https://github.com/asherk7/auto-ml
Creating a basic Auto-ML system
https://github.com/asherk7/auto-ml
ml swe
Last synced: 8 months ago
JSON representation
Creating a basic Auto-ML system
- Host: GitHub
- URL: https://github.com/asherk7/auto-ml
- Owner: asherk7
- License: mit
- Created: 2025-04-30T23:08:28.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-09-14T22:08:36.000Z (9 months ago)
- Last Synced: 2025-09-15T00:17:33.265Z (9 months ago)
- Topics: ml, swe
- Language: Python
- Homepage:
- Size: 49.8 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AutoML System
A lightweight, modular AutoML framework for computer vision and tabular machine learning. Supports training, experiment tracking, and model serving with minimal setup.
---
## Features
* **Tasks**:
* Computer Vision → image classification, object detection
* Tabular → classification, regression, clustering
* **Models**:
* CV → ResNet, EfficientNet, MobileNet (PyTorch Lightning)
* Tabular → Random Forest, Logistic Regression, SVM, KNN, Decision Trees (scikit-learn)
* **Core**:
* Auto preprocessing + augmentation
* Hyperparameter tuning (GridSearchCV)
* Experiment tracking (Weights & Biases)
* ONNX export for inference
* FastAPI serving + SQLite metadata DB
* Docker ready
* **Datasets**: CIFAR-10, Iris, + custom datasets
---
## Quick Start
```bash
# Clone & install
git clone https://github.com/asherk7/auto-ml.git && cd auto-ml
pip install -r requirements.txt
# Check system info
python main.py system-info
```
### Examples
```bash
# Train CV model (ResNet-18 on CIFAR-10)
python main.py train-cv --model-type resnet18 --max-epochs 10
# Train tabular model (Random Forest on Iris)
python main.py train-tabular --model-type random_forest --tune-hyperparameters
# Try multiple models automatically
python main.py auto-train --data-type tabular --task-type classification --models "random_forest,svm,logistic_regression"
# Start API server
python main.py serve --port 8000
```
---
## Project Structure
```
auto-ml/
├── main.py # CLI entry
├── models/ # CV + tabular models
├── training/ # Training logic
├── serving/ # FastAPI endpoints
├── utils/ # DB + helpers
└── examples/ # Example scripts
```
---
## Key Commands
| Command | Use |
| ------------------ | -------------------- |
| `train-cv` | Train vision model |
| `train-tabular` | Train tabular model |
| `auto-train` | Run multiple models |
| `serve` | Start FastAPI server |
| `list-experiments` | Show experiments |
| `list-models` | Show trained models |
---
## Advanced
* W\&B for experiment tracking (`wandb login`)
* Export to ONNX for fast inference
* Docker & docker-compose for deployment
---
## Roadmap
* Optuna for smarter hyperparameter tuning
* Fine-tuning large pretrained models (QLoRA, PEFT)
* Explainability → GradCAM, SHAP/LIME
* Redis + Celery for async serving
* Interactive model builder
* Real-time deployment for latency and inference testing