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In this post, we will be learning about a new Cloud IDE that is both free and user-friendly. It is an upgraded version of Google Colab that allows you to save your projects, use essential plugins, and run generative models on GPU for free.
Lightning AI Studio is a cloud-based AI development platform (similar to Google Colab) that aims to eliminate the hassle of setting up local environments for machine learning projects.
Here are the key features of Lightning AI Studio:
- It integrates popular machine learning tools into a single interface, so you don’t have to context switch between different tools. This allows for building scalable AI apps and endpoints more easily.
- There is no environment setup required. You can code in the browser or connect your local IDE (VSCode or PyCharm). You can also easily switch between CPU and GPU with no environment changes.
- It allows hosting and sharing AI apps built with Streamlit, Gradio, React JS, etc. It also enables multi-user collaboration by coding together.
- It provides unlimited storage and the ability to upload and share files as well as connect S3 buckets.
- It enables training models at a massive scale using thousands of GPUs (Paid option). You can run hyperparameter sweeps, data preprocessing, and model deployment massively in parallel.
- It delivers a local development experience while leveraging the power of cloud infrastructure.
- Discover community templates (Studios) for deploying, fine-tuning, and training models quickly on your cloud with your data in minutes, requiring no setup.
Your Jupyter Notebook / VSCode on the cloud provides scalable hardware for training large language models and running fast inference.
You can create a free account at lightning.ai/sign-up. To get instant verification, make sure to use an official company or .edu email. I have signed up with @kdnuggets.com email, and I got instant access.
Once you have created your account, follow a few simple steps to customize your Studio experience. To get 7 hours of free GPU, you will need to verify your phone number.
Once you complete the initial steps, you will be directed to a sample project that includes a basic Python file. In just a few minutes, your Studio will be ready to process images and fine-tune the Renest model. To get started, simply write your code and execute the file.
The user interface (UI) is quite similar to that of VSCode but with additional options available on the right panel.
The Lighting AI platform offers Studios that are project templates designed by users. These templates include code, environment settings, and data to help you commence your project. You can find various Studios on the platforms such as training, fine-tuning, preprocessing, inference, and hyperparameters sweep templates. You can easily search and scroll through these templates to find the one that suits your requirements.
For example, accessing Mistral 7B API. You click on the “Get” and wait for it to complete.
Click on the `run.ipynb` file and run the first cell.
After accessing the Mistral 7B API, the code returns the result. To review the client code, go to the server folder and open the `client.py` file.
I use VSCode regularly and found it easy to get started with Lightning AI Studio.
If you are comfortable with the Jupyter Notebook UI, you can switch the IDE by clicking on the Jupyter Notebook button on the right panel. The right panel is the place where you will find all kinds of Lightning AI Studio plugins.
To add a new plugin, click the “+” button and choose from IDE, AI Agents, Training, Serving, and Webapps plugins.
It’s that simple. You can now enjoy Studio plugins with VSCode and Jupyter Notebook extensions.
Lightning AI Studio provides a complete platform for your machine learning needs – from experimenting with model architectures to deploying applications. This user-friendly platform comes equipped with all the necessary features, eliminating the need to piece together various tools.
You can leverage the power and scale of the cloud without learning the intricacies of cloud computing or infrastructure management. The developers have abstracted away the complexity, allowing even data scientists with no cloud expertise to develop and deploy solutions independently.
Whether you’re looking to prototype an idea or build a production-grade application, Lightning AI Studio has you covered. The free tier grants access to all the core functionality, including Studio GPUs, to accelerate training. This makes Lightning AI Studio a no-brainer for both learning and creating impactful machine learning applications.
Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master’s degree in Technology Management and a bachelor’s degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.
