Conda pulls pytorch from conda-forge channel instead of pytorch channel

deployment
1

Hi everyone,

At our research lab, we use Z2JH v2.0.0, with jupyter/scipy-notebook:hub-3.0.0 containers. The container has Python 3.10.8 and conda 22.9.0;

No matter if I set strict channel order or not, conda will always pull Pytorch from conda-forge channel instead of pytorch channel. The plan suggested by conda:

  + nsight-compute           2022.4.0.15  0                         nvidia/linux-64          801MB
  + pytorch                       1.12.1  cuda112py310he33e0d6_201  conda-forge/linux-64     512MB
  + pytorch-cuda                    11.6  h867d48c_1                pytorch/noarch             3kB

The environment.yml execute with conda env update -f environment.yml is the following:

name: base

channels:
    - pytorch
    - nvidia
    - conda-forge
    - defaults
    
dependencies:
    - pytorch=1.13.1
    - pytorch-cuda=11.6

Installing manually with mamba install pytorch==1.13.1 pytorch-cuda==11.6 -c pytorch -c nvidia -c defaults -c conda-forge gives the same result.

The output of conda info:

jovyan@jupyter-gcerar:~$ conda info

     active environment : None
       user config file : /home/jovyan/.condarc
 populated config files : /opt/conda/.condarc
          conda version : 22.9.0
    conda-build version : not installed
         python version : 3.10.8.final.0
       virtual packages : __cuda=12.0=0
                          __linux=5.15.0=0
                          __glibc=2.35=0
                          __unix=0=0
                          __archspec=1=x86_64
       base environment : /opt/conda  (writable)
      conda av data dir : /opt/conda/etc/conda
  conda av metadata url : None
           channel URLs : https://conda.anaconda.org/conda-forge/linux-64
                          https://conda.anaconda.org/conda-forge/noarch
          package cache : /opt/conda/pkgs
                          /home/jovyan/.conda/pkgs
       envs directories : /opt/conda/envs
                          /home/jovyan/.conda/envs
               platform : linux-64
             user-agent : conda/22.9.0 requests/2.28.1 CPython/3.10.8 Linux/5.15.0-58-generic ubuntu/22.04.1 glibc/2.35
                UID:GID : 1000:100
             netrc file : None
           offline mode : False

I’m a bit desperate at this point. Even the transition from Z2JH from 1.5 to 2.0 didn’t go without two days of downtime and backup rescue mission.

2

Found the problem. At one point, maintainers of jupyter/*-notebook containers decided to switch from the anaconda to the conda-forge channel as default. The conda could not resolve dependencies with MKL and suggested conda-forge’s PyTorch compiled for CUDA toolkit v11.2.

The solution was to add the anaconda channel explicitly, which resolved the issue. Example:
mamba install pytorch=1.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia -c anaconda -c conda-forge