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 works seamlessly with VSCode. The host appreciates the web-based VSCode interface, which offers a free Studio with 24/7 operation and 22 GPU hours. The platform allows users to transform a simple VSCode instance into a GPU powerhouse by adding a GPU to the instance, which is particularly useful for running large language models or diffusion models. The host demonstrates the process of using Lightning AI, from signing up to running models like LLaMa 3, and shows a significant speed increase when switching to the GPU instance. The video concludes with a comparison of token generation per second between the CPU and GPU instances, highlighting Lightning AI's efficiency and the host's preference for it over Google Colab.

Takeaways

  • ๐ŸŽ‰ The channel AI code King reached 1K subscribers in just one month.
  • ๐Ÿ”‹ Google Colab is commonly used for running high-end models due to its free GPU access, but the presenter prefers local solutions.
  • ๐Ÿ–ฅ๏ธ The presenter dislikes Google Colab's interface and its lack of reliability, such as no persistent storage and potential for getting timed out.
  • ๐ŸŒŸ Lightning AI is introduced as a new and free alternative to Google Colab, offering a web-based VS Code interface.
  • ๐Ÿ’ป Lightning AI provides one free Studio with 24/7 access, four cores, 16 GB RAM, and 22 GPU hours per month.
  • ๐Ÿ› ๏ธ The user can transform the VS Code instance into a GPU powerhouse by adding a GPU to the instance seamlessly.
  • โŒ›๏ธ On the free tier, the GPU can be used for a total of 22 hours per month.
  • ๐Ÿ“ To get started with Lightning AI, one must sign up on their website and wait for access, which typically takes about 2 to 3 days.
  • ๐Ÿ“Š The platform offers live CPU usage metrics and the ability to switch between different machine types and interfaces.
  • ๐Ÿš€ The presenter demonstrates running LLaMa 3 on Lightning AI, showing a significant speed increase when using the GPU instance.
  • ๐Ÿ“ˆ The GPU version of LLaMa 3 processed about 43 tokens per second, a substantial improvement over the CPU version.
  • ๐Ÿ“Œ The presenter will no longer use Google Colab and encourages viewers to try Lightning AI and share their thoughts in the comments.

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 is Lightning AI.

  • What are some of the advantages of using Lightning AI over Google Colab according to the speaker?

    -Lightning AI offers a web-based VS Code interface, allows for persistent storage, provides terminal access, and gives the user the ability to fully customize their environment. It also provides a more reliable experience without the need for frequent re-setup.

  • 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 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 typically takes about 2 to 3 days to gain access, with an email notification upon approval.

  • 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 switch the instance to a GPU instance in Lightning AI?

    -To switch the instance to a GPU instance, the user clicks on the first option on the right sidebar and then chooses the GPU option.

  • What is the response time difference when running LLM or diffusion models on the default CPU machine versus a GPU instance?

    -On the default CPU machine, the response time is about three tokens per second, which is slow. On a GPU instance, the response time is instantaneous, with about 43 tokens per second.

  • What does the speaker suggest about the reliability of Google Colab?

    -The speaker suggests that Google Colab is not reliable as it often does not allocate a GPU, lacks persistent storage, and can time out if not active for even 5 minutes.

  • What is the speaker's preferred method for running high-end models locally?

    -The speaker prefers to use a local environment that is simple to use, provides terminal access, allows for fully customized behaviors, and supports persistent storage.

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

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

  • What is the speaker's opinion on the interface of Google Colab?

    -The speaker does not like the interface of Google Colab, describing it as outdated and reminiscent of the 1990s.

  • What are the additional options provided by Lightning AI for the user interface?

    -Lightning AI allows the user to change the interface from VS Code to Jupyter, which makes the interface look like Google Colab, and also provides options like Google's Tensor Board.

Outlines

00:00

๐ŸŽ‰ Channel Milestone and Introduction to Lightning AI

The speaker expresses gratitude for reaching 1,000 subscribers within a month and introduces the topic of using Google Colab for running high-end models due to its free GPU access. However, they share their preference for local operations and the inconveniences they face when using Colab, such as an outdated interface, lack of persistent storage, and reliability issues. They then introduce 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. The description includes a step-by-step guide on how to sign up, access, and utilize the platform, highlighting its benefits over traditional methods.

05:02

๐Ÿš€ Comparing CPU and GPU Performance on Lightning AI

The speaker demonstrates the performance difference between running a language model, LLaMa-3, on a CPU versus a GPU instance within Lightning AI. Initially, they install LLaMa-3 on the default CPU machine and observe a slow token generation rate of about three tokens per second. They then switch the instance to a GPU instance by changing the machine type in the platform's interface. After the switch, they experience a significant improvement in performance, with a token generation rate of approximately 43 tokens per second. The speaker concludes by sharing their decision to no longer use Colab and encourages viewers to share their thoughts in the comments and to subscribe to the channel for future updates.

Mindmap

Keywords

๐Ÿ’กGoogle Colab

Google Colab is a cloud-based platform provided by Google that allows users to write and execute Python code in a virtual environment. It is popular among AI enthusiasts and researchers because it offers free access to GPUs, which are essential for running high-end machine learning models. In the video, the speaker mentions using Google Colab but prefers a local setup and is looking for an alternative due to issues with the interface and reliability.

๐Ÿ’กHigh-End LLMs (Large Language Models)

High-End LLMs refer to sophisticated artificial intelligence models that are designed to process and understand large volumes of natural language data. These models are often used for tasks such as language translation, text summarization, and content generation. The video discusses the use of such models and the need for powerful computational resources like GPUs to run them efficiently.

๐Ÿ’กDiffusion Models

Diffusion models are a type of generative model used in machine learning, particularly for generating high-fidelity images or audio from a given input. They work by gradually adding noise to data and then learning to reverse the process, which results in the generation of new, synthetic data. The video script mentions the use of diffusion models in the context of running intensive computational tasks.

๐Ÿ’ก

๐Ÿ’กLocal Setup

A local setup refers to running software, applications, or computational tasks on a user's own computer or personal device rather than using a cloud-based or remote service. The speaker in the video prefers a local setup for their work but occasionally uses Google Colab when the task requires more computational power than their local machine can provide.

๐Ÿ’กLightning AI

Lightning AI is a web-based alternative to Google Colab that the video introduces as a new option for running high-end machine learning models. It provides users with a free studio that can run 24/7, offering a VS Code interface and the ability to attach a GPU for intensive tasks. The platform aims to provide a more reliable and customizable experience compared to Google Colab.

๐Ÿ’กVS Code Interface

VS Code, or Visual Studio Code, is a popular source-code editor developed by Microsoft. It supports debugging, Git integration, syntax highlighting, intelligent code completion, and code refactoring. In the context of the video, Lightning AI offers a web-based VS Code interface, allowing users to work in a familiar environment and providing terminal access for more customized behaviors.

๐Ÿ’กPersistent Storage

Persistent storage refers to a type of storage that retains data even after the system is powered off or restarted. In the video, the speaker criticizes Google Colab for not offering persistent storage, which means that users lose their data and environment setup when they close the browser. Lightning AI, on the other hand, provides persistent storage so that users can find their previous data whenever they open the platform.

๐Ÿ’กGPU Access

GPU, or Graphics Processing Unit, access is the ability to use a GPU for computational tasks. GPUs are especially beneficial for running machine learning models and other data-intensive applications because they can perform many calculations in parallel, significantly speeding up the processing time. The video discusses how Lightning AI allows users to attach a GPU to their instances for such tasks.

๐Ÿ’กFree Tier

The free tier refers to the level of service that is provided at no cost to the user. In the context of the video, Lightning AI offers a free tier that includes one free studio with 24/7 access and 22 GPU hours per month. This allows users to try out the platform and perform certain tasks without incurring costs.

๐Ÿ’กToken Per Second

Token per second is a metric used to measure the performance of language models, indicating how many tokens (the basic units of text in natural language processing) the model can process in one second. In the video, the speaker uses this metric to compare the speed of running a language model on a CPU versus a GPU, highlighting the significant difference in performance.

๐Ÿ’กMachine Learning

Machine learning is a subset of artificial intelligence that involves the use of data and algorithms to enable machines to learn from and make predictions or decisions without being explicitly programmed. The video discusses the application of machine learning through the use of high-end LLMs and diffusion models, which are types of machine learning models.

Highlights

AI code King reached 1K subscribers in 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 lacks persistent storage, requiring re-setup after browser closure.

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

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

Lightning AI offers a web-based VS Code interface and a free 24/7 Studio.

Users receive 22 free GPU hours with Lightning AI.

The platform automatically powers down the Studio after inactivity.

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

Free tier users can only use the GPU for a total of 22 hours per month.

The process to access Lightning AI involves signing up and waiting for an email notification.

Once logged in, users can create a studio and choose between CPU and GPU options.

Lightning AI provides live CPU usage metrics and other interface options.

The platform allows for the installation of models like LLaMa-3 directly through the terminal.

LLaMa-3 runs significantly faster on the GPU instance compared to the CPU instance.

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

The video concludes with an invitation for viewers to share their thoughts in the comments.

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