Content Comparison

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The “conda install” on the above page gives the installation commands:

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The conda part in the red circle needs to be changed to mamba, and the bioconda part in the blue box is the channel name of this multiqc package. Channel is similar to the name of an online folder, that mamba can find and download the correct package. This is important for the next step.

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There are some packages can only be found on pypi.org but not anaconda.org, then the only option to install them is via pip, inside an activated mamba env. Notably that the current official installation guide for both tensorflow and pytorch prefers pip, but the conda-forge channel still supports them, and our public tensorflow/pytorch environments were built via mamba.

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1. Connect to the VPN

2. Open a command line interface on Sol by navigating to sol.asu.edu on your browser and
   selecting the "Sol Shell Access" option from the "System" menu option. Or by SSHing into
   Sol using the command ssh <asurite>@sol.asu.edu

3. interactive -p htc -c 4 -t 30

4. module load mamba/latest

5. Since we are on a CPU compute node right now, we need CUDA drivers so the env we are making can used GPU nodes in the future. That means we need a CUDA module:
   module load cuda-12.6.1-gcc-12.1.0

6. Now we need to go inside an env. Two options here:

   1. If you have an existing env: source activate myENV

   2. If you want to build a new env:

      Code Block
      mamba create -n myENV -c conda-forge python=3.12 source activate myENV

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1. Open a shell/ terminal to access Sol or Phx

2. Add the packages to the existing mamba env

3. Recreate the jupyter kernel using the mkjupy command. You can use a new name if you want to keep the old kernel.

4. Launch a new jupyter session on the Web Portal, and look for this new kernel.

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