2. This is how web UI configurations looks like
   

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Hi, ok im really close now to working system
Debug image is uploading to s3, im seeing the files, all ok there

Problem now is viewing these images in web UI
Going to Debug Samples panel in Task drops me a popup to fill in s3 credentials

I cant figure out what the right setup is for the creds to work
This is what I have now (Note that we dont have region)

@<1523701435869433856:profile|SmugDolphin23> Any ideas how to fix this?

But there are stil some wierd issues, i cannot see the files uploaded in bucket

WebApp: 1.14.1-451 • Server: 1.14.1-451 • API: 2.28

I solved the problem.
I had to add tensorboard loggger and pass it to pytorch_lightning trainer logger=logger
Is that normal?

@<1523701070390366208:profile|CostlyOstrich36> Any news on this? We are currently stuck without this fix, cant finish up clearml setup

@<1523701070390366208:profile|CostlyOstrich36> 👀

Yes, credetials seems to work
Im trying to figure out not why I dont see the uploaded files / folders

• I checked maybe clearml task uses fileserver instead but i dont see any files in fileserver folder
• Nothing is uploaded in bucket (i will ask IT guy to check if im uploading any files in logs)
  

I have tried:
Airflow - Pain to setup, old UI and other problems

Prefect - Literaly just tried to setup a simple distributed system, took me a week, I do not recommend this tool at all, horrible documentation, noone helps at slack.

Dagster - Absolute beauty, nice UI, easy to setup (as a pip package or just a docker + postgres), i highly recommend this tool. Takes a bit to get used to it. I will in coming week try this combo of dagster + clearml, where i periodically check some things and if...

Im pretty sure

how to get rid of this auto appended line

I hope that its all the experiments

i can add "source /workspace/.venv/bin/activate", to clearml.conf docker_init_bash_script
However it then tries to access pip, but i dont need no pip, how to disable it, i already have my packages, and uv doesnt even require pip

is there any way to see if I even have the data in mongodb?

Is it possible to split the large elasticsearch indexes? I know elasticsearch has something called rollover, but im not sure that clearml supports this

Im doing all of this because there isnt (or im not aware of) any good way understand what datasets are on workers

7 out of 30 GB is currently used and is quite stable

has 8 cores, so nothing fancy even

You can check out boto3 python client (This is what we use to download / upload all S3 stuff), but minio-client probably already uses it under the hood.
We also use aws cli to do some downloading, it is way faster than python.

Regarding pdfs, yes, you have no choice but to preprocess it

@<1523701482157772800:profile|AnxiousSeal95> I see a lot of people here migrating data from one data source to another.
For us it was that we experimented with Clearml to get the feeling and we used clearml built in file storage to save debug images an all other artifacts.

Then we grew rapidly and we had to migrate to S3 storage.
I had to write a script that goes through elasticsearch and mongo db to point to new S3 links wher the data was migrated to.
I do however understand that migration...

I was on 1.7 version and now im on latest 1.11
Cant get screenshow yet (copying data), will add later.
What worries me is that config and agent folders are empty. I can reconfigure all agents, no problems.
But where is info about projects stored?

I also see that elastisearch and mongo has some data

Our datasets are more than 1TB in size and will grow in size (probably 4TB and up), this means we also need 4TB local storage just to upload the dataset back in zipped format. This is not a good solution.

What we can do I guess is do the downloading locally by some chunks of files?
Download locally 100 files, add_to_clearml dataset, repeat

Yes, but does add_external_files makes chunked zips as add_files do?

im also batch uploading, maybe thats the problem?

• The dataset is about 1TB containing 1 million files
• I dont have the SSD space locally to do the upload
• So i download a part of the dataset, use add_files() and then upload() to that batch
• Upload the dataset
  I noticed that each batch is slower and slower