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I have created an Azure function and I am trying to build a Azure pipeline. The pipeline fails at install application dependencies with the below error.
ERROR: Cannot install -r requirements.txt (line 8) and azure-storage-blob==2.1.0 because these package versions have conflicting dependencies.
The conflict is caused by:
The user requested azure-storage-blob==2.1.0
azure-storage-file-datalake 12.7.0 depends on azure-storage-blob<13.0.0 and >=12.12.0
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict.
Below is the code in the install dependencies.
python -m venv worker_venv
source worker_venv/bin/activate
pip install setuptools
pip install -r requirements.txt
In my requirements.txt file the azure-storage-blob version is 2.1.0. Should I remove the version part in the requirements.txt file and trying running the pipeline. Is there any other way to fix this issue.
Thank you.
I just removed the version in the requirements.txt file and it was successful.
Should I remove the version part in the requirements.txt file and
trying running the pipeline. Is there any other way to fix this issue.
Yes, of course you have other way to fix this issue. But you need to make sure your code is backward compatible, if your code must be based on azure-storage-blob version 2.1.0, then you can't use azure-storage-file-datalake version 12.7.0.
If your code is compatible with 'azure-storage-blob<13.0.0 and >=12.12.0', then you can specify any version within this range in the requirements.txt file.
For example, you can do this:
requirements.txt
azure-functions
azure-storage-blob==12.12.0
azure-storage-file-datalake==12.7.0
Now your direct remove version is valid because the current latest version of azure-storage-blob is 12.12.0, which can meet the dependency requirements of azure-storage-file-datalake 12.7.0:
https://pypi.org/project/azure-storage-blob/12.12.0/#history
But if you use the 12.7.0 version of azure-storage-file-datalake, you will still have version conflicts in the near future. Because if you do not specify the version number, the pip tool will install the latest version by default. If the latest version of azure-storage-blob is greater than or equal to 13.0.0 in the future, you will encounter an error again.
I recently had to bump a google cloud library due to a conflict that was generating a bug. Long story short, I had
google-cloud-pubsub==1.4.2
which I had to bump to 1.4.3. This in turn reverted google-api-core module to 1.16.0, which generated a conflict with another module google-cloud-secret-manager which required a higher version of google-api-core.
Now, I have removed google-cloud-secret-manager. But, If I try to install the module again to the last version however, it will bump me google-api-core to a version not compatible with google-cloud-pubsub. What I want to do instead is to pip install google-cloud-secret-manager to the highest possible version that is compatible with google-api-core==1.16.0 without manually trying to install all the versions until i find the right match. Is it something possible?
Is there a pip install fix dependency version command that could allow me to easily install google-cloud-secret-manager that will not change the version of the dependency module google-api-core to a different version? Thank you
You can achieve this with a constraints file. Just put all your constraints into that file:
google-api-core==1.16.0
Then you can install via:
python -m pip install -c constraints.txt google-cloud-secret-manager
This will try every version of google-cloud-secret-manager, starting from the most recent version, until it finds a version that is compatible with the given constraints.
I've been trying to install a package through pip on my rpi 3 model B
my operating system is raspbian. Debian based pip version is 21.0.1 and python version is 3.7.4
the command I'm using is:
python3 -m pip install librosa
the problem is that the dependency resolver takes way too long to resolve the conflicts.
and after a few hours, it keeps repeating this line over and over again for hours ( I even left the installation running for 2 days overnights )
INFO: pip is looking at multiple versions of <Python from requires-Python> to determine which version is compatible with other requirements. this could take a while.
INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. If you want to abort this run you can press ctrl + c to do so.
I've tried using a stricter constraint such as adding "numpy > 1.20.0" and other stuff but now the popped up and I have no clue what I can do now.
So as of pip 20.3, a new (not always working) resolver has been introduced. As of pip 21.0 the old (working) resolver is unsupported and slated for removal dependent on pip team resources.
Changes to the pip dependency resolver in 20.3
I have hit the same issue trying to build jupyter, my solution was to pin pip back to the 20.2 release which is the last release with the old resolver. This got past the point my builds were choking at using the new resolver under pip 21.1.1.
A second approach that might work (untested) is to use the flag:
--use-deprecated=legacy-resolver
which appears to have been added when 20.3 switched over to the new resolver. This would allow the benefits of newer pip releases, until the backtracking issue is resolved, assuming it works.
What is happening, according to the devs on this Github issue, is "pip downloads multiple versions [of each package] because the versions it downloaded conflict with other packages you specified, which is called backtracking and is a feature. The versions need to be downloaded to detect the conflicts." But it takes a very long time to download all of these versions. Pip explains this in detail, along with ways to resolve it or speed it up, at https://pip.pypa.io/en/latest/topics/dependency-resolution/.
If you run
pip install -r requirements.txt --use-deprecated=legacy-resolver
you will not get this backtracking behavior, but your install will complete, and you will see an error at the end that is useful for troubleshooting:
ERROR: pip's legacy dependency resolver does not consider dependency conflicts when selecting packages. This behaviour is the source of the following dependency conflicts.
apache-airflow-providers-amazon 2.6.0 requires boto3<1.19.0,>=1.15.0, but you'll have boto3 1.9.253 which is incompatible.
package_xyz 0.0.1 requires PyJWT==2.1.0, but you'll have pyjwt 1.7.1 which is incompatible.
Upgrading my pip to 21.3.1 worked
python.exe -m pip install --upgrade pip
I've just uploaded a new version of my package to PyPi (1.2.1.0-r4): I can download the egg file and install it with easy_install, and the version checks out correctly. But when I try to install using pip, it installs version 1.1.0.0 instead. Even if I explicitly specify the version to pip with pip install -Iv tome==1.2.1.0-r4, I get this message: Requested tome==1.2.1.0-r4, but installing version 1.1.0.0, but I don't understand why.
I double checked with parse_version and confirmed that the version string on 1.2.1 is greater than that on 1.1.0 as shown:
>>> from pkg_resources import parse_version as pv
>>> pv('1.1.0.0') < pv('1.2.1.0-r4')
True
>>>
So any idea why it's choosing to install 1.1.0 instead?
This is an excellent question. It took me forever to figure out. This is the solution that works for me:
Apparently, if pip can find a local version of the package, pip will prefer the local versions to remote ones. I even disconnected my computer from the internet and tried it again -- when pip still installed the package successfully, and didn't even complain, the source was obviously local.
The really confusing part, in my case, was that pip found the newer versions on pypi, reported them, and then went ahead and re-installed the older version anyway ... arggh. Also, it didn't tell me what it was doing, and why.
So how did I solve this problem?
You can get pip to give verbose output using the -v flag ... but one isn't enough. I RTFM-ed the help, which said you can do -v multiple times, up to 3x, for more verbose output. So I did:
pip install -vvv <my_package>
Then I looked through the output. One line caught my eye:
Source in /tmp/pip-build-root/ has version 0.0.11, which satisfies requirement <my_package>
I deleted that directory, after which pip installed the newest version from pypi.
Try forcing download the package again with:
pip install --no-cache-dir --upgrade <package>
Thanks to Marcus Smith, who does amazing work as a maintener of pip, this was fixed in version 1.4 of pip which was released on 2013-07-23.
Relevant information from the changelog for this version
Fixed a number of issues (#413, #709, #634, #602, and #939) related to
cleaning up and not reusing build directories. (Pull #865, #948)
I found here that there is a known bug in pip that it won't check the version if there's a build directory with unpacked sources. I have checked this on my troubling package and after deleting its sources from build directory pip installed the required version.
If you are using a pip version that comes with some distribution packages (ex. Ubuntu python-pip), you may need to install a newer pip version:
Update pip to latest version:
sudo pip install -U pip
In case of "virtualenv", skip "sudo":
pip install -U pip
Following command may be required, if your shell report something like -bash: /usr/bin/pip: No such file or directory after pip update:
hash -d pip
Now install your package as usual:
pip install -U foo
or
pip install foo==package.version.here
Got the same issue to update pika 0.9.5 to 0.9.8. The only working way was to install from tarball: pip install https://pypi.python.org/packages/source/p/pika/pika-0.9.8.tar.gz.
In my case the python version used (3.4) didn't satisfy Django 2.1 dependencies requirements (python >= 3.5).
For my case I had to delete the .pip folder in my home directory and then I was able to get later versions of multiple libraries. Note that this was on linux.
pip --version
pip 18.1 from /usr/lib/python2.7/site-packages/pip (python 2.7)
virtualenv --version
15.1.0
Just in case that anyone else hassles with upgrading torchtext (or probably any other torch library):
Although https://pypi.org/project/torchtext/ states that you could run pip install torchtext I had to install it similiar to torch by specifying --find-links aka -f:
pip install torchtext===0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
What irritated me was that PyCharm pointed me to the new version, but couldn't find it when attempting to upgrade to it. I guess that PyCharm uses its own mechanism to spot new versions. Then, when invoking pip under the hood, it didn't find the new version without the --find-links option.
In my case I am pip installing a .tar.gz package from Artifactory that I make a lot of updates to. In order to overwrite my cached Python files and always grab/install the latest I was able to run:
pip install --no-cache-dir --force-reinstall <path/to/tar.gz>
You should see this re-download any necessary files and install those, instead of using your local cache.
10 years on and pip still fails to work as expected 😖.
I wasted a couple of hours now banging my head against the wall trying to find out why pip won't install a development version of my package. In my case, there are versions 0.0.4 and 0.0.5.dev1 in a private gitlab.com package registry (hence the --extra-index-url argument below), but I believe that's not relevant to the problem.
Following a lot of the advice on this page, I create a test venv in a far away folder, clear the pip cache, uninstall the package in question, etc. first to rule out the most common problems:
$ pip cache purge && \
pip uninstall --yes my-package && \
pip install --extra-index-url "https://_:${GITLAB_PASSWORD_TOOLS_VAULTTOOLS}#gitlab.com/api/v4/projects/<project-id>/packages/pypi/simple" \
--no-cache-dir \
--pre \
--upgrade my-package
output (using empty lines to separate output for commands):
WARNING: No matching packages
Files removed: 0
Found existing installation: my-package 0.0.4
Uninstalling my-package-0.0.4:
Successfully uninstalled my-package-0.0.4
Looking in indexes: https://pypi.org/simple, https://_:****#gitlab.com/api/v4/projects/<project-id>/packages/pypi/simple
Collecting my-package
Downloading https://gitlab.com/api/v4/projects/<project-id>/packages/pypi/files/f07 ... 397/my_package-0.0.5.dev1-py3-none-any.whl (16 kB)
Downloading https://gitlab.com/api/v4/projects/<project-id>/packages/pypi/files/775 ... 70e/my_package-0.0.4-py3-none-any.whl (16 kB)
...
Successfully installed my-package-0.0.4
So pip does see the dev package version, but chooses the earlier one nonetheless.
In an attempt to figure out what's going on, I published a 0.0.5 version: Error persists, pip sees all three versions, but still installs 0.0.4.
In a further, increasingly desperate attempt, I removed any versions prior to 0.0.5* from the gitlab.com package registry.
Only now, pip would bother to actually display some useful information:
$ (same command as above)
... (similar output as above) ...
ERROR: Cannot install my-package==0.0.5 and my-package==0.0.5.dev1 because these package versions have conflicting dependencies.
The conflict is caused by:
my-package 0.0.5 depends on my-other-package<0.2.5 and >=0.2.4
my-package 0.0.5.dev1 depends on my-other-package<0.2.5 and >=0.2.4
To fix this you could try to:
1. loosen the range of package versions you've specified
2. remove package versions to allow pip attempt to solve the dependency conflict
ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/topics/dependency-resolution/#dealing-with-dependency-conflicts
OK, so there is something wrong with my package dependencies. Thanks for letting me know.
Seriously - I tried hard for a couple of hours using all kinds of pip ... -vvv and/or fixed versions such as e.g. my-package==0.0.5.dev1 - but I did not manage to get any useful output out of pip - until I wiped the entire history from my package registry 🤬.
Hope this at least helps someone in the same situation.
I found that if you use microversions, pip doesn't seem to recognize them. For example, we couldn't get version 1.9.9.1 to upgrade.
In my case, someone had published the latest version of a package with python2, so attempting to pip3 install it grabbed an older version that had been built with python3.
Handy things to check when debugging this:
If pip install claims to not be able to find the version, see whether pip search can see it.
Take a look at the "Download Files" section on the pypi repo -- the filenames might suggest what's wrong (in my case i saw -py2- there clear as day).
As suggested by others, try running pip install --no-cache-dir in case pip isn't bothering to ask the internet because it already has your answer locally.
I had hidden unversioned files under the Git tab in PyCharm that were being installed with pip install . even though I didn't see the files anywhere else.
Took a long time to find it for me, posting this in hope that it'll help somebody else.
if you need the path for your package do pip -v list. Example see related post when using pip -e Why is an old version of a package of my python library installing by itself with pip -e?
A tweet reads:
Don't use easy_install, unless you
like stabbing yourself in the face.
Use pip.
Why use pip over easy_install? Doesn't the fault lie with PyPI and package authors mostly? If an author uploads crap source tarball (eg: missing files, no setup.py) to PyPI, then both pip and easy_install will fail. Other than cosmetic differences, why do Python people (like in the above tweet) seem to strongly favor pip over easy_install?
(Let's assume that we're talking about easy_install from the Distribute package, that is maintained by the community)
From Ian Bicking's own introduction to pip:
pip was originally written to improve on easy_install in the following ways
All packages are downloaded before installation. Partially-completed installation doesn’t occur as a result.
Care is taken to present useful output on the console.
The reasons for actions are kept track of. For instance, if a package is being installed, pip keeps track of why that package was required.
Error messages should be useful.
The code is relatively concise and cohesive, making it easier to use programmatically.
Packages don’t have to be installed as egg archives, they can be installed flat (while keeping the egg metadata).
Native support for other version control systems (Git, Mercurial and Bazaar)
Uninstallation of packages.
Simple to define fixed sets of requirements and reliably reproduce a set of packages.
Many of the answers here are out of date for 2015 (although the initially accepted one from Daniel Roseman is not). Here's the current state of things:
Binary packages are now distributed as wheels (.whl files)—not just on PyPI, but in third-party repositories like Christoph Gohlke's Extension Packages for Windows. pip can handle wheels; easy_install cannot.
Virtual environments (which come built-in with 3.4, or can be added to 2.6+/3.1+ with virtualenv) have become a very important and prominent tool (and recommended in the official docs); they include pip out of the box, but don't even work properly with easy_install.
The distribute package that included easy_install is no longer maintained. Its improvements over setuptools got merged back into setuptools. Trying to install distribute will just install setuptools instead.
easy_install itself is only quasi-maintained.
All of the cases where pip used to be inferior to easy_install—installing from an unpacked source tree, from a DVCS repo, etc.—are long-gone; you can pip install ., pip install git+https://.
pip comes with the official Python 2.7 and 3.4+ packages from python.org, and a pip bootstrap is included by default if you build from source.
The various incomplete bits of documentation on installing, using, and building packages have been replaced by the Python Packaging User Guide. Python's own documentation on Installing Python Modules now defers to this user guide, and explicitly calls out pip as "the preferred installer program".
Other new features have been added to pip over the years that will never be in easy_install. For example, pip makes it easy to clone your site-packages by building a requirements file and then installing it with a single command on each side. Or to convert your requirements file to a local repo to use for in-house development. And so on.
The only good reason that I know of to use easy_install in 2015 is the special case of using Apple's pre-installed Python versions with OS X 10.5-10.8. Since 10.5, Apple has included easy_install, but as of 10.10 they still don't include pip. With 10.9+, you should still just use get-pip.py, but for 10.5-10.8, this has some problems, so it's easier to sudo easy_install pip. (In general, easy_install pip is a bad idea; it's only for OS X 10.5-10.8 that you want to do this.) Also, 10.5-10.8 include readline in a way that easy_install knows how to kludge around but pip doesn't, so you also want to sudo easy_install readline if you want to upgrade that.
Another—as of yet unmentioned—reason for favoring pip is because it is the new hotness and will continue to be used in the future.
The infographic below—from the Current State of Packaging section in the The Hitchhiker's Guide to Packaging v1.0—shows that setuptools/easy_install will go away in the future.
Here's another infographic from distribute's documentation showing that Setuptools and easy_install will be replaced by the new hotness—distribute and pip. While pip is still the new hotness, Distribute merged with Setuptools in 2013 with the release of Setuptools v0.7.
Two reasons, there may be more:
pip provides an uninstall command
if an installation fails in the middle, pip will leave you in a clean state.
REQUIREMENTS files.
Seriously, I use this in conjunction with virtualenv every day.
QUICK DEPENDENCY MANAGEMENT TUTORIAL, FOLKS
Requirements files allow you to create a snapshot of all packages that have been installed through pip. By encapsulating those packages in a virtualenvironment, you can have your codebase work off a very specific set of packages and share that codebase with others.
From Heroku's documentation https://devcenter.heroku.com/articles/python
You create a virtual environment, and set your shell to use it. (bash/*nix instructions)
virtualenv env
source env/bin/activate
Now all python scripts run with this shell will use this environment's packages and configuration. Now you can install a package locally to this environment without needing to install it globally on your machine.
pip install flask
Now you can dump the info about which packages are installed with
pip freeze > requirements.txt
If you checked that file into version control, when someone else gets your code, they can setup their own virtual environment and install all the dependencies with:
pip install -r requirements.txt
Any time you can automate tedium like this is awesome.
pip won't install binary packages and isn't well tested on Windows.
As Windows doesn't come with a compiler by default pip often can't be used there. easy_install can install binary packages for Windows.
UPDATE: setuptools has absorbed distribute as opposed to the other way around, as some thought. setuptools is up-to-date with the latest distutils changes and the wheel format. Hence, easy_install and pip are more or less on equal footing now.
Source: http://pythonhosted.org/setuptools/merge-faq.html#why-setuptools-and-not-distribute-or-another-name
As an addition to fuzzyman's reply:
pip won't install binary packages and isn't well tested on Windows.
As Windows doesn't come with a compiler by default pip often can't be
used there. easy_install can install binary packages for Windows.
Here is a trick on Windows:
you can use easy_install <package> to install binary packages to avoid building a binary
you can use pip uninstall <package> even if you used easy_install.
This is just a work-around that works for me on windows.
Actually I always use pip if no binaries are involved.
See the current pip doku: http://www.pip-installer.org/en/latest/other-tools.html#pip-compared-to-easy-install
I will ask on the mailing list what is planned for that.
Here is the latest update:
The new supported way to install binaries is going to be wheel!
It is not yet in the standard, but almost. Current version is still an alpha: 1.0.0a1
https://pypi.python.org/pypi/wheel
http://wheel.readthedocs.org/en/latest/
I will test wheel by creating an OS X installer for PySide using wheel instead of eggs. Will get back and report about this.
cheers - Chris
A quick update:
The transition to wheel is almost over. Most packages are supporting wheel.
I promised to build wheels for PySide, and I did that last summer. Works great!
HINT:
A few developers failed so far to support the wheel format, simply because they forget to
replace distutils by setuptools.
Often, it is easy to convert such packages by replacing this single word in setup.py.
Just met one special case that I had to use easy_install instead of pip, or I have to pull the source codes directly.
For the package GitPython, the version in pip is too old, which is 0.1.7, while the one from easy_install is the latest which is 0.3.2.rc1.
I'm using Python 2.7.8. I'm not sure about the underlay mechanism of easy_install and pip, but at least the versions of some packages may be different from each other, and sometimes easy_install is the one with newer version.
easy_install GitPython