                        one or more `requirements.yaml` or `pyproject.toml`
                        files using `pip-compile` from `pip-tools`. This
                        command consolidates all pip dependencies defined in
                        the `requirements.yaml` or `pyproject.toml` files and
                        compiles them into a single `requirements.txt` file,
                        taking into account the specific versions and
                        dependencies of each package.
    pip                 Get the pip requirements for the current platform
                        only.
    conda               Get the conda requirements for the current platform
                        only.
    version             Print version information of unidep.

options:
  -h, --help            show this help message and exit

unidep merge

Use unidep merge to scan directories for requirements.yaml file(s) and combine them into an environment.yaml file. See unidep merge -h for more information:

usage: unidep merge [-h] [-o OUTPUT] [-n NAME] [--stdout]
                    [--selector {sel,comment}] [-d DIRECTORY] [-v]
                    [--platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}]
                    [--depth DEPTH] [--skip-dependency SKIP_DEPENDENCY]
                    [--ignore-pin IGNORE_PIN] [--overwrite-pin OVERWRITE_PIN]

Combine multiple (or a single) `requirements.yaml` or `pyproject.toml` files
into a single Conda installable `environment.yaml` file. Example usage:
`unidep merge --directory . --depth 1 --output environment.yaml` to search for
`requirements.yaml` or `pyproject.toml` files in the current directory and its
subdirectories and create `environment.yaml`. These are the defaults, so you
can also just run `unidep merge`.

options:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output file for the conda environment, by default
                        `environment.yaml`
  -n NAME, --name NAME  Name of the conda environment, by default `myenv`
  --stdout              Output to stdout instead of a file
  --selector {sel,comment}
                        The selector to use for the environment markers, if
                        `sel` then `- numpy # [linux]` becomes `sel(linux):
                        numpy`, if `comment` then it remains `- numpy #
                        [linux]`, by default `sel`
  -d DIRECTORY, --directory DIRECTORY
                        Base directory to scan for `requirements.yaml` or
                        `pyproject.toml` file(s), by default `.`
  -v, --verbose         Print verbose output
  --platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}, -p {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}
                        The platform(s) to get the requirements for. Multiple
                        platforms can be specified. By default, the current
                        platform (`linux-64`) is used.
  --depth DEPTH         Maximum depth to scan for `requirements.yaml` or
                        `pyproject.toml` files, by default 1
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.

unidep install

Use unidep install on one or more requirements.yaml files and install the dependencies on the current platform using conda, then install the remaining dependencies with pip, and finally install the current package with pip install [-e] .. See unidep install -h for more information:

usage: unidep install [-h] [-v] [-e] [--skip-local] [--skip-pip]
                      [--skip-conda] [--skip-dependency SKIP_DEPENDENCY]
                      [--no-dependencies]
                      [--conda-executable {conda,mamba,micromamba}]
                      [--dry-run] [--ignore-pin IGNORE_PIN]
                      [--overwrite-pin OVERWRITE_PIN]
                      files [files ...]

Automatically install all dependencies from one or more `requirements.yaml` or
`pyproject.toml` files. This command first installs dependencies with Conda,
then with Pip. Finally, it installs local packages (those containing the
`requirements.yaml` or `pyproject.toml` files) using `pip install [-e]
./project`. Example usage: `unidep install .` for a single project. For
multiple projects: `unidep install ./project1 ./project2`. The command accepts
both file paths and directories containing a `requirements.yaml` or
`pyproject.toml` file. Use `--editable` or `-e` to install the local packages
in editable mode. See `unidep install-all` to install all `requirements.yaml`
or `pyproject.toml` files in and below the current folder.

positional arguments:
  files                 The `requirements.yaml` or `pyproject.toml` file(s) to
                        parse or folder(s) that contain those file(s), by
                        default `.`

options:
  -h, --help            show this help message and exit
  -v, --verbose         Print verbose output
  -e, --editable        Install the project in editable mode
  --skip-local          Skip installing local dependencies
  --skip-pip            Skip installing pip dependencies from
                        `requirements.yaml` or `pyproject.toml`
  --skip-conda          Skip installing conda dependencies from
                        `requirements.yaml` or `pyproject.toml`
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --no-dependencies     Skip installing dependencies from `requirements.yaml`
                        or `pyproject.toml` file(s) and only install local
                        package(s). Useful after installing a `conda-lock.yml`
                        file because then all dependencies have already been
                        installed.
  --conda-executable {conda,mamba,micromamba}
                        The conda executable to use
  --dry-run, --dry      Only print the commands that would be run
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.

unidep install-all

Use unidep install-all on a folder with packages that contain requirements.yaml files and install the dependencies on the current platform using conda, then install the remaining dependencies with pip, and finally install the current package with pip install [-e] ./package1 ./package2. See unidep install-all -h for more information:

usage: unidep install [-h] [-v] [-e] [--skip-local] [--skip-pip]
                      [--skip-conda] [--skip-dependency SKIP_DEPENDENCY]
                      [--no-dependencies]
                      [--conda-executable {conda,mamba,micromamba}]
                      [--dry-run] [--ignore-pin IGNORE_PIN]
                      [--overwrite-pin OVERWRITE_PIN]
                      files [files ...]

Automatically install all dependencies from one or more `requirements.yaml` or
`pyproject.toml` files. This command first installs dependencies with Conda,
then with Pip. Finally, it installs local packages (those containing the
`requirements.yaml` or `pyproject.toml` files) using `pip install [-e]
./project`. Example usage: `unidep install .` for a single project. For
multiple projects: `unidep install ./project1 ./project2`. The command accepts
both file paths and directories containing a `requirements.yaml` or
`pyproject.toml` file. Use `--editable` or `-e` to install the local packages
in editable mode. See `unidep install-all` to install all `requirements.yaml`
or `pyproject.toml` files in and below the current folder.

positional arguments:
  files                 The `requirements.yaml` or `pyproject.toml` file(s) to
                        parse or folder(s) that contain those file(s), by
                        default `.`

options:
  -h, --help            show this help message and exit
  -v, --verbose         Print verbose output
  -e, --editable        Install the project in editable mode
  --skip-local          Skip installing local dependencies
  --skip-pip            Skip installing pip dependencies from
                        `requirements.yaml` or `pyproject.toml`
  --skip-conda          Skip installing conda dependencies from
                        `requirements.yaml` or `pyproject.toml`
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --no-dependencies     Skip installing dependencies from `requirements.yaml`
                        or `pyproject.toml` file(s) and only install local
                        package(s). Useful after installing a `conda-lock.yml`
                        file because then all dependencies have already been
                        installed.
  --conda-executable {conda,mamba,micromamba}
                        The conda executable to use
  --dry-run, --dry      Only print the commands that would be run
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.

unidep conda-lock

Use unidep conda-lock on one or multiple requirements.yaml files and output the conda-lock file. Optionally, when using a monorepo with multiple subpackages (with their own requirements.yaml files), generate a lock file for each subpackage. See unidep conda-lock -h for more information:

usage: unidep conda-lock [-h] [--only-global] [--lockfile LOCKFILE]
                         [--check-input-hash] [-d DIRECTORY] [-v]
                         [--platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}]
                         [--depth DEPTH] [--skip-dependency SKIP_DEPENDENCY]
                         [--ignore-pin IGNORE_PIN]
                         [--overwrite-pin OVERWRITE_PIN]

Generate a global `conda-lock.yml` file for a collection of
`requirements.yaml` or `pyproject.toml` files. Additionally, create individual
`conda-lock.yml` files for each `requirements.yaml` or `pyproject.toml` file
consistent with the global lock file. Example usage: `unidep conda-lock
--directory ./projects` to generate conda-lock files for all
`requirements.yaml` or `pyproject.toml` files in the `./projects` directory.
Use `--only-global` to generate only the global lock file. The `--check-input-
hash` option can be used to avoid regenerating lock files if the input hasn't
changed.

options:
  -h, --help            show this help message and exit
  --only-global         Only generate the global lock file
  --lockfile LOCKFILE   Specify a path for the global lockfile (default:
                        `conda-lock.yml` in current directory). Path should be
                        relative, e.g., `--lockfile ./locks/example.conda-
                        lock.yml`.
  --check-input-hash    Check existing input hashes in lockfiles before
                        regenerating lock files. This flag is directly passed
                        to `conda-lock`.
  -d DIRECTORY, --directory DIRECTORY
                        Base directory to scan for `requirements.yaml` or
                        `pyproject.toml` file(s), by default `.`
  -v, --verbose         Print verbose output
  --platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}, -p {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}
                        The platform(s) to get the requirements for. Multiple
                        platforms can be specified. By default, the current
                        platform (`linux-64`) is used.
  --depth DEPTH         Maximum depth to scan for `requirements.yaml` or
                        `pyproject.toml` files, by default 1
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.

unidep pip-compile

Use unidep pip-compile on one or multiple requirements.yaml files and output a fully locked requirements.txt file using pip-compile from pip-tools. See unidep pip-compile -h for more information:

usage: unidep pip-compile [-h] [-o OUTPUT_FILE] [-d DIRECTORY] [-v]
                          [--platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}]
                          [--depth DEPTH] [--skip-dependency SKIP_DEPENDENCY]
                          [--ignore-pin IGNORE_PIN]
                          [--overwrite-pin OVERWRITE_PIN]
                          ...

Generate a fully pinned `requirements.txt` file from one or more
`requirements.yaml` or `pyproject.toml` files using `pip-compile` from `pip-
tools`. This command consolidates all pip dependencies defined in the
`requirements.yaml` or `pyproject.toml` files and compiles them into a single
`requirements.txt` file, taking into account the specific versions and
dependencies of each package. Example usage: `unidep pip-compile --directory
./projects` to generate a `requirements.txt` file for all `requirements.yaml`
or `pyproject.toml` files in the `./projects` directory. Use `--output-file
requirements.txt` to specify a different output file.

positional arguments:
  extra_flags           Extra flags to pass to `pip-compile`. These flags are
                        passed directly and should be provided in the format
                        expected by `pip-compile`. For example, `unidep pip-
                        compile -- --generate-hashes --allow-unsafe`. Note
                        that the `--` is required to separate the flags for
                        `unidep` from the flags for `pip-compile`.

options:
  -h, --help            show this help message and exit
  -o OUTPUT_FILE, --output-file OUTPUT_FILE
                        Output file for the pip requirements, by default
                        `requirements.txt`
  -d DIRECTORY, --directory DIRECTORY
                        Base directory to scan for `requirements.yaml` or
                        `pyproject.toml` file(s), by default `.`
  -v, --verbose         Print verbose output
  --platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}, -p {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}
                        The platform(s) to get the requirements for. Multiple
                        platforms can be specified. By default, the current
                        platform (`linux-64`) is used.
  --depth DEPTH         Maximum depth to scan for `requirements.yaml` or
                        `pyproject.toml` files, by default 1
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.

unidep pip

Use unidep pip on a requirements.yaml file and output the pip installable dependencies on the current platform (default). See unidep pip -h for more information:

usage: unidep pip [-h] [-f FILE] [-v]
                  [--platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}]
                  [--skip-dependency SKIP_DEPENDENCY]
                  [--ignore-pin IGNORE_PIN] [--overwrite-pin OVERWRITE_PIN]
                  [--separator SEPARATOR]

Get the pip requirements for the current platform only. Example usage: `unidep
pip --file folder1 --file folder2/requirements.yaml --seperator ' ' --platform
linux-64` to extract all the pip dependencies specific to the linux-64
platform. Note that the `--file` argument can be used multiple times to
specify multiple `requirements.yaml` or `pyproject.toml` files and that --file
can also be a folder that contains a `requirements.yaml` or `pyproject.toml`
file.

options:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  The `requirements.yaml` or `pyproject.toml` file to
                        parse, or folder that contains that file, by default
                        `.`
  -v, --verbose         Print verbose output
  --platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}, -p {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}
                        The platform(s) to get the requirements for. Multiple
                        platforms can be specified. By default, the current
                        platform (`linux-64`) is used.
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.
  --separator SEPARATOR
                        The separator between the dependencies, by default ` `

unidep conda

Use unidep conda on a requirements.yaml file and output the conda installable dependencies on the current platform (default). See unidep conda -h for more information:

usage: unidep conda [-h] [-f FILE] [-v]
                    [--platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}]
                    [--skip-dependency SKIP_DEPENDENCY]
                    [--ignore-pin IGNORE_PIN] [--overwrite-pin OVERWRITE_PIN]
                    [--separator SEPARATOR]

Get the conda requirements for the current platform only. Example usage:
`unidep conda --file folder1 --file folder2/requirements.yaml --seperator ' '
--platform linux-64` to extract all the conda dependencies specific to the
linux-64 platform. Note that the `--file` argument can be used multiple times
to specify multiple `requirements.yaml` or `pyproject.toml` files and that
--file can also be a folder that contains a `requirements.yaml` or
`pyproject.toml` file.

options:
  -h, --help            show this help message and exit
  -f FILE, --file FILE  The `requirements.yaml` or `pyproject.toml` file to
                        parse, or folder that contains that file, by default
                        `.`
  -v, --verbose         Print verbose output
  --platform {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}, -p {linux-64,linux-aarch64,linux-ppc64le,osx-64,osx-arm64,win-64}
                        The platform(s) to get the requirements for. Multiple
                        platforms can be specified. By default, the current
                        platform (`linux-64`) is used.
  --skip-dependency SKIP_DEPENDENCY
                        Skip installing a specific dependency that is in one
                        of the `requirements.yaml` or `pyproject.toml` files.
                        This option can be used multiple times, each time
                        specifying a different package to skip. For example,
                        use `--skip-dependency pandas` to skip installing
                        pandas.
  --ignore-pin IGNORE_PIN
                        Ignore the version pin for a specific package, e.g.,
                        `--ignore-pin numpy`. This option can be repeated to
                        ignore multiple packages.
  --overwrite-pin OVERWRITE_PIN
                        Overwrite the version pin for a specific package,
                        e.g., `--overwrite-pin 'numpy==1.19.2'`. This option
                        can be repeated to overwrite the pins of multiple
                        packages.
  --separator SEPARATOR
                        The separator between the dependencies, by default ` `

:hammer_and_wrench: Troubleshooting

pip install fails with FileNotFoundError

When using a project that uses local_dependencies: [../not/current/dir] in the requirements.yaml file:

local_dependencies:
  # File in a different directory than the pyproject.toml file
  - ../common-requirements.yaml

You might get an error like this when using a pip version older than 22.0:

$ pip install /path/to/your/project/using/unidep
  ...
  File "/usr/lib/python3.8/pathlib.py", line 1222, in open
    return io.open(self, mode, buffering, encoding, errors, newline,
  File "/usr/lib/python3.8/pathlib.py", line 1078, in _opener
    return self._accessor.open(self, flags, mode)
FileNotFoundError: [Errno 2] No such file or directory: '/tmp/common-requirements.yaml'

The solution is to upgrade pip to version 22.0 or newer:

pip install --upgrade pip

:warning: Limitations

• Conda-Focused: Best suited for Conda environments. However, note that having conda is not a requirement to install packages that use UniDep.
• Setuptools and Hatchling only: Currently only works with setuptools and Hatchling, not flit, poetry, or other build systems. Open an issue if you'd like to see support for other build systems.
• No logic operators in platform selectors and no Python selectors.

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Try unidep today for a streamlined approach to managing your Conda environment dependencies across multiple projects! 🎉👏