libthx 0.1.2

Architecture experimentation and training infrastructure.

theseus

Have you ever wanted to train a language model from scratch but hate writing boilerplate? Previously the solution to this is to work at a frontier lab with Research Engineers:tm:.

Now the solution is to make Jack:tm: (and also a cast of frontier coding models) do your research engineering. It will probably break a lot but what the heck at least I tried.

Download

It depends on who gave you computors to make warm:

• cuda13: uv sync --group all --group cuda13
• cuda12: uv sync --group all --group cuda12
• you love Google: uv sync --group all --group tpu
• you bought your own computors: uv sync --group all --group cpu

Quick Start

Use the CLI.

# List available jobs
theseus jobs

# Generate a config for data tokenization
theseus configure data/tokenize_variable_dataset tokenize.yaml \
    data.name=fineweb data.max_samples=1000000

# Run the tokenization locally
theseus run tokenize-fineweb tokenize.yaml ./output

# Generate a config for pretraining
theseus configure gpt/train/pretrain train.yaml \
    --chip h100 -n 8

# Run training locally
theseus run my-gpt-run train.yaml ./output

Quick Start, but You Have Infra

Set up ~/.theseus.yaml (see examples/dispatch.yaml), then submit jobs to remote clusters:

theseus submit my-run train.yaml --chip h100 -n 8

Quickish Start

For programmatic configuration and rapid prototyping:

from theseus.quick import quick
from theseus.registry import JOBS

with quick("gpt/train/pretrain", "/path/to/output", "my-run") as j:
    j.config.training.per_device_batch_size = 16
    j.config.logging.checkpoint_interval = 4096
    j()  # run locally

# Or save config for later submission:
with quick("gpt/train/pretrain", "/path/to/output", "my-run") as j:
    j.config.training.per_device_batch_size = 16
    j.save("config.yaml", chip="h100", n_chips=8)

Not Quick Start at All

When you (or Claude) manage to find some time to chill you can actually extend this package. The package is organized based around theseus.job.BasicJobs. They can be extended with checkpointing and recovery tools.

The main entrypoint to start hacking:

1. take a look at how to compose a model together in theseus.model.models.base
2. bodge together anything you want to change and make a new model in the models folder (be sure to add it to theseus.model.models.__init__)
3. write an experiment, which is a RestoreableJob. A very basic one can just inherit the normal trainer, and then that's about it. see theseus.experiments.gpt to get started (be sure to add it to theseus.experiments.__init__)

# theseus/experiments/my_model.py
from theseus.training.base import BaseTrainer, BaseTrainerConfig
from theseus.model.models import MyModel

class PretrainMyModel(BaseTrainer[BaseTrainerConfig, MyModel]):
    MODEL = MyModel
    CONFIG = BaseTrainerConfig

    @classmethod
    def schedule(cls):
        return "wsd"

JuiceFS Integration

When you are on many remote computors but bursty you may go "aw schucks I need to copy like 50TB of pretraining data around that's so lame!"

Don't worry, we gotchu. If you use the submit API, we have a way to ship your root directory around by using a thing called JuiceFS, which is a distributed filesystem.

In your ~/.theseus.yaml, add the mount field to your cluster config:

clusters:
  hpc:
    root: /mnt/juicefs/theseus
    work: /scratch/theseus
    mount: redis://:[e-mail]:6379/0
    cache_size: 100G
    cache_dir: /scratch/juicefs-cache

(an incomplete list of) Features

• CLI & Programmatic API: Configure and run jobs via theseus CLI or the quick() Python API
• Remote Dispatch: Submit jobs to SLURM clusters or plain SSH hosts via ~/.theseus.yaml
• Checkpointing & Recovery: Jobs are RestoreableJobs with built-in checkpoint/restore support
• Data Pipelines: Tokenize datasets (blockwise or streaming) with data/tokenize_* jobs
• JuiceFS Integration: Distributed filesystem support for sharing data across clusters
• Multi-backend: CUDA 11/12/13, TPU, and CPU via uv sync --group
• Extensible: Add models in theseus.model.models, experiments in theseus.experiments, and datasets in theseus.data.datasets
• Dataclass Configs: Type-safe configuration via dataclasses with OmegaConf, easy configuration with theseus.config.field dataclass extension, and Hydra-style cheeky cli overrides (model.hidden_size=1024)

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