# Nimrod
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
[](https://github.com/pre-commit/pre-commit)
[](https://pytorch.org/get-started/locally/)
[](https://hydra.cc/)
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## Description
This is a repo with minimal tooling, modules, models and recipes to get
easily get started with deep learning training and experimentation with
an emphasis on speech, audio and language modeling.
## Install
you need python \<3.12
``` sh
pip install slg-nimrod
```
## Usage
Check recipes in `recipes/` folder. For instance:
``` bash
git clone https://github.com/slegroux/nimrod.git
cd nimrod/recipes/images/mnist
python train.py datamodule.num_workers=10 trainer.max_epochs=20 trainer.accelerator='gpu'
head conf/train.yaml
```
All the parameters of the experiment are editable and read from a .yaml
file which details:
- data and logging directory paths
- data module with data source path and batching parameters
- model architecture
- trainer with hardware acceleration and number of epochs
- callbacks for early stopping and automatic logging to Wandb
## Docker
You might want to use docker containers for reproductible development
environment or run your project in the cloud
``` bash
make container
docker pull slegroux/nimrod
docker run -it --rm -p 8888:8888 slegroux/nimrod /bin/bash
```
You can also use docker-compose to define services and volumes
``` bash
cd .devcontainer
docker-compose up
docker-compose down
```
## Develop
``` bash
pip install -e .
```
## Authors
2023 Sylvain Le Groux <sylvain.legroux@gmail.com>
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"description": "# Nimrod\n\n<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->\n\n[](https://github.com/pre-commit/pre-commit)\n[](https://pytorch.org/get-started/locally/)\n[](https://hydra.cc/)\n[](https://github.com/pre-commit/pre-commit)\n\n## Description\n\nThis is a repo with minimal tooling, modules, models and recipes to get\neasily get started with deep learning training and experimentation with\nan emphasis on speech, audio and language modeling.\n\n## Install\n\nyou need python \\<3.12\n\n``` sh\npip install slg-nimrod\n```\n\n## Usage\n\nCheck recipes in `recipes/` folder. For instance:\n\n``` bash\ngit clone https://github.com/slegroux/nimrod.git\ncd nimrod/recipes/images/mnist\npython train.py datamodule.num_workers=10 trainer.max_epochs=20 trainer.accelerator='gpu'\nhead conf/train.yaml\n```\n\nAll the parameters of the experiment are editable and read from a .yaml\nfile which details:\n\n- data and logging directory paths\n- data module with data source path and batching parameters\n- model architecture\n- trainer with hardware acceleration and number of epochs\n- callbacks for early stopping and automatic logging to Wandb\n\n## Docker\n\nYou might want to use docker containers for reproductible development\nenvironment or run your project in the cloud\n\n``` bash\nmake container\ndocker pull slegroux/nimrod\ndocker run -it --rm -p 8888:8888 slegroux/nimrod /bin/bash\n```\n\nYou can also use docker-compose to define services and volumes\n\n``` bash\ncd .devcontainer\ndocker-compose up\ndocker-compose down\n```\n\n## Develop\n\n``` bash\npip install -e .\n```\n\n## Authors\n\n2023 Sylvain Le Groux <sylvain.legroux@gmail.com>\n",
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