DocsGetting Started
Quick Start Guide
Get CyxWiz up and running in minutes. This guide covers the fastest path to building and running all components.
Prerequisites Check
# Check required tools cmake --version # 3.20+ git --version # 2.0+ rustc --version # 1.70+ python --version # 3.8+
Clone and Build
# Clone repository git clone https://github.com/CYXWIZ-Lab/CYXWIZ.git cd CyxWiz # Run setup script (installs dependencies) ./scripts/setup.sh # Linux/macOS # or setup.bat # Windows (Developer Command Prompt) # Build everything ./scripts/build.sh # Linux/macOS # or build.bat # Windows
Run Components
# CyxWiz Engine (Desktop Client) ./build/linux-release/bin/cyxwiz-engine # CyxWiz Server Node (Compute Worker) ./build/linux-release/bin/cyxwiz-server-node # CyxWiz Central Server (Orchestrator) cd cyxwiz-central-server && cargo run --release
Your First ML Model
Using the Node Editor
- Launch CyxWiz Engine
- Create New Project: File > New Project
- Open Node Editor: View > Node Editor
- Build a Simple Network:
[Data Input] -> [Dense 784->128 ReLU] -> [Dense 128->10 Softmax] -> [Model Output]
- Add Nodes:
- - Right-click canvas > Add Node > Data > DataInput
- - Right-click canvas > Add Node > Layers > Dense
- - Connect nodes by dragging from output pins to input pins
- Configure Dense Layer:
- - Select node
- - In Properties panel: Units=128, Activation=ReLU
- Generate Code: Edit > Generate Code > PyTorch
Using Python Scripting
Open the Console panel (View > Console) and try:
import cyxwiz as cyx
# Create a simple model
model = cyx.Sequential([
cyx.layers.Dense(128, activation='relu', input_shape=(784,)),
cyx.layers.Dropout(0.2),
cyx.layers.Dense(64, activation='relu'),
cyx.layers.Dense(10, activation='softmax')
])
# Print summary
model.summary()
# Compile
model.compile(
optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy']
)
# Load sample data
(X_train, y_train), (X_test, y_test) = cyx.datasets.mnist.load_data()
# Train
history = model.fit(X_train, y_train,
epochs=5,
batch_size=32,
validation_split=0.2)
# Evaluate
loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test accuracy: {accuracy:.4f}")Keyboard Shortcuts
Global
| Ctrl+N | New Project |
| Ctrl+O | Open Project |
| Ctrl+S | Save |
| Ctrl+Z | Undo |
| Ctrl+Shift+P | Command Palette |
Node Editor
| A | Add Node |
| X | Delete Selected |
| D | Duplicate |
| F | Frame Selected |
| Space | Quick Search |