I'm using python through Anaconda, and would like to use a new feature (http://scikit-learn.org/dev/modules/neural_networks_supervised.html) in scikit-learn that's currently only available in the development version 0.18.dev0.
However, doing the classical conda update doesn't seem to work, as conda doesn't list any dev packages. What would be the simplest way to install a development version into my Anaconda? (For what it's worth, I'm using 64-bit windows 7.)
You can only use conda to install a package if someone has built and made available binaries for the package. Some packages publish nightly builds that would allow this, but scikit-learn is not one of them.
To install the bleeding-edge version in one command, you could use pip; e.g.:
$ conda install pip
$ pip install git+git://github.com/scikit-learn/scikit-learn.git
but keep in mind that this requires compiling all the C extensions within the library, and so it will fail if your system is not set up for that.
I had scikit-learn 0.17 which did not have MLPClassifier. I just did a conda update like below:
conda update scikit-learn
conda takes care of updating all dependent packages and after the update it works!
You should build your own scikit-learn package on Anaconda. I did it in about 10 mins (repo)(package). The conda tutorial on how to build packages was helpful. There are probably more ways than one to do this, but I just downloaded the scikit-learn github repo, dropped it into a new repo, added a directory that housed my conda recipe, and then built the package from the recipe which pointed to the source code I just downloaded.
Related
I'm testing poetry and I was wondering if it is possible to install prebuilt packages from conda-forge, as cartopy without relying on conda (so keeping a 100% poetry process). I googled this a bit but the only way I found is to install poetry within a conda venv using pip and then installing from conda-forge using conda and then tweaking poetry files to make it aware of the conda venv so that the TOML is written properly.
Packages like cartopy are a pain to install if not from a prebuilt version, if possible I'd change my conda stack to poetry stack if something like poetry add [?conda-forge?] cartopy works
Thanks.
Not currently possible. Conda is a generic package manager, not just a Python package manager. Furthermore, there is no dedicated metadata in Conda packages to discriminate whether or not they are Python packages, which I think would be a prerequisite for Poetry being able to determine whether the Conda package is even valid for installation. Hence, what OP requests cannot be a thing, or at least would it be a major undertaking to make it one.
However, others have requested similar features, so someone hopeful for such functionality could subscribe to notifications on those, or follow the Feature Roadmap.
I followed the instructions here: Can't find package on Anaconda Navigator. What to do next?
I clicked Open terminal from environment on Anaconda navigator, and then used "pip3 install lmfit" in the terminal. But after installing the lmfit package using pip3, I still cannot find it in the conda list. What should I do?
The Problem
At the time of this question, Conda builds of pip had only just started including a pip3 entrypoint,1 therefore pip3 is very likely referring to a non-Conda version of Python and that is where the package was installed. Try checking which pip3 to find out where it went.
Recommendation
Conda First
Generally, it is preferable to use Conda to install packages in Conda environments, and in this case the package is available via the Conda Forge channel:
conda install -c conda-forge lmfit
Contrary to M. Newville's answer, this recommendation to prefer Conda packages is not about benefiting Conda developers, but instead a rule of thumb to help users avoid creating unstable or unreproducible environments. More info about the risks of mixing pip install and conda install can be found in the post "Using Pip in a Conda Environment".
Nevertheless, the comment that not all packages (and specifically lmfit) are found in the default repository and this complicates installation by requiring resorting to third-party channels is a good point. In fact, because third-parties are free to use different build stacks there are known problems with mixing packages built by Anaconda and those from Conda Forge. However, these issues tend to be rare and limited to compiled code. Additionally, adding trusted channels to a configuration and setting channel priorities heuristically solves the issue.
As for risks in using third-party channels, arbitrary Anaconda Cloud user channels are risky: one should only source packages from channels you trust (just like anything else one installs). Conda Forge in particular is well-reputed and all feedstocks are freely available on GitHub. Moreover, many Python package builds on Conda Forge are simply wrappers around the PyPI build of the package.
PyPI Last
Sometimes it isn't possible to avoid using PyPI. When one must resort to installing from PyPI, it is better practice to use the pip entrypoint from an activate environment, rather than pip3, since only some Conda builds of pip include pip3. For example,
conda activate my_env
pip install lmfit
Again, following the recommendations in "Using Pip in a Conda Environment", one should operate under the assumption that any subsequent calls to conda (install|upgrade|remove) in the environment could have undefined behavior.
PyPI Only
For the sake of completeness, I will note that a stable way of using Conda that is consistent with the recommendations is to limit Conda to the role of environment creation and use pip for all package installation.
This strategy is perhaps the least burden on the Python-only user, who doesn't want to deal with things like finding the Conda-equivalent package name or searching non-default channels. However, its applicability seems limited to Python-only environments, since other libraries may still need to resort to conda install.
[1]: Conda Forge and Anaconda started consistently including pip3 entrypoints for the pip module after version 20.2.
Installing a pure Python package, such as lmfit with the correct version of pip install lmfit should be fine.
Conda first is recommended to make the life of the conda maintainers and packagers easier, not the user's life. FWIW, I maintain both kinds of packages,
and there is no reason to recommend conda install lmfit over pip install lmfit.
In fact, lmfit is not in the default anaconda repository so that installing it requires going to a third-party conda channel such as conda-forge. That adds complexity and risk to your conda environment.
Really, pip install lmfit should be fine.
I used homebrew to install graph-tool on a macOS 10.14 using the command brew install graph-tool, but I cannot import it in python (that is installed through anaconda in /opt/anaconda3/bin/python). How can I make Python able to import graph-tool?
Prefer Conda Packages
If you want to use something in a Conda environment, then the best practice is to install through Conda. In this case, the graph-tool package is available through Conda Forge and can be installed with
conda install -c conda-forge graph-tool
Personally, since I started using Conda in 2016, I've found no reason to use Homebrew or MacPorts. There might be some exceptions, but this package isn't one.
conda install not working
Sometimes the packages you already have installed can conflict with the ones you want to add. This is especially the case when it comes to massive envs like the base in Anaconda. Best practice for using Conda is to avoid installing in base and instead create envs for each project or project type. This also helps you avoid breaking code you've already written because sometimes installs can trigger a version change in a package you were using.
In this case, you could make a new env, say foo, with
conda create --name foo -c conda-forge graph-tool
You can list any additional packages you know you'll need after graph-tool.
Python 3.7 alpha version is out, but I haven't been able to find any post on how to update to python 3.7 using Anaconda - maybe they will wait for the official release? Any suggestions?
This can be installed via conda with the command conda install -c anaconda python=3.7 as per https://anaconda.org/anaconda/python.
Though not all packages support 3.7 yet, running conda update --all may resolve some dependency failures.
Python 3.7 is now available to be installed, but many packages have not been updated yet. As noted by another answer here, there is a GitHub issue tracking the progress of Anaconda building all the updated packages.
Until someone creates a conda package for Python 3.7, you can't install it. Unfortunately, something like 3500 packages show up in a search for "python" on Anaconda.org (https://anaconda.org/search?q=%22python%22) so I couldn't see if anyone has done that yet.
You might be able to build your own package, depending on what OS you want it for. You can start with the recipe that conda-forge uses to build Python: https://github.com/conda-forge/python-feedstock/
In the past, I think Continuum have generally waited until a stable release to push out packages for new Pythons, but I don't work there, so I don't know what their actual policy is.
To see just the Python releases, do conda search --full-name python.
The September 4th release for 3.7 recommends the following:
conda install python=3.7 anaconda=custom
If you want to create a new environment, they recommend:
conda create -n example_env numpy scipy pandas scikit-learn notebook
anaconda-navigator
conda activate example_env
run conda navigator, you can upgrade your packages easily in the friendly GUI
conda create -n py37 -c anaconda anaconda=5.3
seems to be working.
The Anaconda website mentions that the installer has 100 of pre-built packages. Even the installer size of 500mb hints that there should be some pre-built packages.
Yet when we want to use any of the packages we have to install them through the command eg. conda install nltk
Which basically downloads the package from internet and then installs it. Which seems counterintuitive since it is already mentioned on website that nltk is present in the installer.
Can anybody throw some light on this?
There are two parts:
Conda - Package & environment management system. This gives you the
conda command and serves a similar function as pip and
virtualenv.
Anaconda - Python package distribution containing 100's of scientific
packages that are tests and verified to work together.
If you install Miniconda, you will just get conda without the full Anaconda distribution. If you install Anaconda, you will get both the conda management system and the Python distribution. You can also get Anaconda after only having installed conda by running conda install Anaconda.