# Build TurboPilot

TurboPilot is a C++ program that uses the [GGML](https://github.com/ggerganov/ggml) project to parse and run language models.

### Dependencies

To build turbopilot you will need CMake, Libboost, a C++ toolchain and GNU Make.

#### Ubuntu

On Ubuntu you can install these things with:

```bash
sudo apt-get update
sudo apt-get install libboost-dev cmake build-essential
```

#### MacOS

If you use [brew](https://brew.sh/) you can simply add these dependencies by running:

```bash
brew install cmake boost
```

### Checkout Submodules

Make sure the ggml subproject is checked out with `git submodule init` and `git submodule update`

### Prepare and Build

Configure cmake to build the project with the following:

```bash
mkdir ggml/build
cd ggml/build
cmake ..
```

If you are running on linux you can optionally compile a static build with `cmake -D CMAKE_EXE_LINKER_FLAGS="-static" ..` which should make your binary portable across different flavours of the OS.

From here you can now build the components that make up turbopilot:

```bash
make codegen codegen-quantize codegen-serve
```

Where:

- *codegen* is a command line tool for testing out prompts in a lightweight way (a lot like llama.cpp)
- *codegen-serve* is the actual REST server that can be used to connect to VSCode
- *codegen-quantize* is the tool for quantizing models exported by the conversion script. For more details see [Converting and Quantizing The Models](https://github.com/ravenscroftj/turbopilot/wiki/Converting-and-Quantizing-The-Models).

### Building with OpenBLAS


[BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) libraries accelerate mathematical operations. You can use the OpenBLAS implementation with Turbopilot to make generation faster - particularly for longer prompts.

When you run cmake, you can additionally set `-D GGML_OPENBLAS=On` to enable BLAS support.

E.g. `cmake .. -D GGML_OPENBLAS=On`

### Building with CuBLAS

CuBLAS is the [BLAS](https://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms) library provided by nvidia that runs linear algebra code on your GPU. This can speed up the application significantly, especially when working with long prompts.

#### Install Cuda SDK for your Operating System

You will need `nvcc` and the `libcublas-dev` dependencies as a bare minimum. Follow the guide from nvidia [here](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/) for more detailed installation instructions.

#### Configuring Cmake with CuBLAS

You will need to set `-DGGML_CUBLAS=ON` and also pass the path to your `nvcc` executable with `-DCMAKE_CUDA_COMPILER=/path/to/nvcc`.

Full example: `cmake -DGGML_CUBLAS=ON -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc ..`