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Merge pull request #71109 from samuel100/patch-2
Update dsvm-tools-deep-learning-frameworks.md
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articles/machine-learning/data-science-virtual-machine/dsvm-tools-deep-learning-frameworks.md

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@@ -111,7 +111,7 @@ Deep learning frameworks on the DSVM are listed below.
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| Category | Value |
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| ------------- | ------------- |
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| Version(s) supported | 1.2.0 (Ubuntu 16.04), 1.4.0 (Ubuntu 18.04, Windows 2019) |
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| Version(s) supported | 1.2.0 (Ubuntu 16.04), 1.7.1 (Ubuntu 18.04, Windows 2019) |
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| Supported DSVM editions | Windows Server 2019<br>Ubuntu 18.04<br> Ubuntu 16.04 |
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| How is it configured / installed on the DSVM? | Installed in [Python 3.5](dsvm-tools-languages.md#python-linux-edition). Sample Jupyter notebooks are included, and samples are in /dsvm/samples/pytorch. |
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| How to run it | Terminal: Activate the correct environment, and then run Python.<br/>* [JupyterHub](dsvm-ubuntu-intro.md#how-to-access-the-ubuntu-data-science-virtual-machine): Connect, and then open the PyTorch directory for samples. |
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| Version(s) supported | 1.0.3 |
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| Supported DSVM editions | Ubuntu 16.04 |
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| How is it configured / installed on the DSVM? |Theano is installed in Python 2.7 (_root_), and in Python 3.5 (_py35_) environment. |
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| How to run it | Terminal: Activate the Python version you want (root or py35), run Python, and then import Theano.<br/>* Jupyter: Select the Python 2.7 or 3.5 kernel, and then import Theano. <br/>To work around a recent math kernel library (MKL) bug, you need to first set the MKL threading layer as follows:<br/><br/>`export MKL_THREADING_LAYER=GNU` |
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| How to run it | Terminal: Activate the Python version you want (root or py35), run Python, and then import Theano.<br/>* Jupyter: Select the Python 2.7 or 3.5 kernel, and then import Theano. <br/>To work around a recent math kernel library (MKL) bug, you need to first set the MKL threading layer as follows:<br/><br/>`export MKL_THREADING_LAYER=GNU` |

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