.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, C# scripting and .NET Interactive. 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 8.6.0) version 1.11.8 with GPT ready!

MyCaffe now supports Transformer Models and GPT! 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

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 510, Windows 10-22H2/Windows 11-22H2, Driver 522.06

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports Visual Studio 2022 and CUDA 11.8.0/cuDNN 8.6.0 and Windows 11!

IMPORTANT NOTES: When using CUDA 11.8/cuDNN 8.6 with MyCaffe, you may receive errors during training. If this occurs, just download and install the MyCaffe CUDA 11.7 Support Installation which is located here: Once installed, the CUDA 11.7 version is used when first running the MyCaffe Test Application from the initial dialog. 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 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

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 510/driver 522.06 • Windows 11 22H2 • Windows 10 22H2, OS Build 19045.2251, SDK 10.0.19041.0 • Added new GELU Layer. • Added new CudaDnnDll channel_copy function. • Added new CudaDnnDll.channel_copyall fuction. • Added new CudaDnnDll mask function. • Added new gemm batch support to CudaDnnDll. • Added new CausalSelfAttentionLayer. • Added new CudaDnnDll channel_fillfrom function. • Added new LayerNormLayer. • Added support for channel_sum within channels. • Added new TransformerBlockLayer. • Added new TokenizedDataLayer. • Added new SoftmaxCrossEntropy2LossLayer. • Added new AdamWSolver. • Added LoadFromNumpy support to Blob. • Added SaveToNumpy support to Blob. • Added new GPT support.

The following bug fixes are in this release: • Fixed bug in TransposeLayer backward • Fixed bug in SoftmaxCrossEntropyLoss layer when axis > 1 • Fixed bug in create run net with SoftmaxCrossEntropyLoss not converting.

Easily run minGPT[3], Single-Shot Multi-Box Nets[4][5], import/export ONNX AI Models, run Triplet Nets[6][7], run Siamese Nets[8][9], 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 minGPT[3], Single-Shot Multi-Box[4][5], Triplet Net[6][7], Siamese Net[8][9], Deep Q-Learning with NoisyNet and Experienced Replay, Policy Gradient, 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 GPUs cool as you train, but also gives you detailed information on each of your GPUs (such as temperature, fan speed, overclock, and usage), and allows you to easily mine Ethereum. When not training AI, put those GPUs 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] GitHub: karpathy/minGPT by Andrej Karpathy, 2022

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

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

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

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

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

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

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MyCaffe AI Platform