# Install Python dependencies for Llama.cpp

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/)
- [AFM-4.5B deployment on Google Cloud Axion with Llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/00_overview/)
- [Provision a Google Cloud Axion Arm64 environment](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/01_launching_an_axion_instance/)
- [Configure your Google Cloud Axion Arm64 environment](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/02_setting_up_the_instance/)
- [Build Llama.cpp on Google Cloud Axion Arm64](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/03_building_llama_cpp/)
- [Install Python dependencies for Llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/04_install_python_dependencies_for_llama_cpp/)
- [Download and optimize the AFM-4.5B model for Llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/05_downloading_and_optimizing_afm45b/)
- [Run inference with AFM-4.5B using Llama.cpp](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/06_running_inference/)
- [Benchmark and evaluate AFM-4.5B quantized models on Axion](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/07_evaluating_the_quantized_models/)
- [Review your AFM-4.5B deployment on Axion](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/08_conclusion/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-gcp/_next-steps/)

## Set up a Python environment for Llama.cpp
In this step, you’ll create a Python virtual environment and install the dependencies required to run AFM-4.5B with Llama.cpp. This ensures a clean, isolated environment for model optimization on Google Cloud Axion.

## Create a virtual environment
```
virtualenv env-llama-cpp
```
This command creates a new Python virtual environment named `env-llama-cpp`, which has the following benefits:
- Provides an isolated Python environment to prevent package conflicts between projects
- Creates a local directory containing its own Python interpreter and installation space
- Ensures Llama.cpp dependencies don’t interfere with your global Python setup
- Supports reproducible and portable development environments

## Activate your virtual environment
```
source env-llama-cpp/bin/activate
```
This command does the following:
- Runs the activation script, which modifies your shell environment
- Updates your shell prompt to show `env-llama-cpp`, indicating the environment is active
- Updates `PATH` to use so the environment’s Python interpreter
- Ensures all `pip` commands install packages into the isolated environment

## Upgrade pip
Before installing dependencies, upgrade pip:
```
pip install --upgrade pip
```
- Ensures you have the latest version of pip
- Helps avoid compatibility issues with modern packages
- Applies the `--upgrade` flag to fetch and install the newest release
- Brings in security patches and better dependency resolution logic

## Install project dependencies
Use the following command to install all required Python packages:
```
pip install -r requirements.txt
```
This command:
- Uses the `-r` flag to read the list of dependencies from `requirements.txt`
- Installs the exact package versions required for the project
- Ensures consistency across development environments and contributors
- Includes packages for model loading, inference, and Python bindings for `llama.cpp`

This step sets up everything you need to run AFM-4.5B in your Python environment.

## Verify installed Python packages
After installation, your environment includes:
- **NumPy**: numerical computations and array operations
- **Requests**: HTTP operations and API calls
- **Other packages**: dependencies required by Llama.cpp’s Python bindings and utilities

You can now run Python scripts that integrate with the compiled Llama.cpp binaries.

> **Tip**: Before running any Python commands, make sure your virtual environment is activated.