Sinapsis Framework Converter: Simplifying Cross-Platform Model Conversion with Templates
The Sinapsis Framework Converter introduces a comprehensive suite of tools for converting machine learning models across popular deep learning frameworks. By leveraging the power of Templates, this release seamlessly integrates with the broader Sinapsis framework, enabling developers to optimize model deployment, experimentation, and interoperability across diverse environments.
This release not only simplifies cross-platform model conversion but also empowers developers to build scalable and efficient workflows using the power of templates. Whether you’re deploying models in production, experimenting with new frameworks, or sharing models across teams, the Sinapsis Framework Converter has you covered.
Key Features
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KerasTensorFlowConverter
Converts Keras models to native TensorFlow format.
Enables seamless integration with TensorFlow workflows, bridging the gap between high-level Keras APIs and low-level -
TensorFlow operations.
Templates simplify the conversion process, allowing developers to customize workflows for specific use cases. -
ONNXTRTConverter
Converts ONNX models to TensorRT for high-performance inference on NVIDIA hardware.
Templates streamline the optimization process, ensuring models are ready for deployment in production-grade environments. -
TensorFlowONNXConverter
Converts TensorFlow models to the ONNX format, enabling interoperability with a wide range of platforms and tools.
Templates provide pre-configured workflows for common conversion scenarios, reducing development time and effort. -
TorchONNXConverter
Converts PyTorch models to ONNX, facilitating model sharing and deployment beyond the PyTorch ecosystem.
Templates enable developers to adapt models for use in diverse environments, ensuring flexibility and scalability. -
TorchTRTConverter
Converts PyTorch models directly to TensorRT, ideal for deploying PyTorch models in low-latency, production-grade environments.
Templates simplify the process of optimizing models for NVIDIA hardware, ensuring maximum performance. -
Model Deployment: Quickly convert models to formats optimized for production environments, such as TensorRT for NVIDIA hardware.
Experimentation: Easily switch between frameworks to experiment with different tools and libraries.
Interoperability: Share models across teams and platforms by converting them to widely supported formats like ONNX.
Documentation and Resources
Documentation: https://docs.sinapsis.tech/docs/
GitHub Repository: https://github.com/Sinapsis-AI/sinapsis-framework-converter