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PC Docs for using Mamba

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kaviraj 4 months ago
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      SETUP-PC.md
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      SETUP-PC.pdf

19
SETUP-PC.md

@ -61,6 +61,7 @@ If this Part 2 gives you any problems, there is an alternative Part 2B below tha
- 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. - 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` - 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. - 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.
- If the creation of environment using **Conda** is taking too long, a better alternative is **Mamba** which is faster in terms of installation time and dependency resolution. To use **Mamba** as package manager, please go to Part 2C.
- 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 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. You should see `(llms)` in your prompt, which indicates you've activated your new environment.
@ -103,6 +104,24 @@ From within the `llm_engineering` folder, type: `jupyter lab`
If there are any problems, contact me! If there are any problems, contact me!
### Part 2C - Mamba - Alternative to Conda for faster creation of environment.yml
1. **Install Mamba:**
Open the Anaconda Prompt installed in part 2, run the following to install mamba: `conda install -n base -c conda-forge mamba`
2. **Navigate to Project**
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.
3. **Create environment.yml**
Use the following command to create the enviroment: `mamba env create -f environment.yml`
4. **Activate Environment**
You can continue using Conda for the subsequent steps, as Mamba is fully compatible with Conda. Therefore, to activate your environmen, use the following command: `conda activate llms`
### Part 3 - OpenAI key (OPTIONAL but recommended) ### 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). 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).

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