PungentLouse55 , make sure you fix the metric objective and args:
Add "General/" prefix to the list of arguments to optimize, and change the objective metric from "Accuracy" to "epoch_accuracy"

You can however pass a specific Task ID and it will reuse it "reuse_last_task_id=aabb11", would that help?

Hmm I'm sorry it might be "continue_last_task", can you try:
Task.init(..., continue_last_task="aabb11")

Yep that is it 🙂

Hi PungentLouse55
it depends on the trains-server version you are running.
If the trains-server >= 0.16 then you have to add "Args/" prefix. If you are running an older version, then you should not add any prefix.

from clearml import TaskTypes

That will only work if you are using the latest from the GitHub, I guess the example code was modified before a stable release ...

Maybe failed pipelines with zero steps count as completed

zero steps counts as successful.
That said, how could it have zero steps if one of the steps failed? no?

I don't know whether you have access to the backend,

Creepy , no I do not 🙂

I can't make anything appear in the console part of the ui

clearml_task.logger.report_text("some text") should work

Yes

Hmm EmbarrassedPeacock82
Let's try with
--input-size -1 60 1 --aux-config input.format=FORMAT_NCHWBTW: this seems like a triton LSTM configuration issue, we might want to move the discussion to the Triton server issue, wdyt?

Hi JitteryCoyote63
Do you have a specific example in mind ?

Hi SpotlessWorm70

OMP: Error #15: Initializing libiomp5.dylib, but found libomp.dylib already initialized.
OMP: Hint This means that multiple copies of the OpenMP runtime have been linked into the program.

This seems like OpenMP issue
I would assume something is off with the local environment (not really connected to clearml but to one of the frameworks, for example TF, Keras, etc.)

🙂

Hi ConfusedPig65
Any keras model will be automatically uploaded if you pass an upload url to the Task init:
task = Task.init('examples', 'keras upload test', output_uri=" ")(You can also pass to output_uri s3://buckket/folder or change the default output_uri in the clearml.conf file)
After this line any keras model will be automatically uploaded (you will see it under the Artifacts Tab)
Accessing models from executed tasks:
` trains_task = Task.get_task('task_uid_here')
last_check...

Hi EnviousStarfish54
docker on windows , with nvidia runtime support is only with WSL (I think)
https://docs.nvidia.com/cuda/wsl-user-guide/index.html#installing-wip
https://medium.com/@dalgibbard/docker-with-gpu-support-in-wsl2-ebbc94251cf5

However, once I extract the zips (or download the dataset through Python API or CLI) not all the files are there.

and all the files are registered in the metadata? coulf you add --verbose to the sync command to see what it is doing

"clearml-data add --folder ./*" seems to fix this issue though it doesn't preserve my directory structure

This is also odd, it should Not flatten the folder structure. What is your OS / Python / clearml version?
Is this reproducible ? if so, how ...

Hmm, I think it is this line:

WARNING - Model configuration only supports dictionary or string objects
done

Let me check something.

what if for some old tasks I get WARNING:root:Could not delete Task ID=a0908784a2a942c3812f947ec1f32c9f, 'Task' object has no attribute 'delete'? What's the best way of cleaning them?

This seems like an old SDK no?

@<1564422644407734272:profile|DistressedCoyote60> could you open a GitHub issue on it in clearml-agent, just so we know of the problem and fix it for next version ?

Hi @<1716987924207112192:profile|CostlyOctopus40>

is opensearch supported in ClearML instead of Elasticsearch ? please shed some light on that

Long story short, maybe?! but this is not officially supported.
We only support elasticsearch, the opensearch fork is not officially supported and since we continue to use more advanced features of Elastic, it might be that the API will not be compatible in the future.
Out of curiosity, why are you using opensearch?

What do you have in the artifacts of this task id: 4a80b274007d4e969b71dd03c69d504c

the unclear part is how do I sample another point in the optimization space from the optimizer

Just so I'm clear on the issue, you want multiple machines to access the internals of the optimizer class ? or Do you just want a way to understand what is the optimizer sampling space (i.e. the parameters and options per parameter) ?

Hi BlandPuppy7 , is this Trains related, are you trying to integrate it, and need help?

So basically the APIClient is a pythonic interface to the RestAPI, so you can do the following
See if this one works
# stats from he last 60 seconds for worker in workers: print(client.workers.get_stats(worker_ids=[worker.id], from_date=int(time()-60),to_date=int(time()), interval=60, ))

Hi ReassuredTiger98
Are you referring to the UI (as much as I understand there was an improvement, but generally speaking, it still needs the users to have the S3 credentials in the UI client, not backend)
Or are you asking on the cleanup service ?

Hi @<1598487094601191424:profile|MysteriousCow84>
You should put it in the dedicated section:
None

I cannot reproduce, tested with the same matplotlib version and python against the community server

Hi AbruptWorm50

I am currently using the repo cache,

What do you mean by "using the repo cache" ? This is transparent, the agent does that, users should not access that folder?

I also looked at the log you send, why do you think it is re-downloading the repo?

Hi ShortElephant92
You could get a local copy from the local server, then switch credentials to the hosted server and upload again, would that work?

Yes, but I'm not sure that they need to have separate task

Hmm okay I need to check if this can be easily done
(BTW, the downside of that, you can only cache a component, not a sub-component)

Correct, but do notice that (1) task names are not unique and you can change them after the Task was executed (2) when you clone the Task, you can actually rename it, when an agent is running the Task, basically the init function is ignored, because the Task already exists. Make sense ?