I notice if I am trying to remove huge conda packages that occupy hundreds of megabytes in space, running conda remove <package> will take forever. Some examples of these huge packages are pystan, spacy-model-en_core_web_lg.
It is stuck at with no error messages;
Collecting package metadata (repodata.json): done
Solving environment:
Any hints how to fix this problem?
I am using anaconda, python 3.8, windows 10.
Conda's remove operation still needs to satisfy all the other specifications for the environment, so Conda invokes its solver and this can be complicated. Essentially, it re-solves the entire environment sans the specified package, compares that against the existing state, then makes a plan based on the difference.
I very much doubt there is anything directly impactful about size of package, which OP alludes to. Instead, things that negatively impact solving are:
having a large environment (e.g., anaconda package is installed)
channel mixing - in particular, including the conda-forge channel at equal or higher priority as defaults in an environment with the anaconda package; that package and all its dependencies are intended to be sourced from the anaconda channel
having an underspecified environment (see conda env export --from-history to see your explicit specifications); e.g., an environment with a python=3.8 specification will be easier on the solver than just a python specification
In general, using smaller specialized (e.g., per-project) environments, rather than large monolithic ones helps avoid such problems. The anaconda package is particularly problematic.
Try Mamba
Other than adopting better practices, one can also get significantly faster solves with Mamba, a drop-in compiled replacement for conda. Try it out:
## install Mamba in base env
conda install -n base conda-forge::mamba
## use it like you would the 'conda' command
mamba remove -n foo bar
everyone:
Because of the speed of network, when I conda install some packages, there will exist some related packages can not be downloaded completely. But we can not install packages have been downloaded successfully without other "related" packages(maybe "related" means the best march in version, but not necessary).
For example, When I install pytorch, it need numpy-1.14.2, but I am with numpy-1.15.1. I don't need verson 1.14.2 numpy in practice.
So I am a little confused how to make "conda" trying to install packages have been downloaded successfully, ignoring download failed packages?
Thanks!
EricKani
From the conda documentation there are two options that may help https://docs.conda.io/projects/conda/en/latest/commands/install.html
--no-update-deps
Do not update or change already-installed dependencies.
--no-deps
Do not install, update, remove, or change dependencies. This WILL lead to broken environments and inconsistent behavior. Use at your own
risk.
I believe by default conda tries with --no-update-deps first and then if that fails tries to update deps; giving it that option will make sure some version of each needed package is installed, if not necessarily the latest.
You could try --no-deps as well, which will literally prevent conda from installinh ANYTHING other than the exact packages you tell it to, but things may not work with that.
I'm using a station without admin rights and without pip. I need to use PyCharm (already installed) so as a workaround I installed Anaconda Navigator (doesn't require admin) and am using an environment in Anaconda as my interpreter in PyCharm.
I'm a bit confused regarding the conda install and the packages offered there. Are they all the same as the ones offered by the Python Package Index? Do developers only upload their work once to pypi.org and then it appears on both pip and conda installations or does it not include every single python package out there?
Thanks and I apologize if the question doesn't belong to this section of stack exchange.
First here is a link to an anther great post with a similar question: What is the difference between pip and conda?
But here is a response from my point of view and understanding:
Pip libraries specifically focus on packages related to the python. Conda uses those too, however, it also provides packages not related to python.
The best package example available is HDF5 it was not originally integrated into pip and Conda had their own hdf5 package. Pip has a similar package called h5py.
Also, conda's virtualization environments are what so appealing about it. In a way, Conda is like Docker.
Conda Hdf5: https://anaconda.org/anaconda/hdf5
Pip h5py: https://pypi.org/project/h5py/
Conda Cloud has the ability to read the PyPi libraries index, so it will be aware of newly uploaded packages.
Sorry if my response was not clear enough! English is not my first language, plus I was in the same boat as you a year ago.
I have a conda environment for one of my projects. It contains a setup.py that defines an install_requirements option. Conda seems to insist on using its own channels on all of the requirements. Some do not exist in the Conda catalogue though, but can definitely be installed through pip.
Is there a way to tell python setup.py install to use pip on these particular requirements? Preferably inside the setup.py?
No, there is no way to use pip to install packages during the conda build process. Conda insists on using conda packages as dependencies for all conda packages. In my opinion, this is a good restriction because it ensures you'll have a self-consistent environment and until very recently, conda and pip did not play very nicely together. In addition, pip has its own dependency solver that may give different/incompatible versions of dependent packages to the ones that conda would solve for.
For pure Python packages, its not very hard to generate a conda package, and you can upload it to conda forge so that it is generally available. See the conda-forge website, which states
Fork conda-forge/staged-recipes
Create a new branch from the staged-recipes master branch.
Add a new conda recipe in the "recipes" directory. There is an example of a well written recipe there. Further guidance on writing good recipes.
Propose the change as a pull request. Your recipe will automatically be built on Windows, Linux and OSX to test that it works, but the distribution will not yet be available on the conda-forge channel.
Once the recipe is ready it will be merged and new "feedstock" repository will automatically be created for the recipe. The build and upload processes take place in the feedstock, and once complete the package will be available on the conda-forge channel
I recently discovered Conda after I was having trouble installing SciPy, specifically on a Heroku app that I am developing.
With Conda you create environments, very similar to what virtualenv does. My questions are:
If I use Conda will it replace the need for virtualenv? If not, how do I use the two together? Do I install virtualenv in Conda, or Conda in virtualenv?
Do I still need to use pip? If so, will I still be able to install packages with pip in an isolated environment?
Conda replaces virtualenv. In my opinion it is better. It is not limited to Python but can be used for other languages too. In my experience it provides a much smoother experience, especially for scientific packages. The first time I got MayaVi properly installed on Mac was with conda.
You can still use pip. In fact, conda installs pip in each new environment. It knows about pip-installed packages.
For example:
conda list
lists all installed packages in your current environment.
Conda-installed packages show up like this:
sphinx_rtd_theme 0.1.7 py35_0 defaults
and the ones installed via pip have the <pip> marker:
wxpython-common 3.0.0.0 <pip>
Short answer is, you only need conda.
Conda effectively combines the functionality of pip and virtualenv in a single package, so you do not need virtualenv if you are using conda.
You would be surprised how many packages conda supports. If it is not enough, you can use pip under conda.
Here is a link to the conda page comparing conda, pip and virtualenv:
https://docs.conda.io/projects/conda/en/latest/commands.html#conda-vs-pip-vs-virtualenv-commands.
I use both and (as of Jan, 2020) they have some superficial differences that lend themselves to different usages for me. By default Conda prefers to manage a list of environments for you in a central location, whereas virtualenv makes a folder in the current directory. The former (centralized) makes sense if you are e.g. doing machine learning and just have a couple of broad environments that you use across many projects and want to jump into them from anywhere. The latter (per project folder) makes sense if you are doing little one-off projects that have completely different sets of lib requirements that really belong more to the project itself.
The empty environment that Conda creates is about 122MB whereas the virtualenv's is about 12MB, so that's another reason you may prefer not to scatter Conda environments around everywhere.
Finally, another superficial indication that Conda prefers its centralized envs is that (again, by default) if you do create a Conda env in your own project folder and activate it the name prefix that appears in your shell is the (way too long) absolute path to the folder. You can fix that by giving it a name, but virtualenv does the right thing by default.
I expect this info to become stale rapidly as the two package managers vie for dominance, but these are the trade-offs as of today :)
EDIT: I reviewed the situation again in 04/2021 and it is unchanged. It's still awkward to make a local directory install with conda.
Virtual Environments and pip
I will add that creating and removing conda environments is simple with Anaconda.
> conda create --name <envname> python=<version> <optional dependencies>
> conda remove --name <envname> --all
In an activated environment, install packages via conda or pip:
(envname)> conda install <package>
(envname)> pip install <package>
These environments are strongly tied to conda's pip-like package management, so it is simple to create environments and install both Python and non-Python packages.
Jupyter
In addition, installing ipykernel in an environment adds a new listing in the Kernels dropdown menu of Jupyter notebooks, extending reproducible environments to notebooks. As of Anaconda 4.1, nbextensions were added, adding extensions to notebooks more easily.
Reliability
In my experience, conda is faster and more reliable at installing large libraries such as numpy and pandas. Moreover, if you wish to transfer your preserved state of an environment, you can do so by sharing or cloning an env.
Comparisons
A non-exhaustive, quick look at features from each tool:
Feature
virtualenv
conda
Global
n
y
Local
y
n
PyPI
y
y
Channels
n
y
Lock File
n
n
Multi-Python
n
y
Description
virtualenv creates project-specific, local environments usually in a .venv/ folder per project. In contrast, conda's environments are global and saved in one place.
PyPI works with both tools through pip, but conda can add additional channels, which can sometimes install faster.
Sadly neither has an official lock file, so reproducing environments has not been solid with either tool. However, both have a mechanism to create a file of pinned packages.
Python is needed to install and run virtualenv, but conda already ships with Python. virtualenv creates environments using the same Python version it was installed with. conda allows you to create environments with nearly any Python version.
See Also
virtualenvwrapper: global virtualenv
pyenv: manage python versions
mamba: "faster" conda
In my experience, conda fits well in a data science application and serves as a good general env tool. However in software development, dropping in local, ephemeral, lightweight environments with virtualenv might be convenient.
Installing Conda will enable you to create and remove python environments as you wish, therefore providing you with same functionality as virtualenv would.
In case of both distributions you would be able to create an isolated filesystem tree, where you can install and remove python packages (probably, with pip) as you wish. Which might come in handy if you want to have different versions of same library for different use cases or you just want to try some distribution and remove it afterwards conserving your disk space.
Differences:
License agreement. While virtualenv comes under most liberal MIT license, Conda uses 3 clause BSD license.
Conda provides you with their own package control system. This package control system often provides precompiled versions (for most popular systems) of popular non-python software, which can easy ones way getting some machine learning packages working. Namely you don't have to compile optimized C/C++ code for you system. While it is a great relief for most of us, it might affect performance of such libraries.
Unlike virtualenv, Conda duplicating some system libraries at least on Linux system. This libraries can get out of sync leading to inconsistent behavior of your programs.
Verdict:
Conda is great and should be your default choice while starting your way with machine learning. It will save you some time messing with gcc and numerous packages. Yet, Conda does not replace virtualenv. It introduces some additional complexity which might not always be desired. It comes under different license. You might want to avoid using conda on a distributed environments or on HPC hardware.
Another new option and my current preferred method of getting an environment up and running is Pipenv
It is currently the officially recommended Python packaging tool from Python.org
Conda has a better API no doubt. But, I would like to touch upon the negatives of using conda since conda has had its share of glory in the rest of the answers:
Solving environment Issue - One big thorn in the rear end of conda environments. As a remedy, you get advised to not use conda-forge channel. But, since it is the most prevalent channel and some packages (not just trivial ones, even really important ones like pyspark) are exclusively available on conda-forge you get cornered pretty fast.
Packing the environment is an issue
There are other known issues as well. virtualenv is an uphill journey but, rarely a wall on the road. conda on the other hand, IMO, has these occasional hard walls where you just have to take a deep breath and use virtualenv
1.No, if you're using conda, you don't need to use any other tool for managing virtual environments (such as venv, virtualenv, pipenv etc).
Maybe there's some edge case which conda doesn't cover but virtualenv (being more heavyweight) does, but I haven't encountered any so far.
2.Yes, not only can you still use pip, but you will probably have to. The conda package repository contains less than pip's does, so conda install will sometimes not be able to find the package you're looking for, more so if it's not a data-science package.
And, if I remember correctly, conda's repository isn't updated as fast/often as pip's, so if you want to use the latest version of a package, pip might once again be your only option.
Note: if the pip command isn't available within a conda virtual environment, you will have to install it first, by hitting:
conda install pip
Yes, conda is a lot easier to install than virtualenv, and pretty much replaces the latter.
I work in corporate, behind several firewall with machine on which I have no admin acces
In my limited experience with python (2 years) i have come across few libraries (JayDeBeApi,sasl) which when installing via pip threw C++ dependency errors
error: Microsoft Visual C++ 14.0 is required. Get it with "Microsoft Visual C++ Build Tools": http://landinghub.visualstudio.com/visual-cpp-build-tools
these installed fine with conda, hence since those days i started working with conda env.
however it isnt easy to stop conda from installing dependency inside c.programfiles where i dont have write access.