OptiMate

Interactive tool guiding savvy users in achieving the best inference performance out of a given model / hardware setup.

Inference
Transformers
CNN
PyTorch
Hugging Face
TensorFlow
ONNX

🚧 Coming soon 🚧

The OptiMate module is targeted at a sophisticated and savvy type of users, who need to squeeze out every last drop of performance out of a given hardware.

The module is designed to help users to optimize their deep-learning models through the use of profilers and advanced optimization techniques. It also includes a smart assistant that guides the user through the optimization process and provides suggestions to improve the performance of the model.

Each temporary optimization is tracked in a detailed version history, allowing the user to revert to its preferred version at the end of the optimization process.

First, the module leverages profilers to gather information about the model, such as the amount of time it takes for the model to make predictions and the amount of memory used. This information helps in identifying bottlenecks and other inefficiencies in the model.

Then, the module uses various optimization techniques to improve inference performances. These techniques include, among others, model compression, pruning, and quantization, which can help reduce the size and computational demand of the model.

Throughout the process, the smart assistant provides guidance and suggestions to the user. For example, it might suggest which optimization techniques to try out or provide guidance on how to adjust the model parameters to improve its performance.

Overall, the module provides a user-friendly but sophisticated interface to get the most out of any model / hardware setup. Try it out today, and reach out if you have any feedback!

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