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问题:

I tried the conda search --outdated, there are lots of outdated packages, for example the scipy is 0.17.1 but the latest is 0.18.0.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

回答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 go. 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!

回答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

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.

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

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.

回答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.

回答4:

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