Hi @<1523701083040387072:profile|UnevenDolphin73> , can you please elaborate?
And you use the agent to set up the environment for the experiment to run?
ColorfulRaven45 Hi!
I'm afraid this is kind of a plotly limitation. Currently you can switch to full screen view OR hit the maximize graph button (it shows on over) for a better view.
We'd be happy to take a suggestion though 🙂
VexedCat68 I think this will be right up your alley 🙂
https://github.com/allegroai/clearml/blob/master/examples/reporting/hyper_parameters.py#L43
host: "my-minio-host:9000"
The port should be whatever port that is used by your S3 solution
Hi @<1670964701451784192:profile|SteepSquid49> , that sounds like the correct setup 🙂
What were you thinking of improving or do you have some pain points in your current setup?
Hi @<1639074542859063296:profile|StunningSwallow12> , here are the docs for the agent - None
Hi @<1539417873305309184:profile|DangerousMole43> , in that case I think you can simply save the file path as a configuration in the first step and then in the next step you can simply access this file path from the previous step. Makes sense?
Hi @<1544491305910931456:profile|SoggyPuppy25> , can you please attach logs of the two runs? Are both runs executed on the same worker? What are the main environment differences between the machines that ran the experiment locally - OS/Python/ETC
What version of clearml
are you using? Can you try in a clean python virtual env?
Hi @<1547028131527790592:profile|PleasantOtter67> , nothing out of the box. You can however quite easily extract all that information and inject it into a csv programmatically.
I think the bigger question is how would you break it down? Each experiment has several nested properties.
Hi @<1546303277010784256:profile|LivelyBadger26> , I think this is what you are looking for
None
@<1546303277010784256:profile|LivelyBadger26> , it is Nathan Belmore's thread just above yours in the community channel 🙂
Hi @<1547752791546531840:profile|BeefyFrog17> , can you add the full log?
In the installed packages, try removing the version for imageio (Is this a private package?). This looks like the environment (OS/Python version) doesn't support the specific package OR the package is inside a private artifactory
Hi @<1523701457835003904:profile|AbruptHedgehog21> , I'm not sure I understand - How do you use set_base_docker
and what do you expect to happen?
Hmm maybe @<1523701087100473344:profile|SuccessfulKoala55> might have an idea
Hi @<1543766544847212544:profile|SorePelican79> , I don't think you can track the data inside the dataset. Maybe @<1523701087100473344:profile|SuccessfulKoala55> , might have an idea
Hi @<1546303293918023680:profile|MiniatureRobin9> , can you add the full console log?
Can you see if in the APIserver logs something happened during this time? Is the agent still reporting?
Hi @<1541592241250766848:profile|BrightPenguin74> , I think this is what you're looking for
None
Hi @<1625303791509180416:profile|ExasperatedGoldfish33> , I would suggest trying pipelines from decorators. This way you can have very easy access to the code.
None
Hi @<1590152201068613632:profile|StaleLeopard22> , you can simply add the extra index url as part of the agent requirements as such:
agent.package_manager.extra_index_url=["<extra_index_url>",...]
Hi @<1547028074090991616:profile|ShaggySwan64> , so the issue is when writing to the files server? Is it possible that the machine itself is having a hard time to write the data?
StickySheep96 , Is it possible you raised the server locally on your machine and not the EC2 instance?
Hi @<1710827348800049152:profile|ScantChicken68> , I'd suggest first reviewing the onboarding videos on youtube:
None
None
After that, I'd suggest just adding the Task.init()
to your existing code to see what you're getting reported. After you're familiar with the basics then I'd suggest going into the orchestration/pipelines features 🙂
Hi, can you provide an example of how you report them?
Please filter by 'XHR' and see if there are any errors (any return codes that aren't 200)
It is returned in queues.get_all. I'd suggest navigating to the webUI and checking what the webUI is sending to the server (It's all API calls) and replicating that in code with the APIClient