Overview
- Understand what Google Colab is
- Get a list of the top alternatives to Google Colab
- This list is by no means exhaustive.. Feel free to add more in the comments below..
Introduction
For anyone who has storage issues or is unable to afford a system that meets their requirements for data science work, Google Colab has been a blessing.
Working with Colab has opened up so many avenues for me that I previously thought were not possible. We no longer have the restriction of poor computational power in our machines. And free GPUs are at your fingertips.
But, as data scientists, it is important to know all the alternatives available for a particular tool. Then, in this article, We will explore some of the widely used alternatives to Google Colab.
Table of Contents
1- What is Google Colaboratory?
2- Alternatives to Google Colab
- Amazon SageMaker
- CoCalc
- Kernel de Kaggle
- Binder
3- Other alternatives
1- What is Google Colaboratory?
Google Collaborative o Google Colab is a free Jupyter Notebook environment. It's a free cloud-based service from Google, which means you don't have to pay anything. One of the best things about Colab is that you don't need to install anything beforehand. In fact, many of the data science and machine learning libraries, like Pbreathe, NumPy, Tensorflow, Hard, OpenCV they come pre-installed with Colab.
The notebooks you create are saved to your Google Drive. Therefore, Colab also takes advantage of the collaboration features of Google Docs, where you can share your laptop with multiple people easily and everyone can work on the same laptop at the same time without any problem.
Google also provides the use of a free NVIDIA Tesla K80 GPU. If you connect Colab to Google Drive, you will get up to 15 GB of disk space to store your data sets. You can run the session in an interactive Colab Notebook for 12 hours, what is enough for a beginner. Google has its own custom made chips called TPU.
One more thing to keep in mind is that tThe dataset that you upload to the Colab notebook is deleted after the session ends.
But nevertheless, can upgrade to Pro version, which supposedly gives you access to faster GPUs: NVIDIA TESLA T4 GPU or P100, longer run times up to 24 hours and more RAM.
You can read more about Google Colab in the following article:
Alternatives to Google Colab
In the next section, we discuss the 5 best alternatives to google colab.
1. Amazon SageMaker
Amazon SageMaker It is also a cloud-based machine learning platform developed by Amazon in November 2017. Hosted Jupyter notebooks requiring no setup. But it's not free. Yes, must pay for their services, although the trial is free (during the initial two months).
“SageMaker Studio is free to use, you only pay for the AWS services you use in Studio”.
Pros:
- Along with deep learning frameworks like Tensorflow, scikit learn, PyTorch and XGBoost, that are provided by Google Colab, SageMaker also provides MXNet, Chainer y SparkML.
- Offers the following features: Amazon SageMaker Ground Truth, Amazon augmented AI, Amazon SageMaker Studio Notebooks, Preprocessing, Amazon SageMaker Experiments and More.
Cons:
- If you train your model using something incorporated de SageMaker, you cannot implement it outside of SageMaker. This is also the case for Google's AutoML, although all models trained on the ML engine (including those who use the TensorFlow-hub modules) can be deployed anywhere.
- Automatic hyperparameter optimization works best in Colab, in terms of results produced and time required.
- You get new versions of Tensorflow from SageMaker weeks after getting them from Colab.
Here is the guide on how to use SageMaker and its functions.
2. CoCalc
CoCalc or collaborative computing is web-based cloud computing (SaaS) and management platform for computational mathematics courses. It is open source software hosted by SageMath Inc. CoCalc's creator and lead developer is William Stein, former professor of mathematics at the University of Washington. Along with the Jupyter notebook, supports editing of Sage worksheets and Latex documents.
Pros:
- Offers real-time collaboration, which means you can share your notebook with others and everyone can edit it at the same time.
- For the free plan offered by CoCalc, sessions will close after 30 minutes of inactivity, although they can last up to 24 hours, which is twice the time that Colab offers.
- It has a history log feature that records all your changes to the laptop in great detail and allows you to explore those changes using an intuitive slider..
- Languages offered: Python, Sage, R, Octave and many more.
Cons:
- The service is not free. But nevertheless, there is a free plan but a Test project with certain restrictions, in particular, your project is running on a lower quality of hosting and you do not have internet access to download data from other servers.
- GPU is not available, neither in the free plan nor in the upgraded version.
You can start with CoCalc from here.
3. Kaggle Kernel
Kaggle is a popular platform for your data science contests, but nevertheless, they also offer Grain o Notebooks to perform all Machine Learning and Data Science tasks, regardless of skills. Kaggle Kernels is a free platform to run Jupyter notebooks in the browser. Both Colab and Kaggle are products of Google and have many similarities.
Kaggle has updated its cores to have more computing power and memory. Data set 20 GB, 5 GB of disk space, 9 runtime hours and 4 CPU with 16 GB of RAM o, when the GPU is on, there is 2 CPU cores with 13 GB of RAM.
Pros:
- Kaggle provides free access to NVIDIA TESLA P100 GPUs on the cores. This benchmark shows that enabling a GPU in your Kernel results in 12.5X acceleration while training a deep learning model.
- Supports two of the major languages in the field of data science: R y Python.
- Most of the Jupyter Notebook keyboard shortcuts are almost similar in Kaggle Kernels, making it easier for someone who works at Jupyter Notebooks to work at Kaggle.
- Kaggle has a great community to support, learn and validate data science skills.
Cons:
- In general, Kaggle has a lag while running and is slower than Colab.
- Kaggle generally limits the kernel runtime to 9 hours, with a waiting time after 1 time of inactivity.
- A big drawback of both platforms is that the laptops cannot be downloaded in other useful formats.
4. File
Binder is powered by BinderHub, which is an open source tool that implements the Binder service in the cloud. Binder allows you to create custom computing environments that can be shared and used by many remote users. Allows you to enter the URL of any public Git repository and it will open it within the native Jupyter Notebook interface. You can run any notebook in the repository, although the changes you make will not be saved in the repository.
It can be useful when you have a full repository of Jupyter Notebooks. Although there is a limit of users of 100 users for a repository (what is enough, I suppose).
Pros:
- Supported languages: Python, R and Julia.
- Since it is an open source project, is free.
- Binder can run your notebooks directly from GitHub.
Cons:
- Collaboration with others is not available.
- Sessions will close after 20 minutes of inactivity, although they can last 12 hours or more.
- Not suitable for working with large data sets.
Other alternatives
Some of the other alternatives that I did not mention in this article may be:
1- Saturn cloud
3- Datalore
Look at them.
Final notes
The purpose of this article was only to give an idea about the possible alternatives of Google Collaboratory, the final decision is up to you which one you would prefer based on your need. I hope you explore all of these platforms and identify the pros and cons of your line of work..
Also let me know which platform you prefer / use and why.
