It seems like you are correct, everything should just work. Are you still getting the error? What's the clearml agent version?

Are you referring to Poetry ?

Hi GrittyHawk31

but it could not connect to the grafana dashboard through port 3000, is there any particular reason for that? I may have missed something.

Did you run the full docker-compose.yml ?
Are you able to curl to the endpoints ?

BeefyCow3 if you are trying to optimizer a specific metric (i.e. a scalar on a graph). The template Task should report it with the same title/series combination, which should be easy enough to verify in the UI 🙂
You can either report with Tensorboard or with the Trains Logger, either way will work.

(the payload is not the correct form, can that be a problem?

It might, but I assume you will get a different error

Hi GreasyRaven35
You should set the output_uri, in Task init, it will auto upload the model, and register the remote location URL
task = Task.init(..., output_uri=True)You can also specify a target bucket, if you configured credentials (e.g. output_uri=" s3://bucket ")

Hi ImpressionableRaven99
Yes, it is 🙂
Call this one before task.init, and it will run offline (at the end of the execution, you will get a link to the local zip file of the execution)
Task.set_offline(True)Then later you can import it to the system with:
Task.import_offline_session('./my_task_aaa.zip')

yes

argument saying always create from code

can be helpful

@<1523701523954012160:profile|ShallowCormorant89> any chance you can open a github issue on that, just so we do not forget ?

if we can edit the configuration objects of a pipeline, that can be beneficial too. which we're unable to do from UI

Actually you already can, after you clone the pipeline, you can press on details then go to configuration Tab, and edit the pipeline object. The format is HOCON (...

After testing the code again, I see the task parameter dictionary has been removed properly

Great!

However, I still have the same problem with duplicate tasks, as you can see in the image.

Any chance the pipeline script Itself is running from the agent (as opposed to running the pipeline code locally, then the pipelines are executed on the agent)?

GrievingTurkey78
Both are now supported, they basically act the same way 🙂
and log overrides + the final omegaconf

Thanks for answering, Yes, this is exactly what I wanted

Hmm should be possible, how slow is the update that we want to save the time ?

If I access the dataset on the same location directly it works fine:

wait, I'm confused, how is it the datset us there? did it download the dataset?

are you saying this line for example will fail? (assuming you actually have a dataset by that name)

data_path = Dataset.get(dataset_name="002_Datenset_MASAM_for_fintuning", alias="002_Datenset_MASAM_for_fintuning").get_local_copy()

CLI? Code ?

ClumsyElephant70 yes there is 🙂
clearml-agent build --id <task id> --target <folder>(I might have a typo there, but you can basically check the full help clearml-agent build --help )

DeliciousSeal67 the agent will use the "install packages" section in order to install packages for the code. If you clear the entire section (you can do that in the UI or programmatically) then it will revert to requirementsd.txt
Make sense ?

We should probably change it so it is more human readable 🙂

Specifically for model files, if you set the Task.init(..., output_uri=True) it will automatically upload any saved model to the files server (you can also point to any object storage / shared folder)
What's the framework you are using ?

So why is it trying to upload to "//:8081/files_server:" ?
What do you have in the trains.conf on the machine running the experiment ?

JitteryCoyote63 Should be quite safe, there is no major change that I'm aware of on the ClearML side that can effect it.
That said, wait for after the weekend, we are releasing a new ClearML package, I remember there was something with the model logging, it might not directly have something to do with ignite, but worth testing on the latest version.

Hmm that makes sense to me, any chance you can open a github issue so we do not forget ? (I do not think it should be very complicated to fix)

Hi AttractiveWoodpecker16
I think is the correct channel for that question.
(any chance you can move your thread there?)
Specifically just email billing@clear.ml they will cancel (no need to worry about the beginning of the month, just explain and they will not charge over Nov)

EDIT: I know they are working on making it a one click in the UI, main limit is what happens with the data that was stored and was above the free tier threshold, anyhow I think next version will sort that as well.

Hi TeenyFly97

Can I super-impose the graphs while comparing experiments?

Hmm not at the moment, I think someone asked for the option to control it, in both comparison mode and "standalone" mode.
There is a long discussion on this feature here:
https://github.com/allegroai/trains/issues/81#issuecomment-645425450
Feel free to chime in 🙂
I think that the latest agreement is a switch in the UI, separating or collecting (super-imposing) those graphs.

I assume here:
https://github.com/allegroai/trains/blob/04b3fa809bb73d7101d1995327684ebe5b2911e3/trains/storage/cache.py#L47

ColossalDeer61 btw, it turns out the docker-compose services docker was ill configured on the GitHub 😞 I suggest you get the latest copy of it:
curl -o docker-compose.yml

p.s. StraightCoral86 I might be missing something here, please feel free to describe the entire execution scenario and what you are trying to achieve 🙂

feature value distribution over time

You mean how to create this chart? None

Thanks! I think I was able to locate the issue, but I wanted to verify 🙂

I meant even just a link to a blank comparison and one can then add the experiments from that view

Just making sure you are aware, once you are in comparison you can always add Tasks (any Task):
Notice you can press on the "Add experiments", then select Any experiment (including all projects! as filters)
Notice you need to remove all filters (right side red x on the filter Icon)

Simple file transfer test gives me approximately 1 GBit/s transfer rate between the server and the agent, which is to be expected from the 1Gbit/s network.

Ohhh I missed that. What is the speed you get for uploading the artifacts to the server? (you can test it with simple toy artifact upload code) ?

Hi JitteryCoyote63 , let me check, this backwards compatibility might only apply for API version mismatch between the client and server.