Hi Again Eugen,

If I use the hyperparameter tool in ClearML, won't that create a different experiment for every step of the hyperparameter-optimizer? So this will be run across experiments. I could do something with pipelines but since the metrics are already available in the ClearML hyperparameter/metric tables I thought it would make sense to be able to plot against those values.

Strange, the code seems to work perfectly when I run it locally. To make it more confusing, the queue that I enqueue it to when I run it remotely is using agents from the same server that I'm running it locally from.

Are you self hosting a ClearML server?

Since this could happen with a lot of services, maybe it would be worth a retry option? Especially if it's part of a pipeline.

Ah, I think I see the issue. In my head I was crossing ID with URL.

Project 2:

2024-01-22 17:21:56
task 6518c3cd13394aa4abbc8f0dc34eb763 pulled from 8a69a982f5824762aeac7b000fbf2161 by worker bigbrother:10
2024-01-22 17:22:03
Current configuration (clearml_agent v1.7.0, location: /tmp/.clearml_agent.bojpliyx.cfg):
----------------------
agent.worker_id = bigbrother:10
agent.worker_name = bigbrother
agent.force_git_ssh_protocol = true
agent.python_binary = /home/natephysics/anaconda3/bin/python
agent.package_manager.type = pip
agent.package_manager.pip_v...

It seems that the error is related to this part of the code block. However, when I comment this out I get the error I had 2 days ago with the missing configuration object.

Ah, that makes sense. What is supposed to be hidden changes depending on the section your in, which makes sense. Now there needs to a packman sprite easter egg hidden somewhere else.

Let me give that a try. Thanks for all the help.

That's what I was getting at. It wasn't clear to me from the documentation that it saves the state.

So far when I delete a task or dataset using the web interface that has artifacts on S3 it doesn't prompt me for credentials.

Hyperdatasets are the only ones that require a premium. If you're using normal datasets it should be fine.

I might have found the answer. I'll reply if it works as expected.

Thanks for always checking in @<1523701087100473344:profile|SuccessfulKoala55> 😛

Unfortunately, that doesn't seem to have solved the problem. I tried the same thing with https and it seems to skip the lines with the @ symbol like it did before. Honestly, it seems more like it just isn't parsing those lines during the install.

Collecting darts==0.25.0
  Using cached darts-0.25.0-py3-none-any.whl (760 kB)
Collecting lightgbm
  Using cached lightgbm-4.1.0-py3-none-manylinux_2_28_x86_64.whl (3.1 MB)
Collecting prophet
  Using cached prophet-1.1.4-py3-none-manylinux_2_1...

They will be related through the task. Get the task information from the dataset, then get the model information from the task.

Maybe the sleep between scheduler.mark_completed() and scheduler.delete() is too short? But I don't get why deleting the old scheduler task would break the new scheduler. I'm going to try testing by running the scheduler locally.

It's a corporate one. We are also looking into options on Github's end.

No error. Just a new task each time.

That's wonderful! I'll have to give it a try. ;)

Looks good 😄

Alright, I fixed the issue with the scheduler eating itself. But now I'm still getting the same bug as two days ago. So the Scheduler process starts fine and doesn't "crash." But I don't get the config object in the web-app again. It seems to work if I run it locally.

To answer your earlier question, I'm using the app.clear.ml portal so

• WebApp: 3.20.1-1525
• Server: 3.20.1-1299
• API: 2.28
• And my Python ClearML version: 1.14

Actually, clearing the cache on the other project might have fixed it. I just tested it out and it seems to be working.

@<1523701205467926528:profile|AgitatedDove14> Then it isn't working at intended. To test it I started the scheduler and set a simple dead man snitch process to run once a day. In the web-app (on your site app.cleearml.ml), when looking at the scheduler process in the DevOps section, I was able to see a configuration file under artifacts but it was not as all obvious how you'd change that because it wasn't part of the configuration section, it was just an artifact. So I thought maybe it was b...

This doesn't really make a lot of sense. ClearML would be better served for tracking which version of the code you used for a corresponding task and you'd use something like github or gitlab to track code and host your code. You could use ClearML to help you reconstruct the environment and code from a task given it's being tracked by git and hosted somewhere you can access.

Sorry I disappeared (went on a well deserved vacation). The problem is happening because of the ordering of the install. If I install using pip install -r ./requirements.txt then pip installs the packages in the order of the requirements file. However, during the installation process from ClearML, it installs the packages in order UNLESS there's a custom path provided, then it's saved for last. The reason this breaks my code is I have later packages that depend on the custom packages, as ...

Thanks for your reply @<1523701070390366208:profile|CostlyOstrich36> Is there an example where a pipeline is built from existing tasks? I'd like to experiment with it and I don' t see any examples of what you describe with my (clearly lacking) google-fu. What happens if you wrap a function with a task.init() with a pipeline decorator or is that the process you're speaking of?

Provide a bit more detail. What framework are you using?

Should I post this in dev?

I just checked the clearml.conf and I'm not specifying any version of python for the agents.