Conda revisions: letting you ‘rollback’ to a previous version of your environment

June 14, 2016

I now use Anaconda as my primary Python distribution – and my company have also adopted it for use on all of their developer machines as well as their servers – so I like to think I’m a relatively knowledgeable user. However, the other day I came across a wonderful feature that I’d never known about before…revisions!

The best way to explain is by a quick example. If you run conda list --revisions , you’ll get an output like this:

2016-06-10 20:20:37 (rev 10) +affine-2.0.0.post1 +click-6.6 +click-plugins-1.0.3 +cligj-0.4.0 +rasterio-0.35.1 +snuggs-1.3.1 2016-06-10 20:22:19 (rev 11) libpng {1.6.17 -> 1.6.22} 2016-06-10 20:25:49 (rev 12) -gdal-2.1.0

In this output you can see a number of specific versions (or revisions) of this environment (in this case the default conda environment), along with the date/time they were created, and the differences (installed packages shown as + , uninstalled shown as - and upgrades shown as -> ). If you want to revert to a previous revision you can simply run conda install --revision N (where N is the revision number). This will ask you to confirm the relevant package uninstallation/installation – and get you back to exactly where you were before!

So, I think that’s pretty awesome – and really handy if you screw things up and want to go back to a previously working environment. I’ve got a few other hints for you though…

Firstly, if you ‘revert’ to a previous revision then you will find that an ‘inverse’ revision is created, simply doing the opposite of what the previous revision did. For example, if your revision list looks like this:

2016-06-14 21:12:34 (rev 1) +mkl-11.3.3 +numpy-1.11.0 +pandas-0.18.1 +python-dateutil-2.5.3 +pytz-2016.4 +six-1.10.0 2016-06-14 21:13:08 (rev 2) +cycler-0.10.0 +freetype-2.6.3 +libpng-1.6.22 +matplotlib-1.5.1 +pyparsing-2.1.4

and you revert to revision 1 by running conda install --revision 1 , and then run conda list --revisions again, you’ll get this:

2016-06-14 21:13:08 (rev 2) +cycler-0.10.0 +freetype-2.6.3 +libpng-1.6.22 +matplotlib-1.5.1 +pyparsing-2.1.4 2016-06-14 21:15:45 (rev 3) -cycler-0.10.0 -freetype-2.6.3 -libpng-1.6.22 -matplotlib-1.5.1 -pyparsing-2.1.4

You can see that the changes for revision 3 are just the inverse of revision 2.

One more thing is that I’ve found out that all of this data is stored in the history file in the conda-meta directory of your environment ( CONDA_ROOT/conda-meta for your default environment and CONDA_ROOT/envs/ENV_NAME/conda-meta for any other environment). You don’t want to know why I went searching for this file (it’s a long story involving some stupidity on my part), but it’s got some really useful contents:

==> 2016-06-07 22:41:06 <== # cmd: /Users/robin/anaconda3/bin/conda create --name hotbar python=2.7 openssl-1.0.2h-1 pip-8.1.2-py27_0 python-2.7.11-0 readline-6.2-2 setuptools-22.0.5-py27_0 sqlite-3.13.0-0 tk-8.5.19-0 wheel-0.29.0-py27_0 zlib-1.2.8-3 # create specs: ['python 2.7*'] ==> 2016-06-07 22:46:28 <== # cmd: /Users/robin/anaconda3/envs/hotbar/bin/conda install matplotlib numpy scipy ipython jupyter mahotas statsmodels scikit-image pandas gdal tqdm -sqlite-3.13.0-0 +appnope-0.1.0-py27_0 +backports-1.0-py27_0 ...

Specifically, it doesn’t just give you the list of what was installed, uninstalled or upgraded – but it also gives you the commands you ran! If you want, you can extract these commands with a bit of command-line magic:

cat ~/anaconda3/envs/hotbar/conda-meta/history | grep '# cmd' | cut -d" " -f3-

/Users/robin/anaconda3/bin/conda create --name hotbar python=2.7 /Users/robin/anaconda3/envs/hotbar/bin/conda install matplotlib numpy scipy ipython jupyter mahotas statsmodels scikit-image pandas gdal tqdm /Users/robin/anaconda3/envs/hotbar/bin/conda install -c conda-forge rasterio

(For reference, the command-line magic gets the content of the history file, searches for all lines starting with # cmd , and then splits the line by spaces and extracts everything from the 3rd group onwards)

I find environment.yml files to be a bit of a pain sometimes (they’re not always cross-platform compatible – see this issue), so this is quite useful as it actually gives me the commands that I ran to create the environment.

Categorised as: Programming, Python