Expand description
Machine learning framework compatibility
Provides conversion utilities and interfaces for ML frameworks:
- Support for PyTorch, TensorFlow, ONNX, SafeTensors formats
- Model and tensor serialization/deserialization
- Data type conversions between frameworks
- Dataset utilities for ML pipelines
- Seamless integration with ndarray Machine learning framework compatibility layer
Provides conversion utilities and interfaces for seamless integration with popular machine learning frameworks, enabling easy data exchange and model I/O.
Re-exports§
pub use batch_processing::BatchProcessor;pub use batch_processing::DataLoader;pub use converters::get_converter;pub use converters::CoreMLConverter;pub use converters::HuggingFaceConverter;pub use converters::JAXConverter;pub use converters::MLFrameworkConverter;pub use converters::MXNetConverter;pub use converters::ONNXConverter;pub use converters::PyTorchConverter;pub use converters::SafeTensorsConverter;pub use converters::TensorFlowConverter;pub use datasets::MLDataset;pub use optimization::ModelOptimizer;pub use optimization::OptimizationTechnique;pub use quantization::ModelQuantizer;pub use quantization::QuantizationMethod;pub use quantization::QuantizedModel;pub use quantization::QuantizedTensor;pub use serving::ApiConfig;pub use serving::HealthStatus;pub use serving::InferenceRequest;pub use serving::InferenceResponse;pub use serving::LoadBalancer;pub use serving::ModelInfo;pub use serving::ModelServer;pub use serving::ResponseStatus;pub use serving::ServerConfig;pub use serving::ServerMetrics;pub use types::DataType;pub use types::MLFramework;pub use types::MLModel;pub use types::MLTensor;pub use types::ModelMetadata;pub use types::TensorMetadata;pub use validation::BatchValidator;pub use validation::ModelValidator;pub use validation::ValidationConfig;pub use validation::ValidationReport;
Modules§
- batch_
processing - Batch processing support for ML models
- converters
- ML framework converters
- datasets
- Dataset utilities for ML frameworks
- optimization
- Model optimization features
- quantization
- Model quantization support
- serving
- Model serving capabilities with REST API and gRPC support
- types
- Core types for ML framework compatibility
- utils
- Utility functions for ML framework operations
- validation
- Model validation and compatibility checking between frameworks