Merge pull request #86584 from j-martens/master

fixing links

Author: PRMerger9

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

@@ -46,13 +46,13 @@ Deep learning frameworks available on the DSVM include:
 
 |DL tools on DSVM|Windows|Linux|Usage Notes|
 |---------|:-------------------:|:------------------:|-----|
-|[TensorFlow](https://www.tensorflow.org/) | Yes (Windows 2016) | Yes |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). Sample Jupyter notebooks are included on DSVM.<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 TensorFlow directory for samples.  |
+|[TensorFlow](https://www.tensorflow.org/) | Yes (Windows 2016) | Yes |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). Sample Jupyter notebooks are included on DSVM.<br/><br/>**To run it**:<br/>* Terminal: activate the correct environment, then run Python. <br/> * Jupyter: Connect to [Jupyter](provision-vm.md#tools) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then open the TensorFlow directory for samples.  |
 |[PyTorch](https://pytorch.org/)| No | Yes |Installed in [Python 3.5](dsvm-languages.md#python-linux-and-windows-server-2012-edition). Sample Jupyter notebooks are included, and samples are in /dsvm/samples/pytorch.    <br/><br/>**To run it**<br/>* Terminal: activate the correct environment, then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux): Connect, then open the PyTorch directory for samples.  |
-|[Keras](https://keras.io/)| Yes | Yes |API is 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). [Samples](https://github.com/fchollet/keras/tree/master/examples)<br/><br/>**To run it**:<br/>*  Terminal: activate the correct environment, then run Python. <br/> * Jupyter: Download the samples from the GitHub location, 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 sample directory. |
+|[Keras](https://keras.io/)| Yes | Yes |API is 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). [Samples](https://github.com/fchollet/keras/tree/master/examples)<br/><br/>**To run it**:<br/>*  Terminal: activate the correct environment, then run Python. <br/> * Jupyter: Download the samples from the GitHub location, connect to [Jupyter](provision-vm.md#tools) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then open the sample directory. |
 |[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](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) 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_|
@@ -62,5 +62,5 @@ Deep learning frameworks available on the DSVM include:
 |nvidia-smi|Yes | Yes |NVIDIA tool for querying GPU activity. _nvidia-smi_ is available on the system path. <br/><br/>Start a command prompt (on Windows) or a terminal (on Linux), then run _nvidia-smi_.|
 |[TensorFlow Serving](https://www.tensorflow.org/serving/) | No | Yes |A server to inference on a TensorFlow model. _tensorflow_model_server_ is available at the terminal. Samples are available [online](https://www.tensorflow.org/serving/).|
 |[TensorRT](https://developer.nvidia.com/tensorrt) |  No | Yes (Ubuntu) |A deep learning inference server from NVIDIA. TensorRT is installed as an _apt_ package. Samples are available [online](https://docs.nvidia.com/deeplearning/sdk/tensorrt-developer-guide/index.html#samples).|
-|[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/cognitive-toolkit/)|Yes | Yes | 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). Sample Jupyter notebooks are included on DSVM. <br/><br/>**To run it**: <br/>Terminal: Activate the correct environment and 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 CNTK directory for samples. |
+|[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/cognitive-toolkit/)|Yes | Yes | 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). Sample Jupyter notebooks are included on DSVM. <br/><br/>**To run it**: <br/>Terminal: Activate the correct environment and run Python. <br/>Jupyter: Connect to [Jupyter](provision-vm.md#tools) or [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then open the CNTK directory for samples. |
 |Deep Water|No | Yes (Ubuntu) |Deep learning framework for H2O, Deep Water is installed in [Python 3.5](dsvm-languages.md#python-linux-and-windows-server-2012-edition) and is also available in `/dsvm/tools/deep_water`. Sample notebooks are included in JupyterHub. Deep Water requires CUDA 8 with cuDNN 5.1. This is not in the library path by default, as other deep learning frameworks use CUDA 9 and cuDNN 7. To use CUDA 8 + cuDNN 5.1 for Deep Water:<br/><br/>```export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:${LD_LIBRARY_PATH}```<br/>```export CUDA_ROOT=/usr/local/cuda-8.0```<br/><br/>To use Deep Water:<br/>* Terminal: activate the [Python 3.5](dsvm-languages.md#python-linux-and-windows-server-2012-edition) environment, then run _python_. <br/>* JupyterHub: [connect to JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-data-science-virtual-machine-for-linux), then navigate to the deep_water directory to find sample notebooks.|

articles/machine-learning/data-science-virtual-machine/provision-vm.md

@@ -37,7 +37,7 @@ The following tools are included in a DSVM:
 * A standalone Apache Spark instance for local development and testing
 * [JuliaPro](https://juliacomputing.com/products/juliapro.html)
 * Machine learning and data analytics tools:
-  * Deep learning frameworks - a rich set of AI frameworks are included on the VM: [Microsoft Cognitive Toolkit](https://www.microsoft.com/en-us/cognitive-toolkit/), [TensorFlow](https://www.tensorflow.org/), [Chainer](https://chainer.org/), mxNet, and Keras
+  * Deep learning frameworks - a rich set of AI frameworks are included on the VM: [TensorFlow](https://www.tensorflow.org/), [Chainer](https://chainer.org/), mxNet, and Keras
   * [Vowpal Wabbit](https://github.com/JohnLangford/vowpal_wabbit) - a fast machine learning system that supports techniques like online hashing, allreduce, reductions, learning2search, and active and interactive learning
   * [XGBoost](https://xgboost.readthedocs.org/en/latest/) - a tool that provides fast and accurate boosted tree implementation
   * [Rattle](https://togaware.com/rattle/) - the R analytical tool that gets you started with data analytics and machine learning in R. It includes GUI-based data exploration and modeling with automatic R code generation.
@@ -106,7 +106,9 @@ After the VM is created and provisioned, you can remote desktop into it by using
 
 You can also attach a Data Science VM to Azure Notebooks to run Jupyter notebooks on the VM and bypass the limitations of the free service tier. For more information, see [Manage and configure Notebooks projects - Compute tier](../../notebooks/configure-manage-azure-notebooks-projects.md#compute-tier).
 
-## Tools installed on the Microsoft Data Science Virtual Machine
+<a name="tools"></a>
+
+## Tools installed on the DVSM
 
 Learn more about the tools that come installed on the DSVM: