Features
Our community edition provides all the important features to experience the power and usability of model optimization with Neutrino. With the community version, engineers and researchers can verify the seamless integration of Neutrino into standard AI processes, test compatibility with existing model development and explore the benefits of optimization to various products. Feel free to use it as you please! The aim of the community edition is multifold, with examples such as:
Provide hands-on experience with automated model architecture optimization and see first-hand the possibilities with
Deeplite Neutrino
Compare and complement the results obtained using
Deeplite Neutrino
with other open-source and industry model architecture optimization frameworksExport an optimized model to test integration with endpoint applications
Verify the integration of
Deeplite Neutrino
within industry and production pipelinesUtilize
Deeplite Neutrino
to accelerate academic research, expedite results and share your achievements in research papersHave fun! Users can play around with
Deeplite Neutrino
and enjoy the advantages of model architecture optimization in various use-cases
However, for production-grade models and to access some advanced features, we recommend to use the production edition of Neutrino. The production edition is ideal under the following scenarios:
For using a stable, support-friendly production environment with access to Deeplite’s experts
Optimizing advanced, state-of-the-art, and complex deep learning architectures such as object detection and semantic segmentation
Export and consume your optimized models along with trained weights in ONNX or PyTorch JIT format
To be able to use Neutrino in a private or secure environment with no external internet support
Community Edition | Production Edition | |
---|---|---|
Monthly Price |
Free!
|
|
Optimization | ||
Optimize classification CNN models | ||
Type of models to optimize | ||
Optimize for a specific `delta` accuracy | ||
Optimize Object Detection (OD) CNN models | ||
Optimize Segmentation CNN models | ||
Model Quantization | ||
Distributed optimization and training | ||
Exporting options | ||
Export format: ONNX | ||
Export format: TorchScript (.pt) | ||
Export an FP16 quantized ONNX model | ||
Get all the optimization metrics and results | ||
Export the second best optimized model | ||
Export the best optimized model | ||
Caching and Logging | ||
Locally caching and checkpointing through the optimization process | ||
Cloud logging of experimental details | ||
Local(air-gapped) logging of experimental detail | ||
Support and Updates | ||
Free updates and new features | ||
Active support for bugs and requests | (via github) |
(via email, call) |