Hi DangerousDragonfly8

, is it possible to somehow extract the information about the experiment/task of which status has changed?

From the docstring of add_task_trigger
```py def schedule_function(task_id): pass ```This means you are getting the Task ID that caused the trigger, now you can get all the info that you need with Task.get_task(task_id)
` def schedule_function(task_id):
the_task = Task.get_task(task_id)
# now we have all the info on the Task tha...

You mean the entire organization already has Kubeflow, or to better organize something (if this is the second, what are we organizing, pipelines?)

and I install the tar

I think the only way to do that is add it into the docker bash setup script (this is a bash script executed before Task)

You can check the example here, just make sure you add the callback and you are good to go 🙂
https://github.com/allegroai/trains/blob/master/examples/frameworks/keras/keras_tensorboard.py#L107

SweetGiraffe8
That might be it, could you test with the Demo server ?

Hi SmarmySeaurchin8
Could you open a bug on GitHub, so this is not lost? Let's assume 'a' is tracked, how would one change 'a' in the UI?

Sure 🙂
BTW: clearml-agent will mount your host .ssh into the docker to /root/.ssh by default.
So no need to do that manually

Hi @<1559711593736966144:profile|SoggyCow20>

I would first like to say how amazing clearml is!

Thank you! 🙏

Running in Docker mode (v19.03 and above) - using default docker image: nvidia/cuda:10.2-cudnn7-runtime-ubuntu18.04

yes sdk.agent.default_docker.image = python:3.10.0-alpine should be
agent.default_docker.image = python:3.10.0-alpine
Notice the scope is agent, not sdk

Like, let's say I want "a 15GB GPU or better" and there's 4 queues, two of which fit the description... is there any way to set it so that ClearML will just queue it up on whichever one's available?

How do you know that? Also if you know that, what do you know about the queues ?
Generally speaking this type of granularity is k8s, but it has lots of caveats, specifically that you need to Know what you need in term of resources, that you can specify resources that do not exist, and that...

artifact I guess

Hmm, any suggestion on making it more visible or on the interface ? (I mean deleting the cache file is always a solution, but it sounded quite painful to debug, hence the question)

Hi @<1546665638829756416:profile|LovelyChimpanzee39>

anyone know what params I need to pass in order to enable it?

we feel you there 😞 this is an actual plotly feature that we really want to add, but kind of out of our hands: None
feel free to support as there 🤞

Hi WickedGoat98
Regardless on the ingress configuration (which seems like you have the hang of), the API instance itself needs to be configured with persistent volume (the web / file server do not need direct access to the API server).
Can you get the API to run properly ?

Regrading the trains-agent once you have the API/Web/File server configured, you can configure it like the trains-agent-services is configured inside the docker-compose (e.g. set the environment variable with the c...

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

Where are you seeing the returned value?

Hi GiganticTurtle0

Is there a simple way to make 

Task.init

 compatible with

Dask.distributed

 client?

Please tell me more 🙂
I think Dask is trying to pickle you Task object (which is not pickable).
You can however create the Task once with Task.init
and pass the Task ID to the child processes and then use Task.init(..., continue_last_task=task_id_here)
wdyt?

Hi @<1523701066867150848:profile|JitteryCoyote63>

Could you please push the code for that version on github?

oh seems like it is not synced, thank you for noticing (it will be taken care immediately)
Regrading the issue:
Look at the attached images
None does not contain a specific wheel for cuda117 to x86, they use the pip defualt one
![image](https://clearml-web-assets.s3.amazonaws.com/scoold/images/TT9ATQXJ5-F05744CK09L/screenshot...

I assume the task is being launched sequentially. I'm going to prepare a more elaborate example to see what happens.

Let me know if you can produce a mock test, I would love to make sure we support the use case, this is a great example of using pipeline logic 🙂

Thanks CooperativeFox72 ! I'll test and keep you posted 🙂

Meanwhile you can just sleep for 24hours and put it all on the services queue. it should work 🙂
Example here:
https://github.com/allegroai/trains/blob/master/examples/services/cleanup/cleanup_service.py

I would just add git+ None to your requirements (either in the requirements.txt or even better as part of the pipeline/component where you also specify the repo to be used)
The agent will automatically push the crednetilas when it installs the repo as wheel.
wdyt?
btw: you might also get away with adding -e . into the requirements.txt (but you will need to test that one)

Hmm, so currently you can provide help, so users know what they can choose from, but there is no way to limit it.
I know the Enterprise version has something similar that allows users to create a custom "application" from a Task, there you can define a drop and as such, but that might be an overkill here, wdyt?

it would be clearml-server’s job to distribute to each user internally?

So you mean the user will never know their own S3 access credentials?
Are those credentials unique per user or once"hidden" for all of them?

Thanks EnviousStarfish54 we are working on moving them there!
BTW, in the mean time, please feel free to open GitHub issue under train, at least until they are moved (hopefully end of Sept).

One way to circumvent this btw would be to also add/use the 

--python

 flag for 

virtualenv

Notice that when creating the venv , the cmd that is used is basically pythonx.y -m virtualenv ...
By definition this will create a new venv based on the python that executes the venv.
With all that said, it might be there is a bug in virtualenv and in some cases, it does Not adhere to this restriction

Damn 😞

Hmmm, what's your trains version ?

You can always force it with environment variable CUDA_VERSION=10.1

Hi John. sort of. It seems that archiving pipelines does not also archive the tasks that they contain so

This is correct, the rationale is that the components (i.e. Tasks) might be used (or already used) as cached steps ...

BroadMole98 thank you for noticing !
I'll make sure it is fixed (a few other properties are also missing there, not sure why, I'll ask them to take a look)

RoundMosquito25 actually you can 🙂
# check the state every minute while an_optimizer.wait(timeout=1.0): running_tasks = an_optimizer.get_active_experiments() for task in running_tasks: task.get_last_scalar_metrics() # do something herebase line reference
https://github.com/allegroai/clearml/blob/f5700728837188d7d6005726c581c9d74fd91164/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py#L127