More on Paperspace Gradient

Its been a few days since I started to play with Paperspace, and I have come across a couple of interesting features that the platform has – enough for a second post on this topic.

First, GIT integration. Recall that the usual process is to zip the current working directory and submit the resulting file along with the job, the ZIP file is then unzipped in the container in which the job is running and the contents of the ZIP file constitute the working directory. However, if you want to run code that requires, for instance, custom libraries, it is much easier to instruct Paperspace to get the contents of the working directory from GitHub. You can do that by supplying a GIT URL using the --workspace switch. The example below, for instance, instructs Paperspace to pull my code for an RBM from GitHub and to run it as a job.

# Run the RBM as a job on Paperspace. Assume that you have the paperspace NodeJS
# CLI and have done a paperspace login before to store your credentials
~/node_modules/.bin/paperspace jobs create  \
        --workspace "git+" \
        --command "export MPLBACKEND=AGG ; python3 \
        --N=28 --data=MNIST \
        --save=1 \
        --tmpdir=/artifacts \
        --hidden=128 \
        --pattern=256 --batch_size=128 \
        --epochs=40000 \
        --run_samples=1 \
        --sample_size=6,6 \
        --beta=1.0 --sample=200000 \
        --algorithm=PCDTF --precision=32" \
        --machineType K80 \
        --container "paperspace/tensorflow-python" \
        --project "MachineLearning"

Be careful, the spelling of the URL must be exactly like this to be recognized as a GIT URL, i.e. “git+https” followed by the hostname without the “www”, if you use http instead of https or instead of, the job will fail (the documentation at this point could be better, and I have even had to look at the source code of the CLI to figure out the syntax). This is a nice feature, using that along with the job logs, I can easily reconstruct which version of the code has actually been executed, and it supports working in a team that is sharing GitHub repositories well.

Quite recently, Paperspace did apparently also add the option to use persistent storage in jobs to store data across job runs (see this announcement). Theoretically, the storage should be shared between notebooks and jobs in the same region, but as I have not yet found out how to start a notebook in a specific region, I could not try this out.

Another feature that I liked is that the container that you specify can actually be any container from the Docker Hub, for instance ubuntu. The only restriction is that Paperspace seems to overwrite the entrypoint in any case and will try to run bashinside the container to finally execute the command that you provide, so containers that do not have a bash in the standard execution path will not work. Still, you could use this to prepare your own containers, maybe with pre-installed data sets or libraries, and ask Paperspace to run them.

Finally, for those of us who are Python addicts, there is also a Python API for submitting and managing jobs in Paperspace. Actually, this API offers you two ways to run a Python script on Paperspace. First, you can import the paperspace package into your script and then, inside the script, do a, as in the following example.

import paperspace
print('This will only be running on Paperspace')

What will happen behind the scenes is that the paperspace module takes your code, removes any occurrences of the paperspace package itself, puts the code into a temporary file and submits that as a job to Paperspace. You can then work with that job as with any other job, like monitoring it on the console or via the CLI.

That is nice and easy, but not everyone likes to hardcode the execution environment into the code. Fortunately, you can also simply import the paperspace package and use it to submit an arbitrary job, much like the NodeJs based CLI can do it. The code below demonstrates how to create a job using the Python API and download the output automatically (this script can also be found on GitHub).

from paperspace.login import apikey
import paperspace.config

import requests

# Define parameters
params = {}
# We want to use GIT, so we use the parameter workspaceFileName
# instead of workspace
params['workspaceFileName'] = "git+"
params['machineType'] = "K80"
params['command'] = "export MPLBACKEND=AGG ; python3 \
                --N=28 --data=MNIST \
                --save=1 \
                --tmpdir=/artifacts \
                --hidden=128 \
                --pattern=256 --batch_size=128 \
                --epochs=40000 \
                --run_samples=1 \
                --sample_size=6,6 \
                --beta=1.0 --sample=200000 \
                --algorithm=PCDTF --precision=32"
params['container'] = 'paperspace/tensorflow-python'
params['project'] = "MachineLearning"
params['dest'] = "/tmp"

# Get API key
apiKey = apikey()
print("Using API key ", apiKey)
# Create the job. We do NOT use the create method as it cannot
# handle the GIT feature, but assemble the request ourselves
http_method = 'POST'
path = '/' + 'jobs' + '/' + 'createJob'

r = requests.request(http_method, paperspace.config.CONFIG_HOST + path,
                             headers={'x-api-key': apiKey},
                             params=params, files={})
job = r.json()
if 'id' not in job:
    print("Error, could not get jobId")

jobId = job['id']
print("Started job with jobId ", jobId)
params['jobId']  = jobId

# Now poll until the job is complete

if job['state'] == 'Pending':
    print('Waiting for job to run...')
    job ={'jobId': jobId, 'state': 'Running'})

print("Job is now running")
print("Use the following command to observe its logs: ~/node_modules/.bin/paperspace jobs logs --jobId ", jobId, "--tail")

job ={'jobId': jobId, 'state': 'Stopped'})
print("Job is complete: ", job)

# Finally get artifacts
print("Downloading artifacts to directory ", params['dest'])

There are some additional features that the Python API seems to have that I have not yet tried out. First, you can apparently specify an init script that will be run before the command that you provide (though the use of that is limited, as you could put this into your command as well). Second, and more important, you can provide a requirements file according to the pip standard to ask Paperspace to install any libraries that are not available in the container before running your command.

Overall, my impression is that these APIs make it comparatively easy to work with jobs on Paperspace. You can submit jobs, monitor them and get their outputs, and you enjoy the benefit that you are only billed for the actual duration of the job. So if you are interested in a job based execution environment for your Machine Learning models, it is definitely worth a try, even though it takes some time to get familiar with the environment.

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