MyCaffe 0.10.0.190-beta1

MyCaffeControl

A complete C# re-write of Berkeley's open source Convolutional Architecture for Fast Feature Encoding (CAFFE) for Windows C# Developers, now with Policy Gradient Reinforcement Learning, cuDNN LSTM Recurrent Learning, and Neural Style Transfer support!

This is a prerelease version of MyCaffe.
There is a newer prerelease version of this package available.
See the version list below for details.
Install-Package MyCaffe -Version 0.10.0.190-beta1
dotnet add package MyCaffe --version 0.10.0.190-beta1
<PackageReference Include="MyCaffe" Version="0.10.0.190-beta1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version 0.10.0.190-beta1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

CUDA 10.0.130, cuDNN 7.4.2, nvapi 410, Native Caffe up to 10/24/2018, Windows 10-1809, Driver 417.35

MyCaffe now supports Neural Style Transfer using CUDA 10/cuDNN 7.4.2 to implement "A Neural Algorithm of Artistic Style" described by [1] using the VGG model [2], inspired by [3], which uses Caffe [4].

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.

This release of the MyCaffe AI Platform and Test Applications has the following new additions:

  • CUDA 10.0.130/cuDNN 7.4.2 supported (with driver 417.35).
  • Added new ScalarLayer.
  • Added new EventLayer.
  • Finalized TVLossLayer for Neural Style.
  • Finalized LBFGSSolver for Neural Style.
  • Completed Neural Style Transfer.

The following bug fixes are in this release:

  • Fixed bugs in GramLayer for Neural Style.
  • Fixed bugs in Convolution cuDNN stream synchronization (impacted ResNet50 under WDM).
  • Improved Deconvolution cuDNN stream synchronization.

To read more about cuDNN Neural Style Transfer in MyCaffe, see the SignalPop Blog.

Easily 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 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 gives you detailed information on each of your GPU's (such as temperature, fan speed, overclock, and usage), but 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] L. Gatys, A. Ecker, M. Bethge [A Neural Algorithm of Artistic Style] (https://arxiv.org/abs/1508.06576), 2015, arXiv:1508:06576

[2] K. Simonyan, A. Zisserman Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556

[3] ftokarev ftokarev/caffe Github, 2017, Github

[4] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama and T. Darrell, Caffe: Convolutional Architecture for Fast Feature Embedding, June 20, 2014.

CUDA 10.0.130, cuDNN 7.4.2, nvapi 410, Native Caffe up to 10/24/2018, Windows 10-1809, Driver 417.35

MyCaffe now supports Neural Style Transfer using CUDA 10/cuDNN 7.4.2 to implement "A Neural Algorithm of Artistic Style" described by [1] using the VGG model [2], inspired by [3], which uses Caffe [4].

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.

This release of the MyCaffe AI Platform and Test Applications has the following new additions:

  • CUDA 10.0.130/cuDNN 7.4.2 supported (with driver 417.35).
  • Added new ScalarLayer.
  • Added new EventLayer.
  • Finalized TVLossLayer for Neural Style.
  • Finalized LBFGSSolver for Neural Style.
  • Completed Neural Style Transfer.

The following bug fixes are in this release:

  • Fixed bugs in GramLayer for Neural Style.
  • Fixed bugs in Convolution cuDNN stream synchronization (impacted ResNet50 under WDM).
  • Improved Deconvolution cuDNN stream synchronization.

To read more about cuDNN Neural Style Transfer in MyCaffe, see the SignalPop Blog.

Easily 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 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 gives you detailed information on each of your GPU's (such as temperature, fan speed, overclock, and usage), but 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] L. Gatys, A. Ecker, M. Bethge [A Neural Algorithm of Artistic Style] (https://arxiv.org/abs/1508.06576), 2015, arXiv:1508:06576

[2] K. Simonyan, A. Zisserman Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556

[3] ftokarev ftokarev/caffe Github, 2017, Github

[4] Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama and T. Darrell, Caffe: Convolutional Architecture for Fast Feature Embedding, June 20, 2014.

Release Notes

MyCaffe AI Platform

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Version History

Version Downloads Last updated
0.10.1.169-beta1 44 7/8/2019
0.10.1.145-beta1 76 5/31/2019
0.10.1.48-beta1 88 4/18/2019
0.10.1.21-beta1 89 3/5/2019
0.10.0.190-beta1 151 1/15/2019
0.10.0.140-beta1 106 11/29/2018
0.10.0.122-beta1 125 11/15/2018
0.10.0.75-beta1 126 10/7/2018