I exported a conda environment in this way:
conda env export > environment.yml´
Then commited and pulled the environment.yml file to the git repo.
From another computer I cloned the repo and then tried to create the conda environment:
conda env create -f environment.yml
First I got a warning:
Warning: you have pip-installed dependencies in your environment file,
but you do not list pip itself as one of your conda dependencies.
Conda may not use the correct pip to install your packages, and they
may end up in the wrong place. Please add an explicit pip dependency.
I'm adding one for you, but still nagging you
I don't know why conda export does not include pip in the environment definition.
Then I got errors like wrong/unavailable versions of packages:
es-core-news-sm==3.0.0 version not found
I just removed the version part and only left the name of the package and got it work with:
conda env update --prefix ./env --file environment.yml --prune
Here additional details:
I would like to know how can I avoid this behavior?
es-core-news-sm==3.0 does not exist on pypi, where only 3.1 and 2.3.1 are available, hence your error message.
This is of course something very specific to the environment that you have and the packages that you have installed. In your specific case, just removing the version can be a fix, but no guarantee that this will work in all cases.
As for the cause, I can only guess, but what I expect happened in your case is:
You installed es-core-news-sm==3.0 to your environment
The developers of that package created a newer version and decided to delete the old version
Exporting the environment does correctly state that it contains es-core-news-sm==3.0
Creating an environment from the .yaml from step 3 fails, because the packe is not available any longer (see 2.)
An alternative (depending on your usecase) coul;d be to use conda-pack, which can create a packed version of your environment that you can then unpack. This only works though if the OS on the source and target machine are the same
I have a conda environment setup already.
I want to delete a few packages out of it, update few and add a few new packages.
currently, I am doing it manually one after another after source activate <my_exiting_conda>.
conda update x
conda update y
conda remove z --force
conda remove w --force
conda install -c <channel> <package name>
Is there any way using which I can put all these commands in a file and just use it to do all work at once.
Also when I run the above command manually, it asks to hit Y/n for each command? how can I avoid that?
Consider switching to using YAMLs to manage your envs and refrain from using conda update/install/remove commands. When you want to make multiple changes to an env, change them in the YAML, then use:
conda env update -f environment.yaml
This command also has the optional argument --prune which will remove any packages that are not required, i.e., provides the package deletion mechanism you seek.
Note that conda env commands do not provide a transaction review step. For that reason, I would not recommend using it to manage the base env.
Starting from Existing Env
To get a working YAML from an existing env (say foo), try running something like
conda env export -n foo --from-history > foo.yaml
The --from-history argument will only include the explicit specs that you've provided to the env, so the YAML will look closer to what one might make and maintain from scratch.
Note that if there were packages installed through pip they won't appear in the --from-history version. In that case, I would still begin from this version, then export a full YAML to capture any pip specs, and add them to the simpler version.
Yes, you can pass packages in one go
conda update x y
conda remove z w --force
I've had to install a package with pip in a conda environment to get it to work for my application (link).
The package works fine. However, every time I modify the virtual environment in any way, conda tries to install the "missing" package - which would effectively result in downgrading it.
Question: is there a way to mark the pip package as 'manually installed' in the conda venv (e.g. in the same way apt-mark would handle it)? The intention is to get miniconda to leave it alone while still handling the remaining dependencies for the desired additional package.
The pip installed package indeed shows up when typing conda list, with Channel "pypi".
Can give any additional information if needed.
Thanks in advance for any help.
I am using Anaconda. I would like to know how to remove or uninstall unwanted packages from the base environment. I am using another environment for my coding purpose.
I tried to update my environment by using yml file (Not base environment). Unexpectedly some packages installed by yml into the base environment. So now it has 200 python packages which have another environment also. I want to clear unwanted packages in the base environment and I am not using any packages in the base environment. Also, my memory is full because of this.
Please give me a solution to remove unwanted packages in the base environment in anaconda.
It is very hard to remove one by one each package, therefore, I am looking for a better solution.
It is the same as for other environments:
run conda deactivate to be sure you are in the base environment.
run conda list to see the packages that you have installed
run conda remove {package_name} to remove packages
Update:
WARNING: Be careful because there are bugs around this new functionality
https://github.com/conda/conda/issues/6316
I have not tested it myself, so try at your own risk
To reset your base environment you should:
conda list --revisions
This will show you a list of revisions. At this moment, the latest is:
2019-02-11 21:58:57 (rev 19)
conda {4.6.2 -> 4.6.3}
Now run, where 19 can be replaced for the revision you want to use:
conda install --rev 19
If you use:
conda install --rev 1
Then it should reset the base environment to how it looked like when you installed anaconda.
Reference: https://github.com/conda/conda/issues/1032
According to the tickets, if you are not using conda 4.4 release or upwards, you may end up removing conda too because of this bug: https://github.com/conda/conda/issues/6316
conda remove <package_name>
More info can be found: https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-pkgs.html#removing-packages
Please use the below code:
conda uninstall -n base <Package_name>
I tried the conda search --outdated, there are lots of outdated packages, for example the scipy is 0.17.1 but the latest is 0.18.0. However, when I do the conda update --all. It will not update any packages.
update 1
conda update --all --alt-hint
Fetching package metadata .......
Solving package specifications: ..........
# All requested packages already installed.
# packages in environment at /home/user/opt/anaconda2:
#
update 2
I can update those packages separately. I can do conda update scipy. But why I cannot update all of them in one go?
TL;DR: dependency conflicts: Updating one requires (by it's requirements) to downgrade another
You are right:
conda update --all
is actually the way to go1. Conda always tries to upgrade the packages to the newest version in the series (say Python 2.x or 3.x).
Dependency conflicts
But it is possible that there are dependency conflicts (which prevent a further upgrade). Conda usually warns very explicitly if they occur.
e.g. X requires Y <5.0, so Y will never be >= 5.0
That's why you 'cannot' upgrade them all.
Resolving
Update 1: since a while, mamba has proven to be an extremely powerful drop-in replacement for conda in terms of dependency resolution and (IMH experience) finds solutions to problems where conda fails. A way to invoke it without installing mamba is via the --solver=libmamba flag (requires conda-libmamba-solver), as pointed out by matteo in the comments.
To add: maybe it could work but a newer version of X working with Y > 5.0 is not available in conda. It is possible to install with pip, since more packages are available in pip. But be aware that pip also installs packages if dependency conflicts exist and that it usually breaks your conda environment in the sense that you cannot reliably install with conda anymore. If you do that, do it as a last resort and after all packages have been installed with conda. It's rather a hack.
A safe way you can try is to add conda-forge as a channel when upgrading (add -c conda-forge as a flag) or any other channel you find that contains your package if you really need this new version. This way conda does also search in this places for available packages.
Considering your update: You can upgrade them each separately, but doing so will not only include an upgrade but also a downgrade of another package as well. Say, to add to the example above:
X > 2.0 requires Y < 5.0, X < 2.0 requires Y > 5.0
So upgrading Y > 5.0 implies downgrading X to < 2.0 and vice versa.
(this is a pedagogical example, of course, but it's the same in reality, usually just with more complicated dependencies and sub-dependencies)
So you still cannot upgrade them all by doing the upgrades separately; the dependencies are just not satisfiable so earlier or later, an upgrade will downgrade an already upgraded package again. Or break the compatibility of the packages (which you usually don't want!), which is only possible by explicitly invoking an ignore-dependencies and force-command. But that is only to hack your way around issues, definitely not the normal-user case!
1 If you actually want to update the packages of your installation, which you usually don't. The command run in the base environment will update the packages in this, but usually you should work with virtual environments (conda create -n myenv and then conda activate myenv). Executing conda update --all inside such an environment will update the packages inside this environment. However, since the base environment is also an environment, the answer applies to both cases in the same way.
To answer more precisely to the question:
conda (which is conda for miniconda as for Anaconda) updates all but ONLY within a specific version of a package -> major and minor. That's the paradigm.
In the documentation you will find "NOTE: Conda updates to the highest version in its series, so Python 2.7 updates to the highest available in the 2.x series and 3.6 updates to the highest available in the 3.x series."
doc
If Wang does not gives a reproducible example, one can only assist.
e.g. is it really the virtual environment he wants to update or could Wang get what he/she wants with
conda update -n ENVIRONMENT --all
*PLEASE read the docs before executing "update --all"!
This does not lead to an update of all packages by nature. Because conda tries to resolve the relationship of dependencies between all packages in your environment, this can lead to DOWNGRADED packages without warnings.
If you only want to update almost all, you can create a pin file
echo "conda ==4.0.0" >> ~/miniconda3/envs/py35/conda-meta/pinned
echo "numpy 1.7.*" >> ~/miniconda3/envs/py35/conda-meta/pinned
before running the update. conda issues not pinned
If later on you want to ignore the file in your env for an update, you can do:
conda update --all --no-pin
You should not do update --all. If you need it nevertheless you are saver to test this in a cloned environment.
First step should always be to backup your current specification:
conda list -n py35 --explicit
(but even so there is not always a link to the source available - like for jupyterlab extensions)
Next you can clone and update:
conda create -n py356 --clone py35
conda activate py356
conda config --set pip_interop_enabled True # for conda>=4.6
conda update --all
conda config
update:
Currently I would use mamba (or micromamba) as conda pkg-manager replacement
update:
Because the idea of conda is nice but it is not working out very well for complex environments I personally prefer the combination of nix-shell (or lorri) and poetry [as superior pip/conda .-)] (intro poetry2nix).
Alternatively you can use nix and mach-nix (where you only need you requirements file. It resolves and builds environments best.
On Linux / macOS you could use nix like
nix-env -iA nixpkgs.python37
to enter an environment that has e.g. in this case Python3.7 (for sure you can change the version)
or as a very good Python (advanced) environment you can use mach-nix (with nix) like
mach-nix env ./env -r requirements.txt
(which even supports conda [but currently in beta])
or via api like
nix-shell -p nixFlakes --run "nix run github:davhau/mach-nix#with.ipython.pandas.seaborn.bokeh.scikit-learn "
Finally if you really need to work with packages that are not compatible due to its dependencies, it is possible with technologies like NixOS/nix-pkgs.
Imagine the dependency graph of packages, when the number of packages grows large, the chance of encountering a conflict when upgrading/adding packages is much higher. To avoid this, simply create a new environment in Anaconda.
Be frugal, install only what you need. For me, I installed the following packages in my new environment:
pandas
scikit-learn
matplotlib
notebook
keras
And I have 84 packages in total.
I agree with Mayou36.
For example, I was doing the mistake to install new packages in the base environment using conda for some packages and pip for some other packages.
Why this is bad?
1.None of this is going to help with updating packages that have been > installed >from PyPI via pip, or any packages installed using python
setup.py install. conda list will give you some hints about the
pip-based Python packages you have in an environment, but it won't do
anything special to update them.
And I had all my projects in the same one environment! And I used update all -which is bad and did not update all-.
So, the best thing to do is to create a new environment for each project. Why?
2. A Conda environment is a directory that contains a specific collection of Conda packages that you have installed. For example, you
may be working on a research project that requires NumPy 1.18 and its
dependencies, while another environment associated with an finished
project has NumPy 1.12 (perhaps because version 1.12 was the most
current version of NumPy at the time the project finished). If you
change one environment, your other environments are not affected. You
can easily activate or deactivate environments, which is how you
switch between them.
So, to wrap it up:
Create a new environment for each project
Be aware for the differences in conda and pip
3.Only include the packages that you will actually need and update them properly only if necessary.
if working in MS windows, you can use Anaconda navigator. click on the environment, in the drop-down box, it's "installed" by default. You can select "updatable" and start from there
To update all possible packages I used conda update --update-all
It works!
I solved this problem with conda and pip.
Firstly, I run:
conda uninstall qt and conda uninstall matplotlib and conda uninstall PyQt5
After that, I opened the cmd and run this code that
pip uninstall qt , pip uninstall matplotlib , pip uninstall PyQt5
Lastly, You should install matplotlib in pip by this code that pip install matplotlib