Do you mean like sub modules or actually just clone several independent repositories?

I'm not sure that is possible. What is your specific use case?

It's worth a try 🙂

Can you add a log?

SparklingElephant70 , Hi
Can you please provide a screenshot of the error?

Can you give an example for your test_config ?

How are you trying to 'target' the file in the code?

Hi @<1675675716852649984:profile|LackadaisicalLizard46> , I think that's a really neat idea, maybe open a GitHub feature request?

Hi TartBear70 ,

Did you run the experiment locally first? What versions of clearml/clearml-agent are you using?

Hi RoughTiger69 , how are you running the pipeline? Locally or on agents? How is the controller running?

I'll take a large snippet too 😛
Do you have any idea what's the source of this?
TypeError: __init__() got an unexpected keyword argument 'configurations'

Hi @<1752501940488507392:profile|SquareMoth4> , you have to bring your own compute. ClearML only acts as a control plane allowing you to manage your compute. Why not use AWS for example as a simple solution?

Meaning that you should configure your host as follows host: "somehost.com:9000"

Again, I'm telling you, please look at the documentation and what it says specifically on minio like solutions.
The host should be
host: " our-host.com :<PORT>"
And NOT
host: " s3.our-host.com "
Maybe you don't require a port I don't know your setup, but as I said, in the host settings you need to remove the s3 as this is reserved only to AWS S3.

Hi ShortElephant92 , how are you adding files currently? Code or CLI? You can specify the storage in both cases.

Via CLI using the
--output-uritag.
https://clear.ml/docs/latest/docs/clearml_data/clearml_data_cli#create

Via code use the output_url parameter during Dataset.create() call
https://clear.ml/docs/latest/docs/clearml_data/clearml_data_sdk#datasetcreate

Hi @<1625303791509180416:profile|ExasperatedGoldfish33> , I would suggest trying pipelines from decorators. This way you can have very easy access to the code.
None

Are you still having issues?

you should set it on the machine running the agent

Same file I mean the requirements.txt file

So you're using the community server? Response time really depends on the resources of the machine that is running the server and amount of data to filter

Hi @<1742355077231808512:profile|DisturbedLizard6> , you can open a GitHub feature request for this to be added 🙂

After this passes - and you refresh the page even with the popup stuck in infinite loop - does the project get deleted? I think it might be a UI issue

On prem is also K8s? Question is if you run the code unrelated to ClearML on EKS, do you still get the same issue?

Or should I set agent.google.storage {}?

Did you follow the instructions in the docs?

Hi IrateDolphin19 ,

Can you give a bit of a simplistic schema of what you're doing or trying to achieve? Are you using pipelines for this?

Hi MortifiedDove27 , try going into localhost:8080/login

Hi @<1745616566117994496:profile|FantasticGorilla16> , you can always increase disk space on the machine running the server. Another option is to manually delete indices in Elastic, although it would be highly unadvised.

You could also probably clear up space in the file server if it has been heavily used.

DepressedChimpanzee34 , Hi!

The part you want to do faster is the code snippet you provided? Also, I'll check regarding the verbosity 🙂

Hi @<1590514584836378624:profile|AmiableSeaturtle81> , you need to add the port to the credentials when you input them in the webUI

I guess that's a good point but really applicable if your training is CPU intensive. If your training is GPU intensive I guess most of the load goes on the GPU so running over VM (EC2 instances for example) shouldn't have much of a difference but this is worthy of testing.

I found this article talking about performance
https://blog.equinix.com/blog/2022/01/04/3-reasons-why-you-should-consider-running-containers-on-bare-metal/

But it doesn't really say what the difference in performance is...