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Plans

If you signed up for a paid subscription plan before November 20, 2017, please see our Legacy Plans FAQs.

Please see here for Billing FAQs

Which Plan is right for me?

Beginner Plan

If you're just getting started with deep learning, the Beginner plan is for you. It is free! You can create unlimited public projects and datasets, and can run 1 job at a time. It comes with 20 hours of CPU compute time when you sign up.

If you want to run your jobs with GPU support, you can purchase GPU Powerups.

Data Scientist Plan

The Data Scientist plan is for, well, data scientists. It offers private projects/datasets and higher levels of job concurrency and storage. You can also purchase Powerups to add GPU compute hours to supplement your plan.

Teams Plan

If you're on a data science team at work, then the Teams Plan is for you! The Teams Plan offers a centralized, secure hub for your entire team's model development, training, and deployment pipeline. The Teams plan includes consolidated billing, centralized usage tracking, collaborative experiment management, and roles-based permissioning.

For more details on our different plans, visit our pricing page.

What is in the Trial plan?

All new users get a free 2 hour GPU Powerup for a 14 day trial period.

Once you've signed up, take FloydHub for a spin with our Quick Start Guide

How can I run GPU jobs?

To run GPU jobs, you can purchase GPU Powerups from your Powerups Dashboard.

Compute

What is job concurrency?

Job concurrency is the number of jobs you can run in parallel. Each plan has a limit on the number of concurrent jobs you can run. For example, in the Beginner plan, you can only run 1 job at a time. In the Data Scientist plan and Teams plan, you can run up to 8 jobs in parallel.

Having a higher concurrency is useful when you want to parallelize your training, for example while hyperparameter sweeping.

What will happen to my running job when I run out of computing credits?

You job will be shutdown immediately when you run out of computing credits.

If you run long-running jobs and expect them to exceed the computing hours offered by your plan, you can purchase Powerups.

You can also enable auto-refresh on your Powerups to ensure your long-running jobs are never killed because you ran out of computing hours. We'll automatically refresh your selected Powerup so that your job can continue running.

Powerups

What are Powerups?

Powerups are addons that offer you the flexibility of purchasing compute or storage depending on your needs. You can buy Powerups for additional CPU or GPU computing hours, or for additional storage, from your Powerups Dashboard

What Powerups should I buy?

This depends on your computing needs. We offer multiple tiers of Powerups:

  • CPU vs. GPU: Pick the instance that you need for your training. GPUs reduces training time and are expensive.
  • 10 vs. 50 vs. 100 hours: Purchase a pack that suits your computing needs. Note that the larger packs offer compute at a much cheaper rate/hour than smaller packs.
  • Auto-refresh: You can enable auto-refresh on any pack.
  • Storage Powerups: You can purchase storage Powerups to supplement your plan's storage limits.

If you are just starting out and need more computing hours than your plan offers, you can start with the GPU10 Powerup.

If you run long-running jobs, you should purchase the GPU100 with auto-refresh enabled, to ensure that you never run out of computing credits.

If you run critical jobs that are not fault-tolerant, you should purchase the GPU+ Powerup.

How can I buy Powerups?

You can purchase them from your Powerups Dashboard

Why would I enable auto-refresh?

Auto-refresh ensures your long-running jobs are never killed because you ran out of computing hours. We'll automatically refresh your selected Powerup so that your Job can continue running.

Can I buy a Powerup if I am in the Free Plan?

Yes!

Do Powerups expire?

Yes. Compute Powerups are valid for 1 year from the date of purchase.

How will my Powerups be used?

Your compute hours will be consumed in the following order:

  • Hours from your subscription plan
  • Hours from free credits
  • Hours from Powerups

Storage

How much storage do I get?

Each plan comes with its own storage limit. The Beginner plan starts at 10 GB storage, and the Data Scientist and Team plans start at 100 GB. If you need more storage, then you can purchase a Storage powerup - no matter which plan you're on.

What counts against my storage?

Storage is consumed by the datasets that you upload, your code, and the data that your jobs output.

Note that you are only responsible for the data that you own. For example, if you use a public dataset in your job, you won't be charged for it.

Can I buy more storage than my plan offers?

Yes, you can purchase Storage Powerups to increase your storage limits from your Powerups Dashboard

Teams Plan

How many members do I get in a team?

Each team plan starts at 5 members. If you need more than 5 team members, then please let us know at support@floydhub.com and we'll add them to your team at your plan's member rate.

How do I add a team member?

You can easily add a teammate to your team in your team's settings page. You can find your team settings page in either the dropdown in the navbar, or in the "Teams Settings" panel of your Settings page. The People section of your team's settings is where you can manage your team:

  • Add new members
  • Remove members
  • Change member roles

This demonstration shows how to find your team settings, add a member, and change their role from Member to Owner

Create new dataset

What's the difference between a Member and an Owner on a team?

A Member can:

  • Run jobs
  • Create projects and datasets
  • Upload dataset versions
  • View all team jobs, projects, datasets, and team members

An Owner can additionally:

  • View usage and billing information for the team
  • Purchase Powerups for the team
  • Manage the team (add members, remove members, and change roles)

Help make this document better

This guide, as well as the rest of our docs, are open-source and available on GitHub. We welcome your contributions.