nope, the experiment is stuck in RUNNING state

opened it, works now! Thanks.

python -m http://script.as .a.module first_arg second_arg --named_arg value <- something like that

Sounds promising, any ETA for the next version?

Also, tried the continue_pipeline option, didn't work as it couldn't parse the previous step that run...
ValueError: Could not parse reference '${run_experiment.models.output.-1.url}', step run_experiment could not be found

Is there an option to do this from a pipeline, from within the add_step method? Can you link a reference to cloning and editing a task programmatically? nope, it works well for the pipeline when not I don't choose to continue_pipeline

AgitatedDove14 is there any update on the open issue you talked about before? I think it's this one: https://github.com/allegroai/clearml/issues/214

` Exception in thread Thread-5:
Traceback (most recent call last):
File "/opt/pyenv/versions/3.6.8/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/opt/pyenv/versions/3.6.8/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/root/.clearml/venvs-builds/3.6/lib/python3.6/site-packages/clearml/automation/controller.py", line 615, in _daemon
if self._launch_node(self._nodes[name]):
File "/root/.clearml/venvs-builds/3.6/lib/pyt...

Thanks! A followup question - can I make the steps in the pipeline use the latest commit in the branch?

And for some reason this clone is marked as completed. Not sure why, as it failed

Great, good to know!

so no magic "username" key? 😛

yup, it's there in draft mode so I can get the latest git commit when it's used as a base task

cool! just to verify - I'll still need to have the credentials created in the server, right?

Thanks!

right, of course 🙂 so just to make sure I'm running it correctly. I ran python aws_autoscaler.py --run on my laptop and I see the Task on ClearML. Then took a completed task, cloned it and enqueued to the queue defined on the autoscaler. That should spin up an instance, right? (it currently doesn't, and I'm not sure where to debug)

I have access to the machine using SSH from my computer.
There doesn't seem to be any other error in the debug mode.
` Remote machine is ready
Setting up connection to remote session
Starting SSH tunnel

SSH tunneling failed, retrying in 3 seconds
Starting SSH tunnel `

CLEARML_DOCKER_IMAGE=nvidia/cuda:10.1-runtime-ubuntu18.04
How do I pull the image using the agent?

Hooray! That works AND the feature works!
Quick follow up question, is there any way to abort a pipeline and all of the tasks it ran?

I looked there, but couldn't find it. I'm currently experimenting with your free hosted server

yeah, totally. Are there any services OOB like this?

legit, I was thinking only about task tracking, less about user based credentials. good point

I just want to use auth0 (which we already use in the company) in order to manage the users...

it's in the docker image, doesn't the git clone command run in the container?

Sure, redacted most of the params as they are sensitive:
` run_experiment {
base_task_id = "478cfdae5ed249c18818f1c50864b83c"
queue = null
parents = []
timeout = null
parameters {
# Redacted the parameters
}
executed = "d1d361d1059c4f0981200f59d7683773"
}
segment_slides {
base_task_id = "ae13cc979855482683474e9d435895bb"
queue = null
parents = ["run_experiment"]
timeout = null
parameters {
Args/param = """
[
#...

ok, hopefully last question on this subject 🙂
I want to use Jenkins for some pipelines. What I would like to do is have one set of credentials saved on Jenkins. Then whenever a user triggers a pipeline - this is the user that will be marked as the task's user.
If I understand the options you suggested, I'll currently need either to (1) have some mapping between users and their credentials and have all the credentials saved on Jenkins; or, (2) have each user manually add 2 environment varia...

I was thinking about sending the parameters programatically. We have different pipelines that can generate tasks, I would like to be able to tell trains the user who started the pipeline.

yeah, maybe as an option in the Task.init

nope, only port 22 is open for SSH. Is there anyway to set that as the port for clearml-session?

Right... apparently the nvidia-docker wasn't set up. Thanks!