However, are you thinking of including this callbacks features in the new pipelines as well?

Can you see a good use case ? (I mean the infrastructure supports it, but sometimes too many arguments is just confusing, no?!)

@<1523716917813055488:profile|CloudyParrot43> yes server upgrades deleted it 😞 we are redeploying a copy, should take a few min

Yes, it should, why?

1. Yes the main diff between add task and decorator is basically creating dag and " executes " the tasks in parallel, based on the dag dependencies
2. Decorator will also take care of serializing the data in / out of the function. Imagine the pipeline logic is running as python code where the logic will wait for the function to finish only when the result of the function is being used. This means that if you need a parllel loop you can create thread pool.
   Make sense

Switching to process Pool might be a bit of an overkill here (I think)
wdyt?

Weird that this code is also uploading to the 'Plots'. I replicated the same thing as my main script, but main script is still uploading to Debug Samples.

SmarmyDolphin68 are you saying the same code behaves differently ?

Hi WickedBee96

How can I do that?

clearml-task
https://clear.ml/docs/latest/docs/apps/clearml_task#what-is-clearml-task-for

I know this way to run it in the agent only by enqueue the draft after running it on my local machine so is there another way?

Or maybe are you looking for task.execute_remotely
https://clear.ml/docs/latest/docs/references/sdk/task#execute_remotely

I assume now it downloads "more" data as this is running in parallel (and yes I assume that before it deleted the files it did not need)
But actually, at east from a first glance, I do not think it should download it at all...
Could it be that the "run_model_path" is a "complex" object of a sort, and it needs to test the values inside ?

Many thanks!

Pushed 🙂

On all Agents

Can you try to set this in your clearml.conf:

agent.pip_download_cache.enabled: false

this should disable the local caching, of your wheel, I suspect there is some issue with the local cache file in windows...

same: Not Found (#404)
May I suggest to DM it to me (so it is not public)

DrabCockroach54 look here:
https://clear.ml/docs/latest/docs/configs/env_vars#server-connection

2,3 ) the question is whether the serving is changing from one tenant to another, does it?

SmarmySeaurchin8 regrading (2)
I'm not sure the current visualization supports it. I mean we can put "{}", but that would imply you can edit it, which then we have to support, possible but weird, and this is why:
task.connect({'a':{},'b': {'nested': 'value}}will become
'a' = '{}'
'b/nested' = 'value'
But then if you edit to:
'a' = '{'nested': 'value'}'
'b/nested' = 'value'
you have two different ways of presenting the same type of structure...

AdventurousRabbit79 you mean like minio / ceph ?

I can't seem to figure out what the names should be from the pytorch example - where did INPUT__0 come from

This is actually the latyer name in the model:
https://github.com/allegroai/clearml-serving/blob/4b52103636bc7430d4a6666ee85fd126fcb49e2e/examples/pytorch/train_pytorch_mnist.py#L24
Which is just the default name Pytorch gives the layer
https://discuss.pytorch.org/t/how-to-get-layer-names-in-a-network/134238

it appears I need to converted into TorchScript?

Yes, this ...

Hi @<1523701066867150848:profile|JitteryCoyote63>

Hi, how does

agent.enable_git_ask_pass

works

basically it pushes the pass through stdin to git when it asks (it is a git feature)

If that's the case check the free space in the monitoring of the experiment, you will find the free space in GB logged

I'm happy to hear! 😅

but somewhere along the way, the request actually remove the header

Where are you seeing the returned value?

Nice!!!

JitteryCoyote63 yes this is very odd, seems like a pypi flop ?!
On the website they do say there is 0.5.0 ... I do not get it
https://pypi.org/project/pytorch3d/#history

I think they (DevOps) said something about next week, internal roll-out is this week (I think)

https://clear.ml/docs/latest/docs/references/sdk/task#taskimport_offline_session

trains-agent should be deployed to GPU instances, not the trains-server.
The trains-agent purpose is for you to be able to send jobs to a GPU (at least in most cases) instance.
The "trains-server" is a control plane , basically telling the agent what to run (by storing the execution queue and tasks). Make sense ?

QuaintJellyfish58 Notice it tries to access AWS not your minio
"This seems like a bug?! can you quickly verify with previous version ?
Also notice you have to provide the minio section in the clearml.conf so it knows how to access the endpoint:
https://github.com/allegroai/clearml/blob/bd53d44d71fb85435f6ce8677fbfe74bf81f7b18/docs/clearml.conf#L113

In theory, one could go over previously executed tasks, and create a copy of a specific scalar metric.
ShallowCat10 does that make sense in your scenario ?

Let me know if there is an issue 🙂