- A simple note for how to start multi-node-training on slurm scheduler with PyTorch.
- Useful especially when scheduler is too busy that you cannot get multiple GPUs allocated, or you need more than 4 GPUs for a single job.
- Requirement: Have to use PyTorch DistributedDataParallel(DDP) for this purpose.
- Warning: might need to re-factor your own code.
- Warning: might be secretly condemned by your colleagues because using too many GPUs.
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Pretty print tables summarizing properties of tensor arrays in numpy, pytorch, jax, etc. | |
Now on pip! `pip install arrgh` https://github.com/nmwsharp/arrgh |
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"""Stream a response from the OpenAI completion API.""" | |
import os | |
import re | |
import sys | |
import time | |
import random | |
import openai | |
openai.api_key = open(os.path.expanduser("~/.openai")).read().strip() |