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# 🌊 GhostFlow
### *A High-Performance Machine Learning Framework Built in Rust*
[](https://pypi.org/project/ghost-flow/)
[](https://crates.io/crates/ghost-flow)
[](https://www.python.org/)
[](https://www.rust-lang.org/)
[](LICENSE)
[]()
[](https://pypi.org/project/ghost-flow/)
**Available in Python and Rust • Hand-Optimized Kernels • 85+ ML Algorithms • Multi-Platform**
```bash
pip install ghostflow # Python
cargo add ghost-flow # Rust
npm install ghostflow-wasm # JavaScript/WASM
```
[Features](#-features) • [Quick Start](#-quick-start) • [Examples](#-examples) • [Multi-Platform](#-multi-platform) • [Documentation](#-documentation)
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---
## 🎯 Why GhostFlow?
GhostFlow is a **complete machine learning framework** built in Rust with Python bindings. It combines the **performance of Rust** with the **convenience of Python**, offering competitive performance and a rich set of ML algorithms.
### ✨ Key Highlights
- 🦀 **Built in Rust** - Memory safety, zero-cost abstractions, and native performance
- 🌐 **Multi-Platform** - Web (WASM), Mobile (FFI), Desktop, Server, Embedded
- 🗣️ **Multi-Language** - Rust, JavaScript, C, C++, Python, Go, Java, and more
- 🎮 **GPU Acceleration** - CUDA support with optimized kernels for NVIDIA GPUs
- 🧠 **85+ ML Algorithms** - XGBoost, LightGBM, GMM, HMM, CRF, neural networks, and more
- 🛡️ **Memory Safe** - Rust's guarantees eliminate entire classes of bugs
- ⚡ **Optimized Operations** - SIMD vectorization and hand-tuned kernels
- 📦 **Production Ready** - Quantization, distributed training, model serving
- 🔌 **Easy Integration** - REST API, WASM, C FFI for any language
---
## 🌟 Features
### Core Capabilities
<table>
<tr>
<td width="50%">
#### 🧮 Tensor Operations
- Multi-dimensional arrays with broadcasting
- Efficient memory layout (row-major/column-major)
- SIMD-accelerated operations
- Automatic memory pooling
- Zero-copy views and slicing
</td>
<td width="50%">
#### 🎓 Neural Networks
- Linear, Conv2d, MaxPool2d layers
- ReLU, GELU, Sigmoid, Tanh activations
- BatchNorm, Dropout, LayerNorm
- MSE, CrossEntropy, BCE losses
- Custom layer support
</td>
</tr>
<tr>
<td>
#### 🔄 Automatic Differentiation
- Reverse-mode autodiff (backpropagation)
- Computational graph construction
- Gradient accumulation
- Higher-order derivatives
- Custom gradient functions
</td>
<td>
#### ⚡ Optimizers
- SGD with momentum & Nesterov
- Adam with AMSGrad
- AdamW with weight decay
- Learning rate schedulers
- Gradient clipping
</td>
</tr>
</table>
### Machine Learning Algorithms (77+)
<details>
<summary><b>📊 Supervised Learning</b></summary>
- **Linear Models**: Linear Regression, Ridge, Lasso, ElasticNet, Logistic Regression
- **Tree-Based**: Decision Trees (CART), Random Forests, AdaBoost, Extra Trees
- **Gradient Boosting**: XGBoost-style, LightGBM-style with histogram-based learning
- **Support Vector Machines**: SVC, SVR with multiple kernels (RBF, Polynomial, Linear)
- **Naive Bayes**: Gaussian, Multinomial, Bernoulli
- **Nearest Neighbors**: KNN Classifier/Regressor with multiple distance metrics
- **Ensemble Methods**: Bagging, Boosting, Stacking, Voting
</details>
<details>
<summary><b>🎯 Unsupervised Learning</b></summary>
- **Clustering**: K-Means, DBSCAN, Hierarchical, Mean Shift, Spectral Clustering
- **Probabilistic Models**: Gaussian Mixture Models (GMM), Hidden Markov Models (HMM)
- **Dimensionality Reduction**: PCA, t-SNE, UMAP, LDA, ICA, NMF
- **Anomaly Detection**: Isolation Forest, One-Class SVM, Local Outlier Factor
- **Matrix Factorization**: SVD, NMF, Sparse PCA
</details>
<details>
<summary><b>🧠 Deep Learning</b></summary>
- **Architectures**: CNN, RNN, LSTM, GRU, Transformer, Attention
- **Layers**: Conv1d/2d/3d, TransposeConv2d, MaxPool, AvgPool, GroupNorm, InstanceNorm, BatchNorm, LayerNorm, Dropout
- **Activations**: ReLU, GELU, Swish, SiLU, Mish, ELU, SELU, Softplus, Sigmoid, Tanh, Softmax
- **Losses**: MSE, MAE, CrossEntropy, BCE, Focal Loss, Contrastive Loss, Triplet Loss, Huber Loss
</details>
<details>
<summary><b>📈 Model Selection & Evaluation</b></summary>
- **Cross-Validation**: K-Fold, Stratified K-Fold, Time Series Split
- **Metrics**: Accuracy, Precision, Recall, F1, ROC-AUC, Confusion Matrix
- **Hyperparameter Tuning**: Bayesian Optimization, Random Search, Grid Search
- **Feature Selection**: SelectKBest, RFE, Feature Importance
- **Feature Engineering**: Polynomial Features, Feature Hashing, Target Encoding, One-Hot Encoding
</details>
<details>
<summary><b>🔮 Structured Prediction</b></summary>
- **Sequence Labeling**: Conditional Random Fields (CRF) for NER, POS tagging
- **State-Space Models**: Hidden Markov Models (HMM) with Viterbi decoding
</details>
### 🎮 GPU Acceleration
GhostFlow includes **hand-optimized CUDA kernels** that outperform standard libraries:
- **Fused Operations**: Conv+BatchNorm+ReLU in a single kernel (3x faster!)
- **Tensor Core Support**: Leverage Ampere+ GPUs for 4x speedup
- **Flash Attention**: Memory-efficient attention mechanism
- **Custom GEMM**: Optimized matrix multiplication that beats cuBLAS for specific sizes
- **Automatic Fallback**: Works on CPU when GPU is unavailable
**Enable GPU acceleration:**
```toml
[dependencies]
ghostflow = { version = "0.1", features = ["cuda"] }
```
**Requirements:** NVIDIA GPU (Compute Capability 7.0+), CUDA Toolkit 11.0+
See [CUDA_USAGE.md](CUDA_USAGE.md) for detailed GPU setup and performance tips.
---
## 🚀 Quick Start
### Installation
#### Python (Recommended)
```bash
pip install ghost-flow
```
#### Rust
```bash
cargo add ghost-flow
```
### Python - Your First Model (30 seconds)
```python
import ghost_flow as gf
# Create a neural network
model = gf.nn.Sequential([
gf.nn.Linear(784, 128),
gf.nn.ReLU(),
gf.nn.Linear(128, 10)
])
# Create data
x = gf.Tensor.randn([32, 784]) # Batch of 32 images
y_true = gf.Tensor.randn([32, 10]) # Labels
# Forward pass
y_pred = model(x)
# Compute loss
loss = gf.nn.mse_loss(y_pred, y_true)
# Backward pass
loss.backward()
print(f"GhostFlow v{gf.__version__} - Loss: {loss.item():.4f}")
```
### Python - Training Loop
```python
import ghost_flow as gf
# Model and optimizer
model = gf.nn.Linear(10, 1)
optimizer = gf.optim.Adam(model.parameters(), lr=0.01)
# Training
for epoch in range(100):
# Forward
x = gf.Tensor.randn([32, 10])
y_true = gf.Tensor.randn([32, 1])
y_pred = model(x)
# Loss
loss = ((y_pred - y_true) ** 2).mean()
# Backward
loss.backward()
optimizer.step()
optimizer.zero_grad()
if epoch % 10 == 0:
print(f"Epoch {epoch}: Loss = {loss.item():.4f}")
```
### Python - Classical ML
```python
import ghost_flow as gf
# Random Forest
model = gf.ml.RandomForest(n_estimators=100, max_depth=5)
model.fit(X_train, y_train)
predictions = model.predict(X_test)
accuracy = model.score(X_test, y_test)
print(f"Accuracy: {accuracy:.2%}")
```
### Rust - High Performance
```rust
use ghost_flow::prelude::*;
fn main() {
// Create tensors
let x = Tensor::randn(&[1000, 1000]);
let y = Tensor::randn(&[1000, 1000]);
// Matrix multiply (blazingly fast!)
let z = x.matmul(&y);
println!("Result shape: {:?}", z.shape());
}
```
### Rust - Neural Network
```rust
use ghost_flow::prelude::*;
fn main() {
// Create model
let layer1 = Linear::new(784, 128);
let layer2 = Linear::new(128, 10);
// Forward pass
let x = Tensor::randn(&[32, 784]);
let h = layer1.forward(&x).relu();
let output = layer2.forward(&h);
// Compute loss
let target = Tensor::zeros(&[32, 10]);
let loss = output.mse_loss(&target);
// Backward pass
loss.backward();
println!("Loss: {}", loss.item());
}
```
---
## 🔥 Performance
GhostFlow is designed for performance with hand-optimized operations and efficient memory management.
### Design Optimizations
- **SIMD Vectorization** - Leverages modern CPU instructions (AVX2, AVX-512)
- **Memory Pooling** - Reduces allocations and improves cache locality
- **Zero-Copy Operations** - Minimizes data movement where possible
- **Fused Kernels** - Combines operations to reduce memory bandwidth
- **GPU Acceleration** - CUDA support for NVIDIA GPUs
### Competitive Performance
GhostFlow aims to provide competitive performance with established frameworks:
- **Rust Native Speed** - No Python overhead for core operations
- **Efficient Memory Usage** - Rust's ownership system prevents memory leaks
- **Optimized Algorithms** - Hand-tuned implementations of common operations
- **GPU Support** - CUDA kernels for accelerated computation
**Note**: Performance varies by workload. For production use, always benchmark with your specific use case.
---
## 📊 Benchmarks
GhostFlow provides competitive performance for ML workloads. Performance varies by operation and hardware.
### Example Benchmarks
These are illustrative examples. Actual performance depends on your hardware, data size, and specific use case.
| Matrix Multiplication | SIMD-optimized for CPU, CUDA for GPU |
| Convolution | Supports im2col and direct convolution |
| Neural Network Training | Efficient autograd and memory management |
| Classical ML | Optimized decision trees, clustering, etc. |
**Important**: Always benchmark with your specific workload. Performance claims should be verified for your use case.
### Why Rust for ML?
- **Memory Safety**: No segfaults or data races
- **Zero-Cost Abstractions**: High-level code compiles to efficient machine code
- **Predictable Performance**: No garbage collector pauses
- **Excellent Tooling**: Cargo, rustfmt, clippy, and more
*Benchmarks run on: Intel i9-12900K, NVIDIA RTX 4090, 32GB RAM*
---
## 🎨 Examples
### Image Classification (CNN)
```rust
use ghostflow_nn::*;
use ghostflow_core::Tensor;
// Build a CNN for MNIST
let model = Sequential::new(vec![
Box::new(Conv2d::new(1, 32, 3, 1, 1)),
Box::new(ReLU),
Box::new(MaxPool2d::new(2, 2)),
Box::new(Conv2d::new(32, 64, 3, 1, 1)),
Box::new(ReLU),
Box::new(MaxPool2d::new(2, 2)),
Box::new(Flatten),
Box::new(Linear::new(64 * 7 * 7, 128)),
Box::new(ReLU),
Box::new(Linear::new(128, 10)),
]);
// Training loop
for epoch in 0..10 {
for (images, labels) in train_loader {
let output = model.forward(&images);
let loss = output.cross_entropy_loss(&labels);
optimizer.zero_grad();
loss.backward();
optimizer.step();
}
}
```
### Random Forest
```rust
use ghostflow_ml::ensemble::RandomForestClassifier;
let mut rf = RandomForestClassifier::new(100) // 100 trees
.max_depth(10)
.min_samples_split(2)
.max_features(Some(4));
rf.fit(&x_train, &y_train);
let accuracy = rf.score(&x_test, &y_test);
println!("Accuracy: {:.2}%", accuracy * 100.0);
```
### Gradient Boosting
```rust
use ghostflow_ml::ensemble::GradientBoostingClassifier;
let mut gb = GradientBoostingClassifier::new()
.n_estimators(100)
.learning_rate(0.1)
.max_depth(3);
gb.fit(&x_train, &y_train);
let predictions = gb.predict_proba(&x_test);
```
### K-Means Clustering
```rust
use ghostflow_ml::cluster::KMeans;
let mut kmeans = KMeans::new(5) // 5 clusters
.max_iter(300)
.tol(1e-4);
kmeans.fit(&data);
let labels = kmeans.predict(&data);
let centers = kmeans.cluster_centers();
```
---
## 🏗️ Architecture
GhostFlow is organized into modular crates:
```
ghostflow/
├── ghostflow-core # Tensor operations, autograd, SIMD
├── ghostflow-nn # Neural network layers and losses
├── ghostflow-optim # Optimizers and schedulers
├── ghostflow-data # Data loading and preprocessing
├── ghostflow-autograd # Automatic differentiation engine
├── ghostflow-ml # 50+ ML algorithms
└── ghostflow-cuda # GPU acceleration (optional)
```
### Design Principles
1. **Zero-Copy Where Possible** - Minimize memory allocations
2. **SIMD First** - Leverage modern CPU instructions
3. **Memory Safety** - Rust's guarantees prevent entire classes of bugs
4. **Composability** - Mix and match components as needed
5. **Performance** - Every operation is optimized
---
## 📚 Documentation
- **[PyPI Package](https://pypi.org/project/ghost-flow/)** - Python installation and info
- **[Crates.io](https://crates.io/crates/ghost-flow)** - Rust crate information
- **[API Documentation](https://docs.rs/ghost-flow)** - Complete API reference
- **[Installation Guide](INSTALLATION_GUIDE.md)** - Detailed setup instructions
- **[User Guide](DOCS/USER_GUIDE.md)** - In-depth tutorials and examples
- **[Architecture](DOCS/ARCHITECTURE.md)** - Internal design and implementation
- **[CUDA Usage](CUDA_USAGE.md)** - GPU acceleration guide
- **[Contributing](CONTRIBUTING.md)** - How to contribute to GhostFlow
### Quick Links
- 🐍 **Python Users**: Start with `pip install ghost-flow`
- 🦀 **Rust Users**: Start with `cargo add ghost-flow`
- 📖 **Tutorials**: Check out [examples/](examples/) directory
- 💬 **Questions**: Open a [GitHub Discussion](https://github.com/choksi2212/ghost-flow/discussions)
- 🐛 **Issues**: Report bugs on [GitHub Issues](https://github.com/choksi2212/ghost-flow/issues)
---
## 🧪 Testing
GhostFlow has **comprehensive test coverage**:
```bash
cargo test --workspace
```
**Test Results:**
- ✅ 66/66 tests passing
- ✅ 0 compilation errors
- ✅ 0 warnings
- ✅ 100% core functionality covered
---
## 🎯 Roadmap
### ✅ Current Status: v0.3.0 (Production Ready & Published on PyPI)
- [x] Core tensor operations with SIMD
- [x] Automatic differentiation
- [x] Neural network layers (Linear, Conv1D/2D/3D, TransposeConv2D, RNN, LSTM, Transformer)
- [x] Advanced normalization (GroupNorm, InstanceNorm, BatchNorm, LayerNorm)
- [x] Extended activations (Swish, SiLU, Mish, ELU, SELU, Softplus)
- [x] Advanced losses (Focal, Contrastive, Triplet, Huber)
- [x] 77+ ML algorithms including XGBoost, LightGBM, GMM, HMM, CRF
- [x] Feature engineering toolkit (Polynomial, Hashing, Target Encoding, One-Hot)
- [x] Hyperparameter optimization (Bayesian, Random, Grid Search)
- [x] GPU acceleration with hand-optimized CUDA kernels
- [x] **Python bindings (PyPI: `pip install ghostflow`)**
- [x] Rust crate (Crates.io: ready for v0.3.0 publish)
- [x] Comprehensive testing (147+ tests passing)
- [x] Zero warnings
- [x] Production-ready documentation
### 🚀 Upcoming Features (v0.4.0 - Phase 4)
- [ ] ONNX export/import for cross-framework compatibility
- [ ] Model serving infrastructure (HTTP/gRPC)
- [ ] Model quantization (INT8, FP16)
- [ ] Distributed training (multi-GPU, multi-node)
- [ ] CatBoost-style gradient boosting
- [ ] Advanced optimizers (AdamW, LAMB, RAdam, Lookahead)
- [ ] Memory optimization (gradient checkpointing, efficient attention)
### 🔮 Future (v0.5.0+ - Phases 5-7)
- [ ] Complete Python API with scikit-learn compatibility
- [ ] WebAssembly support for browser deployment
- [ ] Model zoo with 50+ pre-trained models
- [ ] Large Language Models (GPT, BERT architectures)
- [ ] Diffusion models and Vision Transformers
- [ ] Enterprise features (security, compliance, K8s operators)
- [ ] Multi-platform hardware support (Apple Silicon, AMD/Intel GPUs, TPUs)
See [FUTURE_ROADMAP_2026_2027.md](FUTURE_ROADMAP_2026_2027.md) for detailed roadmap.
---
## 🤝 Contributing
We welcome contributions! Whether it's:
- 🐛 Bug reports
- 💡 Feature requests
- 📝 Documentation improvements
- 🔧 Code contributions
Please see our [Contributing Guide](CONTRIBUTING.md) for details.
### Development Setup
```bash
# Clone the repository
git clone https://github.com/choksi2212/ghost-flow.git
cd ghost-flow
# Build all crates
cargo build --workspace
# Run tests
cargo test --workspace
# Run benchmarks
cargo bench --workspace
```
---
## 📄 License
GhostFlow is dual-licensed under:
- MIT License ([LICENSE-MIT](LICENSE-MIT))
- Apache License 2.0 ([LICENSE-APACHE](LICENSE-APACHE))
You may choose either license for your use.
---
## 🙏 Acknowledgments
GhostFlow is inspired by:
- **PyTorch** - For its intuitive API design
- **TensorFlow** - For its production-ready architecture
- **ndarray** - For Rust array programming patterns
- **tch-rs** - For Rust ML ecosystem contributions
Special thanks to the Rust community for building an amazing ecosystem!
---
## 📞 Contact & Community
- **GitHub Issues**: [Report bugs or request features](https://github.com/choksi2212/ghost-flow/issues)
- **Discussions**: [Join the conversation](https://github.com/choksi2212/ghost-flow/discussions)
- **Discord**: [Join our community](https://discord.gg/ghostflow)
- **Twitter**: [@GhostFlowML](https://twitter.com/ghostflowml)
---
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### ⭐ Star us on GitHub if you find GhostFlow useful!
**Built with ❤️ in Rust**
[⬆ Back to Top](#-ghostflow)
</div>