Hi @<1652845271123496960:profile|AdorableClams1> , you set up fixed users in your docker compose, I would check there
You need to follow the instructions here - None
Hi 🙂
Are you asking if you can share experiments between a self hosted server and http://app.clear.ml ?
Hi ShallowGoldfish8 , can you elaborate please? You mean train with different data?
Hi @<1638712141588467712:profile|ExuberantTurtle48> , I think you can use Task.create() to write similar code - None
However I would suggest you also investigate the pipelines
UnevenDolphin73 , that's an interesting case. I'll see if I can reproduce it as well. Also can you please clarify step 4 a bit? Also on step 5 - what is "holding" it from spinning down?
I think maybe you're right. Let me double check. I might be confusing it with the previous version
EnormousWorm79 , are you working from different browsers / private windows?
You can use Task.set_base_docker ( None )
To specify arguments, there is an example there 🙂
Currently the UI will give you the timeline up to back a month ago for the usage of workers etc. If you want to go 3 months back and get specifics you'd have to get it directly from the API and extrapolate data yourself
CluelessElephant89 , I think the RAM requirements for elastic might be 2GB, you can try the following hack so it maybe will work.
In the machine that it's running on there should be a docker-compose.yml file (I'm guessing at home directory).
For the following https://github.com/allegroai/clearml-server/blob/master/docker/docker-compose.yml#L41 you can try changing it to ES_JAVA_OPTS: -Xms1g -Xmx1g and this might limit the elastic memory to 1 gb, however please note this might ...
AbruptCow41 , can you please elaborate? You want to move around files to some common folder and then at the end just create the dataset using that folder?
EcstaticMouse10 , this looks like the most relevant for you 🙂
You'll need ES/Mongo to run the ClearML server
Hi :)
I found a comparison here:
https://clear.ml/blog/stacking-up-against-the-competition/
As far as I am aware, there is an on-prem enterprise solution
Hyperparameters are connected to the experiment so your config will be right 🙂
Hi 🙂
How do you provide the package path to the agent? Also can you attach the log of the agent?
GiganticTurtle0 , which ClearML version are you using? From what I can see in the documentation to add the new parameters, you'll have to task.connect() again to add the new args
Hi @<1570220844972511232:profile|ObnoxiousBluewhale25> , what error are you getting?
Can I assume you're running the agent (in daemon mode) on the same machine that you're running the clearml-agent daemon --stop command?
What happens during the run is that plotly plots are shown during run on your computer but they don't show in UI and ONLY after the run is finished the plots show up?
Are your runs long?
@<1715538373919117312:profile|FoolishToad2> , I think you're missing something. ClearML backend only holds references (links) to artifacts. Actual interaction with storage is done directly via the SDK, aka on the machine running the code
Hi @<1600661428556009472:profile|HighCoyote66> , the UI uses the same REST API to communicate with the backend. I would suggest opening developer tools (F12) and seeing what requests are being sent in the network tab. I think it might be auth.login
Hi MoodyCentipede68 , yes I think this is indeed what you're looking for
Hi, SkinnyPanda43 , from what version did you upgrade to which version?
You would need to implement this logic yourself. For example you expose a pipeline argument for the controller (These are the configurations you can control via the UI as well) and then basically have if logic in the controller code that will run/skip steps according to the step you'd like to start from.
Makes sense?
lol! Can you hit F12 and see what the server returns for the call projects.get_all_ex
Hi @<1607909176359522304:profile|UnevenCow76> , I suggest you review the following video on serving - None
This also explains how to visualize different metrics in Grafana