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. The host expresses dissatisfaction with Google Colab's interface and lack of reliability, such as no persistent storage and the need to frequently re-setup the environment. Lightning AI offers a web-based VS Code interface with a free Studio that can run 24/7, and comes with 22 free GPU hours per month. The Studio provides a seamless transition from a standard VS Code instance to a GPU-powered environment, allowing users to run high-end models efficiently. The host demonstrates the process of using Lightning AI, including changing the machine type to GPU and running the LLaMa 3 model, which significantly speeds up with the GPU instance. The host concludes by stating a preference for Lightning AI over Colab for future projects and invites viewers to share their thoughts in the comments.

Takeaways

  • 🎉 The channel AI code King reached 1K subscribers in just one month.
  • 🔥 Google Colab is widely used for running high-end models due to its free GPU access.
  • 😖 The speaker dislikes Google Colab's interface and its lack of reliability, persistent storage, and potential for timeouts.
  • 💡 Lightning AI is introduced as a new and free alternative to Google Colab with a web-based VS Code interface.
  • 🌟 Lightning AI offers one free Studio with 24/7 operation and 22 GPU hours per month.
  • 🖥️ The Studio comes with four cores, 16 GB RAM, and can be accessed through a web-based VS Code.
  • ✅ Users can switch the instance to a GPU-powered environment seamlessly when needed.
  • ⏰ The free tier has a limit of 22 hours of GPU usage per month.
  • 🚀 To get started, sign up on the Lightning AI site and wait for access, which may take 2-3 days.
  • 📈 The platform provides live CPU usage metrics and options to customize the machine type and interface.
  • 📦 Persistent storage is available, allowing users to find their previous data upon re-opening the instance.
  • 🚀 After switching to a GPU instance, the response time for running models significantly improves, offering approximately 43 tokens per second.

Q & A

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

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

  • What are some of the issues the speaker has with Google Colab's interface?

    -The speaker finds Google Colab's interface outdated, likening it to using a 1990s interface. Additionally, there are issues with not always getting a GPU allocation, lack of persistent storage, and the possibility of getting timed out after inactivity.

  • How many GPU hours are included in Lightning AI's free tier?

    -In Lightning AI's free tier, you get 22 GPU hours per month.

  • What is the basic configuration of the free Studio provided by Lightning AI?

    -The free Studio provided by Lightning AI has four cores and 16 GB of RAM.

  • How can you transform the VS Code instance into a GPU powerhouse in Lightning AI?

    -You can transform the VS Code instance into a GPU powerhouse by adding a GPU to the instance through the options provided on the right sidebar of the interface.

  • What is the process to start using Lightning AI after signing up?

    -After signing up, you will be added to a waiting list and will receive access in about 2 to 3 days via email notification. Once you have access, you log in, create a studio, and start using the web-based VS Code interface.

  • How does Lightning AI handle instances when there is no user activity?

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

  • What are the options available on the right sidebar of the Lightning AI interface?

    -The right sidebar of the Lightning AI interface allows you to change your machine type to a GPU option, access the terminal, and change the interface from VS Code to Jupyter, among other options.

  • How does the speaker describe the token production speed when running LLM 3 on the default CPU machine?

    -The speaker describes the token production speed as slow, producing about three tokens per second.

  • What is the response time when running LLM 3 on the GPU instance in Lightning AI?

    -The response time when running LLM 3 on the GPU instance is instantaneous, with a token production rate of about 43 tokens per second.

  • How does the speaker plan to use Lightning AI in the future?

    -The speaker plans to use Lightning AI instead of Google Colab for future projects and videos, as they find it more reliable and user-friendly.

  • What is the speaker's suggestion for viewers who are interested in using Lightning AI?

    -The speaker suggests that interested viewers should sign up on the Lightning AI site and wait for access, which is granted after being on the waiting list for about 2 to 3 days.

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 the video. They discuss the common use of Google Colab for running high-end models due to its free GPU access but share their preference for local work. When needing to run large models for video content, they reluctantly use Colab, criticizing its interface and lack of reliability due to no persistent storage and potential time-outs. The speaker then introduces Lightning AI as an alternative that offers a web-based VS Code interface with persistent storage and customizable behavior. Lightning AI provides one free Studio with 24/7 access and 22 GPU hours monthly. The description includes a step-by-step guide on how to sign up, access, and use the platform, highlighting the ease of switching between CPU and GPU instances.

05:02

🚀 Comparing LLM Performance on CPU vs. GPU with Lightning AI

The speaker demonstrates the use of Lightning AI by running the LLaMa 3 model, first on the default CPU instance and then switching to a GPU instance. They show the significant difference in performance, with the CPU instance producing about three tokens per second, which is quite slow. After switching to a T4 GPU instance, the response time improves dramatically to about 43 tokens per second. The speaker expresses satisfaction with the performance and declares an intention to use Lightning AI for future projects instead of Google Colab. They invite viewers to share their thoughts in the comments and ask for likes and subscriptions to the channel.

Mindmap

Keywords

💡Google Colab

Google Colab is a cloud-based platform that provides free access to computing resources, including GPUs, which are used for running high-end machine learning models. In the video, the speaker mentions using Google Colab but prefers a local setup, highlighting the limitations such as the outdated interface, lack of persistent storage, and unreliability due to time-outs.

💡Lightning AI

Lightning AI is presented as a new and free alternative to Google Colab. It offers a web-based Visual Studio Code (VSCode) interface with one free Studio that can run continuously and provides 22 GPU hours per month. The platform allows users to transform a standard VSCode instance into a GPU-powered environment for running complex models.

💡VSCode

Visual Studio Code (VSCode) is a popular source-code editor developed by Microsoft. In the context of the video, Lightning AI provides a web-based VSCode interface for coding and running machine learning models. It is a preferred choice for the speaker over the Google Colab interface.

💡GPU

A Graphics Processing Unit (GPU) 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 speaker discusses the advantage of having GPU access for running high-end machine learning models, which is provided by both Google Colab and Lightning AI.

💡Persistent Storage

Persistent storage refers to a type of storage where data remains intact even after a system is powered down. The video mentions the lack of persistent storage in Google Colab as a drawback, as it means users lose their data when they close the browser and have to reconfigure their environment each time they return.

💡Terminal Access

Terminal access is the ability to interact with a computer's operating system through a command-line interface. The video emphasizes the importance of having terminal access for full customization and control over the computing environment, which is provided by Lightning AI.

💡Machine Learning Models

Machine learning models are algorithms that learn from data to make predictions or decisions without being explicitly programmed to perform the task. In the video, the speaker discusses running high-end machine learning models, specifically 'llms' or 'diffusion models,' which require significant computational resources.

💡Token

In the context of machine learning and natural language processing, a token is a unit of text, such as a word or character, that is treated as a single element in a machine learning model. The video uses 'tokens per second' as a metric to measure the performance of the machine learning model when running on CPU versus GPU.

💡Free Tier

The free tier refers to the level of service that is provided at no cost to the user. Lightning AI offers a free tier that includes one Studio and 22 GPU hours per month. The video discusses how this free tier can be utilized for running machine learning models without incurring costs.

💡Instance

In computing, an instance refers to a virtual or dedicated hardware environment that runs an operating system or a software application. In the video, the speaker talks about transforming a VSCode instance into a GPU-powered environment using Lightning AI.

💡Time-out

A time-out is a period after which a system or process automatically terminates due to inactivity. The video mentions the inconvenience of Google Colab's time-out feature, which requires users to reconfigure their environment if they are inactive for a short period.

Highlights

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

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

The presenter prefers local processing but uses Colab for large models.

Colab's interface is outdated and not user-friendly.

Colab often fails to allocate a GPU and lacks persistent storage.

Users may experience timeouts on Colab after 5 minutes of inactivity.

Lightning AI is a new web-based VS Code interface alternative to Colab.

Lightning AI offers one free Studio with 24/7 operation and 22 GPU hours.

The free tier of Lightning AI allows seamless GPU attachment and detachment.

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

The platform has a waiting list, with access granted via email notification.

Users can create a studio and start a VS Code interface in a few minutes.

Lightning AI allows changing the machine type from CPU to GPU.

The platform supports switching the interface to Jupyter for a Colab-like look.

Llama 3 can be run on Lightning AI, with a significant speed increase on GPU.

The presenter achieved 43 tokens per second using the GPU instance.

The presenter will no longer use Colab and will use Lightning AI for future projects.

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

A thumbs up and subscription to the channel is requested for viewers who liked the video.