MyCaffe 1.12.2.41

MyCaffe AI Platform (CUDA 11.8.0, cuDNN 8.8.0) with Liquid Neural Networks version 1.12.2.41 ready!

MyCaffe now supports Liquid Neural Network Models! The MyCaffe AI Platform provides an easy AI solution for multiple AI disciplines, including:

• Classification with AlexNet, ResNet, VGG, NoisyNet, and Inception models • Classification with SiameseNet • Classification with TripletNet • Auto Encoders and DANN • Onnx AI Model Support (import and export) • Object detection with Single-Shot Multi Box (SSD) • Reinforcement Learning with Policy Gradient and Deep Q-Learning • Recurrent Learning with CharNet • Neural Style Transfer • Seq2Seq Models • Transformer Models • GPT Models • Temporal Fusion Transformer Models • Liquid Neural Network Models

Speed up AI training with the MyCaffe in-memory database that caches full datasets or drip-fed datasets into your local RAM on one side while feeding the training process on the other with label balanced data. Easily Train on multiple-GPUs with NCCL.

CUDA 11.8.0.522, cuDNN 8.8.0.121, nvapi 515, Windows 10-22H2/Windows 11-22H2, Driver 531.14

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports Visual Studio 2022 and CUDA 11.8.0/cuDNN 8.8.0 and Windows 11!

When using TCC mode, we recommend that ALL headless GPUs are placed in TCC mode for we have experienced stability issues when using a mix of TCC and WDM modes with headless GPUs.

REQUIRED SOFTWARE to use MyCaffe: 1.) Download and install the full version of Microsoft SQL Express 2016 (or later). NOTE: The full version of SQL Express must be installed as opposed to the light version included in Visual Studio. Microsoft SQL Express can be downloaded from https://www.microsoft.com/en-us/sql-server/sql-server-downloads

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 11.8.0.522/cuDNN 8.8.0.121/nvapi 515/driver 531.14 • Windows 11 22H2 • Windows 10 22H2, OS Build 19045.3448, SDK 10.0.19041.0 • Added new GELU Layer. • Upgraded Google ProtoBuf to 3.24.3 • Generified IXImageDatabaseBase interface to IXDatabaseBase • Added In-Memory MyCaffeTemporalDatabase support. • Added ExpandableObjectConverter to all parameters. • Added new SiLU Activation function and layer. • Added new Softplus Activation function and layer. • Added new LeCun Activation function and layer. • Added new LtcUnitCell Layer for Liquid Neural Net support. • Added new CfcUnitCell Layer for Liquid Neural Net support. • Added new CfCLayer for Liquid Neural Net support. • Added new Curve Gym. • Added new record functionality to Gym dialog. • Added new CudaDnn.channel_op function. • Added BWD support to CudaDnn.channel_sum • Added debugging to Numpy TFT data input. • Added background loading to Temporal Database. • Renamed IMGDB_LABEL_SELECTION_ to DB_LABEL_SELECTION_ • Renamed IMGDB_IMAGE_SELECTION_ to DB_ITEM_SELECTION_ • Renamed SourceDescriptor.ImageChannels to Channels • Renamed SourceDescriptor.ImageHeight to Height • Renamed SourceDescriptor.ImageWidth to Width. • Renamed IMGDB_LOAD_METHOD to DB_LOAD_METHOD

The following bug fixes are in this release: • Fixed bug, now correct image database used reported in Test Application. • Fixed bug in CategoricalTransformationLayer regarding resizing. • Fixed bug when image mean missing in DataLayer. • Fixed bug in auto test related to logging access. • Fixed bug in CategoricalTransformationLayer spatial dim. • Fixed bug in NumericTransformationLayer spatial dim. • Fixed bugs causing error in ChannelEmbeddingLayer. • Fixed bugs in NuGet adding TFT layer modules.

Easily run Liquid Neural Networks [3][4], Temporal Fusion Transformer Models[5], Language Translation Encoder/Decoder Transformer Models[6][7], minGPT[8], Single-Shot Multi-Box Nets[9][10], import/export ONNX AI Models, run Triplet Nets[11][12], run Siamese Nets[13][14], Neural Style, train Deep Q-Learning or Policy Gradient 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 these 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.
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] Liquid Time-constant Networks by Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu, 2020, arXiv:2006.04439

[4] Closed-form continuous-time neural networks by Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus, 2022, nature machine intelligence

[5] Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting by Bryan Lim, Sercan O. Arik, Nicolas Loeff, and Tomas Pfister, 2019, arXiv: 1912.09363

[6] GitHub: devjwsong/transformer-translator-pytorch by Jaewoo (Kyle) Song, 2021, GitHub

[7] Attention Is All You Need by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin, 2017, arXiv:1706.03762

[8] GitHub: karpathy/minGPT by Andrej Karpathy, 2022

[9] SSD: Single Shot MultiBox Detector by Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, 2016.

[10] GitHub: SSD: Single Shot MultiBox Detector, by weiliu89/caffe, 2016

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

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

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

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