MyCaffe 0.11.0.188-beta1

MyCaffe AI Platform and Test Application (CUDA 11.0.3, cuDNN 8.0.3) with Distributed AI and ONNX AI Model Support (onnx.ai).

CUDA 11.0.3, cuDNN 8.0.3, nvapi 450, Native Caffe up to 10/24/2018, Windows 10-1909, Driver 452.06

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports Distributed AI!

IMPORTANT NOTE: When using TCC mode, we recommend that ALL headless GPU’s are placed in TCC mode for we have experienced stability issues when using a mix of TCC and WDM modes with headless GPU’s.

REQUIRED SOFTWARE to use MyCaffe: 1.) Download and install Microsoft SQL Express 2016 (or later).

REQUIRED SOFTWARE to build MyCaffe: 1.) Install NVIDIA CUDA 11.0.3 which you can download from https://developer.nvidia.com/cuda-downloads 2.) Install NVIDIA cuDNN 8.0.3 which you can download from https://developer.nvidia.com/cudnn

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 11.0.3/cuDNN 8.0.3 supported (with driver 452.06 or above). • Windows 1909, OS Build 18363.1082 now supported. • Added improved connection options to MyCaffe In-Memory Database for Distributed AI. • Added improved load limit support to MyCaffe In-Memory Database for Distributed AI. • Added capability for loading remote data in MyCaffe In-Memory Database for Distributed AI. • Added optimized Convolution Layer forward pass. • Added improved Nuget Sample making CudaDnnDll selection easier.

The following bug fixes are in this release: • Fixed bugs accessing local help. • Fixed bugs in low-level convolution processing when lWsLimitInBytes = 0. • Fixed bugs related to limited load and auto-refresh. • Fixed bugs related to running MyCaffe Test Application without SQL.

Easily import/export ONNX AI Models, run Triplet Nets[3][4], run Siamese Nets[5][6], Neural Style, train Deep Q-Learning or Policy Gradient[1] models to beat Pong or Cart-Pole, or create the CIFAR-10 and MNIST datasets using the MyCaffe Test Application which you can download from the MyCaffe GitHub site.

Schedule distributed AI work packages, or create and train the Triplet Net[3][4], Siamese Net[5][6], Deep Q-Learning with NoisyNet and Experienced Replay, Policy Gradient[1], Neural Style Transfer, Recurrent Learning, Policy Gradient Reinforcement Learning, Auto-Encoder, DANN and ResNet models by following step-by-step instructions in the SignalPop Tutorials. And, to see other cool examples that show what MyCaffe can do, see the SignalPop Examples.

If you would like to visually design, develop, test and debug your models, see the SignalPop AI Designer specifically designed to enhance your MyCaffe deep learning.

Also, check out the SignalPop Universal Miner that not only keeps your GPU's cool as you train, but also gives you detailed information on each of your GPU's (such as temperature, fan speed, overclock, and usage), and allows you to easily mine Ethereum. When not training AI, put those GPU's to use making some Ether - never let a good GPU go to waste!

Happy ‘deep’ learning!

[1] MyCaffe: A Complete C# Re-Write of Caffe with Reinforcement Learning by D. Brown, 2018.

[2] Caffe: Convolutional Architecture for Fast Feature Embedding by Yangqing Jia, Evan Shelhamer, Jeff Donahue, Sergey Karayev, Jonathan Long, Ross Girshick, Sergio Guadarrama, Trevor Darrell, 2014, arXiv:1408.5093

[3] Deep metric learning using Triplet network by Elad Hoffer and Nir Ailon, 2018, arXiv:1412.6622

[4] In Defense of the Triplet Loss for Person Re-Identification by Alexander Hermans, Lucas Beyer and Bastian Liebe, 2017, arXiv:1703.07737v2

[5] Siamese Network Training with Caffe by Berkeley Artificial Intelligence (BAIR)

[6] Siamese Neural Network for One-shot Image Recognition by G. Koch, R. Zemel and R. Salakhutdinov, ICML 2015 Deep Learning Workshop, 2015.