This release is a pre-release and may not be stable for production use.
mlpipeline
This is a simple framework to organize you machine learning workflow. It automates most of the basic functionalities such as logging, a framework for testing models and gluing together different steps at different stages. This project came about as a result of me abstracting the boilerplate code and automating different parts of the process.
The aim of this simple framework is to consolidate the different sub-problems (such as loading data, model configurations, training process, evaluation process, exporting trained models, etc.) when working/researching with machine learning models. This allows the user to define how the different sub-problems are to be solved using their choice of tools and mlpipeline would handle piecing them together.
Core operations
This framework chains the different operations (sub-problems) depending on the mode it is executed in. mlpipeline currently has 3 modes:
- TEST mode: When in TEST mode, it doesn't perform any logging or tracking. It creates a temporary empty directory for the experiment to store the artifacts of an experiment in. When developing and testing the different operations, this mode can be used.
- RUN mode: In this mode, logging and tracking is performed. In addition, for each experiment run (referred to as a experiment version in mlpipeline) a directory is created for artifacts to be stored.
- EXPORT mode: In this mode, the exporting related operations will be executed instead of the training/evaluation related operations.
In addition to providing different modes, the pipeline also supports logging and recording various details. Currently mlpipeline records all logs, metrics and artifacts using a basic log files as well using mlflow <https://github.com/databricks/mlflow>_.
The following information is recorded:
- The scripts that were executed/imported in relation to an experiment.
- The any output results
- The metrics and parameters
Documentation
The documentation is hosted at ReadTheDocs <https://mlpipeline.readthedocs.io/>_.
Installing
Can be installed directly using the Python Package Index using pip::
pip install mlpipeline
Usage
work in progress
1 maintainer
Release files for mlpipeline 2.0a4.post18
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlpipeline-2.0a4.post18.tar.gz | 24.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlpipeline-2.0a4.post18-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 54.1 kB
Release files / mlpipeline-2.0a4.post18.tar.gz
| Download URL | mlpipeline-2.0a4.post18.tar.gz |
|---|---|
| Size | 24.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
0c8cc067705652d67dcea57b7497cd4907a39015a5a9ca1f754277cff0eba6bb
|
|
BLAKE2b-256 checksum How to use checksums |
14f37fd833b1273da94804ffc2c604f653d55aeea34311bbc51ed35edb837c40
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.7.4
|
Release files / mlpipeline-2.0a4.post18-py3-none-any.whl
| Download URL | mlpipeline-2.0a4.post18-py3-none-any.whl |
|---|---|
| Size | 29.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
2a15a5066594577e850d81fc56406c2bb0d60e6ef99e4343d8f748f7616cdd72
|
|
BLAKE2b-256 checksum How to use checksums |
2cc292a7d86b35ee7a850cd1c81aefaf673559624f2fbfd2df71f03e0a065317
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/41.2.0 requests-toolbelt/0.9.1 tqdm/4.30.0 CPython/3.7.4
|
