Machine Learning tutorial

This page is a beginner's manual concerning how to port a machine learning job to a Compute Canada cluster.

Step 1: Archiving a data set

The filesystems on Compute Canada clusters are designed for a small number of extremely large files. Make sure that the data set which you need for your training is an archive format like tar, which you can then transfer to your job's compute node when the job starts. If you do not respect these rules, you risk causing enormous numbers of I/O operations on the shared filesystem, leading to performance issues on the cluster for all of its users. If you want to learn more about how to handle collections of large number of files, we recommend that you spend some time reading this page.

Assuming that the files which you need are in the directory mydataset:

$ tar cf mydataset.tar mydataset/*

The above command does not compress the data. If you believe that this is appropriate, you can use tar czf.

Step 2: Preparing your virtual environment

We recommend that you try running your job in an interactive job before submitting it using a script (discussed in the following section). You can diagnose problems more quickly using an interactive job. An example of the command for submitting such a job is:

$ salloc --account=def-someuser --gres=gpu:1 --cpus-per-task=6 --mem=32000M --time=1:00

Once the job has started:

• Create and activate a virtual environment in $SLURM_TMPDIR (this variable points to a directory on the local disk, directly attached to the compute node). Do not use Anaconda. For example:

$ virtualenv --no-download $SLURM_TMPDIR/env

• Install the modules that you will need. For TensorFlow, install the module tensorflow_gpu; it is a version of this software optimized for Compute Canada clusters.
• Try to run your program.
• Install any missing modules if necessary.
• Create a file requirements.txt in order to recreate this virtual environment:

(env) $ pip freeze > ~/requirements.txt

Now is a good time to verify that your job reads and writes as much as possible on the compute node's local storage ($SLURM_TMPDIR) and as little as possible on the shared filesystems (home, scratch and project).

Step 3: Preparing your job submission script

Vous devez soumettre vos tâches à l'aide de scripts sbatch, afin qu'elles puissent être entièrement automatisées. Les tâches interactives servent uniquement à préparer et à déboguer des tâches.

Éléments importants d'un script sbatch

1. Compte sur lequel les ressources seront "facturées"
2. Ressources demandées:
   1. Nombre de CPU, suggestion: 6
   2. Nombre de GPU, suggestion: 1 (Utilisez un (1) seul GPU, à moins d'être certain que votre programme en utilise plusieurs. Par défaut, TensorFlow et PyTorch utilisent un seul GPU.)
   3. Quantité de mémoire, suggestion: 32000M
   4. Durée (Maximum Béluga: 7 jours, Graham et Cedar: 28 jours)
3. Commandes bash:
   1. Préparation de l'environnement (modules, virtualenv)
   2. Transfert des données vers le noeud de calcul
   3. Lancement de l'exécutable

Exemple de script

File : ml-test.sh

#!/bin/bash
#SBATCH --gres=gpu:1       # Request GPU "generic resources"
#SBATCH --cpus-per-task=6  # Cores proportional to GPUs: 6 on Cedar, 16 on Graham.
#SBATCH --mem=32000M       # Memory proportional to GPUs: 32000 Cedar, 64000 Graham.
#SBATCH --time=0-03:00     # DD-HH:MM:SS
#SBATCH --output=%N-%j.out

module load python/3.6 cuda cudnn

SOURCEDIR=~/ml-test

# Prepare virtualenv
virtualenv --no-download $SLURM_TMPDIR/env
source $SLURM_TMPDIR/env/bin/activate
pip install --no-index -r $SOURCEDIR/requirements.txt

# Prepare data
mkdir $SLURM_TMPDIR/data
tar xf ~/projects/def-xxxx/data.tar $SLURM_TMPDIR/data

# Start training
python $SOURCEDIR/train.py $SLURM_TMPDIR/data

Morcellement d'une longue tâche

Nous vous recommandons de morceler vos tâches en blocs de 24 heures. Demander des tâches plus courtes améliore votre priorité. En créant une chaîne de tâches, il est possible de dépasser la limite de 7 jours sur Béluga.

1. Modifiez votre script de soumission (ou votre programme) afin que votre tâche puisse être interrompue et continuée. Votre programme doit pouvoir accéder au checkpoint le plus récent. (Voir l'exemple de script ci-dessous.)
2. Vérifiez combien d'epochs (ou d'itérations) peuvent être effectuées à l'intérieur de 24 heures.
3. Calculez combien de blocs de 24 heures vous aurez besoin: n_blocs = n_epochs_total / n_epochs_par_24h
4. Utilisez l'argument --array 1-<n_blocs>%1 pour demander une chaine de n_blocs tâches.

The job submission script will look like this:

File : ml-test-chain.sh

#!/bin/bash
#SBATCH --array=1-10%1   # 10 is the number of jobs in the chain
#SBATCH ...

module load python/3.6 cuda cudnn


# Prepare virtualenv

# Prepare data

# Get most recent checkpoint (this example is for PyTorch *.pth checkpoint files)
export CHECKPOINTS=~/scratch/checkpoints/ml-test
LAST_CHECKPOINT=$(find . -maxdepth 1 -name "$CHECKPOINTS/*.pth" -print0 | xargs -r -0 ls -1 -t | head -1)

# Start training
if [ -n "$LAST_CHECKPOINT" ]; then
    # $LAST_CHECKPOINT is null; start from scratch
    python $SOURCEDIR/train.py --write-checkpoints-to $CHECKPOINTS ...
else
    python $SOURCEDIR/train.py --load-checkpoint $CHECKPOINTS/$LAST_CHECKPOINT --write-checkpoints-to $CHECKPOINTS ...
fi