But adding a simple 

force_download

 flag to the 

get_local_copy

That's sounds like a good idea

So basically take data from tensorboard read it, and report it to the cloud ?

more like testing especially before a pipeline

Hmm yes, that makes sense.
Any chance you can open a github issue on it?
Let me see if I understand, basically, do not limit the clone on execute_remotely, right ?

When did this PipelineDecorator come. Looks interesting 

A few days ago (I think)
It is very cool! checkout the full object proxy interaction on the actual pipeline logic This might be better for your workflow, https://github.com/allegroai/clearml/blob/c85c05ef6aaca4e...

Hi SarcasticSparrow10

which database services are used to...

Mongo & Elastic
You can query everything using ClearML interface, or talk directly with the databases.
Full RestAPI is here:
https://clear.ml/docs/latest/docs/references/api/endpoints
You can use the APIClient for easier pythonic interface:
See example here
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

What is the exact use case you have in mind?

MysteriousBee56 I see...
So yes, you can with the APIClient you have full RESTful access to the backend.
I think there was a similar discussion https://allegroai-trains.slack.com/archives/CTK20V944/p1593524144116300
HandsomeCrow5 how did you end up solving it? I think you had a similar use case?!

This will fix it, the issue is the "no default value" that breaks the casting
@PipelineDecorator.component(cache=False) def step_one(my_arg=""):

but could you try with the latest RC?

BoredHedgehog47 can you test this one? Is it close to your code ?

Hi @<1539055479878062080:profile|FranticLobster21>

hey, how do I use local files as dependencies?

You mean like a repository ?

Can I specify in task what local files do I use that should be packaged?

In a git repo?

Basically the agent can do two things, either replicate a single script or clone a git repo + uncommitted changes

BTW: see if this works:
$ CLEARML_API_HOST_VERIFY_CERT=0 clearml-init

Thank you JuicyOtter4 ! 😍

. Is there a way to programmatically set that in the code?

Something like?
` task = Task.init(...)

probably we should change that to description ?!

task.set_comment("best thing ever") `

My pleasure 🙂

Seems the apiserver is out of connections, this is odd...
SuccessfulKoala55 do you have an idea ?

ohh right, my bad:
docker run -t --rm nvidia/cuda:10.1-base-ubuntu18.04 bash -c "echo 'Binary::apt::APT::Keep-Downloaded-Packages \"true\";' > /etc/apt/apt.conf.d/docker-clean && apt-get update && apt-get install -y git python3-pip && pip install trains-agent && echo done"

Nice!

Basic setup:
glues service per "job template" (e.g. k8s resources, for example cpu requirement, or gpu requirement).
queue per glue service, e.g. cpu_machine queue, and 1xGPU queue
wdyt?

Issue Verified, thanks!

EnviousPanda91 notice that when passing these arguments to clearml-agent you are actually passing default args, if you want an additional argument to Always be used, set the extra_docker_arguments here:
https://github.com/allegroai/clearml-agent/blob/9eee213683252cd0bd19aae3f9b2c65939d75ac3/docs/clearml.conf#L170

Are you asking regrading the k8s integration ?
(This is not a must, you can run the clearml-agent bare-metal on any OS)

Hi @<1628565287957696512:profile|AloofBat92>
Yeah the name is confusing, we should probably change that. The idea is it is a low code / high code , train your own LLM and deploy it. Not really chatgpt 1:1 comparison, more like, GenAI for enterprises. make sense ?

@<1541954607595393024:profile|BattyCrocodile47> first let me say I ❤ the dark theme you have going on there, we should definitly add that 🙂

When I run

python set_triggers.py; python basic_task.py

, they seem to execute, b

Seems like you forgot to start the trigger, i.e.
None
(this will cause the entire script of the trigger inc...

Basically it gives it direct access to the host, this is why it is considered less safe (access on other levels as well, like network)

Hi StaleHippopotamus38

I imagine I could make the changes specified in the warning to 

/etc/security/limits.conf

Yep seems like elastic memory issue, but I think the helm chart takes care of it,
You can see a reference in the docker compose:
https://github.com/allegroai/clearml-server/blob/09ab2af34cbf9a38f317e15d17454a2eb4c7efd0/docker/docker-compose.yml#L41

The easiest if export_task / update_task:
https://allegro.ai/docs/task.html#trains.task.Task.export_task
https://allegro.ai/docs/task.html#trains.task.Task.update_task
Check the structure returned by export_task, you'll find the entire configuration test there,
then, you can use that to update back the Task.

BTW:
Partial update is also supported...

WickedGoat98 if this is the case, you can check this example. Same idea only "manual":
https://github.com/allegroai/trains/blob/master/examples/automation/task_piping_example.py

Check the examples on the github page, I think this is what you are looking for 🙂
https://github.com/allegroai/trains-agent#running-the-trains-agent

actually no it is not, alpine is Not a good baseline, is is very very slim missing a ton of stuff.
I would use bullseye or slim (depending how many aux things you need on the container)
https://hub.docker.com//python/tags?page=1&name=bullseye
https://hub.docker.com//python/tags?page=1&name=slim-bullseye

ok so i accidentally (probably with luck) noticed the max_connection: 2 in the azure.storage config.

NICE!!!! 🎊
But wait where is that set?
None
Should we change the default or add a comment ?

Hi AstonishingWorm64
Is this the same ?
https://github.com/allegroai/clearml-serving/issues/1
(I think it was fixed on the later branch, we are releasing 0.3.2 later today with a fix)
Can you try:
pip install git+

For the on-prem you can check the k8s helm charts it case spin agents for you (static agents).
For the GKE the best solution is the k8s glue:
https://github.com/allegroai/clearml-agent/blob/master/examples/k8s_glue_example.py