https://github.com/khaykingleb/research-playground
Efficient ML/DL implementations across multiple domains with K3s multi-node training setup
https://github.com/khaykingleb/research-playground
automatic-speaker-recognition hydra k3s k8s key-word-spotting opentofu pytorch-lightning terraform text-to-speech
Last synced: about 2 months ago
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Efficient ML/DL implementations across multiple domains with K3s multi-node training setup
- Host: GitHub
- URL: https://github.com/khaykingleb/research-playground
- Owner: khaykingleb
- License: mit
- Created: 2022-07-27T16:40:32.000Z (almost 3 years ago)
- Default Branch: master
- Last Pushed: 2025-01-20T20:36:28.000Z (5 months ago)
- Last Synced: 2025-04-12T11:18:22.198Z (about 2 months ago)
- Topics: automatic-speaker-recognition, hydra, k3s, k8s, key-word-spotting, opentofu, pytorch-lightning, terraform, text-to-speech
- Language: Python
- Homepage:
- Size: 1.82 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Research Playground
> Inspired by [ashleve/lightning-hydra-template](https://github.com/ashleve/lightning-hydra-template) and [NVIDIA/NeMo](https://github.com/NVIDIA/NeMo)
It's a library I created for efficient ML/DL research on various tasks.
## Key features:
- 🚀 Production-ready training pipelines
- 🧠 Actual model implementations
- ⚡️ Easy configuration management with Hydra
- 📊 Experiment tracking with Weights & Biases
- 🔧 Modular architecture for quick prototyping
- 🐳 Docker support for reproducible environments
- ☸️ Multi-GPU training with K3s and Terraform (soon)
## Getting Started
1. Install [asdf](https://asdf-vm.com/guide/getting-started.html) to manage different tools' runtime versions.
2. Update `.env.example` to your needs.
3. Setup your training Hydra config in `configs/experiments/` folder.
4. Choose between local development outside or inside Docker container.
* Outside of Docker (not recommended):
```bash
make init-local
poetry shell && python3 src train --experiment
```
* Inside Docker:
```bash
make init && make build && make run
python3 src train --experiment
```
## Notes
* Use `make help` to see all available commands.
* Use `python3 src --help` to see all available CLI arguments.