Currently we are trying use DLTK to implement our ML scenarios,but there are some questions that need to be fixed.
1. When create users and assign permissions, I had to add "Power" to users to make them can using the "fit" command, but they still cannot access Containers, the error message is "
HTTP 403 Forbidden -- You do not have the capability: admin_all_objects
"
How can I make non-admin users can start their own containers ,and make sure every user's data is totally separated?
2. If we use Docker as Dev and Kubernetes as Production, how can I build the workflow that a user can easily deploy code in these 2 envs, do you have a more detailed manual? thanks.
Hi @muyouming ,
for your 1. question can you check either the user have the role mltk_container_user OR their role capability includes list_mltk_container and control_mltk_container as this should give the the rights to access and control containers.
Your 2. question: assuming that both environments are isolated, e.g. without shared storage, then any notebook from dev would need to get copied over to prod. If you have any versioning / CI/CD workflows or tools in place than you could connect your dev environment e.g. to a GIT repo which is also accessible from the prod environment and only pulls the relevant production parts. Currently there is no build in functions in DLTK so you would need to manage those workflows on your own based on your requirements.
Hope that's helpful?
Thanks, Philipp