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https://github.com/Palashio/libra
Ergonomic machine learning for everyone.
https://github.com/Palashio/libra
auto-ml machine-learning neural-networks
Last synced: 8 days ago
JSON representation
Ergonomic machine learning for everyone.
- Host: GitHub
- URL: https://github.com/Palashio/libra
- Owner: Palashio
- License: mit
- Created: 2020-04-10T23:25:55.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2023-06-12T21:28:58.000Z (over 1 year ago)
- Last Synced: 2024-07-27T13:07:38.740Z (4 months ago)
- Topics: auto-ml, machine-learning, neural-networks
- Language: Python
- Homepage: http://libradocs.org/
- Size: 182 MB
- Stars: 1,912
- Watchers: 89
- Forks: 108
- Open Issues: 48
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE.txt
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# Libra
**An ergonomic machine learning library for non-technical users. Save time. Blaze through ML.**
[](https://travis-ci.org/Palashio/libra)
[](https://pepy.tech/project/libra)
[](https://join.slack.com/t/the-libra-team/shared_invite/zt-ek6bpd47-hdIxXlRAenKfy5JNWe8bgw)
[](https://pypi.org/project/libra/)
[](https://pypi.org/project/libra/)
[](https://libradocs.github.io//)
[]()
Check out our newer machine learning tool [Nylon](https://github.com/Palashio/nylon)!
## Installation
Install latest release version:
```
pip install -U libra
```
Install directory from github:
```
git clone https://github.com/Palashio/libra.git
cd libra
pip install .
```
Alternatively you can build and use the docker image locally with:
```
docker build . -f docker/libra-normal/Dockerfile -t libra
docker run -v /path/to/my/data:/data -it --rm libra
```
Or if you have [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) installed.
```
docker build . -f docker/libra-gpu/Dockerfile -t libra-gpu
docker run -v /path/to/my/data:/data --gpus all -it --rm libra-gpu
```
## Usage: the basics
The core functionality of libra works through the `client` object. A new client object should be created for every dataset that you want to produce results for. All information about the models that're built, the plots that are generated, and the metrics are created will be stored in the object.
You can then call different queries on that client object, and the dataset you passed to it will be used.
```python
from libra import client
newClient = client('path/to/dataset')
newClient.neural_network_query('please model the median number of households')
```
Now, calling
```python
newClient.info()
```
will return a dictionary of all the information that was generated:
```python
dict_keys(['id', 'model', 'num_classes', 'plots', 'target', 'preprocessor',
'interpreter', 'test_data', 'losses', 'accuracy'])
```
Other queries can also be called on the same object, and will be appended to the `models` dictionary.
```python
newClient.svm_query('predict the proximity to the ocean')
newClient.model().keys()
dict_keys(['regression_ANN', svm'])
```
## Tutorials
- Full documentation can be found at [libradocs.org](https://libradocs.org/).
- A list of resources can be found on our [awesome-libra](https://github.com/Palashio/awesome-libra) repository.
---
## Asking for help
Welcome to the Libra community!
If you have any questions, feel free to:
1. [read the docs](https://libradocs.org/).
2. [Search through the issues](https://github.com/Palashio/libra/issues?q=is%3Aissue+is%3Aclosed).
3. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag libra.
4. [Join our slack](https://join.slack.com/t/the-libra-team/shared_invite/zt-ek6bpd47-hdIxXlRAenKfy5JNWe8bgw).
## Demos
## Contact
Shoot me an email at [[email protected]](mailto:[email protected]) if you'd like to get in touch!
Follow me on [twitter](https://twitter.com/_pshah) for updates and my insights about modern AI!