Documentation
Changelog
Start typing to searchโ€ฆ
Back to All
improved

Conversion and Inference Enhancements

August 23rd, 2024

๐Ÿš€ New Features

ONNX & TFLite Inferencer Added

  • Introduced a unified inferencer for ONNX and TFLite models, enabling easy evaluation across formats.

Runtime Config Support for Inference

  • Added configuration options to control runtime behavior during inference, offering more flexibility in deployment.

TensorRT/DRPAI Device & Software Filtering

  • Conversion flows now support device + software version filtering, improving deployment compatibility.

Netspresso Trainer v1.0.0 Integration

  • Upgraded to netspresso_trainer v1.0.0 with enhanced stability and standardized behavior.

PyNPException for GUI

  • Added PyNPException class to streamline error reporting between SDK and GUI environments.

Optimizer & Scheduler Enum

  • Introduced enums for optimizer and scheduler types, simplifying training config definitions.

Timeout Handling for Compression

  • Added a 600-second timeout for compressor requests to prevent indefinite hangs.

Improved Metadata Logging

  • Trainer and converter metadata now include error logs and model info (framework, input_shapes) for better traceability.

DLC Framework Filtering

  • Excluded unsupported DLC framework from trainer configuration options.

๐Ÿž Bug Fixes

  • Fixed incorrect task value in inference runtime configs.
  • Corrected typo in training_result key of trainer metadata.
  • Resolved issue with invalid runtime config options not applying properly.
  • Added validate_token check when generating converter metadata.
  • Introduced GatewayTimeoutException and UnexpectedException for clearer backend error handling.
  • Enhanced training error logs with detailed name and message fields.

๐Ÿง  Why these matter

  • These updates bring more control and reliability to the inference and deployment processes.
  • By enhancing metadata and configuration handling, users can debug more effectively and automate more confidently.
  • The inclusion of timeout handling, structured exceptions, and training options paves the way for smoother integration in both GUI and SDK environments.