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    Edi Santoso
    @repodevs
    Hello Guys,
    I have suggestion, what do you think about adding tree package on default installation?
    Lukas Masuch
    @LukasMasuch
    @repodevs Thanks for the suggestion. We have added the tree package to the Dockerfile. It will be available in the next image version.
    Edi Santoso
    @repodevs
    Nice, Thanks @LukasMasuch!
    Matthew Davis
    @mateothegreat
    sup guys
    GREAT work fyi
    I'm interested in deploying this throughout our org :Zoop:
    interested in doing some paid support?
    Lukas Masuch
    @LukasMasuch
    Hey Matthew, thanks for trying out ml-workspace 👍 We currently cannot provide paid support. However, we would be happy to help you set up a test instance and learn from your requirements & issues. If you like we can schedule a call soon. The ml-hub (for multiuser support) still needs a bit of documentation, so you might need some help for this at the moment.
    Jason K Bellew
    @macshaggy
    hey guys, great job on this I like it. One question, are you thinking about a bleeding edge version that works with python 3.7? Or is 3.7 in the works?
    Benjamin Räthlein
    @raethlein
    Hey Jason, thanks a lot for the nice feedback :) Currently, we do not have planned anything in that direction as far as I know but we will discuss it. Do you have experience with Python 3.7 and know about any breaking changes or compatibility issues with libraries compared to current version 3.6?
    Jason K Bellew
    @macshaggy
    I personally haven't found anything in 3.7 to break any of my 3.6 programs. But my usage is more intermediate, I do use it for work where I work with reviewing metadata, data flows, etc. using NetworkX and everything that I did in 3.6 worked. But beyond NetworkX, numpy, and pandas, I don't really know and couldn't say.
    faizalam1
    @faizalam1
    Anyone online??
    faizalam1
    @faizalam1
    @macshaggy Can you help me with starting ml-workspace?
    Edi Santoso
    @repodevs
    what is your problem?
    Jesper Vang
    @flight505
    soo after I run docker run -p 8080:8080 mltooling/ml-workspace:latest I am not able to access Jupyter .. this is a Macbook the only thing on it is OS Mojave and I am trying to run Ml workspace.. I might mention that I am also new to docker, and I don't know if I should pass a work folder or anything to the command?
    Lukas Masuch
    @LukasMasuch
    @flight505 just running docker run -p 8080:8080 mltooling/ml-workspace:latest should be enough to test the workspace (just access http://localhost:8080). The only requirement is a working docker installation. If it is not working for you, you might try a different port e.g. docker run -p 8081:8080 mltooling/ml-workspace:latest or a restart of the docker daemon/macbook. Have you tried other docker containers? What are the logs you get?
    @macshaggy We will most likely update to Python 3.7 until end of this year. There are still a few dependencies which need Python 3.6 for whatever reason, but most of the important once are working fine on 3.7.
    Derek Chia
    @DerekChia_twitter
    after creating a new conda environment, how do i start a new notebook using it?
    current there's only Python2 and Python3
    Derek Chia
    @DerekChia_twitter
    i.e. How do i add kernels to jupyter
    Lukas Masuch
    @LukasMasuch
    Hey Derek, you need to create a kernel via the ipykernel library. Here is an example:
    1. Create environment: conda create -n myenv python=3.6
    2. Activate environment: source activate myenvy
    3. Install ipykernel: conda install ipykernel
    4. Install kernel from environment: python -m ipykernel install --user --name myenv --display-name "Python (myenv)"
    After this, you just reload the jupyter website and the new kernel should be available
    Derek Chia
    @DerekChia_twitter
    Thanks Lukas, actually i'm aware of these steps, just thinking if it is already in-built in the toolbox for easy access.
    Lukas Masuch
    @LukasMasuch
    There is no easier way to create new conda environments within the workspace. However, the default Python 3 kernel is based on conda and already contains a variety of common machine learning libraries. So, in many cases you probably do not have to create a new environment.
    Derek Chia
    @DerekChia_twitter
    Yea, default Python 3 has a good range of libraries, but sometimes (most of the time) tf 1.14 is more common than tf 2.0.. Sadly..
    Naman Shukla
    @namanUIUC
    Great work guys! I see the jupyterlab got extensions already installed. Couple of questions:
    1. How to choose one of the themes (oriolmirosa/jupyterlab_materialdarker) as I can't find it under Settings -> JupyterLab Themes?
    2. How to install additional extensions ?
    Lukas Masuch
    @LukasMasuch
    @namanUIUC Thanks for the feedback! 1) materialdarker is not preinstalled, however, once you have installed it you will be able to choose it from jupyterlab menu. 2) Within the workspace you can use the terminals to install anything you like, as explained here: https://github.com/ml-tooling/ml-workspace#extensibility For example, running jupyter labextension install @oriolmirosa/jupyterlab_materialdarker in the terminal will install the material darker extension. In Jupyterlab there is also an UI to search and install Jupyterlab extensions (Accessible from the left menu - the puzzel piece)