Can you share the log?

Containers (and Pods) do not share GPUs. There's no overcommitting of GPUs.Actually I am as well, this is Kubernets doing the resource scheduling and actually Kubernetes decided it is okay to run two pods on the Same GPU, which is cool, but I was not aware Nvidia already added this feature (I know it was in beta for a long time)
https://developer.nvidia.com/blog/improving-gpu-utilization-in-kubernetes/
I also see thety added dynamic slicing and Memory Proteciton:
Notice you can control ...

(ignoring still having to fix the problem with 

LazyEvalWrapper

 return values).

fix will be pushed post weekend 🙂

such as displaying the step execution DaG in the PLOTS tab . (edited)

Wait, what are you getting on the DAG plot ? I think we "should" be able to see all the steps

yes

argument saying always create from code

can be helpful

@<1523701523954012160:profile|ShallowCormorant89> any chance you can open a github issue on that, just so we do not forget ?

if we can edit the configuration objects of a pipeline, that can be beneficial too. which we're unable to do from UI

Actually you already can, after you clone the pipeline, you can press on details then go to configuration Tab, and edit the pipeline object. The format is HOCON (...

My pleasure

👍

Regrading the missing packages, you might want to test with:
force_analyze_entire_repo: falsehttps://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L162

Or if you have a full venv you like to store instead:
https://github.com/allegroai/trains/blob/c3fd3ed7c681e92e2fb2c3f6fd3493854803d781/docs/trains.conf#L169

BTW:
What is the missed package?

Still, My problem is calling

pipe.start()

crashes.

is supposed to kill the process
2022-08-19 09:17:56,626 - clearml - WARNING - Terminating local execution processThis is what it writes before killing the local process.
` /opt/homebrew/anaconda3/envs/py39/lib/python3.9/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 16 leaked semaphore objects to clean up at shutdown
warnings.warn('resource_tracker: There appear to be ...

Hi @<1523701523954012160:profile|ShallowCormorant89>
This is generally based on number of agents, or am I missing something ? Also is it based on Task or decorated functions ?

Where do you store those ?

@<1523701523954012160:profile|ShallowCormorant89> can you verify it is reproducible in 1.9.3 ? because if it is I'd like to fix that 🙂

will it be possible for us to configure the "new run" button in a way so that it always clones from a particular pipeline ?

What do you mean by "particular pipeline" ? by default it will clone the last successful one, and by right clicking a specific one you can run a copy of that one. what am I missing ?

Hi @<1729309120315527168:profile|ShallowLion60>
How did you create those credentials ?

I have to admit, I haven't had the time 😞
Trying to get pip to be twice as fast 🤞
https://github.com/pypa/pip/pull/8215
Please keep pinging me, I would really like to follow on it.

Okay this seems correct...
Can you share both yaml files (server & serving) and env file?

Hmm UnevenDolphin73 I just checked with v.1.1.6, the first time the configuration file is loaded is when calling Task.init (if not running with an agent, which is your case).
But the main point I just realized I missed 🤯
"http://"${CLEARML_ENDPOINT}":8080"The code does not try to resolve OS environments there!
Which, well, is a nice feature to add
https://github.com/allegroai/clearml/blob/d3e986393ac8d1a1ea48302224962570ab8e6f9e/clearml/backend_api/session/session.py#L576
should p...

Hi SmallDeer34
Is the Dataset in clearml-data ? If it is then Dataset.get().get_local_copy() will get you a cached local copy of the entire dataset.
If it is not, then you can use StorageManager.get_local_copy(url_here) to download the dataset.

2. Any Argparser is automatically logged (and later can be overridden from the UI). Specifically HfArgumentParser will be automatically logged https://github.com/huggingface/transformers/blob/e43e11260ff3c0a1b3cb0f4f39782d71a51c0191/examples/pytorc...

UnevenDolphin73 following the discussion https://clearml.slack.com/archives/CTK20V944/p1643731949324449 , I suggest this change in the pseudo code
` # task code
task = Task.init(...)

if not task.running_locally() and task.is_main_task():
# pre-init stage
StorageManager.download_folder(...) # Prepare local files for execution
else:
StorageManager.upload_file(...) # Repeated for many files needed
task.execute_remotely(...) `Now when I look at is, it kinds of make sense to h...

Hi @<1536518770577641472:profile|HighElk97>

Is there a way to change the smoothing algorithm?

Just like with TB, this is front-end, not really something you can control ...
That said you can report a smoothed value (i.e. via python) as additional series, wdyt ?

Thanks CleanPigeon16
Could you verify Task "d1d361d1059c4f0981200f59d7683773" exists (and not archived)?

I cannot modify an autoscaler currently running

Yes this is a known limitation, and I know they are working on fixing it for the next version

We basically have flask commands allowing to trigger specific behaviors. ...

Oh I see now, I suspect the issue is that the flask command is not executed from within the git project?!

Hi @<1523701949617147904:profile|PricklyRaven28>
Sorry, we missed that one

we need to invoke it with

accelerate launch

so we use

subprocess.run

So you have two options, either you change the script entry of the Task from your " script.py " to" -m accelerate launch script.py
or you manually do that inside your entry point (i.e. call accelerate launch)
BTW, I "think" we added an "auto detect" for it, so that if you launched it manually this wa...

RoughTiger69 I think this could work, a pseudo example:
` @PipelineDecorator.component(...)
def the_last_step_before_external_stuff():
print("doing some stuff")

@PipelineDecorator.pipeline()
def logic():
the_last_step_before_external_stuff()
if not check_if_data_was_ingested_to_the_system:
print("aborting ourselves")
Task.current_task().abort()
# we will not get here, the agent will make sure we are stopped
sleep(60)
# better safe than sorry
exit(0) `wdyt? (the...

My pleasure 🙂
I do not think it should change anything, just pull the latest agent and reinstall
pip3 install -U clearml-agent

Hmm and how would you imagine a transparent integration here (the example looks like a lot of boilerplate code...)

MysteriousBee56 when you run the trains-agent with --foreground , before it starts the docker it print the full command line, could you send it please?
I can't figure out where the extra ' came from...
Also could you send the trains.conf file?
(feel free to redact and confidential information)

Hi @<1534706830800850944:profile|ZealousCoyote89>

We'd like to have pipeline A trigger pipeline B

Basically a Pipeline is a Task (of a specific Type), so you can have pipeline A function clone/enqueue the pipelineB Task, and wait until it is done. wdyt?

Hi JitteryCoyote63 ,
The easiest would probably be to list the experiment folder, and delete its content.
I might be missing a few things but the general gist should be:
from trains.storage import StorageHelper h = StorageHelper('s3://my_bucket') files = h.list(prefix='s3://my_bucket/task_project/task_name.task_id') for f in files: h.delete(f)Obviously you should have the right credentials 🙂

Hmm
CLEARML_CUSTOM_BUILD_OUTPUT
This might be an enterprise feature, I'm not aware of anything in the open source version

Ohhh I see, yes this is regexp matching, if you want the exact match:
'^{}$'.format(name)

I'm checking now to see where the extra ' could come from