Where communities thrive


  • Join over 1.5M+ people
  • Join over 100K+ communities
  • Free without limits
  • Create your own community
People
Repo info
Activity
    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..