AI Developer Resources
Access high-performance AI computing power at lower costs. NeuralNet enables you to access distributed computing resources at 30-50% lower prices than traditional cloud services.
Seamless Integration
NeuralNet API is fully compatible with mainstream AI frameworks, allowing you to connect your applications to the distributed computing network with just a few lines of code.
- Supports TensorFlow, PyTorch, JAX
- Simple Python and JavaScript SDK
- Complete API documentation and examples
Cost Effectiveness
Compared to traditional cloud services, NeuralNet offers more competitive pricing, making your AI project budget more flexible.
- Save 30-50% compared to traditional cloud services
- Pay-per-second billing, no minimum usage requirements
- Bulk reservation discounts
High Performance Guarantee
Our intelligent scheduling system ensures your AI tasks run on the most suitable hardware, providing a stable high-performance experience.
- Automatic task optimization
- 99.9% availability SLA
- Global node distribution, low-latency access
Supported AI Workloads
Distributed Model Training
Accelerate large-scale AI model training
NeuralNet's distributed training capabilities allow you to train large models in parallel across multiple nodes, significantly reducing training time and costs.
- Supports model parallelism and data parallelism
- Automatic checkpoint saving and recovery
- Suitable for various model architectures (Transformers, CNNs, RNNs, etc.)
Example code:
import neuralnet as nn
# Initialize distributed training environment
nn.init_distributed()
# Configure training parameters
config = nn.TrainingConfig(
model_type="transformer",
batch_size=32,
learning_rate=1e-4,
epochs=10
)
# Start distributed training
trainer = nn.Trainer(config)
trainer.fit(
model=my_model,
train_dataset=train_data,
val_dataset=val_data
)Developer Tools
SDKs and APIs
Comprehensive SDKs supporting multiple programming languages including Python, JavaScript, Go, and Rust, allowing you to integrate NeuralNet using familiar toolsets.
Command Line Tools
Powerful CLI tools that allow you to manage resources, monitor tasks, and deploy models directly from your terminal, supporting automated workflows.
Web Console
Intuitive web interface providing visual monitoring, resource management, and team collaboration features, making it easy to track project progress and performance.
Getting Started Guide
Quick Start
Follow these simple steps to start using NeuralNet's distributed AI computing power:
- 1
Create an Account
Register for a NeuralNet account and connect your Solana wallet for payments and receiving rewards.
- 2
Install the SDK
Install the NeuralNet SDK using your preferred package manager:
pip install neuralnet - 3
Configure API Key
Set up your API key in your environment:
# Linux/macOS export NEURALNET_API_KEY="your_api_key" # Windows set NEURALNET_API_KEY="your_api_key" - 4
Start Using
Import and initialize NeuralNet in your code:
import neuralnet as nn # Initialize client client = nn.Client() # Check available resources resources = client.list_resources() print(f"Available resources: {resources}") # Now you can start using distributed computing power!