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
Hi @<1826066729852211200:profile|DullSwallow71> , I would suggest looking at it from another perspective. Check machine availability and only then push a job into a queue. You can see all the related information to usage in the 'Workers' screen in the 'Orchestration' tab.
Then you can either push jobs manually according to usage or write your own service to sample usage and push jobs accordingly.
Browser thinks it's the same backend because of the domain
Hi @<1594863230964994048:profile|DangerousBee35> , I don't think there is such a mechanism currently. What would the expected/optimal behaviour would be in your use case?
If you killed all processes directly, there can't be any workers on that machine. It means that these two workers are running somewhere else...
What versions of clearml-agent
& clearml
are you using? Is it a self hosted server?
Hi @<1554638166823014400:profile|ExuberantBat24> , you mean dynamic GPU allocation on the same machine?
You certainly can do it with the python APIClient OR through the requests library
I think it is supported only through the API 🙂
StaleButterfly40 Hi!
You could clone the original task and edit the input model of the new task as the output model of the previous task 🙂
Hi @<1706116294329241600:profile|MinuteMouse44> , you need to run in docker mode with --docker
tag to be able to inject env variables
JitteryCoyote63 , if you go to a completed experiment you only see the packages stage installed in the log?
What OS/ClearML-Agent are you running?
Hi @<1546303277010784256:profile|LivelyBadger26> , when you run the agent, you can specify which CPUs to use using the --gpus argument like you used
StaleButterfly40 , do the inputs/outputs have previews or are they just file links?
btw what os are you on?
I think this is what you're looking for then - NoneTask.add_requirements
Hi MoodyCentipede68 ,
I'm not sure how to do it in the bash way (I'm sure there's a solution on google). However for a quick fix - clearml-init
basically creates a configuration file. You can simply inject the file itself into ~/clearml.conf
What do you think?
AbruptWorm50 , the guys tell me that it's under progress and we will be updated in the following minutes 🙂
Hi 🙂
Regarding the input issue - Try defining in your ~/clearml.conf
the following: sdk.development.default_output_uri
to wherever you want it uploaded. I'm guessing that when you're running the original input model is created through the script and downloaded?
Regarding tagging - I think you need to connect tags individually to output models if you wanna connect it only to outputs
That's strange indeed. What if you right click one of the pipeline executions and click on run?
Runs perfectly with Minio too 🙂
Hi RoundMosquito25 , yes you can. You can first filter by enqueued experiments and also in the top left you have a small checkbox. If you click it, you will have an option to select all experiments and not only the ones that are loaded
Looks like it's already there - None
Hi @<1566596960691949568:profile|UpsetWalrus59> , if I'm not mistaken, your code itself needs to support this
Hi @<1594863230964994048:profile|DangerousBee35> , this is pretty much it. I think the default one suggested is a good one
You can delete locally but it should not affect the remote data.
The data itself is stored in the fileserver. Whatever you do locally does not affect the remote storage, only when creating a new version the changes should be stored (Like when using 'clearml-data sync').
VexedCat68 , in the screenshot you provided it looks like the location is being printed. Did you check to see if something is there?
Hi @<1577468638728818688:profile|DelightfulArcticwolf22> , I think this is what you're looking for - CLEARML_AGENT_SKIP_PIP_VENV_INSTALL
CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL
CLEARML_AGENT_FORCE_SYSTEM_SITE_PACKAGES
None
get_parameter
returns the value of a parameter as documented:
https://clear.ml/docs/latest/docs/references/sdk/task#get_parameter
Maybe try https://clear.ml/docs/latest/docs/references/sdk/task#get_parameters