User Guide

This documentation explains how regular users access to Skyway and submit jobs to use cloud services. Please refer to the Skyway home page for more information and news.

Gaining Access

You first need an active RCC User account (see accounts and allocations page). Next, you should contact your PI or class instructors for access to Skyway. Alternatively, you can reach out to our Help Desk at help@rcc.uchicago.edu for assistance.

Connecting

You need to log in to the HPC cluster.

ssh -Y [cnetid]@midway3.rcc.uchicago.edu

For Midway3 users,

  module load skyway

Running jobs on the cloud

You submit jobs to cloud in a similar manner to what do on your HPC cluster. The difference is that you should specify different partitions and accounts corresponding to the cloud services you have access to. Additionally, the instance configuration should be specified via --constraint.

List all the node types available to an account

skyway_nodetypes --account=your-cloud-account
skyway_nodetypes --account=your-gcp-account

To submit jobs to cloud, you must specify a type of virtual machine (VM) by the option --constraint=[VM Type]. For instance, the AWS VM types currently available through Skyway can be found in the table below. Other VM types will be included per requests.

VM Type	AWS EC2 Instance	Configuration	Description
t1	t2.micro	1 core, 1GB RAM	for testing and building software
c1	c5.large	1 core, 4B RAM	for serial jobs
c8	c5.4xlarge	8 cores, 32GB RAM	for medium sized multicore jobs
c36	c5.18xlarge	36 cores, 144GB RAM	for large memory jobs
g1	p3.2xlarge	4 cores, 61 GB RAM, 1x V100 GPU	for GPU jobs
g4	p3.8xlarge	16 cores, 244 GB RAM, 4x V100 GPU	for heavy GPU jobs
g5	p5.2xlarge	8 cores, 32 GB RAM, 1x A10G GPU	for heavy GPU jobs
m24	c5.12xlarge	24 cores, 384GB RAM	for large memory jobs

The following steps show a representative workflow with Skyway.

Allocate/provision an instance

skyway_alloc --account=your-cloud-account --constraint=t1 --time=01:00:00

For a GPU instance, use

skyway_alloc -A your-cloud-account --constraint=g5 --time=00:30:00

List all the running VMs with an account

skyway_list --account=your-cloud-account

Transfer data

To copy a file from the login node to the instance named your-run

skyway_transfer -A your-cloud-account -J your-run training.py

Transfer a file from an instance to the login node

skyway_transfer -A your-cloud-account -J your-run --from-cloud --cloud-path=~/output.txt $HOME/output.txt

Connect to the VM named your-run

skyway_connect --account=your-cloud-account your-run

Once on the VM, do

nvidia-smi
source activate pytorch
python training.py > ~/output.txt
scp output.txt [yourcnetid]@midway3.rcc.uchicago.edu:~/
exit

At this point, there would be a file named output.txt in your Midway3 home folder.

Stop/restart a job

To stop an instance, you use the skyway_stop command with your account and the instance ID:

skyway_stop -A=your-cloud-account -i [instanceID]

The stopped instance does not get charged and the data on the VM is preserved.

To restart a stopped instance, you use the skyway_restart command with your account and the instance ID:

skyway_restart -A=your-cloud-account -i [instanceID] -t 02:00:00

Cancel/terminate/cancel a job

skyway_cancel --account=your-cloud-account [job_name]

Expected behavior: The jobs (VMs) got terminated. When run skyway_list (step 3 above) the VM will not be present.

Note that when a VM is terminated, all the data on the VM is erased. The user should transfer the intermediate output to a cloud storage space or to local storage.

The following steps are for launching interactive and batch jobs.

Submit an interactive job (combinig steps 4, 6 and 7)

skyway_interative --account=your-cloud-account --constraint=t1 --time=01:00:00

For a GPU instance, use

skyway_interative --account=your-cloud-account --constraint=g5 -t 00:30:00

Expected behavior: the user lands on a compute node or a VM on a separate terminal.

Submit a batch job

A sample job script job_script.sh is given as bellow

#!/bin/sh

#SBATCH --job-name=your-run
#SBATCH --account=your-cloud-account
#SBATCH --constraint=g1
#SBATCH --time=06:00:00

skyway_transfer training.py

source activate pytorch
wget [url-of-the-dataset]
python training.py

To submit the job, use the skyway_batch command

skyway_batch job_script.sh

If the job is terminated due to time limit, the instance is stopped. You can find the instance ID with the skyway_list command and restart it to back up the data.

Transfer output data from cloud

skyway_transfer -A your-cloud-account -J your-run --from-cloud --cloud-path=~/model*.pkl .

You can cancel the job using the skyway_cancel command.

Troubleshooting

For further assistance, please contact our Help Desk at help@rcc.uchicago.edu.