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name tinyexp-experiments
description Write, configure and launch experiments with the TinyExp library (a dataclass Exp per file, CLI overrides, CPU/DDP/FSDP training, Ray workers, torchrun/accelerate). Use when the task involves tinyexp or a class derived from TinyExp.

TinyExp experiments

An experiment is one @dataclass class Exp(TinyExp, <mixins>) whose run() holds the logic; every config field is a CLI override (key=value). The installed package documents itself, so read docstrings instead of source: python -c "import tinyexp; print(tinyexp.__doc__)", or for a class python -c "from tinyexp.exp_mixins import RayCfgMixin as m; print(m.__doc__)".

Need Print the __doc__ of
New experiment: minimal example, fields, launcher pairing tinyexp
How overrides are merged and reported tinyexp.TinyExp.set_cfg
Training loop API; picking an accelerator tinyexp.tiny_engine.accelerator.base_accelerator; tinyexp.tiny_engine.accelerator
Code running on Ray workers (minimal example); worker count and resources tinyexp.exp_mixins.RayCfgMixin; tinyexp.exp_mixins.RayCfgMixin.RayCfg
Checkpoint save / load / resume / eval tinyexp.exp_mixins.CheckpointCfgMixin.CheckpointCfg
Logger, W&B, Redis cache mixins tinyexp.exp_mixins.basic_mixins (table, then the nested XXXCfg)
Infinite sampler, fake dataloader, EMA; errors tinyexp.dataset, tinyexp.utils; tinyexp.exceptions
A bundled example: modes, commands, data, hardware tinyexp.examples.<name> (run header)

Bundled examples: mnist_exp (template for a training experiment: CPU or DDP, checkpoints, resume), pi_exp (non-deep-learning distributed job, fastest smoke test), resnet_exp (ImageNet with DDP, FSDP or HF Accelerate and a Redis cache), vit_tp_exp (tensor parallelism, TP-vs-DDP benchmark).

Easy to miss:

  • Check a config change before running: mode=help prints the resolved config and the overridden values, then exits. It checks key names and types, not values.
  • torchrun and accelerate launch need launcher=mp; plain python takes mp (one process) or ray (TinyExp starts Ray workers); tinyexp-run-with-ray-cluster (multi-node, see --help) needs launcher=ray and runs ray stop --force on each node, so do not use it where an existing Ray runtime must survive.
  • With launcher=ray, start with the environment's python -m package.module, not uv run (or set RAY_ENABLE_UV_RUN_RUNTIME_ENV=0).
  • Install with pip install "tinyexp[pytorch]"; for a CUDA/ROCm-specific PyTorch, install tinyexp without the extra, then torch, torchvision and accelerate from that backend.
python -m tinyexp.examples.pi_exp mode=help pi_cfg.total_samples=100000
python -m tinyexp.examples.pi_exp launcher=mp pi_cfg.total_samples=100000
torchrun --nproc-per-node=2 -m tinyexp.examples.mnist_exp launcher=mp accelerator_cfg.accelerator=ddp
tinyexp-run-with-ray-cluster --node-count=2 --node-rank=0 --head-addr=10.0.0.1 --ray-port=6380 -- \
  python -m tinyexp.examples.pi_exp launcher=ray