mirror of
https://github.com/wassname/minicache.git
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wassname (Michael J Clark)
37 lines
1.3 KiB
Markdown
37 lines
1.3 KiB
Markdown
## minicache — tiny disk cache for ML / research code.
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This wraps function calls and stores returns on disk (gzip + cloudpickle). Solves
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the four pain points that stdlib `functools.lru_cache + pickle` and existing
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function-cache libraries (anycache, cachier) hit on ML code:
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- *Loaded models can't be hashed*. So we use a arg blacklist (`exclude=["model", "tok"]`).
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Here, excluded args pass through to the function but never enter the cache key.
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- *Tensors / pandas / closures can't be picked** → we use cloudpickle which extends to many more objects.
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- *Pickle files grow large* → gzip on disk save 20-50%
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## Quick use
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Install
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```sh
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uv add git+https://github.com/wassname/minicache.git
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```
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```py
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from minicache import cached, cache_call
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@cached(exclude=["model", "tok"]) # can't hash model or tokenizer, but model_id will substitute
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def run_eval(model, tok, *, model_id, name, batch_size):
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return tinymfv_evaluate(model, tok, name=name, batch_size=batch_size)
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report = run_eval(model, tok, model_id="qwen-27b", name="classic", batch_size=16)
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# 30 minutes
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report = run_eval(model, tok, model_id="qwen-27b", name="classic", batch_size=16)
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# 0 minutes, gives saved results
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```
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See also
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- anycache https://github.com/c0fec0de/anycache
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- cachier https://github.com/python-cachier/cachier#working-with-unhashable-arguments
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