The GPU server is only accessible using the UdS network.
If you use the GPU server at home, you need to connect to the VPN of UdS, following this instruction:
https://www.hiz-saarland.de/dienste/vpn
As we recommend you to use PyTorch for your final project, we provide a demo of a PyTorch project.
You may use the following link to download it:
For macOS, Debian, and Ubuntu, you may use the Terminal
application provided by the system.
For Windows, you may use PuTTY.
Unzip the downloaded file.
For macOS, Debian, and Ubuntu, you may use the following command to upload the demo to the server:
scp -r hlcv-ss21-gpu-tutorial-main hlcv_team000@conduit.cs.uni-saarland.de:~
Please replace hlcv_team000
with your own account.
For Windows, you may use WinSCP or FileZilla.
In the Terminal
, you may use the following command to login to the GPU server:
ssh hlcv_team000@conduit.cs.uni-saarland.de
Open the folder of the PyTorch demo:
cd hlcv-ss21-gpu-tutorial-main
Submit your job
condor_submit pytorch_docker.sub
Check the state of your job in the condor queue:
condor_q
Aanalyze how many machines can run your job or if there are problems:
condor_q -analyze
condor_q -better
Overview of machines in the cluster:
condor_status
Open the .py
file with vim:
vim pytorch_classifier.py
Use the Python Debugger library to set up the breakpoint:
import pdb
pdb.set_trace()
Submit an interactive job:
condor_submit -i pytorch_docker_interactive.sub
Open the folder of the PyTorch demo:
cd /home/hlcv_team000/hlcv-ss21-gpu-tutorial-main
Please replace hlcv_team000
with your own account.
Run the PyTorch code for debugging:
CUDA_VISIBLE_DEVICES=0 python pytorch_classifier.py
The interactive jobs are killed automatically after one hour to allow other users to get an interactive slot. So please don’t directly run your code with an interactive job.
#!/usr/bin/env python
at the beginning of your .py
file to make it runnable on the GPU serverFor futther questions, you may contact the TAs of HLCV using this mailing list:
hlcv-ss21@lists.mpi-inf.mpg.de.
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