Merge pull request #85763 from j-martens/patch-607

Update dsvm-deep-learning-ai-frameworks.md

Author: PRMerger9

articles/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks.md

@@ -268,3 +268,9 @@ The service takes about one minute to start. Start a web browser and navigate to
 |[Caffe](https://github.com/caffe2/caffe2) | No |Yes (Ubuntu)|Caffe is installed in `/opt/caffe`.   Samples are in `/opt/caffe/examples`. <br/><br/>**To run it**, use X2Go to log in to your VM, then start a new terminal and enter:<br/>`cd /opt/caffe/examples`<br/>`source activate root`<br/>`jupyter notebook`<br/><br/>A new browser window opens with sample notebooks. Binaries are installed in /opt/caffe/build/install/bin.<br/><br/>Installed version of Caffe requires Python 2.7 and won't work with Python 3.5 activated by default. To switch to Python 2.7, run `source activate root` to switch Anaconda environment.|
 |[Caffe2](https://github.com/caffe2/caffe2) | No |Yes (Ubuntu)|Caffe2 is installed in the [Python 2.7 (root) conda environment](dsvm-languages.md#python-linux-and-windows-server-2012-edition). The source is in `/opt/caffe2`.<br/>Sample notebooks are included in JupyterHub.<br/><br/>**To run it**:<br/>* At the terminal: activate the [root Python environment](dsvm-languages.md#python-linux-and-windows-server-2012-edition), start Python, and import caffe2. <br/> * In JupyterHub: [connect to JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then navigate to the Caffe2 directory to find sample notebooks. Some notebooks require the Caffe2 root to be set in the Python code; enter /opt/caffe2. |
 |[Torch](http://torch.ch/) | No |Yes (Ubuntu)|Torch is installed in `/dsvm/tools/torch`. PyTorch is installed in Python 2.7 (_root_), as well as Python 3.5 (_py35_) environment. Torch samples are in `/dsvm/samples/torch` and PyTorch samples are in `/dsvm/samples/pytorch`. |
+|[MXNet](https://mxnet.io/) | Yes (Windows 2016) | Yes|MXNet is installed in `C:\dsvm\tools\mxnet` on Windows and `/dsvm/tools/mxnet` on Linux. Python bindings are installed in Python 3.5 on [Linux and Windows 2012](dsvm-languages.md#python-linux-and-windows-server-2012-edition) and Python 3.6 on [Windows 2016](dsvm-languages.md#python-windows-server-2016-edition). R bindings are also installed on Ubuntu.<br/><br/>Sample Jupyter notebooks are included. <br/><br/>**To run it**:<br/>* Terminal: Activate the correct environment, then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md#tools-installed-on-the-microsoft-data-science-virtual-machine) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then open the mxnet directory for samples.|
+|[MXNet Model Server](https://github.com/awslabs/mxnet-model-server) | No | Yes |A server to create HTTP endpoints for MXNet and ONNX models. _mxnet-model-server_ is available on at the terminal. Samples on the [MXNet Model Server page](https://github.com/awslabs/mxnet-model-server).|
+|[Horovod](https://github.com/uber/horovod) | No | Yes (Ubuntu) |Distributed deep learning framework for TensorFlow. Horovod is installed in Python 3.5 on [Ubuntu](dsvm-languages.md#python-linux-and-windows-server-2012-edition).  [See samples](https://github.com/uber/horovod/tree/master/examples)<br/><br/>**To run it**, activate the correct environment at the terminal, then run Python. |
+|[Theano](https://github.com/Theano/Theano) | No | Yes (Ubuntu) |Theano is installed in Python 2.7 (_root_), as well as Python 3.5 (_py35_) environment.<br/><br/>**To run it**: <br/>* Terminal: Activate the Python version you want (root or py35), run python, then import theano.<br/>* Jupyter: Select the Python 2.7 or 3.5 kernel, then import theano.  <br/>To work around a recent MKL bug, you need to first set the MKL threading layer:<br/><br/>_export MKL_THREADING_LAYER=GNU_|
+|[Chainer](https://chainer.org/) |No | Yes |Chainer is installed in [Python 3.5](dsvm-languages.md#python-linux-and-windows-server-2012-edition). ChainerRL and ChainerCV are also installed. <br/><br/>Sample notebooks are included in JupyterHub.<br/><br/>**To run it**: <br/>* Terminal: Activate the [Python 3.5](dsvm-languages.md#python-linux-and-windows-server-2012-edition) environment, run _python_, then import chainer. <br/> * JupyterHub: [Connect to JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then navigate to the Chainer directory to find sample notebooks.|
+|[NVidia Digits](https://github.com/NVIDIA/DIGITS) | No | Yes (Ubuntu) |Deep learning system from NVIDIA for rapidly training deep learning models. DIGITS is installed in `/dsvm/tools/DIGITS` and is available a service called _digits_.  <br/><br/>**To run it**: <br/>Log in to the VM with X2Go. At a terminal, start the service ```sudo systemctl start digits```. <br/><br/>The service takes about one minute to start. Start a web browser and navigate to `http://localhost:5000`. Note that DIGITS does not provide a secure login and should not be exposed outside the VM.|