gpucce / scaling2_experiments

Slurm Experiments Runner

Create a vitrualenv and install the tiny requirements

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

To run multiple experiments you have to create an experiments.yaml file with a list of datasets and models see the cfg_templates.py file to know the required fields. One can check this train config for a training example and this val config for a validation one.

Once that is in place, one should run

python -m src.generate_scripts --cfg val_experiments.yaml

Let us assume that in experiments.yaml one has set the following

...
sbatch_config:
  experiments_list_file_path: "exps_list.txt"
  sbatch_script_file_path: "sbatch_script.sbatch"
  ...

this will create two files:

• An experiments list file exps_list.txt which holds all the commands needed to run the experiments;
• A sbatch script file sbatch_script.sbatch, that will start a slurm job array with as many experiments as there are pairs of datasets/models in experiments.yaml file.

Now one should be able to start all the jobs with the command:

sbatch sbatch_script.sbatch # autorestart coming soon

NOTE: the logs paths are created when generating the sbatch scripts and not when running the actual experiments. Don't delete them.

Autorestart [Experimental]

One can try using autorestart like this:

python -m src.autorestart_job_array \
    "sbatch sbatch_script.sbatch" \
    --check-interval-secs 3 \ # change depending on the job
    --output-file-template "slurm_logs/slurm-{job_id}_{array_task_id}.out" \
    --verbose 1

NOTE: make sure that --output-file-template <this_path> matches sbatch_config.output: <this_path> . To do this one needs to change {job_id} -> %A and {array_task_id} -> %a so for using

--output-file-template slurm-{job_id}_{array_task_id}.log

in the config one will need

sbatch_config:
  output: slurm-%A_%a.log

TODO

• Add option to ignore experiments
• More testing
• Improve resume