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Included links to all Google Colabs in the README and week 3 folder

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Edward Donner 8 months ago
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commit
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  1. 9
      README.md
  2. 45
      week3/day1.ipynb
  3. 49
      week3/day2.ipynb
  4. 45
      week3/day3.ipynb
  5. 47
      week3/day4.ipynb
  6. 51
      week3/day5.ipynb
  7. 6
      week4/day4.ipynb

9
README.md

@ -68,6 +68,15 @@ HF_TOKEN=xxxx
This file is listed in the `.gitignore` file, so it won't get checked in and your keys stay safe. This file is listed in the `.gitignore` file, so it won't get checked in and your keys stay safe.
### Starting in Week 3, we'll also be using Google Colab for running with GPUs
The colab links are in the Week folders and also here:
- For week 3 day 1, this Google Colab shows what colab can do [colab](https://colab.research.google.com/drive/1DjcrYDZldAXKJ08x1uYIVCtItoLPk1Wr?usp=sharing)
- For week 3 day 2, here is a colab for the HuggingFace [pipelines API](https://colab.research.google.com/drive/1aMaEw8A56xs0bRM4lu8z7ou18jqyybGm?usp=sharing)
- For week 3 day 3, here's the colab on [Tokenizers](https://colab.research.google.com/drive/1WD6Y2N7ctQi1X9wa6rpkg8UfyA4iSVuz?usp=sharing)
- For week 3 day 4, we go to a colab with HuggingFace [models](https://colab.research.google.com/drive/1hhR9Z-yiqjUe7pJjVQw4c74z_V3VchLy?usp=sharing)
- For week 3 day 5, we return to colab to make our [Meeting Minutes product](https://colab.research.google.com/drive/1KSMxOCprsl1QRpt_Rq0UqCAyMtPqDQYx?usp=sharing)
## And that's it! Happy coding! ## And that's it! Happy coding!
### Alternative Setup Instructions if you're a die-hard virtualenv-er ### Alternative Setup Instructions if you're a die-hard virtualenv-er

45
week3/day1.ipynb

@ -0,0 +1,45 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "61f56afc-bc15-46a4-8eb1-d940c332cf52",
"metadata": {},
"source": [
"# Introducing Colab\n",
"\n",
"To see some of what Colab can do, please follow the link below!\n",
"\n",
"https://colab.research.google.com/drive/1DjcrYDZldAXKJ08x1uYIVCtItoLPk1Wr?usp=sharing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

49
week3/day2.ipynb

@ -0,0 +1,49 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "1f2ed49d-7873-4a03-bdc8-bda1c23cb979",
"metadata": {},
"source": [
"# HuggingFace pipelines\n",
"\n",
"For this session we head to Google Colab and use this Notebook to explore the HuggingFace High Level API, pipelines.\n",
"\n",
"https://colab.research.google.com/drive/1aMaEw8A56xs0bRM4lu8z7ou18jqyybGm?usp=sharing\n",
"\n",
"You can use a low cost (or free) T4 GPU runtime for this notebook - and the results look great!\n",
"\n",
"There are instructions in the notebook for setting up your HuggingFace Token and including it as a secret in the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5d84b78-0a02-4a35-8f10-e3f7451d352f",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

45
week3/day3.ipynb

@ -0,0 +1,45 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "61f56afc-bc15-46a4-8eb1-d940c332cf52",
"metadata": {},
"source": [
"# Tokenizers\n",
"\n",
"Please can I bring you back to the wonderful Google Colab where we'll look at different Tokenizers:\n",
"\n",
"https://colab.research.google.com/drive/1WD6Y2N7ctQi1X9wa6rpkg8UfyA4iSVuz?usp=sharing"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

47
week3/day4.ipynb

@ -0,0 +1,47 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "61f56afc-bc15-46a4-8eb1-d940c332cf52",
"metadata": {},
"source": [
"# Models\n",
"\n",
"And now - this colab unveils the heart (or the brains?) of the transformers library - the models:\n",
"\n",
"https://colab.research.google.com/drive/1WD6Y2N7ctQi1X9wa6rpkg8UfyA4iSVuz?usp=sharing\n",
"\n",
"This should run nicely on a low-cost or free T4 box."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

51
week3/day5.ipynb

@ -0,0 +1,51 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "61f56afc-bc15-46a4-8eb1-d940c332cf52",
"metadata": {},
"source": [
"# Meeting minutes creator\n",
"\n",
"In this colab, we make a meeting minutes program.\n",
"\n",
"It includes useful code to connect your Google Drive to your colab.\n",
"\n",
"Upload your own audio to make this work!!\n",
"\n",
"https://colab.research.google.com/drive/1KSMxOCprsl1QRpt_Rq0UqCAyMtPqDQYx?usp=sharing\n",
"\n",
"This should run nicely on a low-cost or free T4 box."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e9289ba7-200c-43a9-b67a-c5ce826c9537",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

6
week4/day4.ipynb

@ -7,7 +7,11 @@
"source": [ "source": [
"# Code Generator\n", "# Code Generator\n",
"\n", "\n",
"The requirement: use a Frontier model to generate high performance C++ code from Python code" "The requirement: use an Open Source model to generate high performance C++ code from Python code\n",
"\n",
"To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n",
"\n",
"It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in productionizing models."
] ]
}, },
{ {

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