anaconda update all possible packages?

I tried the conda search --outdated, there are lots of outdated packages, for example the scipy is 0.17.1 but the latest is 0.18.0.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?

Solution 1:

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

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.

Solution 2:

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.

Solution 3:

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.

Solution 4:

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.