None
Change to:

CLEARML_AGENT_GIT_USER: ${CLEARML_AGENT_GIT_USER:my_git_user_here}

and the same for the password.
You can also just set the environment variables before launching docker-compose, whatever is more convenient for you

@<1523710674990010368:profile|GreasyPenguin14> If I understand correctly you can use tokens as user/pass (it's basically the same interface from the git client perspective, meaning from ClearML

git_user = gitlab-ci-token
git_pass = <the_actual_toke>

WDYT?

Make sense 🙂
Just make sure you configure the git user/pass in the docker-compose so the agent has your credentials for the repo clone.

Nice 🙂
@<1523710674990010368:profile|GreasyPenguin14> for future reference the agent part in the clearml.conf is only created when you call clearml-agent init (no need for it for the python SDK). Full default configuration is here:
None

@<1523710674990010368:profile|GreasyPenguin14> make sure it to uses https not ssh:
edit ~/clearml.conf

force_git_ssh_protocol: false

and that you have both git_user & git_pass set in your clearml.conf

Now that we have the free tier (a.k.a community server) we might change the default behavior.
The idea is always to allow an easy way to on-board and test the system.
ReassuredTiger98
BTW: what's the scenario where your machine reverted to the default configuration (i.e. no configuration file) ?

MagnificentSeaurchin79 YEY!!!!
Very cool!

Do you feel like making it public, I have the feeling a lot of people will appreciate it, this is very useful 🙂

Hi JitteryCoyote63 a few implementation details on the services-mode, because I'm not certain I understand the issue.
The docker-agent (running in services mode) will pick a Task from the services queue, then it will setup the docker for it spin it and make sure the Task starts running inside the docker (once it is running inside the docker you will see the service Task registered as additional node in the system, until the Task ends) once that happens the trains-agent will try to fetch the...

DilapidatedDucks58

all our workers went down after starting the slack bot, is it expected?)

Oh dear... I can;t see any connection... What is the last log you have there?

Hi @<1572395181150310400:profile|DeterminedHare56>
Yes Slack is not the best for knowledge sharing, but it is the easiest for users to communicate over, and it is the easiest to setup and scale.
Specifically you can find historical log of the Slack channel here: None
Which we hoped google will index, but seems like this is still not working as expected, if you have any inputs it will be great to improve it

Yes, the same will work with artifacts, use pass the full url to the artifact_object it should just register it as is.

And is there an easy way to get all the metrics associated with a project?

Metrics are per Task, but you can get the min/max/last of all the tasks in a project. Is that it?

GreasyPenguin14 I think this is what you are looking for
Task.get_project_id('project_name')

logger.report_scalar("loss-train", "train", iteration=0, value=100)
logger.report_scalar("loss=test", "test", iteration=0, value=200)
notice that the title of the graph is its uniue id, so if you send scalars to with the same "title" they will show on the same graph

logger.report_scalar("loss", "train", iteration=0, value=100)
logger.report_scalar("loss", "test", iteration=0, value=200)

Are you using tensorboard or do you want to log directly to trains ?

In the side bar you get the title of the graphs, then when you click on them you can see the diff series on the graphs themselves

Not the file the UI

make sense ?

@<1523720500038078464:profile|MotionlessSeagull22> you cannot have two graphs with the same title, the left side panel presents graph titles. That means that you cannot have a title=loss series=train & title=loss series=test on two diff graphs, they will always be displayed on the same graph.
That said, when comparing experiments, all graph pairs (i.e. title+series) will be displayed as a single graph, where the diff series are the experiments.

Hi @<1692345677285167104:profile|ThoughtfulKitten41>

Is it possible to trigger a pipeline run via API?

Yes! a pipeline is at the end a Task, you can take the pipeline ID and clone and enqueue it

pipeline_task = Task.clone("pipeline_id_here") 
Task.enqueue(pipeline_task, queue_name="services")

You can also monitor the pipeline with the same Task inyerface.
wdyt?

This code will give you one graph titled "loss" with two series: (1) trains (2) loss

Thanks for the detials @<1597762318140182528:profile|EnchantingPenguin77>

clearml.Auto-Scaler - INFO - New instance b97e702d-e2b3-4f28-adab-be59648601ea listening to test-gpu queue

This looks like a new agent was spined on your EC2 account, can you see it in the "Workers" page ?

@<1597762318140182528:profile|EnchantingPenguin77> can you provide the full log?

is it displaying that it is running anything?

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") `

And you want all of them to log into the same experiment ? or do you want an experiment per 60sec (i.e. like the scheduler)

Yep 🙂

I reached over 1M API calls in about one week using clearml-serving

Oh that makes sense now 🙂
If I remember correctly, adding an additional model to a signal clearml-serving instance should not actually change the number of API calls, they are mostly affected by the number of clearml-serving / containers and not in the number of models.

we run in containers without venv, in the main section, and then delete it or use it for similar experimentsIf this is the case then the idea is the venv creation is actually cached, you can turn it on here (unmark the line)
https://github.com/allegroai/clearml-agent/blob/51eb0a713cc78bd35ca15ed9440ddc92ffe7f37c/docs/clearml.conf#L116