Regarding UI - you can either build your own frontend for it or use streamlit / gradio applications (Which are supported in the enterprise license).
About using a model outside of ClearML - You can simply register the model to the model artifactory - None
@<1539417873305309184:profile|DangerousMole43> , I think for this specific ability you would need to re-write your pipeline code with pipelines from decorators
Hi Juan, can you please elaborate? What is pac? What is failing to clone the repo, can you provide an error message?
Hi @<1529995795791613952:profile|NervousRabbit2> , if you're running in docker mode you can easily pass it in the docker_args parameter for example so you can set env variables with -e docker arg
Hi @<1717350332247314432:profile|WittySeal70> , where are the debug samples stored? Have you recently moved the server?
Regarding the packages issue:
What python did you run on originally - Because it looks that 1.22.3 is only supported by python 3.8. You can circumvent this entire issue by running in docker mode with a docker that has 3.7 pre-installed
Regarding the data file loading issue - How do you specify the path? Is it relative?
EcstaticGoat95 , thanks a lot! Will take a look 🙂
SmallDeer34 Hi 🙂
I don't think there is a way out of the box to see GPU hours per project, but it can be a pretty cool feature! Maybe open a github feature request for this.
Regarding on how to calculate this, I think an easier solution for you would be to sum up the runtime of all experiments in a certain project rather than looking by GPU utilization graphs
MelancholyElk85 if you're using add_function_step() it has a 'docker' parameter. You can read more here:
https://clear.ml/docs/latest/docs/references/sdk/automation_controller_pipelinecontroller#add_function_step
That's the controller. I would guess if you fetch the controller you can get it's id as well
How are you writing your pipelines?
What do you mean by organization? In Enterprise, you have users, roles & access controls based on those roles.
AgitatedDove41 , there isn't any throttling in ClearML and it uses the native packages when communicating with AWS (boto3 for example)
Where were you uploading to/from?
Hi @<1673501397007470592:profile|RelievedDuck3> , you simply need to integrate clearml into your code.
from clearml import Task
task = Task.init(...)
More info here:
None
Hi @<1523701868901961728:profile|ReassuredTiger98> , you can fetch the task object, there one of the attributes of the task is it's worker. This way you can see on what machine it is running 🙂
Also can you provide the configuration of the autoscaler? You can export it through the webUI just make sure to scrape off any credentials
It looks like there might be a firewall or something of the sort, please try the curl command from the machine itself to verify
AbruptWorm50 , it looks like the application issue was solved for us 🙂
I noticed that the base docker image does not appear in the autoscaler task'
configuration_object
It should appear in the General section
In network, the plots call returns empty?
Hi @<1898906633770110976:profile|MinuteFlamingo30> , from my understanding this is actually on the roadmap. Currently there is no easy way to check it. Basically any experiment with a lot of scalars or console logs (think like experiments that ran for very long)
Hi @<1858681577442119680:profile|NonchalantCoral99> , please see my reply to Vojta 🙂
I was referring to the SDK python package (ClearML) and ClearML-Agent
DepressedChimpanzee34 , I see. Regarding the things that are not currently implemented, please open a github issue so we can track this 🙂
Hi @<1853245764742942720:profile|DepravedKoala88> , I don't think there is any downsampling when ingesting from Tensorboard. You can always turn off the autologging and only log what you want and downsample accordingly. Keep in mind that on one hand you should avoid bloat on the server and on the other have high enough granularity in your scalars.
What do you think?