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

AICodeKing
26 Apr 202406:36

TLDRAI Code King introduces Lightning AI, a new free alternative to Google Colab that integrates with VSCode and offers a user-friendly interface. It provides a persistent environment with one free studio running 24/7, 22 free GPU hours, and the ability to switch between CPU and GPU instances seamlessly. The video demonstrates the setup and speed improvement when using the platform for running large AI models like LLaMA 3, highlighting a significant upgrade from the limitations of Colab.

Takeaways

  • 🎉 The channel AI code King reached 1K subscribers in one month.
  • 🤖 The video discusses an alternative to Google Colab for running AI models.
  • 💻 Google Colab is commonly used for its free GPU access but has limitations like an outdated interface, lack of persistent storage, and reliability issues.
  • 🌟 The presenter prefers to work locally but sometimes needs to use a platform with a GPU for larger models.
  • 🆕 Introducing Lightning AI, a new web-based VSCode interface that offers a free studio with GPU capabilities.
  • 🕒 Lightning AI provides 22 free GPU hours per month and a 24/7 accessible studio.
  • 🔄 The studio can be switched between CPU and GPU modes seamlessly.
  • 👍 The presenter finds Lightning AI to be simple to use, customizable, and reliable.
  • 🚀 To use Lightning AI, one must sign up and wait for access, which typically takes 2-3 days.
  • 🛠️ The platform offers various options including changing machine type, accessing a terminal, and switching interfaces.
  • 📈 The presenter demonstrates running LLaMA 3 on both CPU and GPU instances, showing significant speed improvement with the GPU.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is introducing Lightning AI, a new and free alternative to Google Colab for running high-end AI models with a web-based VSCode interface.

  • Why does the presenter prefer to do things locally rather than using Google Colab?

    -The presenter prefers to do things locally because they find the Google Colab interface outdated and not reliable due to issues like lack of persistent storage and the need to set up the environment from scratch each time.

  • What are the limitations of Google Colab mentioned in the video?

    -The limitations mentioned include the unattractive interface, no persistent storage, the need to redo environment setup after closing the browser, and the risk of getting timed out after 5 minutes of inactivity.

  • What features does Lightning AI offer that are not available in Google Colab?

    -Lightning AI offers a web-based VSCode interface, a free studio that can run 24/7, terminal access for fully customized behaviors, and the ability to attach and detach a GPU seamlessly.

  • 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 to get started with Lightning AI?

    -To get started with Lightning AI, one needs to sign up on their site, wait for access which may take 2-3 days, and then create a studio once logged in.

  • What is the difference between the CPU and GPU instances in Lightning AI?

    -The CPU instance in Lightning AI is a default machine with four cores and 16 GB RAM, while the GPU instance allows the addition of a GPU for running high-end models, but is limited to 22 hours of GPU usage per month on the free tier.

  • How does the presenter demonstrate the performance difference between CPU and GPU instances in Lightning AI?

    -The presenter runs the same AI model, LLaMA 3, first on the CPU instance and then on the GPU instance, showing a significant speed increase from about 3 tokens per second to 43 tokens per second.

  • What is the presenter's final decision regarding using Lightning AI or Google Colab for future projects?

    -The presenter decides not to use Google Colab anymore and will be using Lightning AI for future projects due to its superior performance and features.

  • How can viewers let the presenter know if they will also use Lightning AI?

    -Viewers can let the presenter know if they will use Lightning AI by leaving a comment on the video.

  • What action does the presenter encourage viewers to take if they liked the video?

    -The presenter encourages viewers to give the video a thumbs up and subscribe to the channel if they liked the video.

Outlines

00:00

🎉 Channel Milestone and Introduction to Lightning AI

The speaker expresses gratitude for reaching 1K subscribers in a month and introduces the topic of using Google Colab for AI tasks. They share their preference for local operations but acknowledge the need for a GPU when running large models. The speaker introduces Lightning AI as a solution that offers a web-based VS Code interface with a free studio, 24/7 availability, and 22 GPU hours per month. The studio can be transformed into a GPU powerhouse seamlessly and provides persistent storage. The process of signing up, accessing the platform, and setting up the environment is explained, along with a demonstration of running a model on the platform.

05:02

🚀 Comparing CPU and GPU Performance on Lightning AI

The speaker demonstrates the performance difference between running a model on a CPU and a GPU instance within Lightning AI. Initially, they install and run the model 'llama 3' on the default CPU machine, achieving a token production rate of three tokens per second. The process of switching the instance to a GPU instance is shown, which involves selecting the GPU option from the sidebar. After switching to a T4 GPU, the model's performance significantly improves, producing 43 tokens per second. The speaker concludes by expressing their intention to use Lightning AI for future projects and invites viewers to share their thoughts in the comments.

Mindmap

Keywords

💡LightningAI

LightningAI is a new and free alternative to Google's Colab, which is highlighted in the video for its ability to provide a web-based VSCode interface with access to a free GPU. It is positioned as a solution for those who prefer local development but occasionally need the computational power of a GPU for running complex machine learning models. The video demonstrates how LightningAI can be utilized to enhance the development experience by offering persistent storage and customizable behaviors.

💡Google Colab

Google Colab is a cloud-based platform that provides free access to GPUs for running machine learning models. It is widely used in the AI community, but the video points out some of its drawbacks, such as an outdated interface, lack of persistent storage, and unreliability due to time-outs. The presenter prefers alternatives that offer a more modern and reliable development environment.

💡VSCode

VSCode, or Visual Studio Code, is a popular source code editor developed by Microsoft. It supports a wide array of programming languages and is extensible with extensions installed from the marketplace. In the context of the video, LightningAI provides a web-based VSCode interface, allowing users to code and run their programs in a familiar environment with the added benefit of GPU acceleration when needed.

💡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 video, the GPU is crucial for running high-end machine learning models, and LightningAI offers the ability to attach a GPU to the user's instance for such tasks, making it a powerful tool for AI development.

💡Studio

In the context of LightningAI, a 'Studio' refers to a user's personal development environment that can be accessed through a web interface. The video explains that each user gets one free Studio with four cores and 16 GB of RAM, which can be used 24/7 for coding and other tasks, and can be transformed into a GPU-powered environment when necessary.

💡Persistent Storage

Persistent storage refers to a type of storage that retains data even after the system is powered off. The video emphasizes the importance of persistent storage in development environments, as it allows developers to save their work and not lose progress when they close their browser or are inactive, unlike the situation with Google Colab.

💡Interface

The term 'interface' in the video refers to the user interface of the development environment. LightningAI offers a web-based VSCode interface, which is familiar to developers, and also provides the option to switch to a Jupyter-like interface, which resembles Google Colab's interface, although the presenter prefers the former.

💡Machine Learning Models

Machine learning models are algorithms that allow computers to learn from and make predictions or decisions based on data. The video discusses the use of high-end machine learning models, such as LLMs (Large Language Models) or diffusion models, which require significant computational power, and how LightningAI facilitates their execution with GPU support.

💡Instance

An 'instance' in the context of cloud computing is a virtual machine that runs on a cloud infrastructure. The video describes how LightningAI allows users to create and interact with a Studio instance that can be customized with different machine types, including options for CPU-only or GPU-enhanced configurations.

💡Token

In the context of machine learning, particularly in language models, a 'token' often refers to a unit of text, such as a word or character, that the model processes. The video uses 'tokens per second' as a metric to demonstrate the performance difference between running a model on a CPU versus a GPU, with the GPU version showing significantly higher throughput.

Highlights

LightningAI is a new and free alternative to Google Colab that works with VSCode.

LightningAI offers a web-based VSCode interface with one free Studio that can run 24/7.

Users get 22 GPU hours on the free tier with LightningAI.

The interface allows for seamless transformation from a VSCode instance to a GPU-powered environment.

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

The platform automatically switches off the instance when there's no activity, conserving resources.

Users can sign up for LightningAI through their website and expect access within 2 to 3 days.

LightningAI offers a live CPU and other usage metrics display on the interface.

The platform allows changing the machine type from default to GPU with a simple click.

LightningAI provides options to switch the interface from VSCode to Jupyter, resembling Google Colab.

The platform supports running high-end models like LLaMA 3 efficiently.

Users can install LLaMA 3 directly through the terminal provided by LightningAI.

LightningAI's GPU instance significantly speeds up the token generation rate for models like LLaMA 3.

The creator of the video will no longer use Google Colab, opting for LightningAI instead.

LightningAI provides a more reliable and customizable experience compared to Google Colab.

The platform is ideal for those who prefer to work locally but need occasional access to a GPU.

LightningAI's interface includes options for terminal access and machine type customization.

The platform offers a free tier with 15 credits per month, allowing for 22 hours of T4 GPU usage.

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