MyCaffe 0.10.0.75-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 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 0.10.0.75-beta1
dotnet add package MyCaffe --version 0.10.0.75-beta1
<PackageReference Include="MyCaffe" Version="0.10.0.75-beta1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add MyCaffe --version 0.10.0.75-beta1
The NuGet Team does not provide support for this client. Please contact its maintainers for support.

CUDA 10.0.130, cuDNN 7.3.1, Native Caffe up to 3/26/2018, Windows 10-1803, Driver 411.70 and 416.16
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.

MyCaffe now supports multi-threaded based Reinforcement Learning using CUDA 10 on the Arcade-Learning-Environment [1]!

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

  • CUDA 10.0.130/cuDNN 7.31 now supported (with driver 411.70 and 416.16).
  • Added new AleControl for access to Arcade-Learning-Environment
  • Added new ATARI gym support via AleControl
  • Added ability to turn off policy gradient accelerated learning.
  • Added random exploration support.

The following bug fixes are in this release:

  • Fixed bugs in discounted return calculation
  • Fixed bugs in Blob.NormalizeData

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 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), it allows you to easily start mining 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] The Arcade Learning Environment: An Evaluation Platform for General Agents by Marc G. Bellemare, Yavar Naddaf, Joel Veness and Michael Bowling, 2012-2013. Source code available on GitHub at https://github.com/mgbellemare/Arcade-Learning-Environment

CUDA 10.0.130, cuDNN 7.3.1, Native Caffe up to 3/26/2018, Windows 10-1803, Driver 411.70 and 416.16
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.

MyCaffe now supports multi-threaded based Reinforcement Learning using CUDA 10 on the Arcade-Learning-Environment [1]!

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

  • CUDA 10.0.130/cuDNN 7.31 now supported (with driver 411.70 and 416.16).
  • Added new AleControl for access to Arcade-Learning-Environment
  • Added new ATARI gym support via AleControl
  • Added ability to turn off policy gradient accelerated learning.
  • Added random exploration support.

The following bug fixes are in this release:

  • Fixed bugs in discounted return calculation
  • Fixed bugs in Blob.NormalizeData

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 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), it allows you to easily start mining 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] The Arcade Learning Environment: An Evaluation Platform for General Agents by Marc G. Bellemare, Yavar Naddaf, Joel Veness and Michael Bowling, 2012-2013. Source code available on GitHub at https://github.com/mgbellemare/Arcade-Learning-Environment

Release Notes

MyCaffe AI Platform

Version History

Version Downloads Last updated
0.10.1.169-beta1 35 7/8/2019
0.10.1.145-beta1 74 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
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