I think the 3rd one, let me know what worked for you
@<1557175205510516736:profile|ShallowSwan53> , ClearML automatically logs all your models in it's model registry during your experiment's run. You can also use the OutputModel class for manual reporting as well.
I'm afraid I don't have anything like that 😞
I think you can periodically upload them to s3, I think the StorageManager would help with that. Do consider that artifacts are logged in the system with links (each artifact is a link in the end) So even if you upload it to and s3 bucket in the backend there will be a link leading to the file-server so you would have to amend this somehow.
Why not upload specific checkpoints directly to s3 if they're extra heavy?
Also, please go into the UI - go to the experiment that was executed remotely. Open developer tools (F12) and see what is returned when you navigate to the plots page in the UI
Hi :)
I'm guessing you're running a self hosted version? I think that access rules are a feature in the enterprise version only.
Hi PetiteRabbit11 , can you please elaborate on what you mean?
Looks like you're having some connectivity to the files server
2024-11-14 07:05:30,888 - clearml.storage - INFO - Uploading: 5.00MB / 12.82MB @ 35.88MBs from /tmp/state.vykhyxpt.json
2024-11-14 07:05:31,111 - clearml.storage - INFO - Uploading: 10.00MB / 12.82MB @ 22.36MBs from /tmp/state.vykhyxpt.json
1731567938707 labserver error WARNING:urllib3.connectionpool:Retrying (Retry(total=2, connect=2, read=5, redirect=5, status=None)) after connection broken by 'NewConnectionError('<urllib...
Hi @<1526734383564722176:profile|BoredBat47> , do you mean new debug samples or old ones? Please note that older debug samples were registered to the previous URL
I recall a big fix to plots in server version 1.6.0, can you try upgrading to see if it fixes the issue?
That is fully supported on premise, even in air gapped environments 🙂
DilapidatedDucks58 , I think this is what you want. You just configure it and don't need to changing anything in code.
https://github.com/allegroai/clearml/blob/92210e2c827ff20e06700807d25783691629808a/docs/clearml.conf#L106
You can pass any boto3 parameter here!
@<1547028079333871616:profile|IdealElephant83> , what are you trying to do during the code execution?
Is it a self hosted server?
I have tried
task.upload_artifact('/text/temp', 'temp.txt')
but it's not working (I can access the task, but as soon as I click artifacts tab, it shows 404 error).
Can you please elaborate on this? Can you please share a screenshot?
Can you provide a task id for such a task?
@<1523701977094033408:profile|FriendlyElk26> , try upgrading to the latest version, I think it should be fixed on the latest version
Hi @<1717350332247314432:profile|WittySeal70> , where are the debug samples stored? Have you recently moved the server?
I'll try to see if it reproduces on my side 🙂
Hi @<1523706826019835904:profile|ThoughtfulGorilla90> , it's not possible since workspaces are connected to the email itself. I would suggest writing some automation to extract the relevant projects/experiments from one workspace and register them into the new workspace. The API would be the best way to go. You would need to extract all information about the experiment itself and then also extract all the logs/scalars/plots and then simply register everything in the new workspace.
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Is this commit local or was it pushed to some branch?
I see. I'm guessing you have pretty extensive use in the form of artifacts/debug samples. You can lower the storage usage by deleting some experiments/models though the UI. That should free up some space 🙂
It's totally possible, I think you need to do research on it. There are probably a few ways to do it too. I see CLEARML_API_ACCESS_KEY & CLEARML_API_SECRET_KEY in the docker compose - None
You should do some more digging around. One option is to see how you can generate a key/secret pair and inject them via your script into mongoDB where the credentials are stored. Another way is to see how the UI ...
@<1772433273633378304:profile|VexedWoodpecker50> , I think there is some misconfiguration or misunderstanding. Can you elaborate on how you set up the server and worker?
Hi WackyRabbit7 ,
Is this what you're looking for?
https://github.com/allegroai/clearml-serving
Regarding pipelines, did you happen to play with this example? - None
The idea is that each step in the pipeline including the pipeline controller are tasks in the system. So you have to choose separate queues for steps and also the controller. The controller by default maps the 'services' queue, but you can control also that.
I am not very familiar with KubeFlow but as far as I know it is mainly for orchestration whereas ClearML offers a full E2E solution 🙂
Just to make sure we're on the same page, you're referring the machine statistics or ALL scalars don't show up?
Hi @<1523701295830011904:profile|CluelessFlamingo93> , are you self hosting or using the community server?
Hi @<1625303791509180416:profile|ExasperatedGoldfish33> , I would suggest trying pipelines from decorators. This way you can have very easy access to the code.
None