How Does Work?

Yuval Greenfield

Yuval Greenfield

December 4, 2019 · 3 min read

Many compute jobs take hours to complete, especially when data is growing too fast for hardware to keep up. Sharing the load across local and cloud machines can reduce hour-long tasks to minutes, but it requires development effort and expertise. provides an easy and cost-effective way to use multiple machines to make compute jobs faster. Whether you need to run a simulation, process footage, or train a model, simplifies distributing compute tasks across your own hardware, the cloud, or both. Let’s take a look at how works. Read on, or check out this overview video.

When you log in, you’ll see’s dashboard which shows the list of jobs that are running or completed as well as any results they generated.

Image of Dashboard

Launch Jobs from the Dashboard

You can also start new jobs through’s dashboard. Simply drag and drop a python script plus any additional data files. For example, I can use to speed up a video analysis script by dragging it onto’s “New job” form along with the source video files. Normally, a script like this would take 30 minutes to run locally. But by breaking the video up into smaller sections, and distributing the analysis across ten machines with, it now finishes processing in roughly three minutes. That’s 10 times faster!

Gif of How to Create a Job in

In this example, all we needed to do was drag over the python script and the video files, then click the launch button. took care of the rest. This means you don’t have to worry about spinning machines up and down, moving code or data around, and getting results back.

Many users will prefer to use the command line interface. This is what the same job would look like when launched from the terminal.

disco add –name process_video –script –input “part*.mp4

You can see the job a name, python script, and the wildcard asterisk which gets all the video files uploaded from the current directory.

Monitor Job Progress

You can monitor your job’s progress on the site, or wait for the job results in the terminal using the “–wait” option.

Image of Monitoring a Job on Dashboard

Deployment Options supports a variety of different deployment configurations. You can bring your own devices, your own cloud, or rely on as a purely managed service.

Visual of Options for Using Disco

If you’d like to better utilize your local machines or scale out to the cloud, makes it easy and efficient. Contact us today to get started with a free account.

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Yuval Greenfield

Yuval Greenfield

December 4, 2019 · 3 min read