Fine-Tuning GPT2 on Colab GPU… For Free!

Models these days are very big, and most of us don’t have the resources to train them from scratch. Luckily, HuggingFace has generously provided pretrained models in PyTorch, and Google Colab allows usage of their GPU (for a fixed time). Otherwise, even fine-tuning a dataset on my local machine without a NVIDIA GPU would take a significant amount of time. While the tutorial here is for GPT2, this can be done for any of the pretrained models given by HuggingFace, and for any size too.

Setting Up Colab to use GPU… for free

Go to Google Colab and create a new notebook. It should look something like this.

Image for post

Set to use GPU by clicking Runtime > Change runtime type

Image for post

Image for post

Then click Save.

Installing Dependencies

We would run pip3 install transformers normally in Bash, but because this is in Colab, we have to run it with !

!pip3 install transformers

Getting WikiText Data

You can read more about WikiText data here. Overall, there’s WikiText-2 and WikiText-103. We’re going to use WikiText-2 because it’s smaller, and we have limits in terms of how long we can run on GPU, and how much data we can load into memory in Colab. To download and run, in a cell, run

%%bash
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
unzip wikitext-2-raw-v1.zip

#gpu #google-colab #gpt-2 #fine-tuning #machine-learning #deep learning

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Fine-Tuning GPT2 on Colab GPU… For Free!

Fine-Tuning GPT2 on Colab GPU… For Free!

Models these days are very big, and most of us don’t have the resources to train them from scratch. Luckily, HuggingFace has generously provided pretrained models in PyTorch, and Google Colab allows usage of their GPU (for a fixed time). Otherwise, even fine-tuning a dataset on my local machine without a NVIDIA GPU would take a significant amount of time. While the tutorial here is for GPT2, this can be done for any of the pretrained models given by HuggingFace, and for any size too.

Setting Up Colab to use GPU… for free

Go to Google Colab and create a new notebook. It should look something like this.

Image for post

Set to use GPU by clicking Runtime > Change runtime type

Image for post

Image for post

Then click Save.

Installing Dependencies

We would run pip3 install transformers normally in Bash, but because this is in Colab, we have to run it with !

!pip3 install transformers

Getting WikiText Data

You can read more about WikiText data here. Overall, there’s WikiText-2 and WikiText-103. We’re going to use WikiText-2 because it’s smaller, and we have limits in terms of how long we can run on GPU, and how much data we can load into memory in Colab. To download and run, in a cell, run

%%bash
wget https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
unzip wikitext-2-raw-v1.zip

#gpu #google-colab #gpt-2 #fine-tuning #machine-learning #deep learning

Free GPU Instances with SSH Access and Visual Studio Code Server

As most of you already know, Google Research offers free instances of Jupyter Notebook environment, with GPU and TPU, that runs entirely in the cloud. The platform is called Colaboratory (“Colab” for short) and it is a great resource when you want to perform quick experiments or prototype new machine learning models because no setup is required: all you need is a browser!
However, a simple Jupyter Notebook interface could be quite limiting when things get tough: we could need to have a SSH session! Let’s do it!
Step 1. Get a token from ngrok
Sign up for free on ngrok and get an Authtoken from the authentication setting on the left.

#ssh #deep-learning #colab #gpu #visual-studio-code

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🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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