Alternative to Colab Pro: Comparing Google's Jupyter Notebooks to Gradient Notebooks

Google Colab Pro is a substantial improvement to free-tier Colab but there are still a number of limitations that make alternatives to Colab Pro like Paperspace Gradient appealing.

6 months ago   •   8 min read

By David

Introduction

Google Colaboratory is probably the most popular hosted Jupyter notebook service in the world. Colab is an appealing choice for millions of users because it's free, requires only a Google account to access, and generally has decent speeds and availability.

Google Colab has a number of drawbacks however – especially when it comes to limitations on the free plan. Colab's free GPU instances (most frequently K80 GPUs released in 2014) are often underpowered. Connectivity can be unreliable as instances will disconnect frequently or can be pre-empted by other users during inactivity. And instances often do not come with enough RAM – particularly when working with larger datasets.

After releasing Google Colab publicly in 2017, Google released Colab Pro in early 2020 to offer high specs including faster GPUs, guaranteed runtimes and availability, and additional RAM.

Colab Pro solves a number of number of issues for machine learning engineers and data scientists – faster GPUs, longer sessions, fewer interrupts, terminal access, and additional RAM – however Colab Pro is still limited in many ways:

  • Colab Pro is unavailable to residents of all but a few countries
  • Colab Pro limits GPU to NVIDIA P100 or T4
  • Colab Pro limits RAM to 25 GB
  • Colab Pro limits sessions to 24 hours
  • Colab Pro will timeout sessions just like Colab
  • Colab Pro does not offer a full version of JupyterLab
  • Colab Pro does not allow downloading files to local machine
  • Colab Pro does not guarantee resources so your instance may not be available

In this blogpost we're going to do our best to highlight the weaknesses of Google Colab Pro and make the case for Paperspace Gradient as an alternative to Colab Pro.

Our experience draws on years of success providing an alternative to Google Colab and Colab Pro called Paperspace Gradient to hundreds of thousands of machine learning engineers and data scientists.

Let's dive in!

Introducing Paperspace Gradient as an alternative to Colab Pro

Gradient Notebooks from Paperspace are an appealing alternative to Google Colab Pro. Gradient Notebooks are trusted by hundreds of thousands of developers and data scientists from around the world and Gradient is one of the recommended cloud notebooks for the mostly popular deep learning course in the world – fast.ai.

Some of the features that Gradient Notebooks offer that Colab Pro does not include:

  • Wider selection of GPUs including NVIDIA V100
  • More RAM (up to 30 GB per instance)
  • Full version of JupyterLab
  • More CPU (QTY 8 vCPUs compared to QTY 2 vCPUs for Google Colab Pro)
  • Sessions are not interruptible / pre-emptable
  • No inactivity penalty
Running Fast.ai in Paperspace Gradient

Let's get into some comparisons.

Pricing

Google Colab is free while Google Colab Pro is $9.99/mo.

Gradient has both free and paid tiers, which are delineated as follows:

Gradient Subscription Type Cost Details
Free $0/mo - Free instances only
- Notebooks are public
- Limit 1 concurrent notebook
- Limit 12 hours max per session
- 5GB persistent storage
G1 (Individual) $8/mo - Free and Paid instances
- Private notebooks
- Limit 5 concurrent notebooks
- Unlimited session length
- 200GB persistent storage
G2 (Individual) $24/mo - Free and Paid instances
- Private notebooks
- Limit 10 concurrent notebooks
- Unlimited session length
- 1TB persistent storage
T1 (Team) $12/user/mo - Free and Paid instances
- Private notebooks
- Limit 10 concurrent notebooks
- Unlimited session length
- 500GB persistent storage
- Private team collaboration
- Private managed cluster
T2 (Team) $49/user/mo - Free and Paid instances
- Private notebooks
- Limit 50 concurrent notebooks
- Unlimited session length
- 1TB persistent storage
- Private team collaboration
- Private managed cluster

And Gradient instance pricing looks like this:

Instance Type Price per Hour
Free (M4000) $0.00/hr
Free (P5000) $0.00/hr
P4000 $0.51/hr
P5000 $0.78/hr
P6000 $1.10/hr
V100 $2.30/hr
P5000 x4 $3.12/hr
P6000 x4 $4.40/hr

System specs

For starters, let's take a look at the system specs of the instance types that you will get with both Colab and Colab Pro.

Most notable is that the majority of Colab sessions will initialize with a K80 GPU and 12 GB of RAM.

Feature Google Colab Google Colab Pro
GPUs Mostly K80 K80, P100, T4
CPUs 2 x vCPU 2 x vCPU
RAM Mostly 12GB 24GB
Guaranteed Resources No No
Price Free $9.99/month

Meanwhile, in Paperspace Gradient, GPU instances will always come with 8 vCPUs and 30 GB RAM – even free instances!

When you create a new notebook with Gradient, you select a Free or Paid instance.

GPU instance comparison

When it comes to GPUs, neither Google Colab no Colab Pro will let you choose your GPU. Instead, Google assigns you a GPU. This GPU is often a K80 (released in 2014) on Google Colab while Colab Pro will mostly provide T4 and P100 GPUs.

GPUs available in Colab and Colab Pro

GPU Price Architecture Launch Year GPU RAM CPUs System RAM Current Street Price (2021)
K80 Free (Colab Free-tier) Kepler 2014 12 GB 2 vCPU 13 GB $399
T4 $9.99/mo (Colab Pro) Turing 2018 16 GB 2 vCPU 13 GB upgradeable to 25 GB $2,347
P100 $9.99/mo (Colab Free-tier and Colab Pro) Pascal 2016 16 GB 2 vCPU 13 GB upgradeable to 25 GB $2,830

In Gradient, Paperspace offers both NVIDIA M4000 and NVIDIA P5000 as free-tier instances as well as instances all the way up to V100 for paid tier.

GPUs available in Gradient Notebooks

GPU Price Architecture Launch Year GPU RAM CPUs System RAM Current Street Price (2021)
M4000 Free! Maxwell 2015 8 GB 8 vCPU 30 GB $459
P4000 Free! or $0.51/hr Pascal 2017 8 GB 8 vCPU 30 GB $908
P5000 Free! or $0.78/hr Pascal 2016 16 GB 8 vCPU 30 GB $1,888
P6000 $1.10/hr Pascal 2016 24 GB 8 vCPU 30 GB $2,499
V100 $2.30/hr Volta 2017 16 GB 8 vCPU 30 GB $8,450

RAM comparison

Free-tier Colab will almost always provide ~12 GB of RAM with limited access to high-memory VMs which have 25 GB RAM. Colab Pro increases availability of high-memory VMs (25 GB RAM).

Alternatively, Paperspace ensures that all instances come with a minimum of 30 GB RAM.

Google Colab Google Colab Pro Gradient Notebooks
Mostly standard VMs with 12 GB RAM Mostly high-memory VMs with 25 GB RAM All instances have at least 30 GB RAM

Storage comparison

With Colab it is impossible to predict how much disk space an instance will provide locally. Although Google Drive is used for persistent storage, users have reported local storage as low as a few GB to upwards of 300 GB just by luck of the draw.

Alternatively, Gradient provides 200GB of local storage. Gradient provides a set amount of storage space so you always know how much disk space you will have.

Additionally, Gradient provides persistent storage which means that you can share disk space across multiple notebooks! This is extremely handy for datasets and other shared storage resources.

Google Colab Google Colab Pro Gradient Notebooks
Local storage ~40 GB to ~300 GB ~40 GB to ~300 GB 200 GB
Persistent storage Google Drive Google Drive $0/mo - 5 GB
$8/mo - 200 GB
$24/mo - 1 TB

Resources Not Guaranteed

Neither Colab nor Colab Pro guarantee resources. This is mentioned several times in the Colab literature and is a large source of annoyance for many Colab and Colab Pro users.

Source: Google Colab FAQs (https://colab.research.google.com)

"Resources not guaranteed" means that Google can disconnect your instance at any time for any reason. And as many Colab users can attest – this seems to happen frequently!

We have heard countless stories of users being booted off a Colab or Colab Pro instance for a couple minutes of inactivity, or for maxxing out resources, or for any number of seemingly arbitrary reasons. The frustration of being "pre-empted" even when you are paying for Colab Pro is real.

Paperspace on the other hand does not pre-empt your instances. Once you are running an instance your session will only end once the auto-shutdown limit is reached or you turn your instance off manually. If you are running a free instance, auto-shutdown will be set to 6 hours.

Time limitation comparison

To spell out a few of the time management differences between Colab, Colab Pro, and Gradient Notebooks, let's refer to this table:

Google Colab Google Colab Pro Gradient Notebooks
12 hours maximum session duration 24 hours maximum session duration No maximum session duration
Pre-empted after a few minutes of inactivity Pre-empted after a few minutes of inactivity Never pre-empted
No auto-shutdown No auto-shutdown Custom auto-shutdown intervals

The biggest difference is that once you have secured an instance on Gradient, unlike with Colab, you will not be booted off the instance against your will.

Architecture and fundamental limitations of putting a wrapper on JupyterLab

Google Colab and Colab Pro are both limited implementations of JupyterLab – basically a thin wrapper around core Jupyter features. Colab and Colab Pro offer much of the same functionality as JupyterLab, but in an abbreviated package with far fewer options.

Gradient also offers a custom IDE which is a wrapper on top of JupyterLab. This IDE is designed to bring powerful Paperspace features into notebooks like instance selection, data management, and so forth.

But Gradient Notebooks also offer a full version of JupyterLab which is always available if you need it.

The toggle to open a notebook in JupyterLab is available in the lefthand sidebar of any running notebook on Gradient

In this sense, Gradient Notebooks are not only comparable to Colab and Colab Pro in terms of simplicity, but also to much more fully featured JupyterLab experiences like Google's GCP AI Platform Notebooks, Microsoft's Azure ML Notebooks, or Amazon's AWS SageMaker Studio Notebooks.

Availability

When Colab Pro was introduced in 2020, an enormous pain point for many was regional availability. The original Colab Pro release countries were:

Countries where Colab Pro was available initially
USA
Canada

As of March 2021, Colab Pro has expanded availability to these countries:

Countries where Colab Pro was recently made available
Japan
Brazil
Germany
France
India
United Kingdom
Thailand

While it's a good sign that Colab Pro has recently opened to users in more countries, Colab Pro is still blocked in most countries on the planet.

Google Colab Pro is blocked in all but 9 countries

Meanwhile, Paperspace does not block access to users of any country. Paperspace currently has three data centers (US West, US East, and EU) with additional data centers planned for the future.

We also hear from many users that Paperspace instances are far more performant than Colab instances even if they are not located near a Paperspace data center!

Preloaded dependencies

Both Colab Pro and Gradient Notebooks come with a number of popular dependencies and libraries pre-installed. Colab Pro instances come with around ~380 pre-installed dependencies while Gradient Notebooks come with around ~220 pre-installed dependencies.

The biggest difference is that Colab notebooks come pre-loaded with a large number of Google-specific libraries and packages.

Support

No matter what kind of instance you're running on Paperspace – be it a free CPU instance, or a Free P5000 instance, or a beefy V100 paid instance – a friendly and helpful support team is always one message away.

At Paperspace our mean time to respond (MTTR) is just a couple of hours.

If you need support from Google for a Colab product – well, good luck with that!

Conclusion

If you're looking for an alternative to Colab Pro that won't pre-empt you (interrupt your instances), won't degrade your instance arbitrarily, will provide you with more RAM (30 GB compared to 12 GB or 25 GB), more CPUs (8 vCPUs compared to 2 vCPUs), a full JupyterLab experience, and will not block you based on your country – you might want to give Gradient Notebooks a shot.

Gradient Notebooks will always have a free-tier plan and will always be backed by a friendly team of engineers and machine learning enthusiasts who are working to make it easier to launch and run ML projects.

Spread the word

Keep reading