- 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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sudo add-apt-repository -y ppa:apt-fast/stable | |
sudo add-apt-repository -y ppa:graphics-drivers/ppa | |
sudo apt-get update | |
sudo apt-get -y install apt-fast | |
# prompts | |
sudo apt-fast -y upgrade | |
sudo apt-fast install -y python3-pip ubuntu-drivers-common libvorbis-dev libflac-dev libsndfile-dev cmake build-essential libgflags-dev libgoogle-glog-dev libgtest-dev google-mock zlib1g-dev libeigen3-dev libboost-all-dev libasound2-dev libogg-dev libtool libfftw3-dev libbz2-dev liblzma-dev libgoogle-glog0v5 gcc-6 gfortran-6 g++-6 doxygen graphviz libsox-fmt-all parallel exuberant-ctags vim-nox python-powerline python3-pip ack lsyncd | |
sudo apt-fast install -y tigervnc-standalone-server firefox mesa-common-dev |
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# (c) Matthew Wardrop 2019; Licensed under the MIT license | |
# | |
# This script provides the ability to run a notebook in the same Python | |
# process as this script, allowing it to access to variables created | |
# by the notebook for other purposes. In most cases, this is of limited | |
# utility and not a best-practice, but there are some limited cases in | |
# which this capability is valuable, and this script was created for | |
# such cases. For all other cases, you are better off using the | |
# `nbconvert` execution API found @: | |
# https://nbconvert.readthedocs.io/en/latest/execute_api.html |
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import torch | |
x = torch.randn(1, 3, 800, 1200) | |
n, _, h, w, = x.shape | |
rnd = torch.rand(2, n, 1, 2).sort(-1).values | |
r, c = torch.linspace(0, 1, h+2)[None, None], torch.linspace(0, 1, w+2)[None, None] | |
mask = (((r > rnd[0, :, :, :1]) & (r < rnd[0, :, :, 1:])).unsqueeze(-1) * | |
((c > rnd[1, :, :, :1]) & (c < rnd[1, :, :, 1:])).unsqueeze(-2))[:, :, 1:-1, 1:-1].expand_as(x) |
Using this software requires agreeing to the license agreement.
cuDNN | CUDA9.0 | CUDA9.2 | CUDA10.0 | CUDA10.1 | CUDA10.2 | CUDA11.0 |
---|---|---|---|---|---|---|
8.0.2 | - | - | - | 10.1-8.0.2 (991.32MB) | 10.2-8.0.2 (1017.23MB) | 11.0-8.0.2 (1.32GB) |
7.6.1 | 9.0-7.6.1 (392.82MB) | 9.2-7.6.1 (396.89MB) | [10.0-7.6.1](https://developer.download.nvidia.com/compute/redist/cudnn/v7.6.1/cudnn-10.0-linux-x64-v7.6.1.34.tg |
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#!/usr/bin/env bash | |
set -e | |
echo | |
if ! [[ $(id -u) = 0 ]]; then | |
echo "Please run 'sudo ./install-wireguard.sh'" >&2 | |
exit 1 | |
fi | |
read -e -p "Use VPN for *all* internet traffic? [y/n] " -i n ROUTE_ALL |
For a version without the collapsible details sections (so you can search the whole thing in your browser), click here.
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from fastcore.utils import * | |
host = 8888,'localhost' | |
sock = start_server(*host) | |
print(f'Serving on {host}...') | |
while True: | |
conn,addr = sock.accept() | |
with conn: | |
data = conn.recv(1024) | |
print(data.decode()) |
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