- 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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\documentclass[letter]{article} | |
\pdfoutput=1 | |
\usepackage{hyperref} | |
\hypersetup{ | |
pdfinfo={ | |
Title={title}, | |
Author={author}, | |
} | |
} | |
\usepackage{pdfpages} |
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from __future__ import print_function | |
import torch | |
import torch.nn as nn | |
import torch.nn.functional as F | |
from torch.autograd import Variable | |
def sample_gumbel(shape, eps=1e-20): | |
U = torch.rand(shape).cuda() | |
return -Variable(torch.log(-torch.log(U + eps) + eps)) |
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from __future__ import print_function | |
import imageio | |
from PIL import Image | |
import numpy as np | |
import keras | |
from keras.layers import Input, Dense, Conv2D, MaxPooling2D, AveragePooling2D, ZeroPadding2D, Dropout, Flatten, Concatenate, Reshape, Activation | |
from keras.models import Model | |
from keras.regularizers import l2 | |
from keras.optimizers import SGD |
At the top of the file there should be a short introduction and/ or overview that explains what the project is. This description should match descriptions added for package managers (Gemspec, package.json, etc.)
Show what the library does as concisely as possible, developers should be able to figure out how your project solves their problem by looking at the code example. Make sure the API you are showing off is obvious, and that your code is short and concise.