Machine Learning with DotCompute
This guide demonstrates how to build machine learning models and inference pipelines using DotCompute's GPU acceleration capabilities.
🚧 Documentation In Progress - Machine learning examples are being developed with complete training and inference examples.
Overview
DotCompute provides optimized operations for:
- Neural network training on GPUs
- Batch inference acceleration
- Model optimization and quantization
- Distributed training across multiple GPUs
Training Loop
Basic Training Loop
TODO: Provide example of basic neural network training:
- Forward pass implementation
- Loss calculation
- Backward pass computation
- Parameter updates
Distributed Training
TODO: Document multi-GPU training patterns:
- Data parallelism
- Model parallelism
- Gradient synchronization
Optimization Strategies
TODO: Explain optimizer implementations:
- SGD with momentum
- Adam optimizer
- Mixed precision training
Inference
Single Sample Inference
TODO: Provide inference pipeline example:
- Model loading
- Input preparation
- Forward pass
- Output processing
Batch Inference
TODO: Document batch processing:
- Batching strategies
- Memory efficiency
- Throughput optimization
Model Serving
TODO: Explain production inference deployment:
- Model serialization
- Runtime optimization
- Latency reduction
Advanced Topics
Model Quantization
TODO: Document quantization techniques
Knowledge Distillation
TODO: Explain knowledge distillation
Transfer Learning
TODO: Cover transfer learning patterns
Performance Tips
TODO: List optimization techniques for ML workloads
Examples
TODO: Link to complete example projects