AdventurousButterfly15

Despite having manually installed this torch version, during task execution agent still tries to install it somehow and fails:

Are you running the agent in venv mode? or docker mode?
Notice that in docker mode it inherits the python packages from the container, and adds/reinstalls missing packages. In venv mode it creates a New clean venv (there is no way to inherit a venv, venv can only inherit from system wide installed packages)

The idea is that you cannot e...

How do I best utilize clearml in this scenario such that any coworker of mine is able to reproduce my work with the same pipeline?

Basically this sounds to me like proper software developemnt design (i.e. the class vs stages).
In order to make sure Anyone can reproduce it, you mean anyone can rerun the "pipeline" ? If this is the case just add Task.init (maybe use a specific Task type) and the agents will make sure this is Fully reproducible.
If you mean the data itself is stored, the...

Under your profile you should be able to see it

is number of calls performed, not what those calls were.

oh, yes this is just a measure of how many API calls are sent.
It does not really matter which ones

In case of scalars it is easy to see (maximum number of iterations is a good starting point

I guess last followup question, is there a way to cap costs?

Scale tier ? (I know it is not per usage, but it is probably more than 15$ per user 🙂 )

well from 2 to 30sec is a factor of 15, I think this is a good start 🙂

VictoriousPenguin97 basically spin down sereverA (this should flush all DBs) then copy /opt/clearml to the new server and spin it with docker-compose. As long as the new server is on the same address as the previous one, everything should work out of the box

Nice!

Hmm EmbarrassedPeacock82
Let's try with
--input-size -1 60 1 --aux-config input.format=FORMAT_NCHWBTW: this seems like a triton LSTM configuration issue, we might want to move the discussion to the Triton server issue, wdyt?

Hi JitteryCoyote63
The easiest is to inherit the ResourceMonitor class and change the default logging rate (you could also disable some of the metrics).
https://github.com/allegroai/clearml/blob/701fca9f395c05324dc6a5d8c61ba20e363190cf/clearml/task.py#L565
Then pass the new class to Task.init as auto_resource_monitoring

Hi JitteryCoyote63 report_frequency_sec=30. controller how frequently monitoring events are sent to the server, default is every 30 seconds (you can change the UI display to wall-time to review). You can change it to 180 so it will only send an event every 3 minutes (for example).
sample_frequency_per_sec is the sampling frequency it uses internally, then it will average the results over the course of the report_frequency_sec time window, and send the averaged result on the repo...

🤞

So I think there are two bugs here?
--args overrides="key=value" does not work request: add --hydra to override hydra arguments (and if this is added the first one is not needed)Is that correct?

is it displaying that it is running anything?

That sounds about right to me 🙂

Apparently the error comes when I try to access from

get_model_and_features

the pipeline component

load_model

. If it is not set as pipeline component and only as helper function (provided it is declared before the components that calls it (I already understood that and fixed, different from the code I sent above).

ShallowGoldfish8 so now I'm a bit confused, are you saying that now it works as expected ?

If this is the case why not have the stream process call the rest api, then move forward with the result? This way it scales out of the box, the main "conceptual" difference is that the restapi is used internally, and the upside is the event streaming processing becomes part of the application layer, not tied with the compute cost of the model , wdyt?

Hi @<1653207659978952704:profile|LovelyStork78>

I have a docker container with all the dependencies.

Well I think the main question is are you using the clearml-agent to launch jobs/experiments? If you do it makes sense to specify your docker as "base docker image" (in the UI look for under the Execution tab, Container).
This means the agent will use the pre-installed environment and will add anything that your Task needs on top of it, this of course includes pushing your codebase i...

Ok, but it must be somewhere in the bst class

It is the XGboost callback feature, basically just reporting everything xgbosst reports:
None

Yes that makes sense, if the overhead of the additional packages is not huge, I do not think it is worth the maintenance 🙂
BTW clearml-agent has full venv caching that you can turn on, so when running remotely you are not "paying" for the additional packages being installed:
Un-comment this line 🙂
https://github.com/allegroai/clearml-agent/blob/51eb0a713cc78bd35ca15ed9440ddc92ffe7f37c/docs/clearml.conf#L116

Hi QuaintJellyfish58
This is odd, this "undefined" project is also marked as "Example" which would explain why you cannot delete it, but not how you ended up with one
Any idea on what changed on your server ?

QuaintJellyfish58 this is very odd, and the "undefined" is always marked as example?

Because of that, I cannot create a task in this project programmatically locally because it tries to access the bucket and fails. And there is no easy way to change the default output location (not in the web UI, not in the sdk)

JitteryCoyote63 hmm that is a pickle ...
let me check the code ...

Okay fixed, you will be able to override it with output_uri=False (which is ignored on remote execution if you have a project default or Task output uri set in the UI).
Make sense ?

well it should fail, but I think the error message should be fixed 🙂
maybe:
ValueError: dataset 'tmp_datset' not found in projectlavi-testing' `wdyt?

EmbarrassedSpider34

Sync_folder and upload
Several times along the code and then

Do notice they overwrite one another...

Fix was pushed to GitHub 🙂

It should print to console...
print(task.get_output_log_web_page())

Yey! MysteriousBee56 kudos on keep trying!
I'll make sure we report those errors, because this debug process should have much shorter 🙂