New beta release 0.9.1.21, with a new focus on Windows 10 (1709+)

We have just released a new beta with a new focus on Windows 10 (specifically 1709 and above).  With this release moving forward, Windows 10 will be our primary platform of focus, yet we will continue to run our test cycles on Windows 7.

New Features and Fixes

The following main additions have been added to this release:

  • Support for newly released CUDA 9.1 (patches 1-3) and cuDNN 7.1.1
  • Fixed memory overwrites caused during the convolution backward pass when group > 1.
  • New updated, faster installation.
New Installation Notes

We have changed our installation process in that we now install the SignalPop In-Memory Database Service under the Local Service account.  Before using the LOAD_FROM_SERVICE image loading method you will need to do one of the following:

1.) Make sure the Local Service account has access to your DNN database tables (Use the SQL Server Management Studio to make these changes).

2.) Alternatively, change the Service Account used by the SignalPop In-Memory Database service to an account that has access to the tables within the DNN database.  This link will show you how to do just that.

New Maintenance Release Available – version 0.9.0.427

We have just dropped a new maintenance release version 0.9.0.427.  This release includes the following improvements:

1.) Dramatically improved start-up time.
2.) t-SNE algorithm includes bugs fixed related to very small ‘% of NN to circle’
3.) Dataset naming improved.
4.) First database creation improved.

For a list of all bugs fixed, see our bugs section in the Developer area.

NOTE: Your existing product license key will work with this new release, just install this version and you are ready to go!

If you don’t have the SignalPop AI Designer, you can download an evaluation version for free from the Products area.

Known Issues
  • IMPORTANT: When using the AlexNet (32×32) or (56×56) resource template, the second convolution layer ‘conv2’ uses a group setting of 2.  This causes a known CUDA error when using the CUDNN engine.  We recommend for now changing the ‘conv2’ group setting to 1 to work around the issue while we work on a fix.

New beta release now synced up with native Caffe through 2/1/2018!

We have just released a new beta release – version 0.9.0.409 that is fully synced up with the native Caffe open-source project up through 2/1/2018.

New features added in this version include the following:

1.) The deconvolution layer now supports the CUDNN engine.
2.) The BilinearFill has been updated.
3.) All NVIDIA cuDNN errors are now supported up through version 7.0.5.
4.) All NVIDIA CUDA errors are now supported up through version 9.1.
5.) The CUDA.9 low-level interface DLL now use compute_35 and sm_35 (for compute_30, sm_30) use the CUDA.8 low-level interface DLL.
6.) NCCL has been updated to resolve issues caused when training in multi-GPU configuration.

Happy learning!

Export datasets and projects directly into your Docker containers!

The new release of the SignalPop AI Designer (v. 0.9.0.391) now allows you to easily export both your datasets and projects directly into your Docker containers!  With this feature, you can easily develop, edit and test your models (and datasets) locally in the visual SignalPop AI Designer and then quickly deploy them via SFTP into your production Docker container running native Caffe locally or in the cloud.

To get going all that you need to do is setup an SFTP Docker container (such as atmoz/sftp on DockerHub) and link it to your native Caffe Docker container (such as nvidia/caffe also on DockerHub) via a shared Docker volume.

The following Docker commands will get you started:

$ docker volume create mycaffe-vol
$ docker container run 
  -v mycaffe-vol:/home/signalpop/mycaffe 
  -p 2222:22 
  -d atmoz/sftp signalpop:password:1001::mycaffe/files
$ docker container run -it 
  -v mycaffe-vol:/workspace/mycaffe nvidia/caffe

Once up and running, you can then easily export datasets or projects from the SignalPop AI Designer right into your native Caffe Docker container!

To export your dataset (or project) simply right click on the dataset and select the ‘Export’ menu.

Exporting a dataset to a Docker container.

Once the export completes, just ls to the /workspace/mycaffe/files/data directory on your native Caffe Docker container and you will see the set of images for both the test and training set of the CIFAR-10 dataset.

For more information, see the “Exporting to Docker” section in the Getting Started document located in the Developers area and also shipped with the SignalPop AI Designer.

New Maintenance Release Available – version 0.9.0.378

We have just dropped a new maintenance release version 0.9.0.378.  This release includes a number of bug fixes and an improved support for importing public models like GoogleNet, AlexNet and VGG.  To try it out, just open the File | Import menu and select the new Get Public Models button on the Import Project dialog.

Several notable bugs fixed in this release include:

1.) Dataset creators now properly use file-based data.
2.) Dataset creators now create the image mean.
3.) The real-time debugger Visualize menu is now available.

For a list of all bugs fixed, see our bugs section in the Developer area.

NOTE: Your existing product license key will work with this new release, just install this version and you are ready to go!

Welcome to the SignalPop AI Designer!

We just released the SignalPop AI Designer, a comprehensive AI development environment for Windows C# developers using the MyCaffe AI Platform!  With the AI Designer, you can easily develop your own AI models right on your development computer using all of the development tools that you are familiar with!

All that you need to start deep learning is a Windows 7 or 10 based PC with a NVIDIA GPU, Microsoft SQL Express (which is free), and the SignalPop AI Designer and you are ready to go – see the SignalPop AI Designer Product page for more details on the minimum requirements needed to start building AI.  Once your models are built they are ready to be used with the open-source MyCaffe AI platform which is easily integrated into your Visual Studio projects via the MyCaffe package on NuGet.

Now you can manage your datasets from CIFAR-10 to MNIST to your own custom datasets.  The T-SNE algorithm integrated into the AI Designer then allows you to visualize how learnable each data set is.  And with the debugging features of the AI Designer, you can debug models as they train in real-time which can help diagnose why your model is not performing as expected.  In addition, several of the included model evaluators allow you to visualize trained models using Deconvolution and Unpooling or using the DeepDraw and DeepDream algorithms which give you a view of what the network actually sees.

For more information on this product, log in and take a look at the Getting Started document which describes most of the product features in detail.

In addition, check out the open-source MyCaffe project on GitHub used to build the underlying AI platform that the SignalPop AI Designer uses.

Also, see us on Nuget.org by searching for the MyCaffe package.

Time to start (deep) learning!

Featured Video

Using the SignalPop AI Designer (soon to be released) we imported the GoogleNet model, (originally trained by Berkeley Artificial Intelligence Research) and boot-strapped our training of the CIFAR-10 dataset up to 88% in about 30 minutes with the SignalPop AI Designer.  Next, we generated the images from the 10 CIFAR-10 classes using the SignalPop AI Designer’s Dream Evaluator and the MyCaffe implementation of the Deep Draw algorithm (inspired by the Google deep dream algorithm).  We then synchronized the deep draw images with great electronic dance music by CLOV15.

For more cool examples created with the SignalPop AI Designer, see our Examples section.

Sign up for a free account to get access to white papers, the free SignalPop AI Designer download and more!

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