.NET Framework 4.0
dotnet add package MyCaffe --version
NuGet\Install-Package MyCaffe -Version
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package.
<PackageReference Include="MyCaffe" Version="" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version
#r "nuget: MyCaffe,"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package.
// Install MyCaffe as a Cake Addin
#addin nuget:?package=MyCaffe&version=

// Install MyCaffe as a Cake Tool
#tool nuget:?package=MyCaffe&version=

MyCaffe AI Platform (CUDA 11.8.0, cuDNN 8.8.0) with GPT and ChatGPT technology - version ready!

MyCaffe now supports Encoder/Decoder Transformer Models like those used in ChatGPT! The MyCaffe AI Platform provides an easy AI solution for multiple AI disciplines, including:

• ChatGPT Models • GPT Models • Transformer Models • Object detection with Single-Shot Multi Box (SSD) • Onnx AI Model Support (import and export) • Classification with AlexNet, ResNet, VGG, NoisyNet, and Inception models • Classification with SiameseNet • Classification with TripletNet • Auto Encoders and DANN • Reinforcement Learning with Policy Gradient and Deep Q-Learning • Recurrent Learning with CharNet • Neural Style Transfer • Seq2Seq 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, cuDNN, nvapi 515, Windows 10-22H2/Windows 11-22H2, Driver 516.94

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.

IMPORTANT NOTES: 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.

IMPORTANT NOTES: If you receive a build error triggered by the 'xcopy' post-build event, change the Post-build event command line to: copy "$(SolutionDir)packages\MyCaffe.\NativeBinaries\x64*.*" "$(TargetDir)"

REQUIRED SOFTWARE to use MyCaffe: 1.) Download and install 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

2.) Download and install the MyCaffe Test Application for easy access to required NVIDIA DLL's. The MyCaffe Test Application which you can download from the MyCaffe GitHub site.

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 515/driver 516.94 • Windows 11 22H2 • Windows 10 22H2, OS Build 19045.2604, SDK 10.0.19041.0 • Added GELU support to TransformerBlockLayer. • Added GELU_BERT support to TransformerBlockLayer. • Added Activation Graphing support to MyCaffe Auto Tests. • Upgraded to Google ProtoBuf 3.22.0 • Upgraded to NewtonJson 13.0.2 • Added ‘i4’, ‘i8’, and ‘f8’ type support to Blob LoadFromNumpy • Added new MultiheadAttentionLayer. • Added CudaDnn mask_batch support. • Added Blob.Compare support. • Added EncoderBlock support to TransformerBlockLayer • Added DecoderBlock support to TransformerBlockLayer • Added new TokenizedDataPairsLayer. • Added new TokenizedDataPairsLayerPy. • Added new algorithm support to SoftmaxLayer including LOG • Added new NLLLossLayer. • Added new CudaDnn channel_duplicate. • Added new CudaDnn matmul • Added new CudaDnn transpose_hw • Added new CudaDnn add3 • Added new CudaDnn channel_mean • Added support to CudaDnn channel_max/min to return indexes. • Added gpu support to ArgmaxLayer • Added Blob.SaveToImage. • Added support for loading very large numpy arrays.

The following bug fixes are in this release: • Improved internal blobs for better memory sharing. • Improved overall GPU memory use. • Fixed bug in Blob.CopyFrom wrt shape comparison. • Fixed bug in CudaDnn layer where large indexes are now properly passed. • Fixed bug in CausalSelfAttentionLayer related to batch training. • Optimized memory use on GPT and Encoder/Decoder models.

Easily run Encoder/Decoder for Language Translation[3], minGPT[4], Single-Shot Multi-Box Nets[5][6], import/export ONNX AI Models, run Triplet Nets[7][8], run Siamese Nets[9][10], 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.

Visually design, develop, test and train these models with the SignalPop AI Desigenr 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.

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

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

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

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

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

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

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

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

Product Compatible and additional computed target framework versions.
.NET Framework net40 is compatible.  net403 was computed.  net45 was computed.  net451 was computed.  net452 was computed.  net46 was computed.  net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
Compatible target framework(s)
Additional computed target framework(s)
Learn more about Target Frameworks and .NET Standard.

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version Downloads Last updated 382 2/21/2023 558 11/23/2022 903 8/8/2022 641 6/10/2022 202 2/11/2022 244 9/11/2021 224 5/19/2021 200 2/3/2021 245 11/21/2020 262 10/17/2020 296 9/24/2020 352 8/6/2020 425 5/31/2020 370 1/21/2020 353 11/29/2019 358 10/28/2019 355 9/17/2019 466 7/8/2019 470 5/31/2019 484 4/18/2019 466 3/5/2019 635 1/15/2019 572 11/29/2018 596 11/15/2018 626 10/7/2018

MyCaffe AI Platform