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Hello, I was taking a look at "Deep Learning Toolkit for Splunk" and was wondering if someone could point me to the docs for securing the different services (i.e. Juypter, TensorBoard, etc) used by the app? (By securing, I mean locking it down to users coming from Splunk).
From what I think I saw in the code the different services are just running in the Docker env on different ports and the "Deep Learning Toolkit for Splunk" is aware of which ports things are running on and just opens a new tab in your browser linking out to the different services directly. Is that correct, or does it integrate with Splunk more and the auth is handled in some way there?
Or do you have to build your own containers to lock things down?
Thanks!
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Hi @bplaxco - currently the recommendation would be to add all specific security requirements into the container itself and build according to your needs. There is an open source GitHub repo for building own containers: https://github.com/splunk/splunk-mltk-container-docker
As there are various deployment scenarios for different container environments this would give you most control. However, we're currently also working to standardise and better secure DLTK within the latest (work in progress) version 4.0 available on https://github.com/splunk/deep-learning-toolkit
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Hi @bplaxco - currently the recommendation would be to add all specific security requirements into the container itself and build according to your needs. There is an open source GitHub repo for building own containers: https://github.com/splunk/splunk-mltk-container-docker
As there are various deployment scenarios for different container environments this would give you most control. However, we're currently also working to standardise and better secure DLTK within the latest (work in progress) version 4.0 available on https://github.com/splunk/deep-learning-toolkit
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Thanks for the info 🙂
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