mllogs
Local experiment logging for machine learning.
Installation
pip install mllogs
Usage
from mllogs import MLLogsClient
client = MLLogsClient()
client.start_run(
name="baseline",
run_type="training",
)
client.log_param("learning_rate", 0.01)
client.log_metric("accuracy", 0.92)
client.set_tag("model", "logistic_regression")
run = client.end_run()
client.list_runs()
Retrieve or delete a saved run:
client.get_run(id)
client.delete_run(id)
Features
- Start and end experiment runs
- Log parameters, metrics, and tags
- Automatically persist completed runs to local storage
- Load, list, and delete saved runs
Development
Install from source with development dependencies:
pip install -e ".[dev]"
Run the test suite:
pytest
License
MIT
1 maintainer
Metadata
1 maintainer
Release files for mllogs 0.2.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mllogs-0.2.1.tar.gz | 5.7 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mllogs-0.2.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 10.9 kB
Release files / mllogs-0.2.1.tar.gz
| Download URL | mllogs-0.2.1.tar.gz |
|---|---|
| Size | 5.7 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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|
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Transparency logRelease files / mllogs-0.2.1-py3-none-any.whl
| Download URL | mllogs-0.2.1-py3-none-any.whl |
|---|---|
| Size | 5.2 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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|
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|
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
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