Developer Resources

Everything you need to start building decentralized AI applications with PYRAI.

Resources

Documentation

Comprehensive guides and API references

Code Examples

Sample projects and code snippets

Tutorials

Step-by-step guides for building on PYRAI

Video Guides

Visual walkthroughs and explanations

SDK Downloads

Official PYRAI SDKs for various languages

Community Forum

Connect with other PYRAI developers

Quick Start Example


import pyrai

# Initialize PYRAI client
client = pyrai.Client(api_key="YOUR_API_KEY")

# Define a simple neural network model
model = pyrai.models.Sequential([
  pyrai.layers.Dense(64, activation='relu', input_shape=(10,)),
  pyrai.layers.Dense(32, activation='relu'),
  pyrai.layers.Dense(1, activation='sigmoid')
])

# Compile the model
model.compile(optimizer='adam', loss='binary_crossentropy')

# Train the model on the decentralized network
model.fit(x_train, y_train, epochs=10, batch_size=32)

# Make predictions
predictions = model.predict(x_test)

# Save the model to PYRAI's decentralized storage
model.save('my_model.h5')

# Load the model from PYRAI's decentralized storage
loaded_model = pyrai.models.load_model('my_model.h5')

Online Demo

Python Editor

Output Console

> Run the code to see output