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conda search --outdated, there are lots of outdated packages, for example the scipy is 0.17.1 but the latest is 0.18.0.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.
conda update --all --alt-hint Fetching package metadata ....... Solving package specifications: .......... # All requested packages already installed. # packages in environment at /home/user/opt/anaconda2: #
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 its 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).
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.
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."
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
Currently I would use mamba (or micromamba) as conda pkg-manager replacement
Because the idea of conda is nice but it is not working out very well for complex environments I personally prefer the combination of
poetry [as superior pip/conda .-)] (intro poetry2nix).
Alternatively you can use
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:
And I have 84 packages in total.
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
I solved this problem with
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
To update all possible packages I used
conda update --update-all
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:
3.Only include the packages that you will actually need and update them properly only if necessary.