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Deeplite Neutrino™

With the emergence of deep learning on mobile and embedded devices, AI solutions must satisfy strict computing requirements, which often limits performance and prevents people from moving to these devices. Deeplite created Neutrino, an intelligent optimization engine for Deep Neural Networks (DNNs) deployed on cloud servers and edge devices. Neutrino helps AI experts automatically optimize high-performance DNN models to satisfy target computation constraints. The engine inputs large, initial DNN models and constraints to produce smaller, highly-efficient models that meet these constraints.

Contents:

Documentation

Why Neutrino™ ?

Neutrino is a deep learning library for optimizing and accelerating deep neural networks to make them faster, smaller and more energy-efficient. Neural network designers can specify a variety of pre-trained models, datasets and target computation constraints and ask the engine to optimize the network. High-level APIs are provided to make the optimization process easy and transparent to the user. Neutrino can be biased to concentrate on compression (relative to disk size taken by the model) or latency (forward call’s execution time) optimization.

A quick glance on the results obtained using Deeplite Neutrino™ can be found in the table below. While optimizing the models, a maximum affordable drop in the performance is provided as input delta. For classification models, the delta is set to 1%, while for objection detection (OD) and segmentation models, the delta is set to 0.03 mAP and 0.05 mIOU, respectively. We can observe that with such low (and, almost negligble) drop in the performance, the classification models can be optimized upto ~30x of its original size, while OD and segmentation models can be optimized upto ~8x of its original size.

Community Release

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 frameworks
  • Export an optimized model to test integration with endpoint applications
  • Verify the integration of Deeplite Neutrino within industry and production pipelines
  • Utilize Deeplite Neutrino to accelerate academic research, expedite results and share your achievements in research papers
  • Have fun! Users can play around with Deeplite Neutrino and enjoy the advantages of model architecture optimization in various use-cases

For detailed comparison of features on our community and production editions, refer to the documentation

Get Your Free Community License

The community license key is completely free-to-obtain and free-to-use. Fill out this simple form to obtain the license key for the Community Version of Deeplite Neutrino™.

Installation

Use pip to install neutrino-engine from PyPi repository. We recommend creating a new python virtualenv, then pip install using the following commands.

    pip install --upgrade pip
    pip install neutrino-engine
    pip install neutrino-torch

For other methods of installation and detailed instructions, refer to the documentation

Examples

We provide various examples on how to optimize different kinds of deep learning models such as classification, object detection, and segmentation models using Deeplite Neutrino™.

For a detailed understanding of various customization parameters and setings, refer to the documentation

Deeplite Torch Zoo

The deeplite-torch-zoo package is a collection of popular CNN model architectures and benchmark datasets for PyTorch framework. The models are grouped under different datasets and different task types such as classification, object detection, and segmentation. The primary aim of this deeplite-torch-zoo is to booststrap applications by starting with the most suitable pretrained models. In addition, the pretrained models from deeplite-torch-zoo can be used as a good starting point for optimizing model architectures using our Deeplite Neutrino™.

For a list of models available in Deeplite Torch Zoo and the benchmark results, refer to the documentation

Deeplite Profiler

To be able to use a deep learning model in research and production, it is essential to understand different performance metrics of the model beyond just the model's accuracy. deeplite-profiler helps to easily and effectively measure the different performance metrics of a deep learning model. In addition to the existing metrics in the deeplite-profiler, users could seamlessly contribute any custom metric to measure using the profiler. deeplite-profiler could also be used to compare the performance between two different deep learning models, for example, a teacher and a student model. deeplite-profiler currently supports PyTorch and TensorFlow Keras (v1) as two different backend frameworks.

For a list of different metrics available in the Deeplite Profiler, refer to the documentation

Distributed Training and Optimization in Neutrino™

Neutrino™ can run in distributed mode(multi-GPU, multi-server nodes) using horovod. Please note that the distributed mode is currently only availble to GPU's, CPU support is yet to be added. To be able to use distributed mode, you need to have docker installed and running on your system. The setup process is straightforward:

  • Get the docker file from here.
  • Build your docker image:
    sudo docker build -t neutrino:latest -f Dockerfile.gpu .
    sudo nvidia-docker run -it --shm-size=32g  -v /home/JohnDoe/:/neutrino  neutrino:latest
  • To test Neutrino™ in distributed mode, from within your docker container, checkout and navigate to the neutrino-examples repository, then run the following command:
    horovodrun -np 1 -H localhost:1 python src/hello_neutrino_classifier.py --arch resnet18 --dataset cifar100 --delta 1 --horovod

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