LightningAI: STOP PAYING for Google's Colab with this NEW & FREE Alternative (Works with VSCode)

AICodeKing
26 Apr 202406:36

TLDRIn this video, the host introduces Lightning AI, a new and free alternative to Google Colab that offers a web-based VSCode interface and is ideal for running high-end models. The host, who prefers local work but occasionally needs to run large models for videos, dislikes Google Colab's interface and its lack of reliability due to issues like non-persistent storage and time-outs. Lightning AI provides a free Studio with four cores and 16 GB RAM, which can be accessed 24/7 and can be seamlessly upgraded to a GPU instance for intensive tasks. The free tier includes 22 GPU hours per month. The host demonstrates the process of signing up, creating a studio, and running a model on both CPU and GPU instances, showing a significant speed improvement with the GPU version. The video concludes with a recommendation to try Lightning AI and a call for viewer engagement in the comments.

Takeaways

  • 🎉 The channel AI Code King reached 1K subscribers in just one month.
  • 🔍 Google Colab is commonly used for running high-end models due to its free GPU access, but has limitations like an outdated interface and lack of persistent storage.
  • 🚀 The presenter prefers to work locally but uses Colab for large models due to its GPU capabilities.
  • 💻 Lightning AI is introduced as a new and free alternative to Google Colab, providing a web-based VS Code interface with terminal access and customization.
  • ⏰ Lightning AI offers one free Studio that can run 24/7 with 22 GPU hours per month on the free tier.
  • 🌐 The Studio provided by Lightning AI automatically shuts down when inactive and can be restarted when needed.
  • 📦 Persistent storage is available, allowing users to access their previous data each time they open the Studio.
  • 📝 Users can sign up for Lightning AI and expect to gain access within 2 to 3 days via email notification.
  • 🔧 The platform allows changing the machine type from CPU to GPU and back, and also provides options to switch between VS Code and Jupyter interfaces.
  • 🚀 A demonstration shows running LLaMa 3 on Lightning AI, with a significant speed increase when switching to a GPU instance.
  • 👍 The presenter expresses satisfaction with Lightning AI and plans to use it instead of Colab for future work.

Q & A

  • What milestone did the AI Code King channel achieve recently?

    -The AI Code King channel recently reached 1K subscribers, which was a significant milestone for the channel.

  • Why do people commonly use Google Colab for running high-end models?

    -People use Google Colab because it provides free access to a GPU, which is necessary for running high-end machine learning models or diffusion models.

  • What are some of the drawbacks mentioned about using Google Colab?

    -The drawbacks mentioned include an outdated interface, lack of persistent storage, no guaranteed GPU allocation, and the possibility of being timed out after inactivity, which requires starting the environment setup again.

  • What is Lightning AI and how does it differ from Google Colab?

    -Lightning AI is a web-based VS Code interface that offers a free Studio with persistent storage and the ability to add a GPU for running high-end models. It differs from Google Colab by providing a more modern interface, terminal access, and the convenience of persistent storage.

  • How many GPU hours are included in the free tier of Lightning AI?

    -The free tier of Lightning AI includes 22 GPU hours per month.

  • What is the process of signing up for Lightning AI?

    -To sign up for Lightning AI, one needs to go to the Lightning AI site, sign up, and join a waiting list. Access is granted in about 2 to 3 days through an email notification.

  • How can you transform a VS Code instance into a GPU instance on Lightning AI?

    -You can transform a VS Code instance into a GPU instance by clicking on the first option on the right sidebar and choosing the GPU option.

  • What is the difference in performance when running LLM models on the default CPU machine versus a GPU instance on Lightning AI?

    -On the default CPU machine, the performance was about three tokens per second, which is slow. On a GPU instance, the performance increased to about 43 tokens per second, which is significantly faster.

  • What are the additional options available on the Lightning AI interface?

    -Additional options on the Lightning AI interface include changing the machine type, accessing a terminal, switching the interface to Jupyter for a look similar to Google Colab, and using Google's Tensor Board.

  • How does the speaker feel about using Google Colab in the future?

    -The speaker expresses a preference for not using Google Colab anymore and plans to use Lightning AI for future projects.

  • What is the speaker's call to action for viewers of the video?

    -The speaker asks viewers to share if they will also use Lightning AI in the comments and to like and subscribe to the channel for more content.

  • What is the significance of the token rate when running LLM models?

    -The token rate indicates the speed at which the model can process and generate output. A higher token rate, such as the 43 tokens per second achieved on the GPU instance, means faster processing and is desirable for efficiency.

Outlines

00:00

🎉 Channel Milestone and Introduction to Lightning AI

The speaker begins by expressing gratitude for reaching 1,000 subscribers in just a month since starting the channel. They discuss the common use of Google Colab for running high-end AI models due to its free GPU access but express personal preference for local operations. However, for running large models, they reluctantly use Colab, criticizing its outdated interface and lack of persistent storage. The speaker then introduces Lightning AI as a solution that offers a web-based VS Code interface with persistent storage and the ability to add a GPU for intensive tasks. They explain the features of Lightning AI, including a free Studio that can run 24/7 and an allocation of 22 GPU hours per month. The process of signing up, accessing, and setting up a Lightning AI Studio is outlined, followed by a demonstration of running the LLaMa 3 model on a CPU and then on a GPU instance.

05:02

🚀 Comparing LLaMa 3 Performance on CPU vs. GPU

The speaker demonstrates the installation of the LLaMa 3 model and runs a test to measure its performance on a default CPU machine, yielding about three tokens per second, which they note as being quite slow. They then guide the viewer on how to switch the instance to a GPU instance within the Lightning AI platform. After selecting the T4 GPU option and waiting for the instance to refresh, they run the same test again and observe a significant improvement in performance, achieving about 43 tokens per second. The speaker concludes by expressing satisfaction with the Lightning AI platform and stating an intention to use it for future projects, inviting viewers to share their thoughts in the comments and to subscribe for more content.

Mindmap

Keywords

💡Google Colab

Google Colab is an online platform provided by Google that allows users to write and execute Python code in a cloud-based environment. It is popular among AI researchers and developers for its free access to GPUs, which are essential for running high-end machine learning models. In the video, the speaker discusses their preference for a local setup but acknowledges the necessity of using Google Colab for certain tasks due to its GPU capabilities.

💡GPU

A GPU, or Graphics Processing Unit, is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. In the context of the video, the GPU is crucial for running complex AI models, as it provides the necessary computational power to process these models efficiently.

💡LLM (Large Language Model)

An LLM refers to a Large Language Model, which is a type of artificial intelligence model designed to process and understand large volumes of human language data. These models are often used in natural language processing tasks. The video mentions running high-end LLMs and how the use of a GPU can significantly speed up their operation.

💡Diffusion Model

A diffusion model in AI is a type of generative model that is used to create data that resembles a given dataset, such as images or audio. It is part of the broader field of machine learning and is often used for tasks like image synthesis. The video discusses using diffusion models in the context of AI, where the models require substantial computational resources.

💡VSCode

VSCode stands for Visual Studio Code, which is a free, open-source code editor developed by Microsoft. It is highly popular among developers for its rich feature set, including support for debugging, Git control, syntax highlighting, intelligent code completion, and customization through extensions. In the video, VSCode is mentioned as the interface for the new alternative platform, Lightning AI.

💡Terminal Access

Terminal access refers to the ability to interact with a computer's operating system through a command-line interface, allowing users to execute commands and run programs. In the context of the video, having terminal access is important for developers and researchers as it provides them with a powerful tool for customizing their environment and running specific commands necessary for their work.

💡Persistent Storage

Persistent storage in computing refers to a type of storage that retains data even after the system is powered off. This is in contrast to volatile memory, which loses data when power is cut. The video emphasizes the importance of persistent storage for AI work, as it allows users to save their work and not lose progress when they close their browser or the system is restarted.

💡Lightning AI

Lightning AI is presented in the video as a new and free alternative to Google Colab. It offers a web-based VSCode interface and provides users with a free Studio that can run 24/7 with the option to attach a GPU for intensive tasks. The platform aims to provide a more reliable and customizable experience for running AI models compared to Google Colab.

💡Studio

In the context of the video, a Studio refers to a workspace provided by Lightning AI where users can code and run their AI models. The studio is equipped with a CPU and can be upgraded to include a GPU for more demanding tasks. It is a central concept in the video, as it represents the environment where the user interacts with the platform.

💡T4 GPU

The T4 GPU is a specific model of graphics processing unit developed by Nvidia, designed for AI inference and other compute workloads. It is mentioned in the video as one of the GPU options available on Lightning AI, which the user can select to enhance the computational power of their Studio for running AI models.

💡Token

In the context of language models and natural language processing, a token refers to a unit of text, such as a word or punctuation mark, that is processed by the model. The video script mentions tokens per second as a metric for measuring the speed at which the LLM processes text. A higher number of tokens per second indicates faster processing.

Highlights

AI Code King reached 1K subscribers in just one month since starting the channel.

Google Colab is widely used for running high-end models due to its free GPU access.

The presenter prefers to work locally but uses Colab for big models due to its limitations.

Google Colab's interface is considered outdated and unreliable.

Colab does not provide persistent storage, requiring users to re-setup their environment after each session.

Lightning AI is introduced as a new and free alternative to Google Colab.

Lightning AI offers a web-based VS Code interface with one free Studio that operates 24/7.

Users receive 22 free GPU hours per month on Lightning AI's free tier.

The free tier instance has four cores and 16 GB RAM, and can be accessed through a web-based VS Code.

Lightning AI allows seamless transition from a VS Code instance to a GPU-powered environment.

The platform automatically switches off the instance when inactive and can be restarted as needed.

Lightning AI provides persistent storage, retaining data even after closing the browser.

The platform offers a waiting list for new users, with access granted within 2 to 3 days.

Users can create a studio and choose between CPU and GPU options within the interface.

Lightning AI provides real-time CPU usage metrics and allows switching to a Jupyter-like interface.

The presenter demonstrates running LLaMa 3 on a default CPU machine and then on a GPU instance.

The GPU version of LLaMa 3 shows a significant speed improvement, processing 43 tokens per second.

The presenter plans to use Lightning AI for future projects instead of Google Colab.

The video encourages viewers to share their thoughts on using Lightning AI in the comments.

The channel requests likes and subscriptions for more content.

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