Metadata-Version: 2.1
Name: dlmml
Version: 0.0.3
Summary: Converts IR to Code. Heart of Auto-DL
Home-page: https://github.com/Auto-DL/DLMML
Author: Auto-DL
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

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# DLMML

The intermediary representation of the generated model.

## How to run

#### Install form PyPI (recommended):

```sh
pip install dlmml 
```
*Yep, that's it :)*

#### Steps to build package locally: 

1. ```sh
    # clone the repo
    git clone https://github.com/Auto-DL/DLMML.git
    ```

2. *Activate your environment* (not compulsory but highly recommended)

3. `python -m build`

4. `pip install dist\dlmml-*.tar.gz`

5. [Extra Step] Check using pytest by either running `pytest` or `python setup.py pytest`

#### What is the notebooks folder about:

- Testing and Prototyping is what we do there 
- Run the `jupyter-notebook` command
- Using the GUI, navigate and run the notebooks
- [This](https://github.com/Auto-DL/DLMML/blob/master/notebooks/Code%20Generator%20PlayBook.ipynb) can be a good starting point


## Where to go next?

#### To know more about the project and initiative, please visit our [website](https://auto-dl.github.io/)

#### Curious to know about our front-end or backend development? [Here](https://github.com/Auto-DL/Generator), Have a look :)

## Note
- To know more about the technicalities of the project, read the our [developer guidelines](https://github.com/Auto-DL/DLMML/blob/master/docs/devguide.md).
- For more detailed instructions to run the DLMML part. Read our [User guidelines](https://github.com/Auto-DL/DLMML/blob/master/docs/userguide.md) 

## Contributing
Please take a look at our [contributing](https://github.com/Auto-DL/DLMML/blob/master/docs/contributing.md) guidelines if you're interested in helping!

#### Features to add
- Add model generation code for pytorch

- Check if generated code is correct (current thought is to call model.compile and return errors if any)

- Add different model evaluation parameters

- Test for backward compatibility of libraries versions

- Benchmarking parameters

- Visualization and data preprocessing steps to be added


