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

AICodeKing
26 Apr 202406:36

TLDRAI Code King's video introduces Lightning AI, a new and free alternative to Google Colab that offers a web-based VSCode interface and integrates seamlessly with GPU for running high-end models. The host shares his dissatisfaction with Colab's interface and lack of persistent storage, and how Lightning AI solves these issues. With Lightning AI, users get a free Studio that operates 24/7 and includes 22 GPU hours per month. The platform allows users to switch between CPU and GPU instances easily and provides a persistent workspace. The video demonstrates the performance of running LLM models on both platforms, with Lightning AI showing significantly faster token processing speeds when using the GPU.

Takeaways

  • 🎉 The channel AI Code King reached 1K subscribers in just one month.
  • 🔋 Google Colab is widely used for accessing free GPUs to run high-end models, but the presenter prefers local solutions.
  • 🚀 The presenter dislikes Google Colab's interface and lack of reliability due to no persistent storage and potential timeouts.
  • 💡 Lightning AI is introduced as a new, free alternative to Google Colab, providing a web-based VS Code interface with GPU capabilities.
  • 🌐 With Lightning AI, users get one free Studio with 24/7 access, four cores, 16 GB RAM, and 22 GPU hours per month.
  • 🔧 The platform allows seamless transition between a standard VS Code instance and a GPU-enhanced environment.
  • ⏰ Lightning AI automatically shuts down the instance after inactivity and can be restarted as needed.
  • 📦 Persistent storage is available, so users can access their previous data upon re-opening the instance.
  • 🚀 The free tier of Lightning AI includes a T4 GPU option, which can be used for a total of 22 hours per month.
  • 📝 The platform provides a terminal, machine type switching, and the ability to change the interface to Jupyter-like or use Google Tensor Board.
  • 📈 A demonstration shows a significant speed increase when running an LLM model on the GPU instance compared to the CPU instance.

Q & A

  • What is the name of the new free alternative to Google Colab mentioned in the video?

    -The new free alternative to Google Colab mentioned in the video is Lightning AI.

  • 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 main advantage of using Lightning AI over Google Colab according to the speaker?

    -The main advantage of using Lightning AI over Google Colab is that it provides a web-based VSCode interface, allows for persistent storage, and offers a more reliable and customizable experience.

  • What is the process to get access to Lightning AI?

    -To get access to Lightning AI, one needs to sign up on their website. There is a waiting list, and it may take about 2 to 3 days to get access, with an email notification upon availability.

  • How does Lightning AI handle inactivity to conserve resources?

    -Lightning AI automatically switches off the instance when there's no activity, and it can be spun up again when needed.

  • What is the default machine type provided by Lightning AI?

    -The default machine type provided by Lightning AI is an instance with four cores and 16 GB of RAM.

  • How can the user transform the VSCode instance into a GPU powerhouse in Lightning AI?

    -The user can transform the VSCode instance into a GPU powerhouse by adding a GPU to the instance through the options provided on the right sidebar.

  • What is the response time improvement when running LLMs on the GPU instance compared to the CPU instance in Lightning AI?

    -The response time improves significantly when running LLMs on the GPU instance, with the video demonstrating an output of about 43 tokens per second compared to approximately 3 tokens per second on the CPU instance.

  • How many credits are provided in the free tier of Lightning AI, and how does it relate to the T4 GPU usage?

    -The free tier provides 15 credits every month, which allows the use of the T4 GPU for 22 hours.

  • What is the name of the interface option that makes the interface look like Google Colab?

    -The interface option that makes the interface look like Google Colab is called Jupyter.

  • What is the speaker's recommendation for running high-end LLMs or diffusion models in the future?

    -The speaker recommends using Lightning AI for running high-end LLMs or diffusion models in the future instead of Google Colab.

  • How can viewers let the speaker know if they are interested in using Lightning AI?

    -Viewers can express their interest in using Lightning AI by commenting on the video and/or giving it a thumbs up and subscribing to the channel.

Outlines

00:00

🎉 Celebrating 1K Subscribers and Introducing Lightning AI

The speaker begins by expressing gratitude for reaching 1,000 subscribers in just a month and then discusses the common use of Google Colab for running high-end language models due to its free GPU access. However, the speaker prefers local operations and only uses Colab reluctantly due to its outdated interface, lack of persistent storage, and unreliability. The speaker then introduces Lightning AI as a solution that offers a web-based VS Code interface with a free Studio that can run 24/7, along with 22 free GPU hours per month. The Studio can be transformed into a GPU-powered environment when needed, and the speaker guides the audience on how to sign up, access, and use the platform, including changing the machine type to GPU and running a language model to demonstrate the performance difference between CPU and GPU instances.

05:02

🚀 Comparing CPU and GPU Performance on Lightning AI

The speaker demonstrates the performance of running the LLaMA 3 language model on Lightning AI's platform, first on a default CPU machine and then on a GPU instance. Initially, the model produces about three tokens per second, which is considered slow. The speaker then switches the instance to a GPU instance by selecting the GPU option in the platform's interface. After a brief waiting period for the instance to switch, the model's performance significantly improves, producing about 43 tokens per second. The speaker concludes by stating a preference for using Lightning AI over Google Colab for future projects and encourages viewers to share their thoughts in the comments and to subscribe to the channel.

Mindmap

Keywords

💡Lightning AI

Lightning AI is a new and free alternative to Google's Colab, providing a web-based VSCode interface for coding and running high-end models. It is highlighted in the video for offering a free Studio that can run continuously and comes with 22 GPU hours per month. This service is presented as a solution to the limitations and inconveniences experienced with Google Colab, such as the lack of persistent storage and unreliable GPU allocation.

💡Google Colab

Google Colab is a cloud-based platform that provides free access to GPU resources for running machine learning models. It is widely used in the AI community but criticized in the video for its outdated interface, lack of persistent storage, and unreliable GPU allocation. The video suggests that Lightning AI offers a more reliable and user-friendly alternative.

💡VSCode

VSCode, short for Visual Studio Code, is a popular source-code editor developed by Microsoft. It is mentioned in the video as the interface provided by Lightning AI, which allows users to code and interact with their projects in a familiar and customizable environment. The integration of VSCode with Lightning AI is presented as a significant advantage over the standard Google Colab interface.

💡GPU

GPU stands for Graphics Processing Unit, which is a specialized hardware component that is highly efficient for performing the complex mathematical operations required in machine learning and AI. In the context of the video, the availability of a free GPU with Lightning AI is a key selling point, as it allows users to run high-end models without investing in expensive hardware.

💡Persistent Storage

Persistent storage refers to a type of data storage that retains data even after the system is powered off or closed. The video criticizes Google Colab for not offering persistent storage, meaning that users lose their data after closing the browser. Lightning AI, on the other hand, is praised for providing persistent storage, allowing users to save their work and pick up where they left off.

💡Studio

In the context of Lightning AI, a Studio refers to a personal workspace that users can create to run their code and models. The video emphasizes that each user gets one free Studio with four cores and 16 GB of RAM, which can be accessed and used continuously, offering a level of customization and flexibility that is not available with Google Colab.

💡Machine Learning Models

Machine learning models are algorithms that allow computers to learn and make predictions or decisions without being explicitly programmed to do so. The video discusses running high-end machine learning models, such as LLMs (Large Language Models) or diffusion models, which require significant computational resources, highlighting the need for platforms like Lightning AI that can support these demanding tasks.

💡LLMs (Large Language Models)

Large Language Models (LLMs) are a type of machine learning model designed to process and understand human language. They are used in various applications, from natural language processing to content generation. The video mentions running LLMs as one of the use cases for Lightning AI, demonstrating the platform's capability to handle complex and resource-intensive tasks.

💡Diffusion Models

Diffusion models are a class of machine learning models used for generating high-fidelity images or samples from a given dataset. They are part of the advanced techniques in AI and require substantial computational power. The video script discusses the use of diffusion models to illustrate the need for a platform like Lightning AI that can provide the necessary GPU resources.

💡Interface Customization

Interface customization refers to the ability to modify the appearance and functionality of a software interface to suit the user's preferences. The video highlights the customization options available in Lightning AI's VSCode interface, allowing users to tailor their environment to their specific needs, which is not possible with Google Colab's more static interface.

💡Token

In the context of language models, a token typically refers to a unit of text, such as a word or a character, that the model processes. The video uses the term 'tokens per second' to measure the performance of a language model, indicating how many tokens the model can process in one second. This metric is used to demonstrate the significant speed improvement when running a model on a GPU instance with Lightning AI.

Highlights

AI Code King reached 1K subscribers in just one month.

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

The presenter prefers local work but uses Colab for large models due to its limitations.

Colab's interface is outdated and unreliable, with no persistent storage.

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

Lightning AI offers a web-based VS Code interface with one free Studio and 22 GPU hours.

The free Studio has four cores and 16 GB RAM, and can be accessed 24/7.

Lightning AI allows seamless transformation of a VS Code instance into a GPU powerhouse.

On the free tier, GPU usage is limited to 22 hours per month.

Users can sign up for Lightning AI and expect access within 2 to 3 days.

The platform provides options to change machine type and interface.

Lightning AI can be used for coding or running large models like LLMs.

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

LLaMa 3 runs at about 3 tokens per second on CPU, and 43 tokens per second on GPU.

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

The video encourages viewers to try Lightning AI and share their experiences.

The presenter dislikes Colab's interface and prefers a more modern, customizable setup.

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

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