Data Science VM

The Data Science and Deep Learning Virtual Machines are customized VM images on Azure, loaded with data science tools used to build intelligent applications for advanced analytics. The Data Science VM is a customized VM image on Microsoft’s Azure cloud built specifically for data science work. It has many popular data science and other tools pre-installed and pre-configured to jumpstart building intelligent applications for advanced analytics. It is available on Windows Server and on Linux.
The Deep Learning VM is a specially-configured variant of the Data Science VM to make it more straightforward to use GPU-based VM instances for training deep learning models.
More details about the Data Science VMs are available in the Azure Data Science Virtual Machine Documentation. If you have a technical issue, please open a question on the developer forums through Stack Overflow.

  1. DSVM - Windows 2019: Pycharm 2019.3 does not properly activate Miniconda environment

    Seems that I run into https://youtrack.jetbrains.com/issue/PY-27234

    Anyone had that issue as well, that when running code from pycharm inside a conda environment, that the environment is not activated and you get messages such as ImportError: DLL load failed: The specified module could not be found? I am not able to run my programs which used to work on DSVM - Windows 2016..

    1 vote
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  2. Include Azure Data Studio in Windows and Linux DSVM images

    Currently, on the Windows DSVM the image provides SQL Server Management Studio (SSMS) which is the classic SQL DB management and development tool.

    Microsoft released a new experience way back in July 2018 called Azure Data Studio (ADS) which provides a variety of new features and some overlapping features with SSMS and runs on Linux and Windows.

    https://docs.microsoft.com/en-us/sql/azure-data-studio/download-azure-data-studio?view=sql-server-ver15

    The new tool is complementary to SSMS and is seeing significant investment related to the Azure Synapse experience. You’ll see many of the features of ADS align with data science activities including support for Notebooks, HDFS integration, flat file import, CSV/JSON export,…

    6 votes
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  3. Add Scala and sbt

    Particularly useful for Spark devlopment

    1 vote
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  4. Make Python 3 the default

    Currently, running just "python" on the Windows DSVM starts 2.7. When using this image in scenarios like Azure Batch then this is unexpected and inconvenient as modern code typically relies on Python 3. I know I can "activate py35" (btw why not Python 3.6?), but still. Is there any reason why Python 3 can't be the default? It's time...

    2 votes
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  5. Update Azure Storage Manager in Ubuntu image or at least remove current out of date version

    I want to use Azure Storage Explorer to access Azure Data Lake. The current version included does not support this nor Azure Files, tiered blob storage. This gives a very bad impression of a very useful tool.

    1 vote
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  6. Have Tab on OS X + X2Go + Ubuntu work as intended

    If you are using OS X and X2Go with an Ubuntu DSVM (as described in the Azure DSVM docs), the TAB key does not work.

    It's a known issue for this combination, and short of remapping the keys manually, which is a process, the tab key will not work.

    When indenting code, this can be an issue.

    Windows + X2Go work fine however.

    1 vote
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  7. Use JupyterLab by Default

    JupyterLab should be the default configuration when logging into the Jupyter server.

    6 votes
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  8. Jupyterhub set password doesn't show

    Title says it ; I wanted to setup a jupyterhub this morning (the tool is very powerfull for collaboration purposes) ; I could not do it because the "set password" doesn't show anywhere, and the port doesn't answer :(

    1 vote
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  9. Please add top Java Machine Learning frameworks

    The DSVM is currently missing a proper environment to develop enterprise applications for Machine Learning and Deep Learning with Java. Despite of the fact that all the Python frameworks are the best tools for modeling and research, to get ready for production the backend in most case is more suitable to be C++, C# or Java, to address performances, multi-threading and backward interoperability with enterprise systems (distributed or not).
    So I would suggest to add Java frameworks for Machine and Deep Learning like

    1 vote
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  10. Please add top Java Machine Learning frameworks

    The DSVM is currently missing a proper environment to develop enterprise applications for Machine Learning and Deep Learning with Java. Despite of the fact that all the Python frameworks are the best tools for modeling and research, to get ready for production the backend in most case is more suitable to be C++, C# or Java, to address performances, multi-threading and backward interoperability with enterprise systems (distributed or not).
    So I would suggest to add Java frameworks for Machine and Deep Learning like

    7 votes
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  11. Can you give us an idea how often the software packages in the VM image are updated?

    It would be helpful for us to know if we deploy a VM, we can expect that the software would have been updated within the last 30, 60, 90 days, etc.

    4 votes
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