tt-train: CPP ML training framework

Overview

This repository contains a high-performance training framework developed in C++ designed to efficiently leverage the computational capabilities of Tenstorrent hardware. The framework is optimized to accelerate model training tasks, providing a seamless interface for training deep learning models on Tenstorrent's advanced hardware architecture.

Install

1. Initialize and update submodules

git submodule update --init --recursive

2. Install CMake 3.30
3. Run setup script to configure env variables, direnv, clang-tidy and clang-format.

source ./init_repo.sh

4. Navigate to tt-metal folder and follow repository instructions to build it

Building the project:

You have two options for building the project:

1. VSCode

• Install the CMake and direnv extensions for VSCode.
• Use UI to build all targets.

2. Terminal

Debug

cmake -DCMAKE_BUILD_TYPE=Debug -B build -GNinja
cmake --build build --config Debug --clean-first

Release

cmake -DCMAKE_BUILD_TYPE=Release -B build -GNinja
cmake --build build --config Release --clean-first

Run

MNIST

Training

# Navigate to the root directory of the repository
./build/sources/examples/mnist_mlp/mnist_mlp --model_path mnist_mlp.msgpack --num_epochs 10

Evaluation

# Navigate to the root directory of the repository
./build/sources/examples/mnist_mlp/mnist_mlp --model_path mnist_mlp.msgpack -e 1

NanoGPT Shakespeare

Training

# Navigate to the root directory of the repository
TT_METAL_LOGGER_LEVEL=FATAL ./build/sources/examples/nano_gpt/nano_gpt --model_path nano_gpt.msgpack --data_path sources/examples/nano_gpt/data/shakespeare.txt

Training loss example from wandb project:

Evaluation

# Navigate to the root directory of the repository
TT_METAL_LOGGER_LEVEL=FATAL ./build/sources/examples/nano_gpt/nano_gpt --model_path nano_gpt.msgpack -e 1 --data_path sources/examples/nano_gpt/data/shakespeare.txt

Contributing

• Create a new branch.
• Make your changes and commit them.
• Add new tests and run existing ones
• Open a pull request (PR).
• Ensure the PR is approved by at least one code owner before merging.