# Nimrod
<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->
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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
### Install using Pip
Install package:
``` sh
pip install slg-nimrod
```
Install espeak for LM:
```bash
brew install espeak #macos
```
Install Spacy english model
```bash
python -m spacy download en_core_web_sm
```
## Usage
Download test data on which to run example recipes:
```bash
# if not already installed on your system
git lfs install
# update changes
git lfs fetch --all
# copy the actual data
git lfs checkout
# or just
git lfs pull # combing both steps above into one (like usual git pull)
```
Check recipes in `recipes/` folder. E.g. for a simple digit recognizer
on MNIST:
``` bash
git clone https://github.com/slegroux/nimrod.git
cd nimrod/recipes/images/mnist
python train.py datamodule.num_workers=8 trainer.max_epochs=20 trainer.accelerator='mps' loggers='tensorboard'
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 .
```
## Hyperparameter tuning
to compare training results on different model parameters:
``` bash
cd nimrod/recipes/images/mnist
python train.py --multirun model.n_h=16,64,256 loggers='tensorboard' trainer.max_epochs=5
```
## Server
### st webapp
Run a simple digit recognizer webapp with GUI
``` bash
cd server
./run_st_app.sh
```
## 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### Install using Pip\n\nInstall package:\n``` sh\npip install slg-nimrod\n```\nInstall espeak for LM:\n```bash\nbrew install espeak #macos\n```\nInstall Spacy english model\n```bash\npython -m spacy download en_core_web_sm\n```\n\n\n## Usage\n\nDownload test data on which to run example recipes:\n\n```bash\n# if not already installed on your system\ngit lfs install \n# update changes\ngit lfs fetch --all\n# copy the actual data\ngit lfs checkout\n# or just\ngit lfs pull # combing both steps above into one (like usual git pull)\n```\n\nCheck recipes in `recipes/` folder. E.g. for a simple digit recognizer\non MNIST:\n\n``` bash\ngit clone https://github.com/slegroux/nimrod.git\ncd nimrod/recipes/images/mnist\npython train.py datamodule.num_workers=8 trainer.max_epochs=20 trainer.accelerator='mps' loggers='tensorboard'\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## Hyperparameter tuning\n\nto compare training results on different model parameters:\n\n``` bash\ncd nimrod/recipes/images/mnist\npython train.py --multirun model.n_h=16,64,256 loggers='tensorboard' trainer.max_epochs=5\n```\n\n## Server\n\n### st webapp\n\nRun a simple digit recognizer webapp with GUI\n\n``` bash\ncd server\n./run_st_app.sh\n```\n\n## Authors\n\n2023 Sylvain Le Groux <sylvain.legroux@gmail.com>\n",
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