The takeaway from the pricing page, I think, is that clearml is free as in speech. If you want super duper support that may cost $ but the folks in the community here do an awesome job in the meantime.

huh.. that example animation of automated driving on their homepage https://plotly.com/ is pretty awesome.

speaking as a google colab/jupyter notebooks person, I know we are missing some tutorials/docs there .. noted on the full blown example/testcase 👍

open to being totally wrong 🙂

laughs okily dokily Sir.. noted and noted 👍
I must admit, I have been using plotly and matplotlib for years and.. I have never used animations once. I am old school though 🙂

adding this to the ever expanding list of "nice to have" features 👍

I am guessing it could be but.. I don't feel that k8s is clearml-session's main focus/push

can you show me the complete output from 'docker-compose ps' please ? 🙂

(when I say fancy, you are free to substitute whatever adjective you wish instead 🙂

Hey Federico, since you are doing this from inside python, you could always call the 'get_parameters_as_dict' from the Task you have cloned, merge/update whichever ones you want to (or not), and then call ' set_parameters_as_dict ' .. I believe that should get you where you want to go 🙂

Hey Paul, glad to see you in here but sorry that you're having issues. Jst a quick clarification if I can, you said 'set up using docker'.. do you mean you did a 'docker-compose up' or some other commands such as only starting specific parts/docker containers only ?

Howdy and Morning @<1687643893996195840:profile|RoundCat60> .. docker when using overlay2 doesn't have it's mount points show up in a 'df' btw, they will only appear in a 'df -a', mostly because since they are simply 'overlays', they don't (technically) consume any space (I mean, the files are still in the /var/lib but not for the space counting practices used by df)

this is why I was suggesting a find, maybe with a 'du' .. actually.. let me try that here.. 2s

I would assume, from the sounds of it, that you are using the dockerfile to pip install python libs.. In which case a pip install clear-ml can also be done at image creation time.. I don't know what other methods you would be using to install python deps.. Easy_install?!?

hello Emanuel 👋
I assume you are going to use python, in which case, inside each ClearML Task there is a method called get_reported_scalars that should have all the data I think.

you may want to read the warning at https://allegro.ai/clearml/docs/rst/references/clearml_python_ref/task_module/task_task.html#clearml.task.Task.get_reported_scalars on this.. and cache yourself as appropriate.. actually, the docs for the API are pretty thorough.. so if this isn't the exact itch you ne...

maybe I am entirely wrong .. I read clearml-session as running a remote 'headless container with jupyter'.. but not on k8s.. or rather... the pre-requisites say that 'ssh access', which you won't have in a k8s cluster (I would hope). I mean.. exposing the ssh port on a k8s cluster is jst a security nightmare.

the one you are in 🙂

oh it's not a problem.. if you can fling up the logs of ES after startup that's probably the next step.. along with a 'docker network list' output 👍

from the look of those two graphs, the underlying data is totally different 😕

mea culpa

I agree with Martin.B, it appears to be a CUDA mismatch. The version of torch is trying to use cuda 10.2 but you have
agent.default_docker.image = nvidia/cuda:10.1-runtime-ubuntu18.04that should probably be
agent.default_docker.image = nvidia/cuda:10.2-runtime-ubuntu18.04

Never a problem Tim.. although it does prompt me to try and figure out a/b model testing myself ... I see everything as a "potential blog post" 😄 😄

I believe you can set it on a 'per container' way as well.

you will probably want to find the culprit, so a find should work wonders. I probably suspect elasticsearch first. It tends to go nuts 😕

Hey Slava, I don't mean to be "that guy" but, I am interested in what do you think a feature store means/implies/should do. The term is still (to my mind) very open to interpretation.. so I would honestly love to hear from you (and others)

The enterprise feature store we have should probably be more named as "data store but with advanced search/update capabilities" but.. that's not as nice sounding.

If you mean feature store as 'data ingestion via a DSL with type checking' then this is no...

that's... a very good question. When I was using Feast, it was that more than one person was interested in using the ingested data, so it became that 'single source of truth'. From then on, ClearML was used to do the actual pipeline flow and training/testing/serving runs and, since it's all python shop, it worked pretty well. We used it offline, since we didn't care about online with having features at inference time. I should probably write up something about this when I have the time come t...

the one on the right, for example, has no data points at around the 19 mark

oh .. no worries at all then.. you are free to do whatever you want to with it.. but I don't think it's designed with what you are trying to do in mind sadly

There will be a roadmap for the community up and on the blog this Monday.. It may not be as detailed as you would like but I am always happy to yak about specific requests 👍 👍

I assume that clearml's is on the right ?

Sadly, I haven't, but if anyone has then please scream because I would love to pick your brain for (yet another) post/article I am writing 😄 😄

I take it you are wanting to use Airflow to replace/extend an existing Jenkins setup ??