Parallel Computing

caution

This documentation is under construction.

Background

In machine learning, there is some inherent randomness (e.g., random starting point when beginning to for the network) and across iterations of training a neural network, error estimates can fluctuate. Additionally, in the case of training a neural network on a biological system, like our Populus trichocarpa trees, the origin of some trees might be easier to predict of practical and biological reasons. For example, the DNA quality could be a practical reason that the tree origin is uncertain, or a biological reason for uncertainty could be that trees from a large region of the species distribution may be homogeneous genetically. Because of this uncertainty and randomness, we want to train the neural network on multiple test sets to get a better understanding of the distribution of origin prediction error.

For this worked example, we have 5 test sets of Populus trichocarpa trees, each with a different random draw of 10% of individuals where their true origin has been replaced with NA. We want to train the neural network with each test set, so that later we can combine results and calculate prediction error from a broader diversity of P. trichocarpa trees.

Array Jobs

The method for solving this embarassingly parallel computing problem is very similar to what we have set up in the last section. We will use a Slurm batch script to submit an array of jobs to be executed in parallel by adding the sbatch directive #SBATCH --array=. In our case, #SBATCH --array=0-4 which will execute 5 jobs, one for each test_set. Let's take a look at the script before we test it out.

Use cat to view the script.

cat locator_NN_array.slurm

And use the text editor nano to edit it as needed. Remember to use hyakalloc to find which accounts and partitions are available to you. If you have a compute parition, replace --parition=ckpt with --partition=compute and your job will be scheduled faster because you will be requesting a job on resources you can use with priority access.

nano locator_NN_array.slurm

# exit nano by holding Ctrl and pressing X; then save it by pushing Y

locator_NN_array.slurm

#!/bin/bash

#SBATCH --job-name=locator_array

#SBATCH --partition=ckpt

#SBATCH --nodes=1

#SBATCH --ntasks-per-node=1

#SBATCH --mem=10G

#SBATCH --array=0-4

#SBATCH --time=3:00:00

#SBATCH -o %x_%A_%a.out

#### Truncated for website view

# here we are saving the list of populus trichocarpa test sets as a variable called FILES

FILES=($(ls -1 data/potr_m_pred*))

# next we assign an array index to each test set and saving each into a variable called FILE

# the Slurm environment variable SLURM_ARRAY_TASK_ID is the index (0-4)

FILE=${FILES[${SLURM_ARRAY_TASK_ID}]}

# command - we pass the variable FILE to the command for each job in the array

# we are also using the index variable SLURM_ARRAY_TASK_ID as a suffix the results from each job

apptainer exec --cleanenv --bind /gscratch locator.sif python /locator/scripts/locator.py --matrix potr_genotypes.txt --sample_data ${FILE} --out locator_out/array_potr_predictions_${SLURM_ARRAY_TASK_ID}

#### Truncated for website view

The work of transforming this batch job into an array job is done by attaching the Slurm environment variable SLURM_ARRAY_TASK_ID to each test set. SLURM_ARRAY_TASK_ID is an index (0-4) being attached to each file in the data/ directory. The file list is saved as a variable FILES and then each file plus its SLURM_ARRAY_TASK_ID index is saved as a variable FILE which is passed as the input with the flag --sample_data. We aslso use the SLURM_ARRAY_TASK_ID index as a suffix for the results that will be saved in the locator_out/ directory.

This single script is scheduling an array of 5 jobs, one for each test set (${FILE}). Each job in the array will run as one task on one node that has 10G of RAM. Each job in the array will produce an output file like locator_array_12345678_0.out using %x as shorthand of the job-name, %A as shorthand for the array-jobID that will be assigned by Slurm when the job is submitted, and %a for the index of the job within the array the array-jobID will replace 12345678 in locator_array_12345678_0.out and there will be 5 output files, one for each job locator_array_12345678_0-4.out.

Once you have edited the script to fit your needs, you can submit it with sbatch.

sbatch locator_NN_array.slurm

# the following is an example result

sbatch: No account specified, defaulting to: account

Submitted batch job 12345678

# Slurm will assign a JobID when the job was submmitted

# it will likely be an 8-digit number, but not 12345678

And use squeue with watch to monitor the progress of the jobs in real time.

watch squeue -u UWNetID

JOBID PARTITION NAME USER ST TIME NODES NODELIST(REASON)

12345678_0 ckpt locator_ UWNetID R 0:07 1 n3263

12345678_1 ckpt locator_ UWNetID R 0:07 1 n3319

12345678_2 ckpt locator_ UWNetID R 0:07 1 n3319

12345678_3 ckpt locator_ UWNetID R 0:07 1 n3396

12345678_4 ckpt locator_ UWNetID R 0:07 1 n3396

# use Ctrl + C to exit the watch command

The watch command executes the squeue command every 2 seconds, allowing you to watch the job in real time. List the currect directory to see the output files there.

ls

data/ locator_array_12345678_4.out locator_out/

locator_array_12345678_0.out locator_array_12345678_5.out locator.sif

locator_array_12345678_1.out locator_job_12345678.out shell-lesson-data/

locator_array_12345678_2.out locator_NN_array.slurm

locator_array_12345678_3.out locator_NN_job.slurm

Use tail to compare two of the output files to each other.

tail locator_array_12345678_0.out

11/11 [==============================] - ETA: 0s - loss: 0.0196

Epoch 316: val_loss did not improve from 0.15270

11/11 [==============================] - 1s 58ms/step - loss: 0.0196 - val_loss: 0.1538 - lr: 2.4414e-07

predicting locations...

R2(x)=0.9602027602199694

R2(y)=0.9934424100402416

mean validation error 0.4544098551826771

median validation error 0.3181686778302888

run time 3.9815464933713276 minutes

tail locator_array_12345678_1.out

11/11 [==============================] - ETA: 0s - loss: 0.0234

Epoch 293: val_loss did not improve from 0.09959

11/11 [==============================] - 1s 61ms/step - loss: 0.0234 - val_loss: 0.1028 - lr: 9.7656e-07

predicting locations...

R2(x)=0.9852468923779139

R2(y)=0.9940615432651215

mean validation error 0.2893548027087871

median validation error 0.18939671640277614

run time 3.3444977164268495 minutes

As you can see, test set 0 (data/potr_m_pred0.txt) took a slightly longer time to execute and the validation error mean and median differ between the runs. Results files locator_out/array_potr_predictions_0_predlocs.txt and locator_out/array_potr_predictions_1_predlocs.txt are distinct as well and contain predictions for the trees that whose origin were NA in the test set. The next step would be to combine the results and calculate the distnace between the true and predicted origin, but that data analysis is outside of the scope of this tutorial.

Congratulations, each job executed in parallel took around 4 minutes to complete, and if you would have executed these serially, it would have taken about 20 minutes. What will you do with all of your extra time? Go forth and parallelize your workflows.