MyCaffe 0.10.1.221-beta1

This is a prerelease version of MyCaffe.
There is a newer version of this package available.
See the version list below for details.
dotnet add package MyCaffe --version 0.10.1.221-beta1
NuGet\Install-Package MyCaffe -Version 0.10.1.221-beta1
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="0.10.1.221-beta1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version 0.10.1.221-beta1
#r "nuget: MyCaffe, 0.10.1.221-beta1"
#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=0.10.1.221-beta1&prerelease

// Install MyCaffe as a Cake Tool
#tool nuget:?package=MyCaffe&version=0.10.1.221-beta1&prerelease

CUDA 10.1.243, cuDNN 7.6.3, nvapi 430, Native Caffe up to 10/24/2018, Windows 10-1903, Driver 430.86

MyCaffe[1] (a complete C# re-write of CAFFE[2]) now supports Deep Q-Learning[3][4] with a NoisyNet[5] and Prioritized Replay Buffer[6], all supported by the new DQN trainer and do so with the newly released CUDA 10.1.243, CuDNN 7.6.3 and the dual RNN/RL training that allows multi-pass training where the first pass involves RNN training and the second pass involves RL training that uses the already trained RNN side of the model.

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: 1.) Install NVIDIA CUDA 10.1.243 which you can download from https://developer.nvidia.com/cuda-downloads 2.) Install NVIDIA cuDNN 7.6.3 which you can download from https://developer.nvidia.com/cudnn 3.) Download and install Microsoft SQL Express 2016 (or later).

This release of the MyCaffe AI Platform and Test Applications has the following new additions: • CUDA 10.1.243/cuDNN 7.6.3 supported (with driver 430.86 or above). • Windows 1903, OS Build 18362.356 now supported. • Added CudaDnn.matrix_mean support. • Added CudaDnn.matrix_stdev support. • Added CudaDnn.matrix_correlation support. • Added CudaDnn.mulbsx matrix vector multiplication support. • Added CudaDnn.divbsx matrix vector division support. • Added new CudaDnn.permute support. • Added new Normalization Layer for SSD. • Added new PriorBox Layer for SSD. • Added new MultiBoxLoss Layer for SSD. • Added legacy compute 3.5 support to cuDNN 10.1 version of CudaDnn DLL. • Added compute checks to the MyCaffe Test Application. • Added data load control to Image Database allowing optional loading of Image Criteria and Debug data. • Added new LoadLite method to MyCaffeControl to load model and solver without MyCaffeImageDatabase.

The following bug fixes are in this release: • Fixed bugs related to allocating a large number of items.

Easily run Neural Style, train Deep Q-Learning[3][4] 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.

Create and train the Deep Q-Learning[3][4], 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] GitHub: Google/dopamine licensed under the Apache 2.0 License;

[4] Dopamine: A Research Framework for Deep Reinforcement Learning by Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare, 2018, arXiv:1812.06110

[5] Noisy Networks for Exploration by Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, Olivier Pietquin, Charles Blundell, Shane Legg, 2018, arXiv:1706.10295

[6] Prioritized Experience Replay by Tom Schaul, John Quan, Ioannis Antonoglou, David Silver, 2016, arXiv:1511.05952

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)
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Learn more about Target Frameworks and .NET Standard.

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MyCaffe/MyCaffe
A complete deep learning platform written almost entirely in C# for Windows developers! Now you can write your own layers in C#!
Version Downloads Last updated
1.12.2.41 350 9/18/2023
1.12.1.82 381 6/8/2023
1.12.0.60 596 2/21/2023
1.11.8.27 758 11/23/2022
1.11.7.7 1,087 8/8/2022
1.11.6.38 814 6/10/2022
0.11.6.86-beta1 344 2/11/2022
0.11.4.60-beta1 323 9/11/2021
0.11.3.25-beta1 404 5/19/2021
0.11.2.9-beta1 288 2/3/2021
0.11.1.132-beta1 332 11/21/2020
0.11.1.56-beta1 327 10/17/2020
0.11.0.188-beta1 366 9/24/2020
0.11.0.65-beta1 392 8/6/2020
0.10.2.309-beta1 498 5/31/2020
0.10.2.124-beta1 419 1/21/2020
0.10.2.38-beta1 422 11/29/2019
0.10.1.283-beta1 415 10/28/2019
0.10.1.221-beta1 414 9/17/2019
0.10.1.169-beta1 529 7/8/2019
0.10.1.145-beta1 524 5/31/2019
0.10.1.48-beta1 547 4/18/2019
0.10.1.21-beta1 525 3/5/2019
0.10.0.190-beta1 691 1/15/2019
0.10.0.140-beta1 629 11/29/2018
0.10.0.122-beta1 655 11/15/2018
0.10.0.75-beta1 686 10/7/2018

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