What is Google Colab?
Colaboratory, sometimes called “Colab,” is a Google Research product. It enables anyone to create and execute arbitrary Python code through the browser. Technically speaking, Colab is a hosted Jupyter notebook service that offers free access to computer resources, including GPUs, and requires no setup. As a better iteration of Jupyter Notebook, Google Colab can be characterized. Data analysis, teaching, and machine learning are three areas where Colab excels.
Google Colab Features
The exciting features that each contemporary IDE offers are abundant in Google Colab, in addition to many others. Below is a list of some of the more fascinating aspects.
- Interactive tutorials for learning neural networks and machine learning.
- Without a local setup, write and run Python 3 code.
- Run commands on the terminal from the Notebook.
- Importing data from outside sources like Kaggle.
- Your notebooks can be saved to Google Drive.
- Google Drive notebooks can be imported.
- GPUs, TPUs, and cloud services for free.
- Integrate Open CV, Tensor Flow, and PyTorch.
- Easily import or publish to/from GitHub.
What is Google Colab Pro?
Colab gives interactive computing top priority. Runtimes will expire if you are not active. Depending on availability and usage trends, notebooks in the free edition of Colab can operate for up to 12 hours. Based on the balance of your compute units, Colab Pro offers you enhanced compute availability.
Generally speaking, notebooks have a maximum operating time of 12 hours, depending on availability and usage patterns. Backend termination is what you can anticipate if you use up all of the compute units on a Pro. If you have adequate computational units, Colab Pro+ allows continuous code execution for up to 24 hours. Idle timeouts only take effect after code execution ends.
Faster GPUs, longer sessions, fewer interruptions, terminal access, and more RAM are just a few problems that Colab Pro and Colab Pro+ address for machine learning engineers and data scientists. Nevertheless, Colab Pro’s offers still need to be more constrained.
- All but a few nations’ citizens cannot use Colab Pro or Pro+.
- GPU restrictions for Colab Pro and Pro+ are NVIDIA P100 or T4.
- RAM is limited in Colab Pro to 32 GB and Pro+ to 52 GB.
- Sessions are limited by Colab Pro and Pro+ to 24 hours.
- While Pro+ offers background execution, Colab Pro does not
- JupyterLab is not available in its entirety through Colab Pro or Pro+.
- Your instance might not be accessible because Colab Pro and Pro+ provide no resource guarantees.
Alternatives of Google Colab
SageMaker on Amazon
Another cloud-based machine learning platform created by Amazon in November 2017 is called Amazon SageMaker. It offers hosted Jupyter notebooks with no setup necessary. However, it isn’t free. Yes, even though the trial is free, you must pay for its services (for the initial two months).
SageMaker offers MXNet, Chainer, and SparkML in addition to the Deep Learning frameworks given by Google Colab, such as Tensorflow, Scikit-Learn, PyTorch, and XGBoost. Amazon SageMaker Ground Truth, Amazon Augmented AI, Amazon SageMaker Studio Notebooks, Preprocessing, Amazon SageMaker Experiments, and many other features are available.
A web-based cloud computing (SaaS) and course administration platform for computational mathematics are called CoCalc, or Collaborative Calculation. It is an open-source program that SageMath Inc. hosts. It offers editing of LaTeX documents and Sage worksheets in addition to the Jupyter notebook. William Stein, a former mathematics professor at the University of Washington, is the originator and primary developer of CoCalc.
Real-time collaboration is a feature that allows you to share your notebook with others and modify it together in real-time. It contains a history recording function that records every change you make to the notebook and lets you browse those changes using a simple slider control. For the CoCalc free plan, sessions will end after 30 minutes of inactivity, even if they can go up to 24 hours, which is twice as long as Colab offers. Python, Sage, R, Octave, and many other languages are available.
Although Kaggle is well-known for its data science competitions, they also offer free Kernels or Notebooks for carrying out machine learning and data science projects independently, without regard to contests. A free platform for running Jupyter notebooks in the browser is called Kaggle Kernels. Colab and Kaggle are both Google products, and they share many features.
Kaggle has modified its kernels to include more significant memory and processing capability. 20GB Dataset, 5GB Disk Space, 9 hours of runtime, 4 CPUs with 16GB RAM, or 2 CPU cores with 13GB RAM when the GPU is turned on
The open-source program BinderHub, which deploys the Binder service in the cloud, powers Binder. You can build unique computing environments using Binder that multiple remote users can share and use. You can enter the URL of any openly accessible Git repository, and it will open in the default Jupyter Notebook interface. Any notebook in the repository may be used, but any modifications you make won’t be saved back to the repository.
It might be helpful when you have a repository chock full of Jupyter Notebooks. Even though a repository’s maximum user count is 100
Microsoft’s Azure laptops and Colab are extremely functionally comparable. A free cloud-sharing feature is offered by both systems. Regarding speed, Azure Notebooks triumphs and outperforms Colab by a wide margin. It contains 4 gigabytes of RAM. Libraries are the name of the connected notebooks that Azure Notebooks develops. Each data file in these libraries is less than 100 megabytes in size. Python, R, and F# are all supported programming languages by Azure Notebooks. Its native Jupyter UI is present. Simple applications are better suited for Azure Notebooks.
IBM DataPlatform Notebooks
With support for open-source choices, IBM debuted Data Science Experience (DSX) and the Watson Data Platform in 2016. These choices included Jupyter notebooks, R, Python, Scala, and Apache Spark. The platform enabling multi-cloud freedom of choice for data science work was eventually launched. This was accomplished with the use of Kubernetes-based product containerization. It can be set up wherever the data is in Docker or CloudFoundry containers. Unlike Google Colab, IBM DataPlatform Notebooks provide containerization for multi-cloud or a hybrid deployment. Data science must be hosted on Colab’s private cloud.
Because it enables users to design, deploy, and operate models anywhere, including on competing public clouds, IBM supports containerization. As DSX Local, DSX is both a component of and, optionally, an independent entity from the Watson Data Platform. It offers collaborative, permission-controlled access to projects, data, tools for data science, services, and community space. R, Python, and Scala are supported by DataPlatform Notebooks, which also keep Jupyter and Apache Zeppelin notebooks. Open-source libraries compatible with DSX include Spark MLlib, TensorFlow, Caffe, Keras, and MXNet.
An open-source web tool called Jupyter Notebook allows users to create and share documents with live code, visualizations, equations, and text. The staff of Project Jupyter is in charge of maintaining the Jupyter Notebook. They are unrelated projects that came about due to the IPython project. Julia, R, and Python are all supported. Their primary applications are in data analysis and computational physics. Jupyter notebooks, like Colab, are more concerned with making work reproducible and understandable. It offers a range of visualizations that are instantly rendered in the notebook. It has two modes, referred to as insert and escape.
Replit, a collaborative IDE with a browser interface. A straightforward yet effective online IDE, editor, compiler, interpreter, and REPL is called Replit. In more than 50 programming languages, you can code, compile, run, and host. On your browser, you can run and save code whenever you like. Replit is compatible with Chromebooks and other devices with a web browser.
- Third-party packages for Linting Debugger
- Live updates and files
- deployment and hosting
- Replit Classroom: Effective teaching and learning tools, invite pupils, tracking progress and automatic grading, Exchange classrooms, and coding
- Put running code on your blog or website.
VS Code is available in the browser thanks to vscode.dev. Start coding after opening a folder on your local computer. No installation is needed. With certain restrictions, it covers all significant languages.
The online code editor for web apps is called StackBlitz. Visual Studio Code serves as its engine. Modern editing capabilities from VS Code are now available in the browser thanks to StackBlitz.
Project Search, Go to Definitions, and other capabilities of Visual Studio Code
any NPM package into your project for import
Debug and preview in a different window
If you go offline, you can still continue editing because StackBlitz runs a live development server in-browser using Progressive Web App APIs.
Share and embed: Each project can be made public.
- Installing the NPM-dependent modules.
- The ability to embed your sandbox anywhere.
- Integrating GitHub.
- Real-time cooperation Real-time collaborative editing in sandboxes.
- Unlisted/Private Sandboxes
- Editor for Monaco identical editing environment as VSCode.
- Free Software.
Plunker is an online community where you can share your web development ideas and create them together.
- Code collaboration in real time
- a configurable, full-featured syntax editor
- a live glimpse of code modifications
- Type-ahead code linting
- Plunks is fully open-source on GitHub and is available for forking, discussing, and sharing under the MIT license.
Prathamesh Ingle is a Consulting Content Writer at MarktechPost. He is a Mechanical Engineer and working as a Data Analyst. He is also an AI practitioner and certified Data Scientist with interest in applications of AI. He is enthusiastic about exploring new technologies and advancements with their real life applications