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Updates to guides and troubleshooting

pull/14/head
Edward Donner 6 months ago
parent
commit
55a9ddf6ce
  1. 239
      README.md
  2. 177
      SETUP-PC.md
  3. BIN
      SETUP-PC.pdf
  4. 182
      SETUP-mac.md
  5. BIN
      SETUP-mac.pdf
  6. 10
      requirements.txt
  7. 256
      week1/Guide to Jupyter.ipynb
  8. 464
      week1/Intermediate Python.ipynb
  9. 73
      week1/day1.ipynb
  10. 20
      week1/troubleshooting.ipynb

239
README.md

@ -11,156 +11,32 @@ I'm so happy you're joining me on this path. We'll be building immensely satisfy
I'm here to help you be most successful with your learning! If you hit any snafus, or if you have any ideas on how I can improve the course, please do reach out in the platform or by emailing me direct (ed@edwarddonner.com). It's always great to connect with people on LinkedIn to build up the community - you'll find me here:
https://www.linkedin.com/in/eddonner/
### An important point on API costs
During the course, I'll suggest you try out the leading models at the forefront of progress, known as the Frontier models. I'll also suggest you run open-source models using Google Colab. These services have some charges, but I'll keep cost minimal - like, a few cents at a time.
Please do monitor your API usage to ensure you're comfortable with spend; I've included links below. There's no need to spend anything more than a couple of dollars for the entire course. You may find that AI providers such as OpenAI requires a minimum credit like \$5 for your region; we should only spend a fraction of it, but you'll have plenty of opportunity to put it to good use in your own projects. During Week 7 you have an option to spend a bit more if you're enjoying the process - I spend about $10 myself and the results make me very happy indeed! But it's not necessary in the least; the important part is that you focus on learning.
### How this Repo is organized
There are folders for each of the "weeks", representing modules of the class, culminating in a powerful autonomous Agentic AI solution in Week 8 that draws on many of the prior weeks.
Follow the setup instructions below, then open the Week 1 folder and prepare for joy.
### The most important part
The mantra of the course is: the best way to learn is by **DOING**. You should work along with me, running each cell, inspecting the objects to get a detailed understanding of what's happening. Then tweak the code and make it your own. There are juicy challenges for you throughout the course. I'd love it if you wanted to push your code so I can follow along with your progress, and I can make your solutions available to others so we share in your progress. While the projects are enjoyable, they are first and foremost designed to be _educational_, teaching you business skills that can be put into practice in your work.
## Setup instructions
I should confess up-front: setting up a powerful environment to work at the forefront of AI is not as simple as I'd like. For most people these instructions will go great; but in some cases, for whatever reason, you'll hit a problem. Please don't hesitate to reach out - I am here to get you up and running quickly. There's nothing worse than feeling _stuck_. Message me, email me or LinkedIn message me and I will unstick you quickly!
The recommended approach is to use Anaconda for your environment. It's a powerful tool that builds a complete science environment. Anaconda ensures that you're working with the right version of Python and all your packages are compatible with mine, even if we're on different platforms.
**Update** Some people have had problems with Anaconda - horrors! The idea of Anaconda is to make it really smooth and simple to be working with the same environment. If you hit any problems with the instructions below, please skip to near the end of this README for the alternative approach using `pip` with `virtualenv`, and hopefully you'll be up and running fast. And please do message me if I can help with anything.
We'll be mostly using Jupyter Lab in this course. For those new to Jupyter Lab / Jupyter Notebook, it's a delightful Data Science environment where you can simply hit shift+return in any cell to run it; start at the top and work your way down! When we move to Google Colab in Week 3, you'll experience the same interface for Python runtimes in the cloud.
### For Windows Users
1. **Install Git** (if not already installed):
- Download Git from https://git-scm.com/download/win
- Run the installer and follow the prompts, using default options
2. **Open Command Prompt:**
- Press Win + R, type `cmd`, and press Enter
3. **Navigate to your projects folder:**
If you have a specific folder for projects, navigate to it using the cd command. For example:
`cd C:\Users\YourUsername\Documents\Projects`
If you don't have a projects folder, you can create one:
```
mkdir C:\Users\YourUsername\Documents\Projects
cd C:\Users\YourUsername\Documents\Projects
```
(Replace YourUsername with your actual Windows username)
3. **Clone the repository:**
- Go to the course's GitHub page
- Click the green 'Code' button and copy the URL
- In the Command Prompt, type this, replacing everything after the word 'clone' with the copied URL: `git clone <paste-url-here>`
4. **Install Anaconda:**
- Download Anaconda from https://docs.anaconda.com/anaconda/install/windows/
- Run the installer and follow the prompts
- A student mentioned that if you are prompted to upgrade Anaconda to a newer version during the install, you shouldn't do it, as there might be problems with the very latest update for PC. (Thanks for the pro-tip!)
5. **Set up the environment:**
- Open Anaconda Prompt (search for it in the Start menu)
- Navigate to the cloned repository folder using `cd path\to\repo` (replace `path\to\repo` with the actual path to the llm_engineering directory, your locally cloned version of the repo)
- Create the environment: `conda env create -f environment.yml`
- Wait for a few minutes for all packages to be installed
- Activate the environment: `conda activate llms`
You should see `(llms)` in your prompt, which indicates you've activated your new environment.
6. **Start Jupyter Lab:**
- In the Anaconda Prompt, from within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipnbk`.
### For Mac Users
1. **Install Git** if not already installed (it will be in most cases)
- Open Terminal (Applications > Utilities > Terminal)
- Type `git --version` If not installed, you'll be prompted to install it
2. **Navigate to your projects folder:**
If you have a specific folder for projects, navigate to it using the cd command. For example:
`cd ~/Documents/Projects`
If you don't have a projects folder, you can create one:
```
mkdir ~/Documents/Projects
cd ~/Documents/Projects
```
3. **Clone the repository**
- Go to the course's GitHub page
- Click the green 'Code' button and copy the URL
- In Terminal, type this, replacing everything after the word 'clone' with the copied URL: `git clone <paste-url-here>`
4. **Install Anaconda:**
- PC people please follow the instructions in [SETUP-PC.md](SETUP-PC.md)
- Mac people please follow the instructions in [SETUP-mac.md](SETUP-mac.md)
- Linux people, the Mac instructions should be close enough!
- Download Anaconda from https://docs.anaconda.com/anaconda/install/mac-os/
- Double-click the downloaded file and follow the installation prompts
### An important point on API costs (which are optional! No need to spend if you don't wish)
5. **Set up the environment:**
During the course, I'll suggest you try out the leading models at the forefront of progress, known as the Frontier models. I'll also suggest you run open-source models using Google Colab. These services have some charges, but I'll keep cost minimal - like, a few cents at a time. And I'll provide alternatives if you'd prefer not to use them.
- Open Terminal
- Navigate to the cloned repository folder using `cd path/to/repo` (replace `path/to/repo` with the actual path to the llm_engineering directory, your locally cloned version of the repo)
- Create the environment: `conda env create -f environment.yml`
- Wait for a few minutes for all packages to be installed
- Activate the environment: `conda activate llms`
(Instructions for open source model alternative coming in next few days - around November 15th)
You should see `(llms)` in your prompt, which indicates you've activated your new environment.
Please do monitor your API usage to ensure you're comfortable with spend; I've included links below. There's no need to spend anything more than a couple of dollars for the entire course. Some AI providers such as OpenAI require a minimum credit like \$5 or local equivalent; we should only spend a fraction of it, and you'll have plenty of opportunity to put it to good use in your own projects. During Week 7 you have an option to spend a bit more if you're enjoying the process - I spend about $10 myself and the results make me very happy indeed! But it's not necessary in the least; the important part is that you focus on learning.
6. **Start Jupyter Lab:**
I'll also show you an alternative if you'd rather not spend anything on APIs.
- In Terminal, from within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipnbk`.
### When we get to it, creating your API keys
Particularly during weeks 1 and 2 of the course, you'll be writing code to call the APIs of Frontier models (models at the forefront of progress). You'll need to join me in setting up accounts and API keys.
- [GPT API](https://platform.openai.com/) from OpenAI
- [Claude API](https://console.anthropic.com/) from Anthropic
- [Gemini API](https://ai.google.dev/gemini-api) from Google
Initially we'll only use OpenAI, so you can start with that, and we'll cover the others soon afterwards. The webpage where you set up your OpenAI key is [here](https://platform.openai.com/api-keys). See the extra note on API costs below if that's a concern. One student mentioned to me that OpenAI can take a few minutes to register; if you initially get an error about being out of quota, wait a few minutes and try again. Another reason you might encounter the out of quota error is if you haven't yet added a valid payment method to your OpenAI account. You can do this by clicking your profile picture on the OpenAI website then clicking "Your profile." Once you are redirected to your profile page, choose "Billing" on the left-pane menu. You will need to enter a valid payment method and charge your account with a small advance payment. It is recommended that you **disable** the automatic recharge as an extra failsafe. If it's still a problem, see more troubleshooting tips in the Week 1 Day 1 notebook, and/or message me!
Later in the course you'll be using the fabulous HuggingFace platform; an account is available for free at [HuggingFace](https://huggingface.co) - you can create an API token from the Avatar menu >> Settings >> Access Tokens.
And in Week 6/7 you'll be using the terrific [Weights & Biases](https://wandb.ai) platform to watch over your training batches. Accounts are also free, and you can set up a token in a similar way.
When you have these keys, please create a new file called `.env` in your project root directory. This file won't appear in Jupyter Lab because it's a hidden file; you should create it using something like Notepad (PC) or nano (Mac / Linux). I've put detailed instructions at the end of this README.
### How this Repo is organized
It should have contents like this, and to start with you only need the first line:
There are folders for each of the "weeks", representing modules of the class, culminating in a powerful autonomous Agentic AI solution in Week 8 that draws on many of the prior weeks.
Follow the setup instructions above, then open the Week 1 folder and prepare for joy.
```
OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
### The most important part
This file is listed in the `.gitignore` file, so it won't get checked in and your keys stay safe.
If you have any problems with this process, there's a simple workaround which I explain in the video.
The mantra of the course is: the best way to learn is by **DOING**. I don't type all the code during the course; I execute it for you to see the results. You should work along with me or after each lecture, running each cell, inspecting the objects to get a detailed understanding of what's happening. Then tweak the code and make it your own. There are juicy challenges for you throughout the course. I'd love it if you wanted to push your code so I can follow along with your progress, and I can make your solutions available to others so we share in your progress. While the projects are enjoyable, they are first and foremost designed to be _educational_, teaching you business skills that can be put into practice in your work.
### Starting in Week 3, we'll also be using Google Colab for running with GPUs
## Starting in Week 3, we'll also be using Google Colab for running with GPUs
You should be able to use the free tier or minimal spend to complete all the projects in the class. I personally signed up for Colab Pro+ and I'm loving it - but it's not required.
@ -183,91 +59,4 @@ The charges for the exercsies in this course should always be quite low, but if
2. For Anthropic: Always use model `claude-3-haiku-20240307` in the code instead of the other Claude models
3. During week 7, look out for my instructions for using the cheaper dataset
## And that's it! Happy coding!
### Alternative Setup Instructions if Anaconda is giving you problems
First please run:
`python --version`
To find out which python you're on. Ideally you'd be using Python 3.11.x, so we're completely in sync. You can download python at
https://www.python.org/downloads/
Here are the steps:
After cloning the repo, cd into the project root directory `llm_engineering`.
Then:
1. Create a new virtual environment: `python -m venv venv`
2. Activate the virtual environment with
On a Mac: `source venv/bin/activate`
On a PC: `venv\Scripts\activate`
3. Run `pip install -r requirements.txt`
4. Create a file called `.env` in the project root directory and add any private API keys, such as below. (The next section has more detailed instructions for this, if you prefer.)
```
OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
5. Run `jupyter lab` to launch Jupyter and head over to the intro folder to get started.
Let me know if you hit problems.
### Guide to creating the `.env` file
**For PC users:**
1. Open the Notepad (Windows + R to open the Run box, enter notepad)
2. In the Notepad, type the contents of the file, such as:
```
OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
Double check there are no spaces before or after the `=` sign, and no spaces at the end of the key.
3. Go to File > Save As. In the "Save as type" dropdown, select All Files. In the "File name" field, type ".env". Choose the root of the project folder (the folder called `llm_engineering`) and click Save.
4. Navigate to the foler where you saved the file in Explorer and ensure it was saved as ".env" not ".env.txt" - if necessary rename it to ".env" - you might need to ensure that "Show file extensions" is set to "On" so that you see the file extensions. Message or email me if that doesn't make sense!
**For Mac users:**
1. Open Terminal (Command + Space to open Spotlight, type Terminal and press Enter)
2. cd to your project root directory
cd /path/to/your/project
(in other words, change to the directory like `/Users/your_name/Projects/llm_engineering`, or wherever you have cloned llm_engineering).
3. Create the .env file with
nano .env
4. Then type your API keys into nano:
```
OPENAI_API_KEY=xxxx
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
5. Save the file:
Control + O
Enter (to confirm save the file)
Control + X to exit the editor
6. Use this command to list files in your file
`ls -a`
And confirm that the `.env` file is there.
Please do message me or email me at ed@edwarddonner.com if this doesn't work or if I can help with anything. I can't wait to hear how you get on.

177
SETUP-PC.md

@ -0,0 +1,177 @@
# LLM Engineering - Master AI and LLMs
## Setup instructions for Windows
Welcome, PC people!
I should confess up-front: setting up a powerful environment to work at the forefront of AI is not as simple as I'd like. For most people these instructions will go great; but in some cases, for whatever reason, you'll hit a problem. Please don't hesitate to reach out - I am here to get you up and running quickly. There's nothing worse than feeling _stuck_. Message me, email me or LinkedIn message me and I will unstick you quickly!
Email: ed@edwarddonner.com
LinkedIn: https://www.linkedin.com/in/eddonner/
I use a platform called Anaconda to set up your environment. It's a powerful tool that builds a complete science environment. Anaconda ensures that you're working with the right version of Python and all your packages are compatible with mine, even if our systems are completely different. It takes more time to set up, and it uses more hard drive space (5+ GB) but it's very reliable once its working.
Having said that: if you have any problems with Anaconda, I've provided an alternative approach. It's faster and simpler and should have you running quickly, with less of a guarantee around compatibility.
### Part 1: Clone the Repo
This gets you a local copy of the code on your box.
1. **Install Git** (if not already installed):
- Download Git from https://git-scm.com/download/win
- Run the installer and follow the prompts, using default options (press OK lots of times!)
2. **Open Command Prompt:**
- Press Win + R, type `cmd`, and press Enter
3. **Navigate to your projects folder:**
If you have a specific folder for projects, navigate to it using the cd command. For example:
`cd C:\Users\YourUsername\Documents\Projects`
Replacing YourUsername with your actual Windows user
If you don't have a projects folder, you can create one:
```
mkdir C:\Users\YourUsername\Documents\Projects
cd C:\Users\YourUsername\Documents\Projects
```
4. **Clone the repository:**
Enter this in the command prompt in the Projects folder:
`git clone https://github.com/ed-donner/llm_engineering.git`
This creates a new directory `llm_engineering` within your Projects folder and downloads the code for the class. Do `cd llm_engineering` to go into it. This `llm_engineering` directory is known as the "project root directory".
### Part 2: Install Anaconda environment
There is an alternative to Part 2 if this gives you problems.
1. **Install Anaconda:**
- Download Anaconda from https://docs.anaconda.com/anaconda/install/windows/
- Run the installer and follow the prompts. Note that it takes up several GB and take a while to install, but it will be a powerful platform for you to use in the future.
2. **Set up the environment:**
- Open **Anaconda Prompt** (search for it in the Start menu)
- Navigate to the "project root directory" by entering something like `cd C:\Users\YourUsername\Documents\Projects\llm_engineering` using the actual path to your llm_engineering project root directory. Do a `dir` and check you can see subdirectories for each week of the course.
- Create the environment: `conda env create -f environment.yml`
- Wait for a few minutes for all packages to be installed - in some cases, this can literally take 20-30 minutes if you've not used Anaconda before, and even longer depending on your internet connection. Important stuff is happening! If this runs for more than 1 hour 15 mins, or gives you other problems, please go to Part 2B instead.
- You have now built an isolated, dedicated AI environment for engineering LLMs, running vector datastores, and so much more! You now need to **activate** it using this command: `conda activate llms`
You should see `(llms)` in your prompt, which indicates you've activated your new environment.
3. **Start Jupyter Lab:**
- In the Anaconda Prompt, from within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up in a browser. If you've not seen Jupyter Lab before, I'll explain it in a moment! Now close the jupyter lab browser tab, and close the Anaconda prompt, and move on to Part 3.
### Part 2B - Alternative to Part 2 if Anaconda gives you trouble
1. **Open Command Prompt**
Press Win + R, type `cmd`, and press Enter
Run `python --version` to find out which python you're on. Ideally you'd be using a version of Python 3.11, so we're completely in sync.
If not, it's not a big deal, but we might need to come back to this later if you have compatibility issues.
You can download python here:
https://www.python.org/downloads/
2. Navigate to the "project root directory" by entering something like `cd C:\Users\YourUsername\Documents\Projects\llm_engineering` using the actual path to your llm_engineering project root directory. Do a `dir` and check you can see subdirectories for each week of the course.
Then, create a new virtual environment with this command:
`python -m venv llms`
3. Activate the virtual environment with
`llms\Scripts\activate`
You should see (llms) in your command prompt, which is your sign that things are going well.
4. Run `pip install -r requirements.txt`
This may take a few minutes to install.
5. **Start Jupyter Lab:**
From within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipynb`. Success! Now close down jupyter lab and move on to Part 3.
If there are any problems, contact me!
### Part 3 - OpenAI key (OPTIONAL but recommended)
Particularly during weeks 1 and 2 of the course, you'll be writing code to call the APIs of Frontier models (models at the forefront of AI).
For week 1, you'll only need OpenAI, and you can add the others if you wish later on.
1. Create an OpenAI account if you don't have one by visiting:
https://platform.openai.com/
2. OpenAI asks for a minimum credit to use the API. For me in the US, it's \$5. The API calls will spend against this \$5. On this course, we'll only use a small portion of this. I do recommend you make the investment as you'll be able to put it to excellent use. But if you'd prefer not to pay for the API, I give you an alternative in the course using Ollama.
You can add your credit balance to OpenAI at Settings > Billing:
https://platform.openai.com/settings/organization/billing/overview
I recommend you disable the automatic recharge!
3. Create your API key
The webpage where you set up your OpenAI key is at https://platform.openai.com/api-keys - press the green 'Create new secret key' button and press 'Create secret key'. Keep a record of the API key somewhere private; you won't be able to retrieve it from the OpenAI screens in the future. It should start `sk-proj-`.
In week 2 we will also set up keys for Anthropic and Google, which you can do here when we get there.
- Claude API at https://console.anthropic.com/ from Anthropic
- Gemini API at https://ai.google.dev/gemini-api from Google
Later in the course you'll be using the fabulous HuggingFace platform; an account is available for free at https://huggingface.co - you can create an API token from the Avatar menu >> Settings >> Access Tokens.
And in Week 6/7 you'll be using the terrific Weights & Biases at https://wandb.ai to watch over your training batches. Accounts are also free, and you can set up a token in a similar way.
### PART 4 - .env file
When you have these keys, please create a new file called `.env` in your project root directory. The filename needs to be exactly the four characters ".env" rather than "my-keys.env" or ".env.txt". Here's how to do it:
1. Open the Notepad (Windows + R to open the Run box, enter `notepad`)
2. In the Notepad, type this, replacing xxxx with your API key (starting `sk-proj-`).
```
OPENAI_API_KEY=xxxx
```
If you have other keys, you can add them too, or come back to this in future weeks:
```
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
Double check there are no spaces before or after the `=` sign, and no spaces at the end of the key.
3. Go to File > Save As. In the "Save as type" dropdown, select All Files. In the "File name" field, type exactly **.env** as the filename. Choose to save this in the project root directory (the folder called `llm_engineering`) and click Save.
4. Navigate to the foler where you saved the file in Explorer and ensure it was saved as ".env" not ".env.txt" - if necessary rename it to ".env" - you might need to ensure that "Show file extensions" is set to "On" so that you see the file extensions. Message or email me if that doesn't make sense!
This file won't appear in Jupyter Lab because jupyter hides files starting with a dot. This file is listed in the `.gitignore` file, so it won't get checked in and your keys stay safe.
### Part 5 - Showtime!!
- Open **Anaconda Prompt** (search for it in the Start menu)
- Navigate to the "project root directory" by entering something like `cd C:\Users\YourUsername\Documents\Projects\llm_engineering` using the actual path to your llm_engineering project root directory. Do a `dir` and check you can see subdirectories for each week of the course.
- Activate your environment with `conda activate llms` (or `llms\Scripts\activate` if you used the alternative approach in Part 2B)
- You should see (llms) in your prompt which is your sign that all is well. And now, type: `jupyter lab` and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipynb`.
And you're off to the races!
Note that any time you start jupyter lab in the future, you'll need to follow these Part 5 instructions to start it from within the `llm_engineering` directory with the `llms` environment activated.
For those new to Jupyter Lab / Jupyter Notebook, it's a delightful Data Science environment where you can simply hit shift+return in any cell to run it; start at the top and work your way down! There's a notebook in the week1 folder with a [Guide to Jupyter Lab](week1/Guide%20to%20Jupyter.ipynb), and an [Intermediate Python](week1/Intermediate%20Python.ipynb) tutorial, if that would be helpful. When we move to Google Colab in Week 3, you'll experience the same interface for Python runtimes in the cloud.
If you have any problems, I've included a notebook in week1 called [troubleshooting.ipynb](week1/troubleshooting.ipynb) to figure it out.
Please do message me or email me at ed@edwarddonner.com if this doesn't work or if I can help with anything. I can't wait to hear how you get on.

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SETUP-mac.md

@ -0,0 +1,182 @@
# LLM Engineering - Master AI and LLMs
## Setup instructions for Mac
Welcome, Mac people!
I should confess up-front: setting up a powerful environment to work at the forefront of AI is not as simple as I'd like. For most people these instructions will go great; but in some cases, for whatever reason, you'll hit a problem. Please don't hesitate to reach out - I am here to get you up and running quickly. There's nothing worse than feeling _stuck_. Message me, email me or LinkedIn message me and I will unstick you quickly!
Email: ed@edwarddonner.com
LinkedIn: https://www.linkedin.com/in/eddonner/
I use a platform called Anaconda to set up your environment. It's a powerful tool that builds a complete science environment. Anaconda ensures that you're working with the right version of Python and all your packages are compatible with mine, even if our systems are completely different. It takes more time to set up, and it uses more hard drive space (5+ GB) but it's very reliable once its working.
Having said that: if you have any problems with Anaconda, I've provided an alternative approach. It's faster and simpler and should have you running quickly, with less of a guarantee around compatibility.
### Part 1: Clone the Repo
This gets you a local copy of the code on your box.
1. **Install Git** if not already installed (it will be in most cases)
- Open Terminal (Applications > Utilities > Terminal)
- Type `git --version` If not installed, you'll be prompted to install it
2. **Navigate to your projects folder:**
If you have a specific folder for projects, navigate to it using the cd command. For example:
`cd ~/Documents/Projects`
If you don't have a projects folder, you can create one:
```
mkdir ~/Documents/Projects
cd ~/Documents/Projects
```
3. **Clone the repository:**
Enter this in the terminal in the Projects folder:
`git clone https://github.com/ed-donner/llm_engineering.git`
This creates a new directory `llm_engineering` within your Projects folder and downloads the code for the class. Do `cd llm_engineering` to go into it. This `llm_engineering` directory is known as the "project root directory".
### Part 2: Install Anaconda environment
There is an alternative to Part 2 if this gives you problems.
1. **Install Anaconda:**
- Download Anaconda from https://docs.anaconda.com/anaconda/install/mac-os/
- Double-click the downloaded file and follow the installation prompts. Note that it takes up several GB and take a while to install, but it will be a powerful platform for you to use in the future.
2. **Set up the environment:**
- Open a new Terminal (Applications > Utilities > Terminal)
- Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path as needed with the actual path to the llm_engineering directory, your locally cloned version of the repo). Do `ls` and check you can see subdirectories for each week of the course.
- Create the environment: `conda env create -f environment.yml`
- Wait for a few minutes for all packages to be installed - in some cases, this can literally take 20-30 minutes if you've not used Anaconda before, and even longer depending on your internet connection. Important stuff is happening! If this runs for more than 1 hour 15 mins, or gives you other problems, please go to Part 2B instead.
- You have now built an isolated, dedicated AI environment for engineering LLMs, running vector datastores, and so much more! You now need to **activate** it using this command: `conda activate llms`
You should see `(llms)` in your prompt, which indicates you've activated your new environment.
3. **Start Jupyter Lab:**
- In the Terminal window, from within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up in a browser. If you've not seen Jupyter Lab before, I'll explain it in a moment! Now close the jupyter lab browser tab, and close the Terminal, and move on to Part 3.
### Part 2B - Alternative to Part 2 if Anaconda gives you trouble
1. **Open a new Terminal** (Applications > Utilities > Terminal)
Run `python --version` to find out which python you're on. Ideally you'd be using a version of Python 3.11, so we're completely in sync.
If not, it's not a big deal, but we might need to come back to this later if you have compatibility issues.
You can download python here:
https://www.python.org/downloads/
2. Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path with the actual path to the llm_engineering directory, your locally cloned version of the repo). Do `ls` and check you can see subdirectories for each week of the course.
Then, create a new virtual environment with this command:
`python -m venv llms`
3. Activate the virtual environment with
`source llms/bin/activate`
You should see (llms) in your command prompt, which is your sign that things are going well.
4. Run `pip install -r requirements.txt`
This may take a few minutes to install.
5. **Start Jupyter Lab:**
From within the `llm_engineering` folder, type: `jupyter lab`
...and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipynb`. Success! Now close down jupyter lab and move on to Part 3.
If there are any problems, contact me!
### Part 3 - OpenAI key (OPTIONAL but recommended)
Particularly during weeks 1 and 2 of the course, you'll be writing code to call the APIs of Frontier models (models at the forefront of AI).
For week 1, you'll only need OpenAI, and you can add the others if you wish later on.
1. Create an OpenAI account if you don't have one by visiting:
https://platform.openai.com/
2. OpenAI asks for a minimum credit to use the API. For me in the US, it's \$5. The API calls will spend against this \$5. On this course, we'll only use a small portion of this. I do recommend you make the investment as you'll be able to put it to excellent use. But if you'd prefer not to pay for the API, I give you an alternative in the course using Ollama.
You can add your credit balance to OpenAI at Settings > Billing:
https://platform.openai.com/settings/organization/billing/overview
I recommend you disable the automatic recharge!
3. Create your API key
The webpage where you set up your OpenAI key is at https://platform.openai.com/api-keys - press the green 'Create new secret key' button and press 'Create secret key'. Keep a record of the API key somewhere private; you won't be able to retrieve it from the OpenAI screens in the future. It should start `sk-proj-`.
In week 2 we will also set up keys for Anthropic and Google, which you can do here when we get there.
- Claude API at https://console.anthropic.com/ from Anthropic
- Gemini API at https://ai.google.dev/gemini-api from Google
Later in the course you'll be using the fabulous HuggingFace platform; an account is available for free at https://huggingface.co - you can create an API token from the Avatar menu >> Settings >> Access Tokens.
And in Week 6/7 you'll be using the terrific Weights & Biases at https://wandb.ai to watch over your training batches. Accounts are also free, and you can set up a token in a similar way.
### PART 4 - .env file
When you have these keys, please create a new file called `.env` in your project root directory. The filename needs to be exactly the four characters ".env" rather than "my-keys.env" or ".env.txt". Here's how to do it:
1. Open Terminal (Applications > Utilities > Terminal)
2. Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path with the actual path to the llm_engineering directory, your locally cloned version of the repo).
3. Create the .env file with
nano .env
4. Then type your API keys into nano, replacing xxxx with your API key (starting `sk-proj-`).
```
OPENAI_API_KEY=xxxx
```
If you have other keys, you can add them too, or come back to this in future weeks:
```
GOOGLE_API_KEY=xxxx
ANTHROPIC_API_KEY=xxxx
HF_TOKEN=xxxx
```
5. Save the file:
Control + O
Enter (to confirm save the file)
Control + X to exit the editor
6. Use this command to list files in your project root directory:
`ls -a`
And confirm that the `.env` file is there.
This file won't appear in Jupyter Lab because jupyter hides files starting with a dot. This file is listed in the `.gitignore` file, so it won't get checked in and your keys stay safe.
### Part 5 - Showtime!!
- Open Terminal (Applications > Utilities > Terminal)
- Navigate to the "project root directory" using `cd ~/Documents/Projects/llm_engineering` (replace this path with the actual path to the llm_engineering directory, your locally cloned version of the repo). Do `ls` and check you can see subdirectories for each week of the course.
- Activate your environment with `conda activate llms` (or `source llms/bin/activate` if you used the alternative approach in Part 2B)
- You should see (llms) in your prompt which is your sign that all is well. And now, type: `jupyter lab` and Jupyter Lab should open up, ready for you to get started. Open the `week1` folder and double click on `day1.ipynb`.
And you're off to the races!
Note that any time you start jupyter lab in the future, you'll need to follow these Part 5 instructions to start it from within the `llm_engineering` directory with the `llms` environment activated.
For those new to Jupyter Lab / Jupyter Notebook, it's a delightful Data Science environment where you can simply hit shift+return in any cell to run it; start at the top and work your way down! I've included a notebook called 'Guide to Jupyter' that shows you more features. When we move to Google Colab in Week 3, you'll experience the same interface for Python runtimes in the cloud.
If you have any problems, I've included a notebook in week1 called [troubleshooting.ipynb](week1/troubleshooting.ipynb) to figure it out.
Please do message me or email me at ed@edwarddonner.com if this doesn't work or if I can help with anything. I can't wait to hear how you get on.

BIN
SETUP-mac.pdf

Binary file not shown.

10
requirements.txt

@ -10,9 +10,6 @@ matplotlib
gensim
torch
transformers
accelerate
sentencepiece
bitsandbytes
tqdm
openai
gradio
@ -34,4 +31,9 @@ chromadb
plotly
jupyter-dash
beautifulsoup4
pydub
pydub
modal
ollama
accelerate
sentencepiece
bitsandbytes

256
week1/Guide to Jupyter.ipynb

@ -10,7 +10,7 @@
"## A Quick Start Guide\n",
"\n",
"Welcome to the wonderful world of Jupyter lab! \n",
"This is a Data Science playground where you can easily write code that builds and builds. It's an ideal environment for: \n",
"This is a Data Science playground where you can easily write code and investigate the results. It's an ideal environment for: \n",
"- Research & Development\n",
"- Prototyping\n",
"- Learning (that's us!)\n",
@ -21,7 +21,7 @@
"\n",
"A long time ago, Jupyter used to be called \"IPython\", and so the extensions of notebooks are \".ipynb\" which stands for \"IPython Notebook\".\n",
"\n",
"On the left is a File Browser that lets you navigate around the weeks and choose different notebooks. But you probably know that already, or you wouldn't have got here!\n",
"On the left is a File Browser that lets you navigate around the directories and choose different notebooks. But you probably know that already, or you wouldn't have got here!\n",
"\n",
"The notebook consists of a series of square boxes called \"cells\". Some of them contain text, like this cell, and some of them contain code, like the cell below.\n",
"\n",
@ -37,6 +37,8 @@
"metadata": {},
"outputs": [],
"source": [
"# Click anywhere in this cell and press Shift + Return\n",
"\n",
"2 + 2"
]
},
@ -47,7 +49,7 @@
"source": [
"## Congrats!\n",
"\n",
"Now run the next cell which sets a value, followed by the cell after it to print the value"
"Now run the next cell which sets a value, followed by the cells after it to print the value"
]
},
{
@ -62,6 +64,18 @@
"favorite_fruit = \"bananas\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07792faa-761d-46cb-b9b7-2bbf70bb1628",
"metadata": {},
"outputs": [],
"source": [
"# The result of the last statement is shown after you run it\n",
"\n",
"favorite_fruit"
]
},
{
"cell_type": "code",
"execution_count": null,
@ -91,10 +105,13 @@
"id": "9442d5c9-f57d-4839-b0af-dce58646c04f",
"metadata": {},
"source": [
"# Now go back and rerun the prior cell with the print statement\n",
"## Now go back and rerun the cell with the print statement, two cells back\n",
"\n",
"See how it prints something different, even though favorite_fruit was changed afterwards? \n",
"The order that code appears in the notebook doesn't matter. What matters is the order that the code is **executed**."
"See how it prints something different, even though favorite_fruit was changed further down in the notebook? \n",
"\n",
"The order that code appears in the notebook doesn't matter. What matters is the order that the code is **executed**. There's a python process sitting behind this notebook in which the variables are being changed.\n",
"\n",
"This catches some people out when they first use Jupyter."
]
},
{
@ -104,16 +121,239 @@
"metadata": {},
"outputs": [],
"source": [
"# More coming here soon!"
"# Then run this cell twice, and see if you understand what's going on\n",
"\n",
"print(f\"My favorite fruit is {favorite_fruit}\")\n",
"\n",
"favorite_fruit = \"apples\""
]
},
{
"cell_type": "markdown",
"id": "a29dab2d-bab9-4a54-8504-05e62594cc6f",
"metadata": {},
"source": [
"# Explaining the 'kernel'\n",
"\n",
"Sitting behind this notebook is a Python process which executes each cell when you run it. That Python process is known as the Kernel. Each notebook has its own separate Kernel.\n",
"\n",
"You can go to the Kernel menu and select \"Restart Kernel\".\n",
"\n",
"If you then try to run the next cell, you'll get an error, because favorite_fruit is no longer defined. You'll need to run the cells from the top of the notebook again. Then the next cell should run fine."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "84b1e410-5eda-4e2c-97ce-4eebcff816c5",
"metadata": {},
"outputs": [],
"source": [
"print(f\"My favorite fruit is {favorite_fruit}\")"
]
},
{
"cell_type": "markdown",
"id": "4d4188fc-d9cc-42be-8b4e-ae8630456764",
"metadata": {},
"source": [
"# Adding and moving cells\n",
"\n",
"Click in this cell, then click the \\[+\\] button in the toolbar above to create a new cell immediately below this one. Copy and paste in the code in the prior cell, then run it! There are also icons in the top right of the selected cell to delete it (bin), duplicate it, and move it up and down.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b51950ca-b512-4829-974f-442bd50e29a5",
"id": "ce258424-40c3-49a7-9462-e6fa25014b03",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "30e71f50-8f01-470a-9d7a-b82a6cef4236",
"metadata": {},
"source": [
"# Cell output\n",
"\n",
"When you execute a cell, the standard output and the result of the last statement is written to the area immediately under the code, known as the 'cell output'. When you save a Notebook from the file menu (or command+S), the output is also saved, making it a useful record of what happened.\n",
"\n",
"You can clean this up by going to Edit menu >> Clear Outputs of All Cells, or Kernel menu >> Restart Kernel and Clear Outputs of All Cells."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a4d021e2-c284-411f-8ab1-030530cfbe72",
"metadata": {},
"outputs": [],
"source": [
"spams = [\"spam\"] * 1000\n",
"print(spams)\n",
"\n",
"# Might be worth clearing output after running this!"
]
},
{
"cell_type": "markdown",
"id": "eac060f2-7a71-46e7-8235-b6ad0a76f5f8",
"metadata": {},
"source": [
"# Using markdown\n",
"\n",
"So what's going on with these areas with writing in them, like this one? Well, there's actually a different kind of cell called a 'Markdown' cell for adding explanations like this. Click the + button to add a cell. Then in the toolbar, click where it says 'Code' and change it to 'Markdown'.\n",
"\n",
"Add some comments using Markdown format, perhaps copying and pasting from here:\n",
"\n",
"```\n",
"# This is a heading\n",
"## This is a sub-head\n",
"### And a sub-sub-head\n",
"\n",
"I like Jupyter Lab because it's\n",
"- Easy\n",
"- Flexible\n",
"- Satisfying\n",
"```\n",
"\n",
"And to turn this into formatted text simply with Shift+Return in the cell.\n",
"Click in the cell and press the Bin icon if you want to remove it."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e1586320-c90f-4f22-8b39-df6865484950",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "1330c83c-67ac-4ca0-ac92-a71699e0c31b",
"metadata": {},
"source": [
"# The exclamation point\n",
"\n",
"There's a super useful feature of jupyter labs; you can type a command with a ! in front of it in a code cell, like:\n",
"\n",
"!pip install \\[some_package\\]\n",
"\n",
"And it will run it at the command line (as if in Windows Powershell or Mac Terminal) and print the result"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "82042fc5-a907-4381-a4b8-eb9386df19cd",
"metadata": {},
"outputs": [],
"source": [
"# list the current directory\n",
"\n",
"!ls"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4fc3e3da-8a55-40cc-9706-48bf12a0e20e",
"metadata": {},
"outputs": [],
"source": [
"# ping cnn.com - press the stop button in the toolbar when you're bored\n",
"\n",
"!ping cnn.com"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a58e9462-89a2-4b4f-b4aa-51c4bd9f796b",
"metadata": {},
"outputs": [],
"source": [
"# This is a useful command that ensures your Anaconda environment \n",
"# is up to date with any new upgrades to packages;\n",
"# But it might take a minute and will print a lot to output\n",
"\n",
"!conda env update -f ../environment.yml --prune"
]
},
{
"cell_type": "markdown",
"id": "4688baaf-a72c-41b5-90b6-474cb24790a7",
"metadata": {},
"source": [
"# Minor things we encounter on the course\n",
"\n",
"This isn't necessarily a feature of Jupyter, but it's a nice package to know about that is useful in Jupyter Labs, and I use it in the course.\n",
"\n",
"The package `tqdm` will print a nice progress bar if you wrap any iterable."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2646a4e5-3c23-4aee-a34d-d623815187d2",
"metadata": {},
"outputs": [],
"source": [
"# Here's some code with no progress bar\n",
"# It will take 10 seconds while you wonder what's happpening..\n",
"\n",
"import time\n",
"\n",
"spams = [\"spam\"] * 1000\n",
"\n",
"for spam in spams:\n",
" time.sleep(0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6e96be3d-fa82-42a3-a8aa-b81dd20563a5",
"metadata": {},
"outputs": [],
"source": [
"# And now, with a nice little progress bar:\n",
"\n",
"import time\n",
"from tqdm import tqdm\n",
"\n",
"spams = [\"spam\"] * 1000\n",
"\n",
"for spam in tqdm(spams):\n",
" time.sleep(0.01)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63c788dd-4618-4bb4-a5ce-204411a38ade",
"metadata": {},
"outputs": [],
"source": [
"# On a different topic, here's a useful way to print output in markdown\n",
"\n",
"from IPython.display import Markdown, display\n",
"\n",
"display(Markdown(\"# This is a big heading!\\n\\n- And this is a bullet-point\\n- So is this\\n- Me, too!\"))\n"
]
},
{
"cell_type": "markdown",
"id": "9d14c1fb-3321-4387-b6ca-9af27676f980",
"metadata": {},
"source": [
"# That's it! You're up to speed on Jupyter Lab.\n",
"\n",
"## Want to be even more advanced?\n",
"\n",
"If you want to become a pro at Jupyter Lab, you can read their tutorial [here](https://jupyterlab.readthedocs.io/en/latest/). But this isn't required for our course; just a good technique for hitting Shift + Return and enjoying the result!"
]
}
],
"metadata": {

464
week1/Intermediate Python.ipynb

@ -0,0 +1,464 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "5c291475-8c7c-461c-9b12-545a887b2432",
"metadata": {},
"source": [
"# Intermediate Level Python\n",
"\n",
"## Getting you up to speed\n",
"\n",
"This course assumes that you're at an intermediate level of python. For example, you should have a decent idea what something like this might do:\n",
"\n",
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n",
"\n",
"If not - then you've come to the right place! Welcome to the crash course in intermediate level python. The best way to learn is by doing!\n"
]
},
{
"cell_type": "markdown",
"id": "542f0577-a826-4613-a5d7-4170e9666d04",
"metadata": {},
"source": [
"## First: if you need a refresher on the foundations\n",
"\n",
"I'm going to defer to an AI friend for this, because these explanations are so well written with great examples. Copy and paste the code examples into a new cell to give them a try.\n",
"\n",
"**Python imports:** \n",
"https://chatgpt.com/share/672f9f31-8114-8012-be09-29ef0d0140fb\n",
"\n",
"**Python functions** including default arguments: \n",
"https://chatgpt.com/share/672f9f99-7060-8012-bfec-46d4cf77d672\n",
"\n",
"**Python strings**, including slicing, split/join, replace and literals: \n",
"https://chatgpt.com/share/672fb526-0aa0-8012-9e00-ad1687c04518\n",
"\n",
"**Python f-strings** including number and date formatting: \n",
"https://chatgpt.com/share/672fa125-0de0-8012-8e35-27918cbb481c\n",
"\n",
"**Python lists, dicts and sets**, including the `get()` method: \n",
"https://chatgpt.com/share/672fa225-3f04-8012-91af-f9c95287da8d\n",
"\n",
"**Python classes:** \n",
"https://chatgpt.com/share/672fa07a-1014-8012-b2ea-6dc679552715"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5802e2f0-0ea0-4237-bbb7-f375a34260f0",
"metadata": {},
"outputs": [],
"source": [
"# Next let's create some things:\n",
"\n",
"fruits = [\"Apples\", \"Bananas\", \"Pears\"]\n",
"\n",
"book1 = {\"title\": \"Great Expectations\", \"author\": \"Charles Dickens\"}\n",
"book2 = {\"title\": \"Bleak House\", \"author\": \"Charles Dickens\"}\n",
"book3 = {\"title\": \"An Book By No Author\"}\n",
"book4 = {\"title\": \"Moby Dick\", \"author\": \"Herman Melville\"}\n",
"\n",
"books = [book1, book2, book3, book4]"
]
},
{
"cell_type": "markdown",
"id": "9b941e6a-3658-4144-a8d4-72f5e72f3707",
"metadata": {},
"source": [
"# Part 1: List and dict comprehensions"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "61992bb8-735d-4dad-8747-8c10b63aec82",
"metadata": {},
"outputs": [],
"source": [
"# Simple enough to start\n",
"\n",
"for fruit in fruits:\n",
" print(fruit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c89c3842-9b74-47fa-8424-0fcb08e4177c",
"metadata": {},
"outputs": [],
"source": [
"# Let's make a new version of fruits\n",
"\n",
"fruits_shouted = []\n",
"for fruit in fruits:\n",
" fruits_shouted.append(fruit.upper())\n",
"\n",
"fruits_shouted"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4ec13b3a-9545-44f1-874a-2910a0663560",
"metadata": {},
"outputs": [],
"source": [
"# You probably already know this\n",
"# There's a nice Python construct called \"list comprehension\" that does this:\n",
"\n",
"fruits_shouted2 = [fruit.upper() for fruit in fruits]\n",
"fruits_shouted2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecc08c3c-181d-4b64-a3e1-b0ccffc6c0cd",
"metadata": {},
"outputs": [],
"source": [
"# But you may not know that you can do this to create dictionaries, too:\n",
"\n",
"fruit_mapping = {fruit:fruit.upper() for fruit in fruits}\n",
"fruit_mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "500c2406-00d2-4793-b57b-f49b612760c8",
"metadata": {},
"outputs": [],
"source": [
"# you can also use the if statement to filter the results\n",
"\n",
"fruits_with_longer_names_shouted = [fruit.upper() for fruit in fruits if len(fruit)>5]\n",
"fruits_with_longer_names_shouted"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "38c11c34-d71e-45ba-945b-a3d37dc29793",
"metadata": {},
"outputs": [],
"source": [
"fruit_mapping_unless_starts_with_a = {fruit:fruit.upper() for fruit in fruits if not fruit.startswith('A')}\n",
"fruit_mapping_unless_starts_with_a"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c97d8e8-31de-4afa-973e-28d8e5cab749",
"metadata": {},
"outputs": [],
"source": [
"# Another comprehension\n",
"\n",
"[book['title'] for book in books]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50be0edc-a4cd-493f-a680-06080bb497b4",
"metadata": {},
"outputs": [],
"source": [
"# This code will fail with an error because one of our books doesn't have an author\n",
"\n",
"[book['author'] for book in books]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "53794083-cc09-4edb-b448-2ffb7e8495c2",
"metadata": {},
"outputs": [],
"source": [
"# But this will work, because get() returns None\n",
"\n",
"[book.get('author') for book in books]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b8e4b859-24f8-4016-8d74-c2cef226d049",
"metadata": {},
"outputs": [],
"source": [
"# And this variation will filter out the None\n",
"\n",
"[book.get('author') for book in books if book.get('author')]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c44bb999-52b4-4dee-810b-8a400db8f25f",
"metadata": {},
"outputs": [],
"source": [
"# And this version will convert it into a set, removing duplicates\n",
"\n",
"set([book.get('author') for book in books if book.get('author')])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "80a65156-6192-4bb4-b4e6-df3fdc933891",
"metadata": {},
"outputs": [],
"source": [
"# And finally, this version is even nicer\n",
"# curly braces creates a set, so this is a set comprehension\n",
"\n",
"{book.get('author') for book in books if book.get('author')}"
]
},
{
"cell_type": "markdown",
"id": "c100e5db-5438-4715-921c-3f7152f83f4a",
"metadata": {},
"source": [
"# Part 2: Generators\n",
"\n",
"We use Generators in the course because AI models can stream back results.\n",
"\n",
"If you've not used Generators before, please start with this excellent intro from ChatGPT:\n",
"\n",
"https://chatgpt.com/share/672faa6e-7dd0-8012-aae5-44fc0d0ec218\n",
"\n",
"Try pasting some of its examples into a cell."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1efc26fa-9144-4352-9a17-dfec1d246aad",
"metadata": {},
"outputs": [],
"source": [
"# First define a generator; it looks like a function, but it has yield instead of return\n",
"\n",
"import time\n",
"\n",
"def come_up_with_fruit_names():\n",
" for fruit in fruits:\n",
" time.sleep(1) # thinking of a fruit\n",
" yield fruit"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eac338bb-285c-45c8-8a3e-dbfc41409ca3",
"metadata": {},
"outputs": [],
"source": [
"# Then use it\n",
"\n",
"for fruit in come_up_with_fruit_names():\n",
" print(fruit)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f6880578-a3de-4502-952a-4572b95eb9ff",
"metadata": {},
"outputs": [],
"source": [
"# Here's another one\n",
"\n",
"def authors_generator():\n",
" for book in books:\n",
" if book.get(\"author\"):\n",
" yield book.get(\"author\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9e316f02-f87f-441d-a01f-024ade949607",
"metadata": {},
"outputs": [],
"source": [
"# Use it\n",
"\n",
"for author in authors_generator():\n",
" print(author)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7535c9d0-410e-4e56-a86c-ae6c0e16053f",
"metadata": {},
"outputs": [],
"source": [
"# Here's the same thing written with list comprehension\n",
"\n",
"def authors_generator():\n",
" for author in [book.get(\"author\") for book in books if book.get(\"author\")]:\n",
" yield author"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dad34494-0f6c-4edb-b03f-b8d49ee186f2",
"metadata": {},
"outputs": [],
"source": [
"# Use it\n",
"\n",
"for author in authors_generator():\n",
" print(author)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "abeb7e61-d8aa-4af0-b05a-ae17323e678c",
"metadata": {},
"outputs": [],
"source": [
"# Here's a nice shortcut\n",
"# You can use \"yield from\" to yield each item of an iterable\n",
"\n",
"def authors_generator():\n",
" yield from [book.get(\"author\") for book in books if book.get(\"author\")]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "05b0cb43-aa83-4762-a797-d3beb0f22c44",
"metadata": {},
"outputs": [],
"source": [
"# Use it\n",
"\n",
"for author in authors_generator():\n",
" print(author)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdfea58e-d809-4dd4-b7b0-c26427f8be55",
"metadata": {},
"outputs": [],
"source": [
"# And finally - we can replace the list comprehension with a set comprehension\n",
"\n",
"def unique_authors_generator():\n",
" yield from {book.get(\"author\") for book in books if book.get(\"author\")}"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3e821d08-97be-4db9-9a5b-ce5dced3eff8",
"metadata": {},
"outputs": [],
"source": [
"# Use it\n",
"\n",
"for author in unique_authors_generator():\n",
" print(author)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "905ba603-15d8-4d01-9a79-60ec293d7ca1",
"metadata": {},
"outputs": [],
"source": [
"# And for some fun - press the stop button in the toolbar when bored!\n",
"# It's like we've made our own Large Language Model... although not particularly large..\n",
"# See if you understand why it prints a letter at a time, instead of a word at a time. If you're unsure, try removing the keyword \"from\" everywhere in the code.\n",
"\n",
"import random\n",
"import time\n",
"\n",
"pronouns = [\"I\", \"You\", \"We\", \"They\"]\n",
"verbs = [\"eat\", \"detest\", \"bathe in\", \"deny the existence of\", \"resent\", \"pontificate about\", \"juggle\", \"impersonate\", \"worship\", \"misplace\", \"conspire with\", \"philosophize about\", \"tap dance on\", \"dramatically renounce\", \"secretly collect\"]\n",
"adjectives = [\"turqoise\", \"smelly\", \"arrogant\", \"festering\", \"pleasing\", \"whimsical\", \"disheveled\", \"pretentious\", \"wobbly\", \"melodramatic\", \"pompous\", \"fluorescent\", \"bewildered\", \"suspicious\", \"overripe\"]\n",
"nouns = [\"turnips\", \"rodents\", \"eels\", \"walruses\", \"kumquats\", \"monocles\", \"spreadsheets\", \"bagpipes\", \"wombats\", \"accordions\", \"mustaches\", \"calculators\", \"jellyfish\", \"thermostats\"]\n",
"\n",
"def infinite_random_sentences():\n",
" while True:\n",
" yield from random.choice(pronouns)\n",
" yield \" \"\n",
" yield from random.choice(verbs)\n",
" yield \" \"\n",
" yield from random.choice(adjectives)\n",
" yield \" \"\n",
" yield from random.choice(nouns)\n",
" yield \". \"\n",
"\n",
"for letter in infinite_random_sentences():\n",
" print(letter, end=\"\", flush=True)\n",
" time.sleep(0.02)"
]
},
{
"cell_type": "markdown",
"id": "04832ea2-2447-4473-a449-104f80e24d85",
"metadata": {},
"source": [
"# Exercise\n",
"\n",
"Write some python classes for the books example.\n",
"\n",
"Write a Book class with a title and author. Include a method has_author()\n",
"\n",
"Write a BookShelf class with a list of books. Include a generator method unique_authors()"
]
},
{
"cell_type": "markdown",
"id": "35760406-fe6c-41f9-b0c0-3e8cf73aafd0",
"metadata": {},
"source": [
"# Finally\n",
"\n",
"Here are some intermediate level details of Classes from our AI friend, including use of type hints, inheritance and class methods. This includes a Book example.\n",
"\n",
"https://chatgpt.com/share/67348aca-65fc-8012-a4a9-fd1b8f04ba59"
]
}
],
"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
}

73
week1/day1.ipynb

@ -5,7 +5,9 @@
"id": "d15d8294-3328-4e07-ad16-8a03e9bbfdb9",
"metadata": {},
"source": [
"# Instant Gratification!\n",
"# Instant Gratification\n",
"\n",
"## Your first Frontier LLM Project!\n",
"\n",
"Let's build a useful LLM solution - in a matter of minutes.\n",
"\n",
@ -13,26 +15,29 @@
"\n",
"Our goal is to code a new kind of Web Browser. Give it a URL, and it will respond with a summary. The Reader's Digest of the internet!!\n",
"\n",
"Before starting, be sure to have followed the instructions in the \"README\" file, including creating your API key with OpenAI and adding it to the `.env` file.\n",
"Before starting, you should have completed the setup for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) and you hopefully launched this jupyter lab from within the project root directory, with your environment activated.\n",
"\n",
"## If you're new to Jupyter Lab\n",
"\n",
"Welcome to the wonderful world of Data Science experimentation! Once you've used Jupyter Lab, you'll wonder how you ever lived without it. Simply click in each \"cell\" with code in it, such as the cell immediately below this text, and hit Shift+Return to execute that cell. As you wish, you can add a cell with the + button in the toolbar, and print values of variables, or try out variations. \n",
"\n",
"If you need to start a 'notebook' again, go to Kernel menu >> Restart kernel. \n",
"\n",
"If you want to become a pro at Jupyter Lab, you can read their tutorial [here](https://jupyterlab.readthedocs.io/en/latest/). But this isn't required for our course; just a good technique for hitting Shift + Return and enjoying the result!\n",
"I've written a notebook called [Guide to Jupyter](Guide%20to%20Jupyter.ipynb) to help you get more familiar with Jupyter Labs, including adding Markdown comments, using `!` to run shell commands, and `tqdm` to show progress.\n",
"\n",
"If you prefer to work in IDEs like VSCode or Pycharm, they both work great with these lab notebooks too. \n",
"\n",
"## If you'd like to brush up your Python\n",
"\n",
"I've added a notebook called [Intermediate Python](Intermediate%20Python.ipynb) to get you up to speed. But you should give it a miss if you already have a good idea what this code does: \n",
"`yield from {book.get(\"author\") for book in books if book.get(\"author\")}`\n",
"\n",
"## I am here to help\n",
"\n",
"If you have any problems at all, please do reach out. \n",
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect.\n",
"I'm available through the platform, or at ed@edwarddonner.com, or at https://www.linkedin.com/in/eddonner/ if you'd like to connect (and I love connecting!)\n",
"\n",
"## More troubleshooting\n",
"\n",
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder for more ideas!\n",
"Please see the [troubleshooting](troubleshooting.ipynb) notebook in this folder to diagnose and fix common problems.\n",
"\n",
"## If this is old hat!\n",
"\n",
@ -59,7 +64,7 @@
"from IPython.display import Markdown, display\n",
"from openai import OpenAI\n",
"\n",
"# If you get an error like \"NameError\" running this cell, then please head over to the troubleshooting notebook!"
"# If you get an error running this cell, then please head over to the troubleshooting notebook!"
]
},
{
@ -75,19 +80,11 @@
"\n",
"Head over to the [troubleshooting](troubleshooting.ipynb) notebook in this folder for step by step code to identify the root cause and fix it!\n",
"\n",
"A summary of some points:\n",
"If you make a change, try restarting the \"Kernel\" (the python process sitting behind this notebook) by Kernel menu >> Restart Kernel and Clear Outputs of All Cells. Then try this notebook again, starting at the top.\n",
"\n",
"1. OpenAI takes a few minutes to register after you set up an account. If you receive an error about being over quota, try waiting a few minutes and try again.\n",
"2. You'll need to set up billing and add the minimum amount of credit at this page [here](https://platform.openai.com/settings/organization/billing/overview). OpenAI requires a minimum of $5 to get started in the U.S. right now - this might be different for your region. You'll only need to use a fraction for this course. In my view, this is well worth the investment for your education and future projects - but if you have any concerns, you can skip this and watch me using OpenAI instead. In week 3 we will start to use free open-source models!\n",
"3. Also, double check you have the right kind of API token with the right permissions. You should find it on [this webpage](https://platform.openai.com/api-keys) and it should show with Permissions of \"All\". If not, try creating another key by:\n",
"- Pressing \"Create new secret key\" on the top right\n",
"- Select **Owned by:** you, **Project:** Default project, **Permissions:** All\n",
"- Click Create secret key, and use that new key in the code and the `.env` file (it might take a few minutes to activate)\n",
"- Do a Kernel >> Restart kernel, and execute the cells in this Jupyter lab starting at the top\n",
"4. As a fallback, replace the line `openai = OpenAI()` with `openai = OpenAI(api_key=\"your-key-here\")` - while it's not recommended to hard code tokens in Jupyter lab, because then you can't share your lab with others, it's a workaround for now\n",
"5. Contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
"Or, contact me! Message me or email ed@edwarddonner.com and we will get this to work.\n",
"\n",
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point."
"Any concerns about API costs? See my notes in the README - costs should be minimal, and you can control it at every point. You can also use Ollama as a free alternative, which we discuss during Day 2."
]
},
{
@ -106,10 +103,10 @@
"\n",
"if not api_key:\n",
" print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
"elif api_key[:8]!=\"sk-proj-\":\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key\")\n",
"elif not api_key.startswith(\"sk-proj-\"):\n",
" print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
"elif api_key.strip() != api_key:\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them\")\n",
" print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
"else:\n",
" print(\"API key found and looks good so far!\")\n"
]
@ -124,8 +121,8 @@
"openai = OpenAI()\n",
"\n",
"# If this doesn't work, try Kernel menu >> Restart Kernel and Clear Outputs Of All Cells, then run the cells from the top of this notebook down.\n",
"# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, ot try the below line instead:\n",
"# openai = OpenAI(api_key=\"your-key-here\")"
"# If it STILL doesn't work (horrors!) then please see the troubleshooting notebook, or try the below line instead:\n",
"# openai = OpenAI(api_key=\"your-key-here-starting-sk-proj-\")"
]
},
{
@ -136,14 +133,12 @@
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"# If you're not familiar with Classes, check out the \"Intermediate Python\" notebook\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped\n",
" \"\"\"\n",
" url: str\n",
" title: str\n",
" text: str\n",
"\n",
" def __init__(self, url):\n",
" \"\"\"\n",
@ -215,13 +210,23 @@
"\n",
"def user_prompt_for(website):\n",
" user_prompt = f\"You are looking at a website titled {website.title}\"\n",
" user_prompt += \"The contents of this website is as follows; \\\n",
" user_prompt += \"\\nThe contents of this website is as follows; \\\n",
"please provide a short summary of this website in markdown. \\\n",
"If it includes news or announcements, then summarize these too.\\n\\n\"\n",
" user_prompt += website.text\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "26448ec4-5c00-4204-baec-7df91d11ff2e",
"metadata": {},
"outputs": [],
"source": [
"print(user_prompt_for(ed))"
]
},
{
"cell_type": "markdown",
"id": "ea211b5f-28e1-4a86-8e52-c0b7677cadcc",
@ -255,6 +260,16 @@
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36478464-39ee-485c-9f3f-6a4e458dbc9c",
"metadata": {},
"outputs": [],
"source": [
"messages_for(ed)"
]
},
{
"cell_type": "markdown",
"id": "16f49d46-bf55-4c3e-928f-68fc0bf715b0",
@ -370,7 +385,7 @@
"source": [
"## An extra exercise for those who enjoy web scraping\n",
"\n",
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them."
"You may notice that if you try `display_summary(\"https://openai.com\")` - it doesn't work! That's because OpenAI has a fancy website that uses Javascript. There are many ways around this that some of you might be familiar with. For example, Selenium is a hugely popular framework that runs a browser behind the scenes, renders the page, and allows you to query it. If you have experience with Selenium, Playwright or similar, then feel free to improve the Website class to use them. In the community-contributions folder, you'll find an example Selenium solution from a student (thank you!)"
]
},
{

20
week1/troubleshooting.ipynb

@ -23,11 +23,18 @@
"\n",
"Try running the next cell (click in the cell under this one and hit shift+return).\n",
"\n",
"If this doesn't work, then you're likely not running in an \"activated\" environment. Please check back in the [README](../README.md) for setting up the Anaconda (or virtualenv) environment and activating it, before running `jupyter lab`.\n",
"If this gives an error, then you're likely not running in an \"activated\" environment. Please check back in Part 5 of the SETUP guide for [PC](../SETUP-PC.md) or [Mac](../SETUP-mac.md) for setting up the Anaconda (or virtualenv) environment and activating it, before running `jupyter lab`.\n",
"\n",
"If you look in the Anaconda window (on PC) or the Terminal window (on Mac), you should see `(llms)` in your prompt where you launch `jupyter lab` - that's your clue that the llms environment is activated.\n",
"If you look in the Anaconda prompt (PC) or the Terminal (Mac), you should see `(llms)` in your prompt where you launch `jupyter lab` - that's your clue that the llms environment is activated.\n",
"\n",
"You might also need to restart the \"Kernel\" (the python process used to run this notebook). To do that, go to the Kernel menu and select \"Restart Kernel and Clear Outputs Of All Cells..\""
"If you are in an activated environment, the next thing to try is to restart everything:\n",
"1. Close down all Jupyter windows, like this\n",
"2. Exit all command prompts / Terminals / Anaconda\n",
"3. Repeat Part 5 from the SETUP instructions to begin a new activated environment and launch jupyter lab\n",
"4. Kernel menu >> Restart Kernel and Clear Outputs of All Cells\n",
"5. Come back to this notebook and try the cell below again.\n",
"\n",
"If **that** doesn't work, then please contact me! I'll respond quickly, and we'll figure it out."
]
},
{
@ -37,7 +44,8 @@
"metadata": {},
"outputs": [],
"source": [
"# This should run with no output - no import errors:\n",
"# This should run with no output - no import errors.\n",
"# Import errors might indicate that you started jupyter lab without your environment activated? See SETUP part 5.\n",
"\n",
"from openai import OpenAI"
]
@ -200,7 +208,7 @@
"Then there's something up with your API key!\n",
"\n",
"First check this webpage to make sure you have a positive credit balance.\n",
"OpenAI requires that you have a positive credit balance and it has minimums. My salespitch for OpenAI is that this is well worth it for your education: for less than the price of a music album, you will build so much valuable commercial experience. But it's not required for this course at all; you can watch me running OpenAI code, and then wait until we get to open source models in week 3.\n",
"OpenAI requires that you have a positive credit balance and it has minimums. My salespitch for OpenAI is that this is well worth it for your education: for less than the price of a music album, you will build so much valuable commercial experience. But it's not required for this course at all; you can watch me running OpenAI code, and then wait until we get to open source models in week 3. Also, I'll show you how to use Ollama to run open-source models locally.\n",
"\n",
"https://platform.openai.com/settings/organization/billing/overview\n",
"\n",
@ -208,7 +216,7 @@
"\n",
"https://platform.openai.com/api-keys\n",
"\n",
"And note that sometimes OpenAI seems to take a few minutes to give you access after you try.\n",
"Sometimes OpenAI may take a few minutes to give you access after you try.\n",
"\n",
"## If all else fails:\n",
"\n",

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