Difference between revisions of "Login to SciComp GPUs"
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1. Setting up the software environment seems to be more easily done using conda. We need to first log into jlab common environment with the below ssh command </p> <br> | 1. Setting up the software environment seems to be more easily done using conda. We need to first log into jlab common environment with the below ssh command </p> <br> | ||
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ssh login.jlab.org | ssh login.jlab.org | ||
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Revision as of 20:40, 26 August 2020
The following is how to use one of the ML scicomp machines that has 4 Titan RTX GPU cards installed.
Steps:
1. Setting up the software environment seems to be more easily done using conda. We need to first log into jlab common environment with the below ssh command
ssh login.jlab.org
You'll be prompted to enter your Jlab account password.
2. We need to log into ifarm with the following ssh command
ssh ifarm190X
In 190X, X can either be 1 or 2.
3. Setting up Python environment
- The software must be set up using a computer other than sciml190X since it needs a level of outside network access not available there.
- We recommend using Conda to manage your python packages and environments.
- Also, the size of the installation is large enough that it won't fit easily in you home directory. Conda likes to install things in ~/.conda so that must be a link to some larger disk.
- If ~/.conda already exists, please delete it since we are going to create a symbolic link named ~/.conda
- Create a folder in your work directory that can be linked to "~/.conda". For me, I created a folder named condaenv in "/work/halld2/home/kishan/". You can simply achieve this by running the following commands mkdir /work/<your hall>/home/<your name>/condaenv ln -s /work/<your hall>/home/<your name>/condaenv ~/.conda