irischang020 / ezpsana

Easy PSANA everywhere!

Easy Photon System ANAlysis

How to:

• bring psana where you need it
• build a psana tailored to your needs

Usage

There are 3 main ways to work with psana: through a JupyterHub, or using a scheduler (LSF or SLURM) in either batch or interactive mode. We describe below how to go about each of those modalities at various facilities: TBD.

Find or build your container

While most of the approach we follow is based on building conda environment, we ultimately build and run containers. Here is how to find or build your own psana-based container!

Check if your container already exists!

All psana-based containers that we build are stored at the following URL Have a look there! If you don't find what you are looking for, proceed to the next step...

Create your container

In order to run the following script, make sure your python has the docker module. If not, pip install docker.

(env)[terminal]$ python create-container.py -h
usage: create-container.py [-h] -b BASE_ENV_FILENAME
                           [-a ADDITIONAL_DEPS_FILENAME] -o OUTPUT_FILENAME
                           [-d] [-c] -r REPOSITORY -t TAG -v VERSION

optional arguments:
  -h, --help            show this help message and exit
  -b BASE_ENV_FILENAME, --base-env-yaml-file BASE_ENV_FILENAME
                        YAML file for base environment
  -a ADDITIONAL_DEPS_FILENAME, --additional-deps-yaml-file ADDITIONAL_DEPS_FILENAME
                        YAML file for additional dependencies
  -o OUTPUT_FILENAME, --output-yaml-file OUTPUT_FILENAME
                        output YAML file name (will be written in the 'docker'
                        directory)
  -d, --build-docker-container
                        Build docker container
  -c, --no-cache        Do not use cache when building the docker container
  -r REPOSITORY, --repository REPOSITORY
                        Repository for the docker image
  -t TAG, --tag TAG     Tag for the docker image
  -v VERSION, --version VERSION
                        Version for the docker image

Run your container

on SDF

If the Singularity image you need is not already on SDF, you can pull it from DockerHub like so:

singularity pull docker://slaclcls/ana-py3:latest

Then, request the resources that you need and run the container once the resources have been allocated:

srun -A LCLS -n 1 --gpus 1 --pty /bin/bash

singularity exec --nv -B /sdf,/gpfs,/scratch,/lscratch /scratch/fpoitevi/singularity_images/ana-py3_latest.sif /bin/bash