Using containers on clusters¶

How to use containers on clusters¶

On every cluster with Slurm, datasets and intermediate results should go in $SLURM_TMPDIR while the final experiment results should go in $SCRATCH. In order to use the container you built, you need to copy it on the cluster you want to use.

Aviso

You should always store your container in $SCRATCH !

Then reserve a node with srun/sbatch, copy the container and your dataset on the node given by SLURM (i.e in $SLURM_TMPDIR) and execute the code <YOUR_CODE> within the container <YOUR_CONTAINER> with:

singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ $SLURM_TMPDIR/<YOUR_CONTAINER> python <YOUR_CODE>

Remember that /dataset, /tmp_log and /final_log were created in the previous section. Now each time, we’ll use singularity, we are explicitly telling it to mount $SLURM_TMPDIR on the cluster’s node in the folder /dataset inside the container with the option -B such that each dataset downloaded by PyTorch in /dataset will be available in $SLURM_TMPDIR.

This will allow us to have code and scripts that are invariant to the cluster environment. The option -H specify what will be the container’s home. For example, if you have your code in $HOME/Project12345/Version35/ you can specify -H $HOME/Project12345/Version35:/home, thus the container will only have access to the code inside Version35.

If you want to run multiple commands inside the container you can use:

singularity exec --nv -H $HOME:/home -B $SLURM_TMPDIR:/dataset/ \
    -B $SLURM_TMPDIR:/tmp_log/ -B $SCRATCH:/final_log/ \
    $SLURM_TMPDIR/<YOUR_CONTAINER> bash -c 'pwd && ls && python <YOUR_CODE>'

Example: Interactive case (srun/salloc)¶

Once you get an interactive session with SLURM, copy <YOUR_CONTAINER> and <YOUR_DATASET> to $SLURM_TMPDIR

# 0. Get an interactive session
$ srun --gres=gpu:1
# 1. Copy your container on the compute node
$ rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
# 2. Copy your dataset on the compute node
$ rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR

Then use singularity shell to get a shell inside the container

# 3. Get a shell in your environment
$ singularity shell --nv \
        -H $HOME:/home \
        -B $SLURM_TMPDIR:/dataset/ \
        -B $SLURM_TMPDIR:/tmp_log/ \
        -B $SCRATCH:/final_log/ \
        $SLURM_TMPDIR/<YOUR_CONTAINER>

# 4. Execute your code
<Singularity_container>$ python <YOUR_CODE>

or use singularity exec to execute <YOUR_CODE>.

# 3. Execute your code
$ singularity exec --nv \
        -H $HOME:/home \
        -B $SLURM_TMPDIR:/dataset/ \
        -B $SLURM_TMPDIR:/tmp_log/ \
        -B $SCRATCH:/final_log/ \
        $SLURM_TMPDIR/<YOUR_CONTAINER> \
        python <YOUR_CODE>

You can create also the following alias to make your life easier.

alias my_env='singularity exec --nv \
        -H $HOME:/home \
        -B $SLURM_TMPDIR:/dataset/ \
        -B $SLURM_TMPDIR:/tmp_log/ \
        -B $SCRATCH:/final_log/ \
        $SLURM_TMPDIR/<YOUR_CONTAINER>'

This will allow you to run any code with:

my_env python <YOUR_CODE>

Example: sbatch case¶

You can also create a sbatch script:

#!/bin/bash
#SBATCH --cpus-per-task=6         # Ask for 6 CPUs
#SBATCH --gres=gpu:1              # Ask for 1 GPU
#SBATCH --mem=10G                 # Ask for 10 GB of RAM
#SBATCH --time=0:10:00            # The job will run for 10 minutes

# 1. Copy your container on the compute node
rsync -avz $SCRATCH/<YOUR_CONTAINER> $SLURM_TMPDIR
# 2. Copy your dataset on the compute node
rsync -avz $SCRATCH/<YOUR_DATASET> $SLURM_TMPDIR
# 3. Executing your code with singularity
singularity exec --nv \
        -H $HOME:/home \
        -B $SLURM_TMPDIR:/dataset/ \
        -B $SLURM_TMPDIR:/tmp_log/ \
        -B $SCRATCH:/final_log/ \
        $SLURM_TMPDIR/<YOUR_CONTAINER> \
        python "<YOUR_CODE>"
# 4. Copy whatever you want to save on $SCRATCH
rsync -avz $SLURM_TMPDIR/<to_save> $SCRATCH

Issue with PyBullet and OpenGL libraries¶

If you are running certain gym environments that require pyglet, you may encounter a problem when running your singularity instance with the Nvidia drivers using the --nv flag. This happens because the --nv flag also provides the OpenGL libraries:

libGL.so.1 => /.singularity.d/libs/libGL.so.1
libGLX.so.0 => /.singularity.d/libs/libGLX.so.0

If you don’t experience those problems with pyglet, you probably don’t need to address this. Otherwise, you can resolve those problems by apt-get install -y libosmesa6-dev mesa-utils mesa-utils-extra libgl1-mesa-glx, and then making sure that your LD_LIBRARY_PATH points to those libraries before the ones in /.singularity.d/libs.

%environment
        # ...
        export LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/mesa:$LD_LIBRARY_PATH

Apuana cluster¶

On the Apuana cluster $SCRATCH is not yet defined, you should add the experiment results you want to keep in /network/scratch/<u>/<username>/. In order to use the sbatch script above and to match other cluster environment’s names, you can define $SCRATCH as an alias for /network/scratch/<u>/<username> with:

echo "export SCRATCH=/network/scratch/${USER:0:1}/$USER" >> ~/.bashrc

Then, you can follow the general procedure explained above.