Pip install proxy linux

User Guide¶

Running pip¶

pip is a command line program. When you install pip, a pip command is added to your system, which can be run from the command prompt as follows:

python -m pip executes pip using the Python interpreter you specified as python. So /usr/bin/python3.7 -m pip means you are executing pip for your interpreter located at /usr/bin/python3.7.

py -m pip executes pip using the latest Python interpreter you have installed. For more details, read the Python Windows launcher docs.

Installing Packages¶

pip supports installing from PyPI, version control, local projects, and directly from distribution files.

The most common scenario is to install from PyPI using Requirement Specifiers

For more information and examples, see the pip install reference.

Basic Authentication Credentials¶

This is now covered in Authentication .

netrc Support¶

This is now covered in Authentication .

Keyring Support¶

This is now covered in Authentication .

Using a Proxy Server¶

When installing packages from PyPI, pip requires internet access, which in many corporate environments requires an outbound HTTP proxy server.

pip can be configured to connect through a proxy server in various ways:

using the —proxy command-line option to specify a proxy in the form [user:passwd@]proxy.server:port

using proxy in a Config file

by setting the standard environment-variables http_proxy , https_proxy and no_proxy .

using the environment variable PIP_USER_AGENT_USER_DATA to include a JSON-encoded string in the user-agent variable used in pip’s requests.

Requirements Files¶

“Requirements files” are files containing a list of items to be installed using pip install like so:

Details on the format of the files are here: Requirements File Format .

Logically, a Requirements file is just a list of pip install arguments placed in a file. Note that you should not rely on the items in the file being installed by pip in any particular order.

In practice, there are 4 common uses of Requirements files:

Requirements files are used to hold the result from pip freeze for the purpose of achieving Repeatable Installs . In this case, your requirement file contains a pinned version of everything that was installed when pip freeze was run.

Requirements files are used to force pip to properly resolve dependencies. pip 20.2 and earlier doesn’t have true dependency resolution, but instead simply uses the first specification it finds for a project. E.g. if pkg1 requires pkg3>=1.0 and pkg2 requires pkg3>=1.0, , and if pkg1 is resolved first, pip will only use pkg3>=1.0 , and could easily end up installing a version of pkg3 that conflicts with the needs of pkg2 . To solve this problem, you can place pkg3>=1.0, (i.e. the correct specification) into your requirements file directly along with the other top level requirements. Like so:

Requirements files are used to force pip to install an alternate version of a sub-dependency. For example, suppose ProjectA in your requirements file requires ProjectB , but the latest version (v1.3) has a bug, you can force pip to accept earlier versions like so:

Requirements files are used to override a dependency with a local patch that lives in version control. For example, suppose a dependency SomeDependency from PyPI has a bug, and you can’t wait for an upstream fix. You could clone/copy the src, make the fix, and place it in VCS with the tag sometag . You’d reference it in your requirements file with a line like so:

If SomeDependency was previously a top-level requirement in your requirements file, then replace that line with the new line. If SomeDependency is a sub-dependency, then add the new line.

It’s important to be clear that pip determines package dependencies using install_requires metadata, not by discovering requirements.txt files embedded in projects.

Constraints Files¶

Constraints files are requirements files that only control which version of a requirement is installed, not whether it is installed or not. Their syntax and contents is a subset of Requirements Files , with several kinds of syntax not allowed: constraints must have a name, they cannot be editable, and they cannot specify extras. In terms of semantics, there is one key difference: Including a package in a constraints file does not trigger installation of the package.

Use a constraints file like so:

Constraints files are used for exactly the same reason as requirements files when you don’t know exactly what things you want to install. For instance, say that the “helloworld” package doesn’t work in your environment, so you have a local patched version. Some things you install depend on “helloworld”, and some don’t.

One way to ensure that the patched version is used consistently is to manually audit the dependencies of everything you install, and if “helloworld” is present, write a requirements file to use when installing that thing.

Constraints files offer a better way: write a single constraints file for your organisation and use that everywhere. If the thing being installed requires “helloworld” to be installed, your fixed version specified in your constraints file will be used.

Constraints file support was added in pip 7.1. In Changes to the pip dependency resolver in 20.3 (2020) we did a fairly comprehensive overhaul, removing several undocumented and unsupported quirks from the previous implementation, and stripped constraints files down to being purely a way to specify global (version) limits for packages.

Installing from Wheels¶

“Wheel” is a built, archive format that can greatly speed installation compared to building and installing from source archives. For more information, see the Wheel docs , PEP 427, and PEP 425.

pip prefers Wheels where they are available. To disable this, use the —no-binary flag for pip install .

If no satisfactory wheels are found, pip will default to finding source archives.

To install directly from a wheel archive:

To include optional dependencies provided in the provides_extras metadata in the wheel, you must add quotes around the install target name:

In the future, the path[extras] syntax may become deprecated. It is recommended to use PEP 508 syntax wherever possible.

For the cases where wheels are not available, pip offers pip wheel as a convenience, to build wheels for all your requirements and dependencies.

pip wheel requires the wheel package to be installed, which provides the “bdist_wheel” setuptools extension that it uses.

To build wheels for your requirements and all their dependencies to a local directory:

And then to install those requirements just using your local directory of wheels (and not from PyPI):

Uninstalling Packages¶

pip is able to uninstall most packages like so:

pip also performs an automatic uninstall of an old version of a package before upgrading to a newer version.

For more information and examples, see the pip uninstall reference.

Listing Packages¶

To list installed packages:

To list outdated packages, and show the latest version available:

To show details about an installed package:

For more information and examples, see the pip list and pip show reference pages.

Searching for Packages¶

pip can search PyPI for packages using the pip search command:

The query will be used to search the names and summaries of all packages.

For more information and examples, see the pip search reference.

Configuration¶

This is now covered in Configuration .

Config file¶

This is now covered in Configuration .

Environment Variables¶

This is now covered in Configuration .

Config Precedence¶

This is now covered in Configuration .

Command Completion¶

pip comes with support for command line completion in bash, zsh and fish.

To setup for bash:

To setup for zsh:

To setup for fish:

Alternatively, you can use the result of the completion command directly with the eval function of your shell, e.g. by adding the following to your startup file:

Installing from local packages¶

In some cases, you may want to install from local packages only, with no traffic to PyPI.

First, download the archives that fulfill your requirements:

Note that pip download will look in your wheel cache first, before trying to download from PyPI. If you’ve never installed your requirements before, you won’t have a wheel cache for those items. In that case, if some of your requirements don’t come as wheels from PyPI, and you want wheels, then run this instead:

Then, to install from local only, you’ll be using —find-links and —no-index like so:

“Only if needed” Recursive Upgrade¶

pip install —upgrade now has a —upgrade-strategy option which controls how pip handles upgrading of dependencies. There are 2 upgrade strategies supported:

eager : upgrades all dependencies regardless of whether they still satisfy the new parent requirements

only-if-needed : upgrades a dependency only if it does not satisfy the new parent requirements

The default strategy is only-if-needed . This was changed in pip 10.0 due to the breaking nature of eager when upgrading conflicting dependencies.

It is important to note that —upgrade affects direct requirements (e.g. those specified on the command-line or via a requirements file) while —upgrade-strategy affects indirect requirements (dependencies of direct requirements).

As an example, say SomePackage has a dependency, SomeDependency , and both of them are already installed but are not the latest available versions:

pip install SomePackage : will not upgrade the existing SomePackage or SomeDependency .

pip install —upgrade SomePackage : will upgrade SomePackage , but not SomeDependency (unless a minimum requirement is not met).

pip install —upgrade SomePackage —upgrade-strategy=eager : upgrades both SomePackage and SomeDependency .

As an historic note, an earlier “fix” for getting the only-if-needed behaviour was:

A proposal for an upgrade-all command is being considered as a safer alternative to the behaviour of eager upgrading.

User Installs¶

With Python 2.6 came the “user scheme” for installation, which means that all Python distributions support an alternative install location that is specific to a user. The default location for each OS is explained in the python documentation for the site.USER_BASE variable. This mode of installation can be turned on by specifying the —user option to pip install .

Moreover, the “user scheme” can be customized by setting the PYTHONUSERBASE environment variable, which updates the value of site.USER_BASE .

To install “SomePackage” into an environment with site.USER_BASE customized to ‘/myappenv’, do the following:

pip install —user follows four rules:

When globally installed packages are on the python path, and they conflict with the installation requirements, they are ignored, and not uninstalled.

When globally installed packages are on the python path, and they satisfy the installation requirements, pip does nothing, and reports that requirement is satisfied (similar to how global packages can satisfy requirements when installing packages in a —system-site-packages virtualenv).

pip will not perform a —user install in a —no-site-packages virtualenv (i.e. the default kind of virtualenv), due to the user site not being on the python path. The installation would be pointless.

In a —system-site-packages virtualenv, pip will not install a package that conflicts with a package in the virtualenv site-packages. The —user installation would lack sys.path precedence and be pointless.

To make the rules clearer, here are some examples:

From within a —no-site-packages virtualenv (i.e. the default kind):

From within a —system-site-packages virtualenv where SomePackage==0.3 is already installed in the virtualenv:

From within a real python, where SomePackage is not installed globally:

From within a real python, where SomePackage is installed globally, but is not the latest version:

From within a real python, where SomePackage is installed globally, and is the latest version:

Ensuring Repeatability¶

This is now covered in Repeatable Installs .

Fixing conflicting dependencies¶

The purpose of this section of documentation is to provide practical suggestions to pip users who encounter an error where pip cannot install their specified packages due to conflicting dependencies (a ResolutionImpossible error).

This documentation is specific to the new resolver, which is the default behavior in pip 20.3 and later. If you are using pip 20.2, you can invoke the new resolver by using the flag —use-feature=2020-resolver .

Understanding your error message¶

When you get a ResolutionImpossible error, you might see something like this:

In this example, pip cannot install the packages you have requested, because they each depend on different versions of the same package ( package_water ):

package_coffee version 0.44.1 depends on a version of package_water that is less than 3.0.0 but greater than or equal to 2.4.2

package_tea version 4.3.0 depends on version 2.3.1 of package_water

Sometimes these messages are straightforward to read, because they use commonly understood comparison operators to specify the required version (e.g. or > ).

However, Python packaging also supports some more complex ways for specifying package versions (e.g.

Any version greater than the specified version.

>3.1 : any version greater than 3.1 .

Any version less than the specified version.

: any version less than 3.1 .

Any version less than or equal to the specified version.

: any version less than or equal to 3.1 .

Any version greater than or equal to the specified version.

>=3.1 : version 3.1 and greater.

Exactly the specified version.

Any version not equal to the specified version.

!=3.1 : any version other than 3.1 .

Any compatible release. Compatible releases are releases that are within the same major or minor version, assuming the package author is using semantic versioning.

=3.1 : version 3.1 or later, but not version 4.0 or later.

=3.1.2 : version 3.1.2 or later, but not version 3.2.0 or later.

Can be used at the end of a version number to represent all,

==3.1.* : any version that starts with 3.1 . Equivalent to

The detailed specification of supported comparison operators can be found in PEP 440.

Possible solutions¶

The solution to your error will depend on your individual use case. Here are some things to try:

1. Audit your top level requirements¶

As a first step it is useful to audit your project and remove any unnecessary or out of date requirements (e.g. from your setup.py or requirements.txt files). Removing these can significantly reduce the complexity of your dependency tree, thereby reducing opportunities for conflicts to occur.

2. Loosen your top level requirements¶

Sometimes the packages that you have asked pip to install are incompatible because you have been too strict when you specified the package version.

In our first example both package_coffee and package_tea have been pinned to use specific versions ( package_coffee==0.44.1b0 package_tea==4.3.0 ).

To find a version of both package_coffee and package_tea that depend on the same version of package_water , you might consider:

Loosening the range of packages that you are prepared to install (e.g. pip install «package_coffee>0.44.*» «package_tea>4.0.0» )

Asking pip to install any version of package_coffee and package_tea by removing the version specifiers altogether (e.g. python -m pip install package_coffee package_tea )

In the second case, pip will automatically find a version of both package_coffee and package_tea that depend on the same version of package_water , installing:

package_coffee 0.46.0b0 , which depends on package_water 2.6.1

package_tea 4.3.0 which also depends on package_water 2.6.1

If you want to prioritize one package over another, you can add version specifiers to only the more important package:

This will result in:

package_coffee 0.44.1b0 , which depends on package_water 2.6.1

package_tea 4.1.3 which also depends on package_water 2.6.1

Now that you have resolved the issue, you can repin the compatible package versions as required.

3. Loosen the requirements of your dependencies¶

Assuming that you cannot resolve the conflict by loosening the version of the package you require (as above), you can try to fix the issue on your dependency by:

Requesting that the package maintainers loosen their dependencies

Forking the package and loosening the dependencies yourself

If you choose to fork the package yourself, you are opting out of any support provided by the package maintainers. Proceed at your own risk!

4. All requirements are loose, but a solution does not exist¶

Sometimes it’s simply impossible to find a combination of package versions that do not conflict. Welcome to dependency hell.

In this situation, you could consider:

Using an alternative package, if that is acceptable for your project. See Awesome Python for similar packages.

Refactoring your project to reduce the number of dependencies (for example, by breaking up a monolithic code base into smaller pieces)

Getting help¶

If none of the suggestions above work for you, we recommend that you ask for help on:

See “How do I ask a good question?” for tips on asking for help.

Unfortunately, the pip team cannot provide support for individual dependency conflict errors. Please only open a ticket on the pip issue tracker if you believe that your problem has exposed a bug in pip.

Dependency resolution backtracking¶

Or more commonly known as “Why does pip download multiple versions of the same package over and over again during an install?”.

The purpose of this section is to provide explanation of why backtracking happens, and practical suggestions to pip users who encounter it during a pip install .

What is backtracking?¶

Backtracking is not a bug, or an unexpected behaviour. It is part of the way pip’s dependency resolution process works.

During a pip install (e.g. pip install tea ), pip needs to work out the package’s dependencies (e.g. spoon , hot-water , cup etc.), the versions of each of these packages it needs to install. For each package pip needs to decide which version is a good candidate to install.

A “good candidate” means a version of each package that is compatible with all the other package versions being installed at the same time.

In the case where a package has a lot of versions, arriving at a good candidate can take a lot of time. (The amount of time depends on the package size, the number of versions pip must try, and other concerns.)

How does backtracking work?¶

When doing a pip install, pip starts by making assumptions about the packages it needs to install. During the install process it needs to check these assumptions as it goes along.

When pip finds that an assumption is incorrect, it has to try another approach (backtrack), which means discarding some of the work that has already been done, and going back to choose another path.

For example; The user requests pip install tea . tea has dependencies of cup , hot-water , spoon amongst others.

pip starts by installing a version of cup . If it finds out it isn’t compatible (with the other package versions) it needs to “go back” (backtrack) and download an older version.

It then tries to install that version. If it is successful, it will continue onto the next package. If not it will continue to backtrack until it finds a compatible version.

This backtrack behaviour can end in 2 ways — either 1) it will successfully find a set of packages it can install (good news!), or 2) it will eventually display a resolution impossible error message (not so good).

If pip starts backtracking during dependency resolution, it does not know how long it will backtrack, and how much computation would be needed. For the user this means it can take a long time to complete.

Why does backtracking occur?¶

With the release of the new resolver ( Changes to the pip dependency resolver in 20.3 (2020) ), pip is now more strict in the package versions it installs when a user runs a pip install command.

Pip needs to backtrack because initially, it doesn’t have all the information it needs to work out the correct set of packages. This is because package indexes don’t provide full package dependency information before you have downloaded the package.

This new resolver behaviour means that pip works harder to find out which version of a package is a good candidate to install. It reduces the risk that installing a new package will accidentally break an existing installed package, and so reduces the risk that your environment gets messed up.

What does this behaviour look like?¶

Right now backtracking behaviour looks like this:

In the above sample output, pip had to download multiple versions of package cup — cup-3.22.0 to cup-3.13.0 — to find a version that will be compatible with the other packages — spoon , hot-water , etc.

These multiple Downloading cup-version lines show pip backtracking.

Possible ways to reduce backtracking occurring¶

It’s important to mention backtracking behaviour is expected during a pip install process. What pip is trying to do is complicated — it is working through potentially millions of package versions to identify the compatible versions.

There is no guaranteed solution to backtracking but you can reduce it — here are a number of ways.

1. Allow pip to complete its backtracking¶

In most cases, pip will complete the backtracking process successfully. It is possible this could take a very long time to complete — this may not be your preferred option.

However, there is a possibility pip will not be able to find a set of compatible versions.

If you’d prefer not to wait, you can interrupt pip (ctrl and c) and use Constraints Files : to reduce the number of package versions it tries.

2. Reduce the number of versions pip will try to backtrack through¶

If pip is backtracking more than you’d like, the next option is to constrain the number of package versions it tries.

A first good candidate for this constraining is the package(s) it is backtracking on (e.g. in the above example — cup ).

pip install tea «cup > 3.13»

This will reduce the number of versions of cup it tries, and possibly reduce the time pip takes to install.

There is a possibility that if you’re wrong (in this case an older version would have worked) then you missed the chance to use it. This can be trial and error.

3. Use constraint files or lockfiles¶

This option is a progression of 2 above. It requires users to know how to inspect:

the packages they’re trying to install

the package release frequency and compatibility policies

their release notes and changelogs from past versions

During deployment, you can create a lockfile stating the exact package and version number for for each dependency of that package. You can create this with pip-tools.

This means the “work” is done once during development process, and so will save users this work during deployment.

The pip team is not available to provide support in helping you create a suitable constraints file.

4. Be more strict on package dependencies during development¶

For package maintainers during software development, give pip some help by creating constraint files for the dependency tree. This will reduce the number of versions it will try.

Getting help¶

If none of the suggestions above work for you, we recommend that you ask for help. Getting help .

Using pip from your program¶

As noted previously, pip is a command line program. While it is implemented in Python, and so is available from your Python code via import pip , you must not use pip’s internal APIs in this way. There are a number of reasons for this:

The pip code assumes that is in sole control of the global state of the program. pip manages things like the logging system configuration, or the values of the standard IO streams, without considering the possibility that user code might be affected.

pip’s code is not thread safe. If you were to run pip in a thread, there is no guarantee that either your code or pip’s would work as you expect.

pip assumes that once it has finished its work, the process will terminate. It doesn’t need to handle the possibility that other code will continue to run after that point, so (for example) calling pip twice in the same process is likely to have issues.

This does not mean that the pip developers are opposed in principle to the idea that pip could be used as a library — it’s just that this isn’t how it was written, and it would be a lot of work to redesign the internals for use as a library, handling all of the above issues, and designing a usable, robust and stable API that we could guarantee would remain available across multiple releases of pip. And we simply don’t currently have the resources to even consider such a task.

What this means in practice is that everything inside of pip is considered an implementation detail. Even the fact that the import name is pip is subject to change without notice. While we do try not to break things as much as possible, all the internal APIs can change at any time, for any reason. It also means that we generally won’t fix issues that are a result of using pip in an unsupported way.

It should also be noted that installing packages into sys.path in a running Python process is something that should only be done with care. The import system caches certain data, and installing new packages while a program is running may not always behave as expected. In practice, there is rarely an issue, but it is something to be aware of.

Having said all of the above, it is worth covering the options available if you decide that you do want to run pip from within your program. The most reliable approach, and the one that is fully supported, is to run pip in a subprocess. This is easily done using the standard subprocess module:

If you want to process the output further, use one of the other APIs in the module. We are using freeze here which outputs installed packages in requirements format.:

If you don’t want to use pip’s command line functionality, but are rather trying to implement code that works with Python packages, their metadata, or PyPI, then you should consider other, supported, packages that offer this type of ability. Some examples that you could consider include:

packaging — Utilities to work with standard package metadata (versions, requirements, etc.)

setuptools (specifically pkg_resources ) — Functions for querying what packages the user has installed on their system.

distlib — Packaging and distribution utilities (including functions for interacting with PyPI).

Changes to the pip dependency resolver in 20.3 (2020)¶

pip 20.3 has a new dependency resolver, on by default for Python 3 users. (pip 20.1 and 20.2 included pre-release versions of the new dependency resolver, hidden behind optional user flags.) Read below for a migration guide, how to invoke the legacy resolver, and the deprecation timeline. We also made a two-minute video explanation you can watch.

We will continue to improve the pip dependency resolver in response to testers’ feedback. Please give us feedback through the resolver testing survey.

Watch out for¶

The big change in this release is to the pip dependency resolver within pip.

Computers need to know the right order to install pieces of software (“to install x , you need to install y first”). So, when Python programmers share software as packages, they have to precisely describe those installation prerequisites, and pip needs to navigate tricky situations where it’s getting conflicting instructions. This new dependency resolver will make pip better at handling that tricky logic, and make pip easier for you to use and troubleshoot.

The most significant changes to the resolver are:

It will reduce inconsistency: it will no longer install a combination of packages that is mutually inconsistent. In older versions of pip, it is possible for pip to install a package which does not satisfy the declared requirements of another installed package. For example, in pip 20.0, pip install «six «virtualenv==20.0.2» does the wrong thing, “successfully” installing six==1.11 , even though virtualenv==20.0.2 requires six>=1.12.0, (defined here). The new resolver, instead, outright rejects installing anything if it gets that input.

It will be stricter — if you ask pip to install two packages with incompatible requirements, it will refuse (rather than installing a broken combination, like it did in previous versions).

So, if you have been using workarounds to force pip to deal with incompatible or inconsistent requirements combinations, now’s a good time to fix the underlying problem in the packages, because pip will be stricter from here on out.

This also means that, when you run a pip install command, pip only considers the packages you are installing in that command, and may break already-installed packages. It will not guarantee that your environment will be consistent all the time. If you pip install x and then pip install y , it’s possible that the version of y you get will be different than it would be if you had run pip install x y in a single command. We are considering changing this behavior (per #7744) and would like your thoughts on what pip’s behavior should be; please answer our survey on upgrades that create conflicts.

We are also changing our support for Constraints Files , editable installs, and related functionality. We did a fairly comprehensive overhaul and stripped constraints files down to being purely a way to specify global (version) limits for packages, and so some combinations that used to be allowed will now cause errors. Specifically:

Constraints don’t override the existing requirements; they simply constrain what versions are visible as input to the resolver (see #9020)

Providing an editable requirement ( -e . ) does not cause pip to ignore version specifiers or constraints (see #8076), and if you have a conflict between a pinned requirement and a local directory then pip will indicate that it cannot find a version satisfying both (see #8307)

Hash-checking mode requires that all requirements are specified as a == match on a version and may not work well in combination with constraints (see #9020 and #8792)

If necessary to satisfy constraints, pip will happily reinstall packages, upgrading or downgrading, without needing any additional command-line options (see #8115 and Options that control the installation process )

Unnamed requirements are not allowed as constraints (see #6628 and #8210)

Links are not allowed as constraints (see #8253)

Constraints cannot have extras (see #6628)

Per our Python 2 Support policy, pip 20.3 users who are using Python 2 will use the legacy resolver by default. Python 2 users should upgrade to Python 3 as soon as possible, since in pip 21.0 in January 2021, pip dropped support for Python 2 altogether.

How to upgrade and migrate¶

Install pip 20.3 with python -m pip install —upgrade pip .

Validate your current environment by running pip check . This will report if you have any inconsistencies in your set of installed packages. Having a clean installation will make it much less likely that you will hit issues with the new resolver (and may address hidden problems in your current environment!). If you run pip check and run into stuff you can’t figure out, please ask for help in our issue tracker or chat.

Test the new version of pip.

While we have tried to make sure that pip’s test suite covers as many cases as we can, we are very aware that there are people using pip with many different workflows and build processes, and we will not be able to cover all of those without your help.

If you use pip to install your software, try out the new resolver and let us know if it works for you with pip install . Try:

installing several packages simultaneously

re-creating an environment using a requirements.txt file

using pip install —force-reinstall to check whether it does what you think it should

using constraints files

the “Setups to test with special attention” and “Examples to try” below

If you have a build pipeline that depends on pip installing your dependencies for you, check that the new resolver does what you need.

Run your project’s CI (test suite, build process, etc.) using the new resolver, and let us know of any issues.

If you have encountered resolver issues with pip in the past, check whether the new resolver fixes them, and read Fixing conflicting dependencies . Also, let us know if the new resolver has issues with any workarounds you put in to address the current resolver’s limitations. We’ll need to ensure that people can transition off such workarounds smoothly.

If you develop or support a tool that wraps pip or uses it to deliver part of your functionality, please test your integration with pip 20.3.

Troubleshoot and try these workarounds if necessary.

If pip is taking longer to install packages, read Dependency resolution backtracking for ways to reduce the time pip spends backtracking due to dependency conflicts.

If you don’t want pip to actually resolve dependencies, use the —no-deps option. This is useful when you have a set of package versions that work together in reality, even though their metadata says that they conflict. For guidance on a long-term fix, read Fixing conflicting dependencies .

If you run into resolution errors and need a workaround while you’re fixing their root causes, you can choose the old resolver behavior using the flag —use-deprecated=legacy-resolver . This will work until we release pip 21.0 (see Deprecation timeline ).

Please report bugs through the resolver testing survey.

Setups to test with special attention¶

Requirements files with 100+ packages

Installation workflows that involve multiple requirements files

Requirements files that include hashes ( Hash-Checking Mode ) or pinned dependencies (perhaps as output from pip-compile within pip-tools )

Continuous integration/continuous deployment setups

Installing from any kind of version control systems (i.e., Git, Subversion, Mercurial, or CVS), per VCS Support

Installing from source code held in local directories

Источник