# NeuroLite 🧠⚡
[](https://badge.fury.io/py/neurolite)
[](https://www.python.org/downloads/)
[](https://opensource.org/licenses/MIT)
[](https://codecov.io/gh/dot-css/neurolite)
**NeuroLite** is a revolutionary AI/ML/DL/NLP productivity library that enables you to build, train, and deploy machine learning models with **minimal code**. Transform complex ML workflows into simple, intuitive operations.
## 🚀 Why NeuroLite?
- **🎯 Minimal Code**: Train state-of-the-art models in less than 10 lines of code
- **🤖 Auto-Everything**: Automatic data processing, model selection, and hyperparameter tuning
- **🌍 Multi-Domain**: Unified interface for Computer Vision, NLP, and Traditional ML
- **⚡ Production Ready**: One-click deployment to production environments
- **🔧 Extensible**: Plugin system for custom models and workflows
- **📊 Rich Visualization**: Built-in dashboards and reporting tools
## 📦 Installation
### Quick Install
```bash
pip install neurolite
```
### Development Install
```bash
git clone https://github.com/dot-css/neurolite.git
cd neurolite
pip install -e ".[dev]"
```
### Optional Dependencies
```bash
# For TensorFlow support
pip install neurolite[tensorflow]
# For XGBoost support
pip install neurolite[xgboost]
# Install everything
pip install neurolite[all]
```
## 🎯 Quick Start
### Image Classification in 3 Lines
```python
from neurolite import train
# Train a computer vision model
model = train(data="path/to/images", task="image_classification")
predictions = model.predict("path/to/new/image.jpg")
```
### Text Classification
```python
from neurolite import train
# Train an NLP model
model = train(data="reviews.csv", task="sentiment_analysis", target="sentiment")
result = model.predict("This product is amazing!")
```
### Tabular Data Prediction
```python
from neurolite import train
# Train on structured data
model = train(data="sales.csv", task="regression", target="revenue")
forecast = model.predict({"feature1": 100, "feature2": "category_a"})
```
### One-Click Deployment
```python
from neurolite import deploy
# Deploy your model instantly
endpoint = deploy(model, platform="cloud", auto_scale=True)
print(f"Model deployed at: {endpoint.url}")
```
## 🌟 Key Features
### 🤖 Automatic Intelligence
- **Auto Data Processing**: Handles missing values, encoding, scaling automatically
- **Auto Model Selection**: Chooses the best model architecture for your data
- **Auto Hyperparameter Tuning**: Optimizes model parameters using advanced algorithms
- **Auto Feature Engineering**: Creates and selects relevant features
### 🎨 Multi-Domain Support
#### Computer Vision
```python
# Image classification, object detection, segmentation
model = train(data="images/", task="object_detection")
results = model.predict("test_image.jpg")
```
#### Natural Language Processing
```python
# Text classification, sentiment analysis, translation
model = train(data="texts.csv", task="text_generation")
generated = model.predict("Once upon a time")
```
#### Traditional ML
```python
# Regression, classification, clustering
model = train(data="tabular.csv", task="classification")
predictions = model.predict(new_data)
```
### 🚀 Production Deployment
```python
from neurolite import deploy
# Deploy to various platforms
deploy(model, platform="aws") # AWS Lambda/SageMaker
deploy(model, platform="gcp") # Google Cloud
deploy(model, platform="azure") # Azure ML
deploy(model, platform="docker") # Docker container
deploy(model, platform="kubernetes") # Kubernetes cluster
```
## 📊 Advanced Features
### Hyperparameter Optimization
```python
from neurolite import train
model = train(
data="data.csv",
task="classification",
optimization="bayesian", # bayesian, grid, random
trials=100,
timeout=3600 # 1 hour
)
```
### Model Ensembles
```python
from neurolite import train
# Automatic ensemble creation
model = train(
data="data.csv",
task="regression",
ensemble=True,
ensemble_size=5
)
```
### Custom Workflows
```python
from neurolite.workflows import create_workflow
# Define custom ML pipeline
workflow = create_workflow([
"data_cleaning",
"feature_engineering",
"model_training",
"evaluation",
"deployment"
])
result = workflow.run(data="data.csv")
```
### Real-time Monitoring
```python
from neurolite import monitor
# Monitor deployed models
monitor.track(model, metrics=["accuracy", "latency", "drift"])
dashboard = monitor.dashboard(model)
```
## 🔧 Configuration
### Global Settings
```python
import neurolite
# Configure global settings
neurolite.config.set_device("gpu") # cpu, gpu, auto
neurolite.config.set_cache_dir("./cache")
neurolite.config.set_log_level("INFO")
```
### Model-Specific Configuration
```python
model = train(
data="data.csv",
task="classification",
config={
"model_type": "neural_network",
"epochs": 100,
"batch_size": 32,
"learning_rate": 0.001,
"early_stopping": True
}
)
```
## 📈 Performance Benchmarks
| Task | Dataset | NeuroLite | Traditional Approach | Time Saved |
|------|---------|-----------|---------------------|-------------|
| Image Classification | CIFAR-10 | 3 lines | 200+ lines | 98.5% |
| Sentiment Analysis | IMDB | 2 lines | 150+ lines | 98.7% |
| Sales Forecasting | Custom | 4 lines | 300+ lines | 98.7% |
## 🛠️ Supported Models
### Computer Vision
- **Classification**: ResNet, EfficientNet, Vision Transformer
- **Object Detection**: YOLO, Faster R-CNN, SSD
- **Segmentation**: U-Net, DeepLab, FCN
### Natural Language Processing
- **Text Classification**: BERT, RoBERTa, DistilBERT
- **Text Generation**: GPT-2, T5, BART
- **Translation**: MarianMT, T5
- **Question Answering**: BERT, RoBERTa
### Traditional ML
- **Classification**: Random Forest, XGBoost, SVM, Logistic Regression
- **Regression**: Linear Regression, Random Forest, Gradient Boosting
- **Clustering**: K-Means, DBSCAN, Hierarchical
- **Ensemble**: Voting, Stacking, Bagging
## 🔌 Plugin System
Extend NeuroLite with custom models and workflows:
```python
from neurolite.plugins import register_model
@register_model("my_custom_model")
class CustomModel:
def train(self, data):
# Custom training logic
pass
def predict(self, data):
# Custom prediction logic
pass
# Use your custom model
model = train(data="data.csv", model="my_custom_model")
```
## 📚 Documentation
- **[Getting Started Guide](https://neurolite.readthedocs.io/getting-started)**
- **[API Reference](https://neurolite.readthedocs.io/api)**
- **[Tutorials](https://neurolite.readthedocs.io/tutorials)**
- **[Examples](https://github.com/dot-css/neurolite/tree/main/examples)**
- **[Plugin Development](https://neurolite.readthedocs.io/plugins)**
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
git clone https://github.com/dot-css/neurolite.git
cd neurolite
pip install -e ".[dev]"
pre-commit install
```
### Running Tests
```bash
pytest tests/ -v
```
### Code Quality
```bash
black neurolite/ tests/
flake8 neurolite/ tests/
mypy neurolite/
```
## 📄 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Built with ❤️ by the NeuroLite Team
- Powered by PyTorch, Transformers, Scikit-learn, and other amazing open-source libraries
- Special thanks to our contributors and the ML community
## 📞 Support
- **Documentation**: [https://neurolite.readthedocs.io](https://neurolite.readthedocs.io)
- **Issues**: [GitHub Issues](https://github.com/dot-css/neurolite/issues)
- **Discussions**: [GitHub Discussions](https://github.com/dot-css/neurolite/discussions)
- **Email**: saqibshaikhdz@gmail.com
---
**Made with ❤️ for the AI/ML community**
Raw data
{
"_id": null,
"home_page": "https://github.com/dot-css/neurolite",
"name": "neurolite",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.8",
"maintainer_email": null,
"keywords": "machine learning, deep learning, nlp, computer vision, automation",
"author": "NeuroLite Team",
"author_email": "NeuroLite Team <team@neurolite.ai>",
"download_url": "https://files.pythonhosted.org/packages/73/45/9ba7df89b21c9543d6ae462a288ad54e17eadc4feddb51dabc6eb19bcb0e/neurolite-0.2.0.tar.gz",
"platform": null,
"description": "# NeuroLite \ud83e\udde0\u26a1\r\n\r\n[](https://badge.fury.io/py/neurolite)\r\n[](https://www.python.org/downloads/)\r\n[](https://opensource.org/licenses/MIT)\r\n[](https://codecov.io/gh/dot-css/neurolite)\r\n\r\n**NeuroLite** is a revolutionary AI/ML/DL/NLP productivity library that enables you to build, train, and deploy machine learning models with **minimal code**. Transform complex ML workflows into simple, intuitive operations.\r\n\r\n## \ud83d\ude80 Why NeuroLite?\r\n\r\n- **\ud83c\udfaf Minimal Code**: Train state-of-the-art models in less than 10 lines of code\r\n- **\ud83e\udd16 Auto-Everything**: Automatic data processing, model selection, and hyperparameter tuning\r\n- **\ud83c\udf0d Multi-Domain**: Unified interface for Computer Vision, NLP, and Traditional ML\r\n- **\u26a1 Production Ready**: One-click deployment to production environments\r\n- **\ud83d\udd27 Extensible**: Plugin system for custom models and workflows\r\n- **\ud83d\udcca Rich Visualization**: Built-in dashboards and reporting tools\r\n\r\n## \ud83d\udce6 Installation\r\n\r\n### Quick Install\r\n```bash\r\npip install neurolite\r\n```\r\n\r\n### Development Install\r\n```bash\r\ngit clone https://github.com/dot-css/neurolite.git\r\ncd neurolite\r\npip install -e \".[dev]\"\r\n```\r\n\r\n### Optional Dependencies\r\n```bash\r\n# For TensorFlow support\r\npip install neurolite[tensorflow]\r\n\r\n# For XGBoost support \r\npip install neurolite[xgboost]\r\n\r\n# Install everything\r\npip install neurolite[all]\r\n```\r\n\r\n## \ud83c\udfaf Quick Start\r\n\r\n### Image Classification in 3 Lines\r\n```python\r\nfrom neurolite import train\r\n\r\n# Train a computer vision model\r\nmodel = train(data=\"path/to/images\", task=\"image_classification\")\r\npredictions = model.predict(\"path/to/new/image.jpg\")\r\n```\r\n\r\n### Text Classification\r\n```python\r\nfrom neurolite import train\r\n\r\n# Train an NLP model\r\nmodel = train(data=\"reviews.csv\", task=\"sentiment_analysis\", target=\"sentiment\")\r\nresult = model.predict(\"This product is amazing!\")\r\n```\r\n\r\n### Tabular Data Prediction\r\n```python\r\nfrom neurolite import train\r\n\r\n# Train on structured data\r\nmodel = train(data=\"sales.csv\", task=\"regression\", target=\"revenue\")\r\nforecast = model.predict({\"feature1\": 100, \"feature2\": \"category_a\"})\r\n```\r\n\r\n### One-Click Deployment\r\n```python\r\nfrom neurolite import deploy\r\n\r\n# Deploy your model instantly\r\nendpoint = deploy(model, platform=\"cloud\", auto_scale=True)\r\nprint(f\"Model deployed at: {endpoint.url}\")\r\n```\r\n\r\n## \ud83c\udf1f Key Features\r\n\r\n### \ud83e\udd16 Automatic Intelligence\r\n- **Auto Data Processing**: Handles missing values, encoding, scaling automatically\r\n- **Auto Model Selection**: Chooses the best model architecture for your data\r\n- **Auto Hyperparameter Tuning**: Optimizes model parameters using advanced algorithms\r\n- **Auto Feature Engineering**: Creates and selects relevant features\r\n\r\n### \ud83c\udfa8 Multi-Domain Support\r\n\r\n#### Computer Vision\r\n```python\r\n# Image classification, object detection, segmentation\r\nmodel = train(data=\"images/\", task=\"object_detection\")\r\nresults = model.predict(\"test_image.jpg\")\r\n```\r\n\r\n#### Natural Language Processing\r\n```python\r\n# Text classification, sentiment analysis, translation\r\nmodel = train(data=\"texts.csv\", task=\"text_generation\")\r\ngenerated = model.predict(\"Once upon a time\")\r\n```\r\n\r\n#### Traditional ML\r\n```python\r\n# Regression, classification, clustering\r\nmodel = train(data=\"tabular.csv\", task=\"classification\")\r\npredictions = model.predict(new_data)\r\n```\r\n\r\n### \ud83d\ude80 Production Deployment\r\n```python\r\nfrom neurolite import deploy\r\n\r\n# Deploy to various platforms\r\ndeploy(model, platform=\"aws\") # AWS Lambda/SageMaker\r\ndeploy(model, platform=\"gcp\") # Google Cloud\r\ndeploy(model, platform=\"azure\") # Azure ML\r\ndeploy(model, platform=\"docker\") # Docker container\r\ndeploy(model, platform=\"kubernetes\") # Kubernetes cluster\r\n```\r\n\r\n## \ud83d\udcca Advanced Features\r\n\r\n### Hyperparameter Optimization\r\n```python\r\nfrom neurolite import train\r\n\r\nmodel = train(\r\n data=\"data.csv\",\r\n task=\"classification\",\r\n optimization=\"bayesian\", # bayesian, grid, random\r\n trials=100,\r\n timeout=3600 # 1 hour\r\n)\r\n```\r\n\r\n### Model Ensembles\r\n```python\r\nfrom neurolite import train\r\n\r\n# Automatic ensemble creation\r\nmodel = train(\r\n data=\"data.csv\",\r\n task=\"regression\",\r\n ensemble=True,\r\n ensemble_size=5\r\n)\r\n```\r\n\r\n### Custom Workflows\r\n```python\r\nfrom neurolite.workflows import create_workflow\r\n\r\n# Define custom ML pipeline\r\nworkflow = create_workflow([\r\n \"data_cleaning\",\r\n \"feature_engineering\", \r\n \"model_training\",\r\n \"evaluation\",\r\n \"deployment\"\r\n])\r\n\r\nresult = workflow.run(data=\"data.csv\")\r\n```\r\n\r\n### Real-time Monitoring\r\n```python\r\nfrom neurolite import monitor\r\n\r\n# Monitor deployed models\r\nmonitor.track(model, metrics=[\"accuracy\", \"latency\", \"drift\"])\r\ndashboard = monitor.dashboard(model)\r\n```\r\n\r\n## \ud83d\udd27 Configuration\r\n\r\n### Global Settings\r\n```python\r\nimport neurolite\r\n\r\n# Configure global settings\r\nneurolite.config.set_device(\"gpu\") # cpu, gpu, auto\r\nneurolite.config.set_cache_dir(\"./cache\")\r\nneurolite.config.set_log_level(\"INFO\")\r\n```\r\n\r\n### Model-Specific Configuration\r\n```python\r\nmodel = train(\r\n data=\"data.csv\",\r\n task=\"classification\",\r\n config={\r\n \"model_type\": \"neural_network\",\r\n \"epochs\": 100,\r\n \"batch_size\": 32,\r\n \"learning_rate\": 0.001,\r\n \"early_stopping\": True\r\n }\r\n)\r\n```\r\n\r\n## \ud83d\udcc8 Performance Benchmarks\r\n\r\n| Task | Dataset | NeuroLite | Traditional Approach | Time Saved |\r\n|------|---------|-----------|---------------------|-------------|\r\n| Image Classification | CIFAR-10 | 3 lines | 200+ lines | 98.5% |\r\n| Sentiment Analysis | IMDB | 2 lines | 150+ lines | 98.7% |\r\n| Sales Forecasting | Custom | 4 lines | 300+ lines | 98.7% |\r\n\r\n## \ud83d\udee0\ufe0f Supported Models\r\n\r\n### Computer Vision\r\n- **Classification**: ResNet, EfficientNet, Vision Transformer\r\n- **Object Detection**: YOLO, Faster R-CNN, SSD\r\n- **Segmentation**: U-Net, DeepLab, FCN\r\n\r\n### Natural Language Processing\r\n- **Text Classification**: BERT, RoBERTa, DistilBERT\r\n- **Text Generation**: GPT-2, T5, BART\r\n- **Translation**: MarianMT, T5\r\n- **Question Answering**: BERT, RoBERTa\r\n\r\n### Traditional ML\r\n- **Classification**: Random Forest, XGBoost, SVM, Logistic Regression\r\n- **Regression**: Linear Regression, Random Forest, Gradient Boosting\r\n- **Clustering**: K-Means, DBSCAN, Hierarchical\r\n- **Ensemble**: Voting, Stacking, Bagging\r\n\r\n## \ud83d\udd0c Plugin System\r\n\r\nExtend NeuroLite with custom models and workflows:\r\n\r\n```python\r\nfrom neurolite.plugins import register_model\r\n\r\n@register_model(\"my_custom_model\")\r\nclass CustomModel:\r\n def train(self, data):\r\n # Custom training logic\r\n pass\r\n \r\n def predict(self, data):\r\n # Custom prediction logic\r\n pass\r\n\r\n# Use your custom model\r\nmodel = train(data=\"data.csv\", model=\"my_custom_model\")\r\n```\r\n\r\n## \ud83d\udcda Documentation\r\n\r\n- **[Getting Started Guide](https://neurolite.readthedocs.io/getting-started)**\r\n- **[API Reference](https://neurolite.readthedocs.io/api)**\r\n- **[Tutorials](https://neurolite.readthedocs.io/tutorials)**\r\n- **[Examples](https://github.com/dot-css/neurolite/tree/main/examples)**\r\n- **[Plugin Development](https://neurolite.readthedocs.io/plugins)**\r\n\r\n## \ud83e\udd1d Contributing\r\n\r\nWe welcome contributions! Please see our [Contributing Guide](CONTRIBUTING.md) for details.\r\n\r\n### Development Setup\r\n```bash\r\ngit clone https://github.com/dot-css/neurolite.git\r\ncd neurolite\r\npip install -e \".[dev]\"\r\npre-commit install\r\n```\r\n\r\n### Running Tests\r\n```bash\r\npytest tests/ -v\r\n```\r\n\r\n### Code Quality\r\n```bash\r\nblack neurolite/ tests/\r\nflake8 neurolite/ tests/\r\nmypy neurolite/\r\n```\r\n\r\n## \ud83d\udcc4 License\r\n\r\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\r\n\r\n## \ud83d\ude4f Acknowledgments\r\n\r\n- Built with \u2764\ufe0f by the NeuroLite Team\r\n- Powered by PyTorch, Transformers, Scikit-learn, and other amazing open-source libraries\r\n- Special thanks to our contributors and the ML community\r\n\r\n## \ud83d\udcde Support\r\n\r\n- **Documentation**: [https://neurolite.readthedocs.io](https://neurolite.readthedocs.io)\r\n- **Issues**: [GitHub Issues](https://github.com/dot-css/neurolite/issues)\r\n- **Discussions**: [GitHub Discussions](https://github.com/dot-css/neurolite/discussions)\r\n- **Email**: saqibshaikhdz@gmail.com\r\n\r\n\r\n---\r\n\r\n**Made with \u2764\ufe0f for the AI/ML community**\r\n",
"bugtrack_url": null,
"license": null,
"summary": "AI/ML/DL/NLP productivity library for minimal-code machine learning workflows",
"version": "0.2.0",
"project_urls": {
"Bug Tracker": "https://github.com/dot-css/neurolite/issues",
"Documentation": "https://neurolite.readthedocs.io",
"Homepage": "https://github.com/dot-css/neurolite",
"Repository": "https://github.com/dot-css/neurolite"
},
"split_keywords": [
"machine learning",
" deep learning",
" nlp",
" computer vision",
" automation"
],
"urls": [
{
"comment_text": null,
"digests": {
"blake2b_256": "4cdb20c489ade51772c2bc87658acfcb39b0dcc2fb66493d7d904163def268f3",
"md5": "3b5500f86325e1c7962ef2f32d298338",
"sha256": "0ea6e1836ba312f9f36ae7da96f0fa9631bb5a441562031061bc803988d342ae"
},
"downloads": -1,
"filename": "neurolite-0.2.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "3b5500f86325e1c7962ef2f32d298338",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.8",
"size": 232324,
"upload_time": "2025-08-02T12:14:35",
"upload_time_iso_8601": "2025-08-02T12:14:35.048940Z",
"url": "https://files.pythonhosted.org/packages/4c/db/20c489ade51772c2bc87658acfcb39b0dcc2fb66493d7d904163def268f3/neurolite-0.2.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": null,
"digests": {
"blake2b_256": "73459ba7df89b21c9543d6ae462a288ad54e17eadc4feddb51dabc6eb19bcb0e",
"md5": "0beb4f652f398fb3089c961d74b5f927",
"sha256": "68e8672faf999bdaf996601eacb6d95cfd9e9bbf5ed39fa9d1a4c0c0bd4197c8"
},
"downloads": -1,
"filename": "neurolite-0.2.0.tar.gz",
"has_sig": false,
"md5_digest": "0beb4f652f398fb3089c961d74b5f927",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.8",
"size": 349391,
"upload_time": "2025-08-02T12:14:37",
"upload_time_iso_8601": "2025-08-02T12:14:37.178794Z",
"url": "https://files.pythonhosted.org/packages/73/45/9ba7df89b21c9543d6ae462a288ad54e17eadc4feddb51dabc6eb19bcb0e/neurolite-0.2.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2025-08-02 12:14:37",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "dot-css",
"github_project": "neurolite",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"lcname": "neurolite"
}
JSON
