A Brief Example
This example is about installing and using one Python package in a Mamba environment.
Step 1 - Search
Multiqc
Go to anaconda.org and search for multiqc
in the search bar:
Click on one of the search results, usually the first one:
Exam the details on the following page, especially the version of the package:
The “conda install” on the above page gives the installation commands:
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.
The command given here CANNOT be directly used on ASU supercomputers, the sections below will show you how to re-write an install command.
Step 2 - Install
Connect to the VPN
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 commandssh <asurite>@sol.asu.edu
interactive -p htc -c 4 -t 30
module load mamba/latest
If the package is for running codes on GPUs, a compatible
cuda
module is also needed.Now we are ready to create env and install packages. It can be done in separated commands, but the fastest way is to do it in a single command, here is how:
Create an environment:
mamba create -n myENV -c conda-forge python=3 source activate myENV
Install Multiqc using the command found in step 1
mamba install -c bioconda multiqc
Combine commands from a, b, c into the fastest single command:
mamba create -n myENV -c conda-forge -c bioconda python=3 multiqc
And answer Y to the promoted question, if everything looks fine
Wait till the installation finish
The -c
flag in the above command means “channel". The channel name must be correct to install the correct package. More information can be found here: Managing Python Modules Through the Mamba Environment Manager
Step 3 - Use
Once the myENV environment is ready, multiqc
can be used directly or within a python session/script.
Connect to the VPN
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 commandssh <asurite>@sol.asu.edu
interactive -p htc -c 4 -t 30
module load mamba/latest
source activate myENV
python
import multiqc
Step 4 - What about pip
?
As explained in Python Package Installation Method Comparison , the only correct way to use pip
on the ASU supercomputers at the moment, is to use it inside an activated mamba env.
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
.
Almost all of the Python packages can be found on anaconda.org, and mamba is always preferred.
Here we take a python package called q2-greengenes2
as an example. The commands to have it installed via pip
to a mamba env are:
Connect to the VPN
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 commandssh <asurite>@sol.asu.edu
interactive -p htc -c 4 -t 30
module load mamba/latest
Two options here
If you have an existing env:
source activate myENV
If you want to build a new env:
pip install q2-greengenes2
Step 5 - Jupyter Notebook
After multiqc
and q2-greengenes2
have been installed to myENV
, we want to use this mamba env in the Jupyter Notebook session on the Sol web portal. So we need to make a Jupyter kernel from this mamba env. More details are covered in Preparing Python Environments for Jupyter and here are the example steps:
Connect to the VPN
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 commandssh <asurite>@sol.asu.edu
interactive -p htc -c 4 -t 30
module load mamba/latest
mkjupy myENV "myENV_kernel"
Find and use
myENV_kernel
Log in to the Sol web portal
On the top bar: Interactive Apps > Jupyter > Fill out request form > Connect to Jupyter
Inside the Jupyter Notebook: Open a Launcher page > Click on
myENV_kernel
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