ml
A collection of machine learning utilities.
Installation
pip install ml
A collection of utilities for ML/NLP research.
Utilities
ml.snapshot
Isolate experiment code on shared / networked filesystems (e.g. Slurm) by copying the current directory into a per-experiment snapshot and running a command inside the copy. Editing your source afterwards won't affect queued or in-flight jobs.
As a library (standard library only):
from ml.snapshot import snapshot
snapshot_dir = snapshot(
"python train.py",
exclude=["*.log", "wandb"],
experiment_id="42",
)
As a CLI (requires the cli extra: pip install 'ml[cli]'):
ml-snapshot --experiment-id 42 --exclude '*.log' 'python train.py'
The base directory defaults to $SNAPSHOT_DIR (or snapshotted_experiments),
and exclude patterns fall back to the comma-separated $SNAPSHOT_EXCLUDE.
Development
This project uses uv for dependency management.
# Install dependencies (including dev tools)
uv sync
# Run the test suite
uv run pytest
# Lint and format
uv run ruff check .
uv run ruff format .
License
1 maintainer
Metadata
1 maintainer
Release files for ml 0.1.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ml-0.1.0.tar.gz | 9.3 kB | Without provenance | Details for ml-0.1.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ml-0.1.0-py3-none-any.whl | Python 3 | none | any | Details for ml-0.1.0-py3-none-any.whl |
Total release size: 18.1 kB
Release files / ml-0.1.0.tar.gz
| Download URL | ml-0.1.0.tar.gz |
|---|---|
| Size | 9.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
uv/0.7.15
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Release files / ml-0.1.0-py3-none-any.whl
| Download URL | ml-0.1.0-py3-none-any.whl |
|---|---|
| Size | 8.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.7.15
|
Security for ml 0.1.0
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