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Kernel Memory on kubernetes #505
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hi @rmelilloii great to hear the solution could help! KM should totally work fine in Kubernetes. I would try first with the docker image mentioned in the main README. Configuration can be provided via a file or env vars, let me know if you encounter any problem. Aside for the basic docker image, there are also optimizations, for example it's possible to turn on/off various aspects of KM, for example you could run ingestion workers on 10 VMs while running the web service only on 2-3 nodes, if that's something that could interest. |
Hello @dluc good morning and happy Tuesday! My initial stateful deployment should be something like:
I am indeed interested in the different workloads for better resource utilisation. To avoid too much noise here I will do tests and post back with results/doubts to help similar needs, maybe adding working examples to the repo. Thanks! |
Hello again o/ Deploy goes green (pod running) but it is on a crash loop. The pod log is not enough to help with a cause. Any suggestion? Elasticsearch and RabbiMQ endpoints are up and accepting connections (Auth validated). Any help is very appreciated. Thanks! Log:
manifest:
|
Some of the env vars can be seen also here https://github.com/microsoft/kernel-memory/blob/main/infra/modules/container-app.bicep For - name: KernelMemory__DataIngestion__EmbeddingGeneratorTypes__0
value: "Elasticsearch" |
Thanks, I really appreciate it. I had the impression that the variable was superseded as it didn't inform any possible value on the "comments", only a reference to a related env. It is up now, play time! :) Do you have any documentation regarding:
Thanks a lot! |
There are two main config settings:
Handlers share state via files which can be stored on disk/azure blobs/mongodb. When using disk, it's harder to share state across VMs, unless you mount the same across. Assuming state is shared with a central storage like blobs or DB or mounted disk, then the service can work across multiple VMs, splitting workload. For instance, this could be one setup to scale the web service separately from the async ingestion workload:
In the async ingestion pipelines, each task is managed by a dedicated handler. Handlers provide another way to load balance across mutliple nodes. E.g. it's possible to control which handlers to execute, using the config setting array |
Context / Scenario
Hello, good morning/afternoon/evening and Happy Monday!
Initially, I must say that I am very impressed with this solution and keen to implement it as an internal service to our k8s clusters.
Question
To my question: I went through all the documentation available. As of now, is it possible to deploy it to kubernetes?
Thanks!
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