Get Free GPU Online — To Train Your Deep Learning Model

Introduction

You must have noticed that for training a very heavy deep learning model, you required a GPU which is mostly available at a very high cost. In simple terms, a GPU is an abbreviation of a Graphical processing unit which means it is a specialized type of processor that is designed to perform complex mathematical calculations quickly and very efficiently. It is mostly used in the gaming industry but nowadays it is commonly used in the field of deep learning, where they have become the most important tool for enhancing the performance of training of a deep neural network. Let’s dive straight into the article where I will be listing down the top 5 platforms that offer cloud-based GPUs free of cost, without even requiring a credit card for sign-up.

Before we get started, here are the parameters on which we are comparing our recommendations:

1. GPU Model & its Memory,
2. Persistent Storage —  storage that retains after power to the device is shut off,
3. Usage Limit — if any
4. Runtime per session, viz., the maximum time your code can run before timeout,
5. Background execution — which allows your model to train, without you needing to keep the code open, and
6. Option to keep your notebooks Private

Table of Contents

1. Top 5 Places to get GPU Online for Free
2. Google Colaboratory
3. Kaggle
4. Amazon SageMaker Studio Lab
5. Gradient by Paperspace
6. Microsoft Azure for Student Account

Top 5 Places to get GPU Online for Free

1. Google Colaboratory

• It’s a product of Google Research and is quite popular among the Data Science community globally.
• The only prerequisite to using Colab is that you need to have a Google Account. So, if you don’t have one, you may create it to get started.
• The free GPU Model you get with Colab is subject to availability. Generally, you may get a Tesla K80, or even Tesla T4, with GPU Memory of up to 16GBs.
• They also have paid subscriptions, called: Colab Pro and Colab Pro+, with which you get more high-end GPU configurations for training larger Deep Learning Models.

Talking about the Pros:

• You get 15GBs of data storage on Google Drive
• Longish Runtime per session of 12 hours
• It also provides you an option to keep your notebooks private

Talking about the Cons:

• Google Colab doesn’t allow background execution

2. Kaggle

If you haven’t used Kaggle so far in your Data Science journey, trust me, you soon will. It is home to over 50,000 public datasets and 400,000 public notebooks and is nothing less than a gold mine for data scientists. Similar to Colab, Kaggle is also owned by Google.

Kaggle offers two GPU options, that you may choose from:

1. Tesla P100, with 16GB of GPU Memory
2. Dual Tesla T4 with 15 GB of GPU memory

Talking about the Pros

• Kaggle offers 20GBs of persistent storage
• It offers a runtime per session is 12 hours
• It allows background execution of your Machine Learning code
• It allows both public and private notebooks
• On top of this, you get easy access to thousands of Kaggle datasets

Talking about the Cons

• There’s a weekly Usage Limit of 33 hours

3. Amazon SageMaker Studio Lab

SageMaker Studio Lab is launched recently by Amazon and is positioned as a direct competitor to Google’s Colab. The best part is you do not need to set up an AWS account or use a credit card. To get started, just sign-up for an account using your email. The free GPU Model you get with SageMaker Studio Lab is the Tesla T4, which has 16GBs of GPU Memory.

Talking about the Pros:

• No credit card is required, like AWS.
• It is the most powerful GPU you get among the free GPU platform options
• You get 15GBs of Persistent Storage, along with background execution

Talking about the Cons:

• It offers a relatively low runtime of 4 hours, and a usage limit of 8 hours per 24 hours.
• And for now, you need to request an account and then wait for a couple of days till the Amazon team reviews your request before you get access.

4. Gradient by Paperspace

The gradient is a product of a company called Paperspace. The free GPU Model you get with Gradient is: Quadro M4000, which has 8GBs of GPU Memory

Talking about the Pros:

• You get a long idle time of 6 hours

Talking about the Cons:

• You get a low GPU Memory of 8GB
• Low persistent storage of 5GB
• No option to keep your notebooks private on the free tier
• As the GPU is subject to availability, there will be times when you face the GPU unavailability issue, making it unreliable.

5. Microsoft Azure for Student Account

As the name suggests, this recommendation is specifically for users who are currently students and pursuing their education with this account: no credit card is required and you get $100 worth of free credit usage, which you may use to purchase Azure GPU resources. For example, you may opt for Tesla T4 (which is the same GPU you get on Amazon Sagemaker Studio Lab). The benefit you have with Azure is that you may decide to choose higher GPU configurations, which by the way might consume your free credits faster.

Talking about the Pros:

• Microsoft Azure for Students is absolutely free of cost.
• You get access to a range of powerful GPUs, with 50GBs of Persistent Storage
• It allows background execution of your Machine Learning code
• It also provides you an option to keep your notebook private

Talking about the Cons:

• Usage is limited by your credits, viz. $100.
• However, you may get further free credits if you keep an eye on Azure promotions on their website and Twitter.

Conclusion

To summarize, these are the top 5 places to get a free GPU online for your Deep Learning work. If you have previously used any one of these platforms, do share your experience in the comments section below. If you liked this article, comment below. If you have come across an issue or topic that you think could be worth discussing in this series, do suggest me in the comments section below. And we will try to do a new article on this.

Thank you for reading. Bye!