syntrix 0.2.1

Syntrix-Base: CPU-first ML framework with tiny models and deterministic training

Syntrix‑Base — A Low‑Resource (CPU‑First) Machine Learning Framework

Train and run modern small models fast on everyday CPUs — simple, transparent, and reproducible.

Highlights

• CPU‑first ergonomics: pinned threads, deterministic seeds, dtype control
• Tiny but modern models: GPT‑mini, SSM‑mini, RNN‑mini
• Reproducible logging: JSONL logs with tokens/sec and environment
• Optional torch.compile with CLI toggle and auto validation

Requirements

• Python >= 3.9
• Linux/macOS (Windows may work via WSL)
• PyTorch (installed automatically via pip install -e .)

Quickstart

1) Clone and install

git clone https://github.com/paredezadrian/syntrix-base.git
cd syntrix-base
python3 -m venv venv && source venv/bin/activate
pip install --upgrade pip
pip install -e .

Install from PyPI (after publish):

pip install syntrix

2) Get sample data (TinyShakespeare)

mkdir -p data
wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt -O data/tinyshakespeare.txt

Or download a mini text8 sample via CLI when training: add --download.text8_mini.

3) Train (YAML + CLI overrides)

syntrix.train \
  --config configs/gpt-mini.yaml \
  --data.file data/tinyshakespeare.txt \
  --train_steps 300 --eval_every 100 --save_every 150 \
  --threads 4 \
  --out_dir runs/gpt-mini_base

Enable torch.compile and auto‑validate throughput (accept only if >= 5% faster):

syntrix.train \
  --config configs/gpt-mini.yaml \
  --data.file data/tinyshakespeare.txt \
  --threads 4 \
  --train_steps 300 --eval_every 100 --save_every 150 \
  --compile --compile.validate --compile.auto --compile.min_improvement 1.05 \
  --out_dir runs/gpt-mini_compile_auto

Outputs:

• Checkpoints: runs/<name>/ckpt.pt
• Logs (JSONL): runs/<name>/log.jsonl with step, val_bpc, tokens_per_s, lr, and an initial env record (Python, PyTorch, threads, dtype, compiled flag).

4) Sample from a checkpoint

syntrix.sample \
  --ckpt runs/gpt-mini_base/ckpt.pt \
  --data.file data/tinyshakespeare.txt \
  --max_new_tokens 200 --temp 0.9

Configuration

• Base configs live in configs/ (e.g., configs/gpt-mini.yaml).
• You can override most settings via CLI flags. Some use dot notation (e.g., --data.file, --download.text8_mini).
• Precision: switch default dtype with --dtype float32|float64. Numeric tests use dtype‑aware tolerances.

CLI Reference

List options:

python -m syntrix.cli_train --help
python -m syntrix.cli_sample --help

Notable flags:

• --threads <int>: sets torch.set_num_threads and pins MKL/OMP threads
• --compile: enable torch.compile if available
• --compile.validate --compile.auto --compile.min_improvement 1.05: benchmark forward throughput and auto‑enable compile only if faster
• --tokenizer <char|bpe> and --bpe_vocab_size <int>
• --use_mmap: use memory‑mapped data loader for large files

Reproducibility & Determinism

• Seeds and threads are initialized consistently via syntrix.utils.seed
• Logs record environment details (threads, Python, PyTorch, dtype)
• Tests cover determinism and tolerance‑aware numeric checks

Benchmarks

For reproducible commands and example results tables, see docs/benchmarks.md.

Contributing

We welcome contributions of all kinds: bug fixes, features, docs, and benchmarks.

• Please read CONTRIBUTING.md for our contribution process, standards, and PR guidelines
• All participants are expected to follow our CODE_OF_CONDUCT.md

Governance & Support

• Issues: Use GitHub Issues for bug reports and feature requests. Include OS, Python, and PyTorch versions, steps to reproduce, and expected vs. actual behavior.
• CI: Pull requests must pass GitHub Actions (pytest on Python 3.10/3.11/3.12).

License

MIT — see LICENSE.

Packaging & Publishing

Versioning Policy

We follow Semantic Versioning (SemVer): MAJOR.MINOR.PATCH.

• Increase MAJOR for incompatible API changes.
• Increase MINOR for added functionality in a backward-compatible manner.
• Increase PATCH for backward-compatible bug fixes.

Release Checklist

1. Ensure all tests pass locally and in CI.
2. Update CHANGELOG.md with a new section for the release.
3. Bump the version in pyproject.toml.
4. Commit changes and tag the release:
   • git commit -m "chore(release): bump version to X.Y.Z"
   • git tag vX.Y.Z && git push origin main --tags
5. Build and upload to PyPI:
   • pip install build twine
   • python -m build
   • twine upload dist/*
6. Create a GitHub Release referencing the tag and the corresponding changelog notes.
7. Verify README badges (CI, CodeQL, PyPI) render correctly.

Installing from PyPI (post‑publish)

pip install syntrix
# verify CLI entry points
syntrix.train --help
syntrix.sample --help