Build neural networks with PuzzleLib
Open Source high level Deep Learning framework with NVIDIA & AMD support
What is PuzzleLib
PuzzleLib is a Deep Learning framework. The framework allows you to collect neural networks, visualize its architecture, train and export it for use on a phone or server application.
PuzzleLib is an analogue to TensorFlow (Google), Torch (Facebook), CNTK (Microsoft).
CPU & GPU Support
Training is better with GPUs, but CPU support can be crucial when t is not possible to use expensive GPUs. PuzzleLib framework supports:
- NVIDIA GPU (CUDA backend)
- AMD GPU (ROCm backend)
- Intel CPU (mkl-dnn backend)
- AMD CPU (numpy backend)
- Elbrus CPU (numpy backend)
It is crucial to have an ability to use Deep Learning on various operation systems. PuzzleLib framework supports all of the most popular operating systems: the Linux family, Windows, Mac OS, iOS and Android.
Neural network training can take several days. Speeding up calculations, even 1.5 to 2 times, significantly affects the entire workflow. In addition, fast data processing is extremely important in real-time applications (for example, speech analysis on a smartphone).
The computational speed in PuzzleLib is comparable to the speed of Facebook Torch and other frameworks. Such a high speed is achieved by:
- GPU support
- parallel computing on multiple GPUs
- use of low-level computing libraries from NVIDIA
- efficient use of processor resources
- the availability of modules for converting neural networks in the TensorRT Engine
Any neural network is a computational graph: every node is a PuzzleLib module that performs specific operations with the data provided. Below is the list of currently supported modules. The modules make it possible to construct any modern Deep Learning architecture.