Maintaining many machine learning experiments requires much manual effort. This lightweight tool helps you currently run a LOT of experiments with simple commands and configurations. You can easily aggregate custom metrics for each experiment with a single line of code.
Install
$ pip install mlrunner
Usage
Download and edit params.yaml, then simply
$ run
When all experiments finish, start a jupyter notebook and analyze results using examine.Examiner.
See the github repo for example use cases.
1 maintainer
Metadata
1 maintainer
Release files for mlrunner 0.5.9
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlrunner-0.5.9.tar.gz | 14.0 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlrunner-0.5.9-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 26.6 kB
Release files / mlrunner-0.5.9.tar.gz
| Download URL | mlrunner-0.5.9.tar.gz |
|---|---|
| Size | 14.0 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.7
|
Release files / mlrunner-0.5.9-py3-none-any.whl
| Download URL | mlrunner-0.5.9-py3-none-any.whl |
|---|---|
| Size | 12.6 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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| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.0 CPython/3.9.7
|
