Absolutely FREE, MASSIVE 29GB RAM GPUs from Kaggle!!!

Summary

Kaggle has upgraded its free GPU Notebooks to 29 GB RAM and 4 CPU cores, making it a powerful alternative to Google Colab. The video demonstrates a significant improvement in model inference time, dropping from 6.3 minutes on Google Colab to 2.3 minutes on Kaggle. It also highlights the ease of importing Google Colab notebooks into Kaggle and the potential for faster fine-tuning with large language models.

Takeaways

• 🚀 Kaggle has upgraded its free GPU notebooks with higher RAM, offering 29 GB of RAM on a T4 machine.
• 🔍 Kaggle is a platform known for machine learning competitions, datasets, and is owned by Google, similar to Google Colab.
• 💡 The increased RAM and CPU cores (from 2 to 4) can significantly speed up model processing and inference times.
• 📈 A test showed that running a model on Kaggle Notebook reduced processing time from 6.3 minutes to 2.3 minutes, nearly a 3x improvement.
• 📚 Kaggle Notebooks can be easily used by importing Jupyter notebooks from Google Colab.
• 🔄 Kaggle provides a 30-hour limit per month for GPU usage, which is a notable constraint compared to Google Colab.
• 🌐 Kaggle notebooks might have higher visibility on Google search engines, making them advantageous for portfolio projects.
• 🧩 The platform supports multiple GPUs, which can be leveraged for parallel processing and handling large language models.
• 💻 Kaggle Notebooks are straightforward to use, with a simple process of importing notebooks and managing files.
• 🔒 While Kaggle and Google Colab are free resources, users should be aware of potential data privacy concerns with Google's platforms.

Q & A

What recent upgrade did Kaggle make to their free machines on Kaggle Notebook?

Kaggle recently upgraded their free machines on Kaggle Notebook by increasing the RAM to 29 GB and providing a T4 machine with higher computational capabilities.

Why is Kaggle being considered as an alternative to Google Colab?

Kaggle is being considered as an alternative to Google Colab due to its increased RAM and computational resources, which can make it faster and more efficient for running machine learning models.

What is the significance of the increased RAM from 12 GB to 29 GB on Kaggle Notebooks?

The increased RAM from 12 GB to 29 GB allows for more efficient handling of large datasets and complex machine learning models, potentially reducing the time required for tasks such as model inference.

How does the increase in CPU cores from two to four affect the performance of Kaggle Notebooks?

The increase in CPU cores from two to four allows for better parallel processing capabilities, which can improve the speed and efficiency of tasks that can be distributed across multiple cores.

What was the time reduction observed when running a model on Kaggle Notebook compared to Google Colab?

The time reduction observed when running a model on Kaggle Notebook compared to Google Colab was from 6.3 minutes to 2.3 minutes, showing a significant improvement in performance.

How can you transfer a notebook from Google Colab to Kaggle Notebook?

You can transfer a notebook from Google Colab to Kaggle Notebook by downloading the .ipynb file from Google Colab, creating a new notebook on Kaggle, and then importing the downloaded notebook.

What limitations does Kaggle have in terms of GPU usage compared to Google Colab?

Kaggle has a limit on GPU usage, which is indicated by a 30-hour counter that resets periodically. This is unlike Google Colab, which does not explicitly state such restrictions.

What are the potential benefits of using Kaggle Notebook for fine-tuning large language models?

The increased RAM and computational resources of Kaggle Notebook can make it easier to fit large language models into memory, potentially reducing the need for sharded models and improving the speed of fine-tuning.

How does Kaggle Notebook's visibility on Google search engines compare to Google Colab?

Kaggle Notebooks are known to rank higher on Google search engines, which can be advantageous for those looking to showcase their work or build a portfolio.

What are some additional benefits of using Kaggle Notebook for machine learning and AI projects?

Kaggle Notebook offers a platform with a large community, competitions, and datasets, which can be beneficial for learning and collaboration in the field of machine learning and AI.

Outlines

00:00

🚀 Kaggle Notebook Upgrades and Google Colab Alternative

Kaggle has recently enhanced its free GPU notebooks with significant upgrades, including a substantial increase in RAM to 29 GB and the addition of four CPU cores. This update positions Kaggle as a strong alternative to Google Colab, especially for machine learning practitioners seeking more computational resources. The video script discusses the improved capabilities of Kaggle's platform, including the ease of importing and running Google Colab notebooks on Kaggle, and the potential for faster model inference and fine-tuning due to the increased RAM. The script also mentions the limitations of Google Colab's free tier, such as time restrictions and timeouts, which Kaggle seems to mitigate with its upgraded offerings.

05:03

🌟 Advantages of Kaggle Notebooks for Deep Learning and AI

The second paragraph delves into the advantages of using Kaggle Notebooks for deep learning and AI tasks, particularly highlighting the benefits of the platform's increased RAM and CPU cores. The script discusses the potential for faster and more efficient model training and inference, especially for large models that previously could not fit into the memory constraints of Google Colab. It also touches on the possibility of leveraging multiple GPUs for parallel processing, which could further enhance performance. Additionally, the script points out that Kaggle notebooks tend to rank higher in Google search results, offering increased visibility for users looking to showcase their work. The video concludes by emphasizing the value of using free resources like Kaggle Notebooks for machine learning and AI projects.

Keywords

💡Kaggle

Kaggle is an online community for data scientists and machine learning practitioners, known for hosting competitions and providing datasets. In the video, Kaggle is highlighted for upgrading its free GPU Notebooks with higher RAM, making it a more attractive platform for machine learning tasks. The script discusses how this upgrade positions Kaggle as a strong alternative to Google Colab.

💡Google Colab

Google Colab is a cloud-based platform for machine learning and data analysis, which offers free access to computing resources including RAM and GPU. The script compares Google Colab with Kaggle, especially after Kaggle's RAM upgrade, suggesting Kaggle as a viable alternative with enhanced capabilities.

💡RAM (Random Access Memory)

RAM is a form of computer memory that can be read from and written to in any order, and it is used to store data that is being processed. The video emphasizes the increase in RAM from 12 GB to 29 GB on Kaggle's free T4 machines, which is significant for running more complex machine learning models and improving performance.

💡T4 GPU

The T4 GPU is a type of graphics processing unit by Nvidia, designed for AI and machine learning tasks. In the script, the T4 GPU is mentioned as being available on Kaggle's upgraded machines, with 14 GB of GPU memory, which is beneficial for handling larger and more intensive computations.

💡Machine Learning Competitions

Machine learning competitions are events where participants apply their skills to solve complex problems using machine learning algorithms. Kaggle is noted in the script as a platform where many such competitions take place, attracting data scientists and machine learning enthusiasts.

💡Datasets

Datasets are collections of data that are used for analysis and training machine learning models. The script mentions that Kaggle provides a wealth of datasets, which is one of the reasons it is popular among machine learning practitioners.

💡Inference

Inference in the context of machine learning refers to the process of making predictions or decisions based on a trained model. The script discusses the improvement in inference time when using Kaggle's upgraded resources, highlighting the efficiency gains for model deployment.

💡Fine-tuning

Fine-tuning is the process of further training a machine learning model on a specific dataset to improve its performance. The script suggests that with the increased RAM on Kaggle, fine-tuning large models, which previously might have been challenging, could now be more feasible.

💡Sharded Model

A sharded model is a technique used when a model is too large to fit into the memory of a single device, and thus it is split across multiple devices. The script refers to sharded models in the context of large models like Amazon Light ML, which might not fit into the memory limits of some platforms but could potentially be managed better with Kaggle's increased RAM.

💡Google Search Engine Ranking

The script mentions that Kaggle notebooks tend to rank higher on Google's search engine compared to Google Colab notebooks. This implies that using Kaggle for projects might increase visibility and recognition, which is beneficial for building a portfolio.

💡Multiple GPUs

The ability to utilize multiple GPUs can significantly speed up processing tasks by distributing the workload. The script indicates that Kaggle's platform supports the use of multiple GPUs, which can be leveraged for parallel processing and handling more complex models.

Highlights

Kaggle has upgraded its free GPU Notebooks with higher RAM, offering 29 GB RAM on a free T4 machine.

This update makes Kaggle a strong alternative to Google Colab, especially for those seeking more RAM for their machine learning tasks.

Kaggle is known for hosting machine learning competitions and providing a wide range of datasets.

The ownership of Kaggle by Google aligns it with Google Colab, both being resources for the machine learning community.

Kaggle's GPU notebooks now feature four CPUs and increased RAM from 12 GB to 29 GB.

The increased RAM and CPU cores can significantly speed up model training and inference processes.

A demonstration of running a model on Kaggle Notebook showed a reduction in processing time from 6.3 minutes to 2.3 minutes.

Kaggle Notebooks are straightforward to use, with the ability to import notebooks from Google Colab.

Kaggle provides a 30-hour counter for GPU usage, resetting on the 29th of each month.

Kaggle Notebooks may have limitations compared to Google Colab, such as time restrictions based on usage frequency.

The potential for fine-tuning large models is enhanced with the increased RAM on Kaggle Notebooks.

Kaggle Notebooks could prevent system crashes when working with large models that previously required sharding.

Kaggle Notebooks may offer higher visibility on Google search engines compared to Google Colab Notebooks.

The availability of multiple GPUs on Kaggle Notebooks allows for parallel processing across GPUs.

Kaggle Notebooks are a free resource provided by Google, with the usual considerations regarding data privacy.

The tutorial demonstrates the practical steps to migrate and utilize a Google Colab notebook on Kaggle.

The video concludes with an endorsement of Kaggle Notebooks as a valuable resource for the machine learning community.

Transcripts

so kaggle recently upgraded their free

machines like that on kaggle Notebook

with higher Ram so you get 29 GB RAM on

a free T4 machine so naturally a lot of

you have been asking me questions give

me like a Google collab alternative and

a couple of years back I made a video

about Google collab Alternatives and one

of that was kagle and now that kaggle

has upgraded its machine especially with

29 GB a lot of our models lot of things

that we do could be faster so I

immediately went to test it and in this

video I'm going to first show you what

is that Improvement then we're going to

show the test and certain nuances that

we can discuss at the end of the video

it's going to be quite a short video to

be really honest to first start with

what is this update and if you are not

familiar with kaggle so kaggle is like

um hacker rank Hacker News top coder for

machine learning there a lot of machine

learning competitions happen lot of data

sets are available in fact I keep on

telling a lot of people that hugging

face capitalized on a market that kaggle

left and kaggle is also owned by Google

where we use Google collab which is also

owned by Google only catch here is that

with kagle gpus we get a limit which

we'll shortly see the first thing is

what is the upgrade that we have got we

have got GPU notebooks with four CPUs so

one they have increased the CPU core the

second one they have increased the ram

previously it was 12 GB of RAM very

similar like Google collab now it is 29

GB of ram that is like huge and uh if

you see here you can go see here like I

can show you like literally live so you

can see that right now it has used 5 GB

of 29 GB and it is a T4 machine you can

see the GPU available with 14 GB memory

now what is this it means for p 100 G

gpus and for T4 gpus notebooks

especially kle notebooks the ram has

been increased from 13 GB to 29 GB and

the CPU code has been increase from two

course to four cores so recently we made

a video where we said Okay I want to run

a model on local machine or Google

collab only on CPU no GPU leveraging no

GPU even if you have got GPU it will use

one CPU and uh the model was like we did

local PDF processing with the Mr AI the

two bit quantized is what we did and

when we asked a question on free Google

collab with 12 GB of RAM it took about

383 seconds which translates roughly

about like 6.3 minutes and uh it I even

mentioned on the video that it is too

much like it's it's actually a lot of

time I did the same thing in this

particular case it's GP GPU uh but the

GPU is not utilized as you can see the

GPU is not utilized and we got exact

same thing nothing else we got 142

seconds which is 2.3 minutes so from 6.3

minutes we came down to 2.3 minutes

using another Free Solution from Google

which is kaggle notebook and it is

pretty straightforward for you to use it

I've got a very vague tutorial about how

to use kaggle notebook but I'll quickly

show you so for example let's say you

have downloaded The Notebook from Google

collab like this is The Notebook that we

created so if you go to Google collab

click file and click download click

download. iynb once you C click

download. ipnb you are going to get it

as a jupyter notebook format on your

local machine then all you have to do is

go to kaggle and once you go to kagle

you can see this create button and click

create new notebook once you click

create a new notebook it is going to

take you to a new notebook screen where

you have to first select the language

accelerator do you want any GPU or not

so this memory improvement is only for

GPU I guess so you would naturally have

to select the T4 machine which once you

select it will um have like a 30 hours

counter so you can see it has a 30 hours

counter and my counter resets on 29th

Saturday uh October 29th and you select

the language here and once you do all

these things go to file and click import

notebook then you get this window there

and all you have to do is click here

drag and drop it that's it and click

import the same notebook that you use

used on Google collab is right now

available inside kagle notebook but it

is as simple as that using a Google

collab notebook inside kaggle unless

until you have some file uploads then

you have to go to you know data um add

data and uh upload a file and all these

kind of things that you have to do it

but generally it's a very pretty

straightforward and easy process for you

to use kaggle notebook this is one the

second thing like I said is it comes

with a CA so you it's not like you can

use as many hours like Google collab so

Google doesn't explicitly say any

restrictions but I have been um you know

like been given time out multiple times

before because Google collab decided

that I have been using Google collab for

a long time and a lot of time so based

on the frequency of your use based on

the time of use Google collab might

often give you a time mode where you

would not be able to use Google collab

so this is another case here and the

other thing that I wanted to mention is

right now we were talking only about

using a model in inference but you know

that we've been doing a lot of fine

tuning which I also want to test it on

kaggle but because we have been using

accelerate which does a lot of memory

management between CPU RAM and GPU vram

the between the system memory and the

graphics memory with 29 GB of RAM I

think it should be fairly faster and

also so easier to fit in a lot of models

previously for which we had to ask a

sharded model like for example Amazon

released a model called Amazon light

mral light couple of days back I went

ahead and asked them that do you plan to

release a sharded model because I could

not fit that exact like a 9gb plus 4GB

model directly within my Google collab

it crashes the system REM crashes and I

don't think those kind of cases would

happen on kaggle Notebook with 29 GB Ram

so I think all in all this is like a

great victory for anybody who practices

deep learning machine learning AI on

free resources like Gaggle notebook or

Google collab and if you prefer to use

Gaggle notebook there are lot of other

advantages like kaggle notebooks usually

rank higher on Google search engine so

if you want to produce if you want to

produce something that is of your own

like a portfolio and you want people to

notice most likely a kaggle notebook

book will have higher visibility on

Google search than a Google collab

notebook these are some advantages I've

always told people to check out kagle so

this video is like a Google collab

alternative with much much higher RAM

and you should definitely check it out

we have got like a 29 GB Ram the final

thing that I forgot to mention is that

you also get multiple gpus so there are

certain libraries that will help you do

parallel processing across multiple gpus

and if you were to leverage that I think

like this this is a great solution that

you can use to do model inference and

also model fine-tuning for any of the

large language models that you typically

deal with and we have just literally

seen a 3X closer to not necessarily 3x

almost closer to 3x Improvement in uh in

the inference time when it was like

completely U

CPU so yeah I hope like this tutorial

was helpful to you in bringing the

latest news from kagle Once again this

is Google resource we just have to use

it and say thank you to Google um if if

you are if you are to use it no strings

attached uh you never know what they're

going to do with your data and I think

all the usual Google related

speculations always exist but while it

is free let's use it see you in another

video Happy prompting