UDocker Containers
Construction of udocker containers directly on CVMFS read-only filesystem in order to speedup their use and improve reproducibility, reliability and avoid interferences between calls; furthermore we will try to optimize compilations when ever possible. This containers can be used directly or run an user command throught a wrapper script.
The container technology is a conveniente way to provide stable software environments or to install them on situations where the configuration is complex or impossible. For example, the tensorflow framework is normally very hard to install on CentOS 7.x systems as found on our worknodes.
Available containers on CVMFS
| Environment | Target Arch. | Arch. Optimizations | Container SO | Applications |
|---|---|---|---|---|
| udoker/tensorflow/cpu/2.4.1 | Epyc_7501, Epyc_7552 | AVX AVX2 FMA | Ubuntu 18.04 | tensorflow-2.4.1 keras-2.43 pandas-1.1.5 madminer-0.8.0 numpy-1.19.5 scipy-1.5.4 |
| udocker/tensorflow/gpu/2.4.1 | Epyc_7552 | AVX AVX2 FMA | Ubuntu 18.04 | CUDA-11.2 tensorflow-2.4.1 keras-2.43 pandas-1.1.5 madminer-0.8.0 numpy-1.19.5 scipy-1.5.4 |
How to use the udocker containers
The containers are meant to be run on workernodes, they will not work on the login servers, launch a batch job or start an interactive session. Note also that the gpu partition is the only one providing GPU devices.
Load environment
Load the appropriate environment, for exemple udocker/tensorflow/gpu/2.4.1:
$ module load udocker/tensorflow/gpu/2.4.1
This will configure the udocker environment and made available the wrapper u_wrapper used to start the container. When the container is started throught the wrapper the /tmp and the user working directory is imported into the container.
Execute a command
Now we can run any command using the wrapper, for example:
$ u_wrapper nvidia-smi -L
******************************************************************************
* *
* STARTING 2bcfad7b-1750-3fb8-9fb1-74acdf4e869e *
* *
******************************************************************************
executing: bash
GPU 0: Tesla T4 (UUID: GPU-8cce58c9-f3f7-839c-50f9-63e21f042152)
GPU 1: Tesla T4 (UUID: GPU-1f698e19-a902-2e73-0a54-44e02fa9c8ee)
Execute an interactive shell
We can run an interactive shell, as long we acquire and interactive allocation:
$ u_wrapper bash -i
******************************************************************************
* *
* STARTING 2bcfad7b-1750-3fb8-9fb1-74acdf4e869e *
* *
******************************************************************************
executing: bash
________ _______________
___ __/__________________________________ ____/__ /________ __
__ / _ _ \_ __ \_ ___/ __ \_ ___/_ /_ __ /_ __ \_ | /| / /
_ / / __/ / / /(__ )/ /_/ / / _ __/ _ / / /_/ /_ |/ |/ /
/_/ \___//_/ /_//____/ \____//_/ /_/ /_/ \____/____/|__/
You are running this container as user with ID 5800002 and group 5800000,
which should map to the ID and group for your user on the Docker host. Great!
tf-docker ~ >
Example of a complete job script
We will run a Deep Learning example.
Get the python script run.py and create a submit script as follow:
$ cat dl.sh
#!/bin/bash
#SBATCH -p gpu
#SBATCH --gres=gpu
#SBATCH --mem=45G
module load udocker/tensorflow/gpu/2.4.1
u_wrapper python run.py
Next, launch the script and wait for completion, once it start to run it should be very fast, about 10 seconds.
$ sbatch dl.sh
Submitted batch job 1435321
$ squeue
JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)
1435321 gpu dl.sh username R 0:01 1 hpc062
$ ls -l
-rwxr-----+ 1 username usergroup 409 May 25 21:06 dl.sh
-rw-r-----+ 1 username usergroup 1417 May 25 20:49 run.py
-rw-r-----+ 1 username usergroup 17034 May 25 21:06 slurm-1435321.out
The output file should be something like the file slurm-1435321.out.