botocore.exceptions.EndpointConnectionError: Could not connect to the endpoint URL: “ None ”

sure

Hi John
Well, I didn’t setup anything extra 🙂

FYI: I also changed the files_server in config

Again throwing Insufficient permissions.

still got same error

Yup, tried that too.

Hi @<1523701070390366208:profile|CostlyOstrich36>
After removing the port number from command given by you.
Re: your message
with the combination of None :port/bucket for --storage ?

Thanks a lot @<1523701070390366208:profile|CostlyOstrich36> 🙏

Though I’m able to connect using minio client with the same creds without any issues.

yes

raise ValueError(“Insufficient permissions (delete failed) for {}“.format(base_url))
ValueError: Insufficient permissions (delete failed) for None

Removed port and set secure as true.

Hi @<1523701435869433856:profile|SmugDolphin23>
This is follow up question from my side.

Is it efficient to use clearML data for hundred of thousands of image dataset. I’ve a concern about performance.

So basically I’m training a transformers classifier. which need at least 300-400K images.

Everytime I start training; first download dataset using minio client and then do further operations.

Can we integrate minio easily with clearML?

Thanks a lot @<1523701435869433856:profile|SmugDolphin23>
will go through this one.
your quick reply really means a lot. Thanks again!!

I also tried passing the default port for minio for host in config
eg: host: “ostore.xyz.tech:9000”

But getting same error.

Also tried changing files_server in config but didn’t work

Am I missing anything in config?

Am I missing anything? because I can clearly see the usage of --config-file using clearml-agent --help

And just FYI: one dataset size can beyond 80GB.

@<1590514584836378624:profile|AmiableSeaturtle81>
Yes, I can also feel that slowness.

But what about if we have millions of pdfs and we need to generate a image dataset along with their bounding boxes. We cannot generate it on the fly while training the model as it will very very very slow.
So I’m doing all preprocessing the upload preprocessed data back to storage.
And while training again pulling preprocessed dataset from storage. 😄

Well I can understand well your problem. @<1590514584836378624:profile|AmiableSeaturtle81>
Please let me know if you have answers for my problem. 😄

And what I feel; pulling data by using some client eg: minio-client for minio is faster than clearml

clearml-data create --storage "

" --project dummyproject --name testingds1

clearml-data - Dataset Management & Versioning CLI
Creating a new dataset:

Error: Insufficient permissions (write failed) for ostore.xyz.tech

it is picking http, it should https?

Thanks @<1523701087100473344:profile|SuccessfulKoala55>

Hi @<1523701070390366208:profile|CostlyOstrich36>
FYI: I’m not using docker agent.

I set the env vars as you mentioned above in config. And I can see the those while starting agent using command: clearml-agent daemon --queue default

While running below code locally eg: python test.py I’m loading the env vars that is working fine.
But my question is when we clone the same task from clearml UI and send it to default queue; So can below piece of code can use those envs?
I tried sendi...

Yes, I tried with default port of minio (9000). didn’t work either.

I passed only None