The following shall hold true for Greene Cluster

NOTE NYU HAS MOVED TO SINGULARITY (please refer to https://github.com/ppriyank/NYU-Singularity, for its setup

p40s and other GPU dont exist anymore, only v100 and rtx8000

Conda
Installing Tensorflow on Prince
Installing Pytorch
Prince
Common Problems
Hacks
Summary

Conda related Things

Installing Conda

Download python 3.6 version of the Anaconda for ubuntu from here : https://www.anaconda.com/distribution/

wget <download link>
unzip <downloaded file> / tar -xf <downloaded file>
chmod +777 <downloaded file>
./<downloaded file>.sh

What virtual environment you have

conda info --envs

Create environment

conda create --name bert python=3.5  
conda activate bert

Delete a conda environment

(if something goes wrong, start fresh) Clean the environment :

conda deactivate 
conda clean -i -l -t -p -s   
conda build purge-all

Conda seems to have a bug, doesn't delete the local files, will eventually exhaust all the storage space

conda remove --name env_name --all

Installing Tensorflow on Prince

Try to follow the sequence of commands as it is. Do not do module load anaconda, doesn't seems to work for me.

Remove any preloaded modules

module purge

Requesting GPUs (not recommended):

Types of GPUs available can be found here : https://wikis.nyu.edu/display/NYUHPC/Clusters+-+Prince

srun -c4 -t5:00:00 --mem=30000 --gres=gpu:p40:1 --pty /bin/bash

requesting specific gpu: --nodelist=gpu-90

Load Cuda/Cudnn modules (strictly follow the order)

(tensorflow==1.7.0)

module load cudnn/9.0v7.0.5  
module load cuda/9.0.176

(tensorflow==1.11.0)

module load cudnn/9.0v7.3.0.29 
module load cuda/9.0.176

(tensorflow==1.12.0)

module load cudnn/10.0v7.6.2.24
module load cuda/10.1.105

just do conda spider cudnn or conda spider cuda to know what versions are available and install accordingly. Must do it before installing tensorflow. If not, pip uninstall tensorflow-gpu==x.x.x and reinstall

Incase of regex error :

module load gcc/9.1.0  
conda install -c conda-forge regex

Install Tensorflow libraries

(Use pip not conda!!) Conda has some sorts of bug. It installs cuda and cudnn as well, which is already installed on prince and in contradictions to the requirements of tensorflow.

pip install h5py nltk pyhocon scipy sklearn
pip install tensorflow-gpu==1.7.0

pip install tensorflow-gpu==1.11.0

Installing Pytorch

conda  env create -f req.yml

req.yml :

 name: env_name
 channels:
     - pytorch
 dependencies:
     - python=3.6
     - pytorch
     - torchvision
     - numpy
     - scikit-learn
     - h5py
     - scipy

Prince Related Things

Submitting Jobs (Recommended)

Create a file file_name.s like
time : hts:mins:seconds
gpu: type: always use p40
mem: 50000 == 5Gb (the max one can request is 10Gb, don't request this much, you don't need it :) )
nodes>1 not allowed (IDK)

#!/bin/bash
#SBATCH --cpus-per-task=4
#SBATCH --nodes=1
#SBATCH --gres=gpu:p40:1
#SBATCH --time=01:00:00
#SBATCH --mem=50000
#SBATCH --job-name=pp1953
#SBATCH [email protected]
#SBATCH --output=slurm_%j.out

OPT=$1 
#command line argument
. ~/.bashrc
module load anaconda3/5.3.1

conda activate <conda env name>
conda install -n <conda env name> nb_conda_kernels
# conda activate 

cd 
cd code/Video-Person-ReID-master/current
python best_cl_centers.py --opt=3 >> gpu-sgd.out

Run the above script as

sbatch file_name.s

squeue -u pp1953
squeue -j 4654238
scancel 4654238

Creating a jupyter notebook on Prince

In case below steps arent working : use this link https://gist.github.com/go-inoue/1203305f83974b5fbbcfd90cd707418d

conda install -n <conda env name> nb_conda_kernels
python -m ipykernel install --user --name build_central --display-name <conda env name>

Now create a sbatch file like this

#!/bin/bash
#SBATCH --cpus-per-task=8
#SBATCH --nodes=1
#SBATCH --time=100:00:00
#SBATCH --gres=gpu:v100:1
#SBATCH --mem=100000
#SBATCH --job-name=pp1953
#SBATCH [email protected]
#SBATCH --output=outputs/slurm_%j.out


. ~/.bashrc
module load anaconda3/5.3.1
module load jupyter-kernels/py3.5

conda activate PPUU
conda install -n PPUU nb_conda_kernels

port=$(shuf -i 10000-65500 -n 1)
/usr/bin/ssh -N -f -R $port:localhost:$port log-0
/usr/bin/ssh -N -f -R $port:localhost:$port log-1

cat<<EOF

Jupyter server is running on: $(hostname)
Job starts at: $(date)

Step 1 :
ssh -L $port:localhost:$port [email protected]

ssh -L $port:localhost:$port $USER@prince

Step 2:


the URL is something: http://localhost:${port}/?token=XXXXXXXX (see your token below)

EOF

unset XDG_RUNTIME_DIR
if [ "$SLURM_JOBTMP" != "" ]; then
    export XDG_RUNTIME_DIR=$SLURM_JOBTMP
fi

jupyter notebook --no-browser --port $port --notebook-dir=$(pwd)

if there is, by any chance, .ssh/config file rights distorted : do chmod 700 .ssh/config

Creating a jupyter notebook on server (unverified)

pip install jupyter
pip install jupyter[notebook]
pip install matplotlib
pip install jupyter_contrib_nbextensions
jupyter contrib nbextension install --user

jupyter notebook --generate-config

For normal servers : edit the following to (uncomment as well remove # ):

c.NotebookApp.ip = '0.0.0.0'
c.NotebookApp.allow_origin = '*'

jupyter notebook --no-browser --port=XXXX(some port)
ssh -N -f -L localhost:YYYY:localhost:XXXX user@ip address

Working with Jiant (allenNLP)

conda install -c conda-forge jsonnet 
module spider gcc 
module load gcc/9.1.0
conda install -c conda-forge regex
pip install allennlp

pip install --user might be better to use
pip install <--> conda install -c conda-forge Don't load python module!!!

Check job history

sacct --format=User,JobID,partition,state,time,start,end,elapsed,nodelist -j 4821655

Retrieve sbatch script for the running job id

scontrol write batch_script 5553599 or scontrol write batch_script 5553599 -

List of CILVR GPUS

1080ti, titanxp, titanblack, k40, k20, k20x, m2090, titanv

CILVR Packages

export PATH="/misc/vlgscratch4/LakeGroup/pathak/anaconda3/bin:$PATH"
conda activate pathak 
conda install -n pathak nb_conda_kernels

Common problems

Conda install reverts the changes of loading cuda/cudnn
use : conda uninstall tensorflow-gpu cudatoolkit cudnn
Tensorflow was compiled with diffent version of cudnn and currently is a different version is loaded. Just load the correct/earlier version of cudnn by which tensorflow-gpu was installed
Tensorflow is not compatible to use gpu. cuda/cudnn used during installation doesn't match with the tensorflow binary from which it was created. pip uninstall tensorflow-gpu or possibly delete the whole environment and follow the above procedure.

Hacks

Google Collab

from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive/My\ Drive/...

Downloading Googe drive link

(ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive)

pip install gdown
gdown https://drive.google.com/uc?id=<id here>

Setting up ssh keys

Local Computer: In ~/.ssh/config Add the following

Host prince
    HostName prince.hpc.nyu.edu
    User net_id
    PubKeyAuthentication yes
    IdentityFile /Users/local_name/.ssh/id_rsa

The last IdentityFile is the private key to use

Server

chmod 700 /home/<net_id>/
chmod 700 ~/.ssh
chmod 600 ~/.ssh/*

Add public key to the ~/.ssh/authorized_keys

Normal SSH

ssh prince
ssh [email protected]
ssh [email protected]
ssh [email protected]

SSH Using Tunnel (Outside NYU)

ssh into gw.hpc.nyu.edu first, or use cisco vpn (allows file transfer)

ssh [email protected]

Mount w/o tunnel

very helpful, if you are not a big vim fan

sshfs -p 22 [email protected]:/scratch/pp1953 ~/project

Mount w/ tunnel


ssh -f [email protected] -L 2222:cassio.cs.nyu.edu:22 -N
sshfs -p 2222 [email protected]:/misc/vlgscratch4/LakeGroup/pathak/Personre-id ~/NYU/temp/

ssh -f <user_name>@<tunnel server> -L 2222:<target server>:22 -N
sshfs -p 2222 <user_name>@127.0.0.1:/desired/path/ local/path/

One single command for the above shall look something like this :

alias storage='ssh -f [email protected] -L 5555:prince1.hpc.nyu.edu:22 -N; sshfs -p 5555 [email protected]:/home/pp1953/code ~/NYU/project'
alias prince='ssh -p 5555 [email protected]'

In case mount is throwing error mount_osxfuse: /Users/ppriyank/project/: Input/output error Then do the following :

pgrep -lf sshfs
kill -9 <pid of the process corresponding to the mount /Users/ppriyank/project/>
sudo umount -f /Users/ppriyank/project/

Then proceed with ususal mount again

Unmounting process remains the same

umount -f ~/NYU/temp/

Using tmux

For god's sake use tmux Do not OPEN mutiple tmux sessions on mutiple login nodes, that just bad practice. I have all my tmux windows running on log-3, so whatever login node you enter, just do ssh log-3 and then launch tmux session

tmux new -s session_name
tmux a -t session_name
control + b -> # (sliding between windows) or control + b -> ' -> # (window >10)

Set mouse scrolling on by : (Mac control + b -> shift + : ->setw -g mouse on or setw -g mode-mouse on )

Pdb multiple Line code

from IPython import embed; embed()

Killing mounted ports (refreshing sshfs)

pgrep -lf sshfs
kill -9 xyz
sudo umount -f /Users/ppriyank/NYU/project2

Simple hack for running jupyter notebook on servers

https://docs.anaconda.com/anaconda/user-guide/tasks/remote-jupyter-notebook/

Summary

sshfs -p 22 [email protected]:/scratch/pp1953 ~/NYU/project
ssh [email protected]
tmux a -t pathak
srun -c4 -t100:00:00 --mem=50000 --gres=gpu:p40:1 --pty /bin/bash
cd /scratch/pp1953/model2/codes/
module load cudnn/9.0v7.3.0.29 
module load cuda/9.0.176
conda activate bert

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