I own an off grid server (linux x86_64 with python3.8) on which I would like to install pypi packages. Up to now, I download manually each package and dependencies from pypi.org. And it is painful.
I would like to use pip3 download feature which is perfect for my needs ! Except the platform I download from is very different (win_amd64 with python3.6). Since I want to get numpy-1.18.5-cp38-cp38-manylinux1_x86_64.whl, I tried :
pip3 download --verbose --only-binary=:all: --abi=cp38 --platform=manylinux1_x86_64 numpy
And, checking the verbose log, for the line which correspond to the expected wheel, I get a it is not compatible with this Python... Of course it isn't !
How could I force pip3 to download the appropriate .whl file even if it is not the correct one for the platform I download it from ?
If you're passing --abi, you have to also pass the --python-version so pip can build the correct platform tag. This should work (untested):
$ pip3 download --only-binary=:all: --python-version=38 --abi=cp38 --platform=manylinux1_x86_64 numpy
Although admitted, pip download is anything but reliable, e.g. when it comes to resolving environment markers and stuff.
Related
I realize that a requirements.txt file can be used to pin the versions used in pip install. Sometimes I don't want to go through all that- and just simply want to protect my installation from downgrades. Is there any way to instruct pip install to do that?
An example: I just installed librosa and it downgraded numpy from 1.24.1 to 1.23.5 . I don't want that behavior to happen unless I explicitly request. On the other hand if there are missing dependencies then please let's grab them.
For this installation of python it is acceptable to take the risk of occasionally ending up with a mismatch due to installing newer versions [ but I don't want older ones].
I'm having issues with pip failing to install editable packages from a local directory. I was able to install the packages manually using commands like pip install -e pkg1. I wanted to use a requirements.txt file to automate future installs, because my coworkers will be working on the same packages. My ideal development workflow is for each developer to checkout the source from version control and run pip install -r requirements.txt. The requirements file would designate all the packages as editable so we can import our code without the need for .pth files but we wouldn't have to keep updating our environments. And by using namespace packages, we can decouple the import semantics from the file structures.
But it's not working out.
I have a directory with packages like so:
index/
pkg1/
src/
pkg1/
__init__.py
pkg1.py
setup.py
pkg2/
src/
...etc.
Each setup.py file contains something like:
from setuptools import setup, find_packages
setup(
name="pkg1",
version="0.1",
packages=find_packages('src'),
package_dir={'':'src'},
)
I generated my requirements.txt file using pip freeze, which yielded something like this:
# Editable install with no version control (pkg1==0.1)
-e c:\source\pkg1
# Editable install with no version control (pkg2==0.1)
-e c:\source\pkg2
...etc...
I was surprised when pip choked on the requirements file that it created for itself:
(venv) C:\Source>pip install -r requirements.txt
c:sourcepkg1 should either be a path to a local project or a VCS url beginning with svn+, git+, hg+, or bzr+
Also, some of our packages rely on other of our packages and pip has been absolutely useless at identifying these dependencies. I have resorted to manually installing packages in dependency order.
Maybe I'm pushing pip to its limits here. The documentation and help online has not been helpful, so far. Most sources discuss editable installation, installation from requirements files, package dependencies, or namespace packages, but never all these concepts at once. Usually when the online help is scarce, it means that I'm trying to use a tool for something it wasn't intended to do or I've discovered a bug.
Is this development process viable? Do I need to make a private package index or something?
How would I install things on Pepper, since I don't know what package manager it uses. I usually use apt on my Ubuntu machine and want to install some packages on Pepper. I'm not sure what package manager Pepper has (if any) and want to install some packages, but also only know the name of the package using apt (not sure if the package name is the same on other package managers). And if possible, would I be able to install apt on Pepper. Thanks.
Note: From the research I've done, Pepper is using NaoQi which is based off Gentoo which uses portage.
You don't have root access on Pepper, which limits what you can install (and apt isn't on the robot anyway).
Some possibilities:
Include your content in Choregraphe projects - when you install a package, the whole directory structure is installed (more exactly, what's listed in the .pml); so you can put arbitrary files on your robot, and you can usually include whatever dependencies your code needs.
Install python packages with pip.
In NAOqi 2.5, a slightly older version of pip is installed that will not always work out of the box; I recommend upgrading it:
pip install --user --upgrade pip
... you can then use the upgraded pip to install other packages, using the upgraded pip, and always --user:
/home/nao/.local/bin/pip install --user whatever-package-you-need
Note however that if you do this and use your packages in your code running on Pepper, that code won't work on other robots until you do pip on them, which is why I usually only do this for tests; for production code I prefer packaging all dependencies in my app's package.
As a workaround if you need to install software (or just newer versions of software) using Gentoo Prefix is an option.
Gentoo Prefix builds a Gentoo OS on any location (no need of root, can be any folder). It includes it's own portage (package manager) to install new software.
I maintain a few projects to work with Pepper and use "any" software I want. Note that they are built for 64b (amd64) and 32b (x86) even though for Pepper only the 32b matter.
gentoo_prefix_ci and gentoo_prefix_ci_32b Which builds nightly the bootstrap of the Gentoo Prefix system. This is a process that takes a while to compile (3-6h depending on your machine) and that breaks from time to time (as upstream packages are updated and bugs are found, Gentoo is a rolling release distribution). Every night updated binary images ready to use can be found in the Releases section.
For ROS users that want to run it on the robot, based on the previous work, I maintain also ros_overlay_on_gentoo_prefix and ros_overlay_on_gentoo_prefix_32b. They provide nightly builds with binary releases of ROS Kinetic and ROS Melodic over Gentoo Prefix using ros-overlay. You can find ready-to-use 'ros_base' and 'desktop' releases.
For purposes related to the RoboCup#Home Social Standard Platform League where the Pepper robot is used I also maintain a specific build that contains a lot of additional software. This project is called pepper_os and it builds 270+ ROS packages, a lot of Python packages (250+ including Theano, dlib, Tensorflow, numpy...) and all the necessary dependencies for these to build (750+ packages). Note that the base image (it's built with Docker) is the actual Pepper 2.5.5.5 image, so it can be used for debugging as it if it was in the real robot (although without sensors and such).
Maybe this approach, or these projects, are useful.
The package manager on pepper is disabled. But you can copy the files to the robot and write your own service that imports any package you might need.
As a supplement on importing:
http://www.about-robots.com/how-to-import-python-files-in-your-pepper-apps.html
To get rid of error :
" SSL3_GET_SERVER_CERTIFICATE:certificate verify failed
".
If you use python and requests package, just add verify=False at the end of your parameters.
r=requests.get(URL,params,header,verify=False)
Works with my Pepper
To get rid of
InsecurePlatformWarning: A true SSLContext object is not available.
install
/home/nao/.local/bin/pip install --user requests[security]
To get rid of:
CryptographyDeprecationWarning: Support for your Python version is deprecated.
install
/home/nao/.local/bin/pip install --user cryptography==2.2.2
If it based on Gentoo maybe we could try to install portage with pip.
pip install portage
Just a thought.
Jython Package installation issue, using pip
Hi, I have installed Jython2.7 configured with pydev in eclipse neon, also configured python 3.6 package
I am able to install packages for python using pip installer?
pip install "packagename"
Below are some of the packages in python/Lib/Site-packages directory
I was able to install all the packages
How do I use pip installer to install packages for jython?
I tried to install Jip package with
jython install setup.py
The binary File got installed in the Jython/Lib/Site-packages folder
However, I am not able to use it.
where and how do I get Jython package binaries like jip?
Also, Please let me know how to search jython packages?
Also, How to make pip install library packages in jython?
Any other configuration like jython home, etc that should be made?
This answer is going to be really generic but I just recently have slogged my way through the setup for jython/jip/pip and here's roughly what I had to do.
Firstly, I'm running Windows 7 64 Bit from behind a proxy (work machine.)
Had to install jython 2.7.0 instead of 2.7.1 because (I think anyway) 2.7.1 requires admin privileges which I don't have on my work PC.
Pip didn't install correctly during the Jython installation and I spent an obscene amount of time trying to get it installed and functioning as I knew it from my cpython days. NOTE: Just because you get pip installed, doesn't mean you can use any package on a python package repo. As of 2.7.0, Jython doesn't have end to end capability to interpret/compile some libraries that rely on certain python wrappers of native OS function calls. I believe 2.7.1 makes solid progress in the direction of supporting all needed native calls but don't quote me on that. For example, I tried to use wxPython to make a simple GUI to test my jython install. Trying to install it from pip kept causing really non-specific error info that took me a lot of time to figure out that the cause was jython simply couldn't compile the wxPython source so beware.
I had to set environment variables 'http_proxy' and 'https_proxy' in the form of http://proxyhosturl:port and https://proxyhosturl:port respectively to get out from behind the proxies without having to invoke pip with the proxy switch every time I called it.
To actually install pip, have a look here. These instructions are for Python and Linux/Unix but the principle is roughly the same. Just use jython -m instead of python -m and ignore the '$' at the start of each command line.
Also be sure to CD to your python_home/bin folder when invoking the ez_install exe.
If that doesn't work (didn't for me), try using get-pip.py script with these instructions https://pip.pypa.io/en/stable/installing/ (remember jython instead of python etc.). Download it, cd to the download location and follow the noted install steps. Worth noting is about half way down the install instructions where it details installing from local archives (source/binary zip or tar.gz archives of pip and setuptools as better described here: https://packaging.python.org/tutorials/installing-packages/#installing-from-local-archives).
The links to the bin archives of pip and setuptools are here:
https://pypi.python.org/pypi/setuptools
https://pypi.python.org/pypi/pip
It may also be worth making sure that your PATH environment variable has the jython/bin path in the variable value. The jython installer should do this but, again, mine did not.
If all goes well, you should be able to invoke pip with the --version switch and if it prints a line with the installed pip version info then you should be good to go
Another quirky issue I had was I could invoke a function of pip one time and any subsequent times I would get a stack trace ending with something along the lines of an object not having a certain property. I fixed that by finding my temp directory by opening a windows explorer instance and typing %TEMP% in the address bar and hitting enter, it should take you to a subdir of your AppData folder and there you may see a folder with the name of the package you were trying to install and the text "_pip" somewhere in the directory name. Delete the directory and try the pip install command again. I had to do this + invoke pip install pip -U to update my install to the latest version. Then pip began behaving correctly in my instance.
pip search numpy (or your library name) will generate a list of results with the same logic it uses to locate your desired package when you call pip install but, again, just because it returns a matching package doesn't mean it will compile when you install it (numpy doesn't work because of the missing java to C native function calls I described earlier.) The trade off is that you can import code artifacts from Java JAR files in your Jython script files and leverage their functionality with relative ease. Between the public Java APIs available and the python packages that work with the jython interpretor, you can (in my experience) come up with a way to accomplish your task. See the following info on JIP, Maven and IDEs.
IDE and jython integration (Eclipse)
- If you are stuck using Eclipse (like me) it actually has pretty decent support for python development. Install the PyDev plugin for Eclipse from Help -> Install Software. Put in this URL https://marketplace.eclipse.org/content/pydev-python-ide-eclipse, hit tab, and select the PyDev plugin and hit 'finish.'
- Setup the jython interpretor info from Windows -> Preferences -> PyDev. Provide the path to your jython.jar file.
- You should now be able to use File -> New PyDev project to create a basic python project and configure it to use your version of Jython and Java.
Brief Overview of Jip and Maven
- jip is a jython package that is invoked very similarly to pip but instead will download JAR files from the Maven Central Repository instead of python packages from pypi.com, for example. See the install instructions described here. Note the install procedure for a global jip install which differ from just pip install jip. https://pypi.python.org/pypi/jip/
- I never got jip to work exactly as I wished because there's not a ton of documentation on it outside of what I already linked. However, if you install a JAR using jip, you have to go to your project in Eclipse and actually add the JARs themselves to your PYTHONPATH in order for import statements and editing to have intellisense and so that you don't get a classnotfound exception at runtime. See following screen shot.
- There is a JIP config file that you can use similar to the pip config ini file but I have yet to find any exhaustive documentation on it's setup.
Note in the above screen shot the first entry in the External libraries entries. By default, pip places installed packages in that directory so to enable eclipse to find them, you need to also ensure that location is entered.
In Conclusion
- I have more to add to this answer and I will do so as soon as possible. In the meantime, see this example project I've loaded into github.
https://github.com/jheidlage1222/jython_java_integration_example
It shows basic config and how to interface with JARs from python code. I used the apache httpcomponents library as an example. Good luck amigo.
I got Numpy and Matplotlib running on Heroku, and I'm trying to install Scipy as well. However, Scipy requires BLAS[1] to install, which is not presented on the Heroku platform. After contacting Heroku support, they suggested me to build BLAS as a static library to deploy, and setup the necessary environment variables.
So, I compiled libblas.a on a 64bit Linux box, and set the following variables as described in [2] :
$ heroku config
BLAS => .heroku/vendor/lib/libfblas.a
LD_LIBRARY_PATH => .heroku/vendor/lib
LIBRARY_PATH => .heroku/vendor/lib
PATH => bin:/usr/local/bin:/usr/bin:/bin
PYTHONUNBUFFERED => true
After adding scipy==0.10.1 in my requirements.txt, the push still fails.
File "scipy/integrate/setup.py", line 10, in configuration
blas_opt = get_info('blas_opt',notfound_action=2)
File "/tmp/build_h5l5y31i49e8/lib/python2.7/site-packages/numpy/distutils/system_info.py", line 311, in get_info
return cl().get_info(notfound_action)
File "/tmp/build_h5l5y31i49e8/lib/python2.7/site-packages/numpy/distutils/system_info.py", line 462, in get_info
raise self.notfounderror(self.notfounderror.__doc__)
numpy.distutils.system_info.BlasNotFoundError:
Blas (http://www.netlib.org/blas/) libraries not found.
Directories to search for the libraries can be specified in the
numpy/distutils/site.cfg file (section [blas]) or by setting
the BLAS environment variable.
It seem that pip is not aware of the BLAS environment variable, so I check the environment using heroku run python:
(venv)bash-3.2$ heroku run python
Running python attached to terminal... import up, run.1
Python 2.7.2 (default, Oct 31 2011, 16:22:04)
[GCC 4.4.3] on linux2
Type "help", "copyright", "credits" or "license" for more information.
>>> import os
>>> os.system('bash')
~ $ echo $BLAS
.heroku/vendor/lib/libfblas.a
~ $ ls .heroku/vendor/lib/libfblas.a
.heroku/vendor/lib/libfblas.a
~ $
And it seems fine. Now I have no idea how to solve this.
[1] http://www.netlib.org/blas/
[2] http://www.scipy.org/Installing_SciPy/Linux
I managed to get this working on the cedar stack by building numpy and scipy offline as bdists and then modifying the heroku python buildpack to unzip these onto the dyno's vendor/venv areas directly. You can also use the buildpack to set environment variables that persist.
Heroku haven't officially published buildpacks yet - search for 'heroku buildpacks' for more thirdparty/heroku ones and information.
My fork of the python build pack is here:
https://wyn#github.com/wyn/heroku-buildpack-python.git
The changes are in the bin/compile where I source two new steps, a scipy/numpy step and an openopt step. The scripts for the two steps are in bin/steps/npscipy and bin/steps/openopt. I also added some variables to bin/release. Note that I am assuming installation through a setup.py file rather than the requirements.txt approach (c.f. https://devcenter.heroku.com/articles/python-pip#traditional_distributions).
I also download the blas/lapack/atlas/gfortran binaries that were used to build numpy/scipy onto the dyno as there are c extensions that need to link through to them. The reason for building everything offline and downloading is that pip-installing numpy/scipy requires you to have a fortran compiler + associated dev environment and this made my slugs too big.
It seems to work, the slug size is now 35mb and scaling seems fast too. All but one of the numpy tests pass and all of the scipy tests pass.
This is still work in progress for me but I thought I'd share.
In case someone else comes to this like I did...
The excellent answer on this question from #coshx sadly no longer works (at least I could not get it to work). What I did, however was the following:
I forked npsicpy-binaries repository from #coshx and changed all the tar files such that they do not have venv as the root folder inside (my fork is here)
I forked the npsp-helloworld repository from #coshx and made it use a requirements.txt file instead of the setup.py (my fork is here - this means that you can use the whole pip approach).
I forked the heroku-buildpack-python repository from Heroku, took the npscipy file from #coshx and changed it to work with this latest version of the build pack (my fork is here - you can see that there is no venv set up, for example).
After doing those three things I have the npsp-helloworld application working perfectly. You just need to make sure you set the buildpack appropriately when creating the application and you are good to go:
$ heroku create --stack=cedar --buildpack=https://github.com/kmp1/heroku-buildpack-python.git
NOTE: I haven't worked out how to make my own binaries yet, so the three libraries (scipy, numpy and scikit-learn) are not the latest versions so make sure when you install it you do (if anyone can build these I would gladly accept a pull request for them):
pip install scipy==0.11.0
pip install numpy==1.7.0
pip install scikit-learn==0.13.1
By the way - I am really sorry if I have not done things in the correct way, etiquette-wise. I'm still learning git and this whole open source thing.
Another good option is the conda buildpack, which allows you to add any of the free Linux64 packages available through Anaconda/Miniconda to a Heroku app. Some of the most popular packages include numpy, scipy, scikit-learn, statsmodels, pandas, and cvxopt. While the buildpack makes it fairly simple to add packages to an app, the downsides are that the buildback takes up a lot of space and that you have to wait on Anaconda to update the libraries in the repository.
If you are starting a new Python app on Heroku, you can add the conda buildpack using the command:
$ heroku create YOUR_APP_NAME --buildpack https://github.com/kennethreitz/conda-buildpack.git
If you have already setup a Python app on Heroku, you can add the conda buildpack to the existing app using the command:
$ heroku config:add BUILDPACK_URL=https://github.com/kennethreitz/conda-buildpack.git
Or, if you need to specify the app by name:
$ heroku config:add BUILDPACK_URL=https://github.com/kennethreitz/conda-buildpack.git --app YOUR_APP_NAME
To use the buildpack, you will need to include two text files in the app directory, requirements.txt and conda-requirements.txt. Just as with the standard Python buildpack, the requirements.txt file lists packages that should be installed using pip. Packages that should be installed using conda are listed in the conda-requirements.txt file. Some of the most useful scientific packages include numpy, scipy, scikit-learn, statsmodels, pandas, and cvxopt. The full list of available conda packages can be found at repo.continuum.io.
For example:
$ cat requirements.txt
gunicorn==0.14.2
requests==0.11.1
$ cat conda-requirements.txt
scipy
numpy
cvxopt
That’s it! You can now add Anaconda packages to a Python app on Heroku.
The slug compiler is not aware of your environment variables, which is why it fails during push, and not once running.
The only real option you have is to look at the user_env_compile addon that's currently in labs beta.
http://devcenter.heroku.com/articles/labs-user-env-compile
For those who wish to use Python 3.4 in production, I've built numpy 1.8.1, scipy 0.14.0, and scikit-learn 0.15-git (0.14 doesn't work with the others for some reason) as binaries on Ubuntu 10.04 LTS 64-bit, which works on the Heroku cedar stack. Here is the git repo.
My heroku buildpack is quite similar to that of kmp. Note that the bin/steps/npscipy file links to my repository of binaries above.
I'm putting this here in case someone stumbled on this like I do. Since I am using python 3.4, thenovices buildpack doesn't work out for me.
I tried to look at conda buildpack, but it seems like a little bit of an overkill for my application which only requires scipy and numpy. What finally works for me is this excellent guide, using a multi buildpacks approach. The scipy installation was indeed a bit long though. Hope this helps! :)