Can you see that the environment is actually being passed ?

The reason is because it is logged as an image, not a plot 🙂

Bugs, definitely GitHub, this is the easiest to track.
Documentation, if these are small issues, Slack is fine, otherwise, GitHub issue.
Regrading the documentation, we are working on another iteration of improvement, but if you find inaccuracies/broken links please report 🙂

None

but does that mean I have to unpack all the dictionary values as parameters of the pipeline function?

I was just suggesting a hack 🙂 the fix itself is transparent (I'm expecting it to be pushed tomorrow), basically it will make sure the sample pipeline will work as expected.
regardless and out of curiosity, if you only have one dict passed to the pipeline function, why not use named arguments ?

Thank you!

LOL I keep typing clara without noticing (maybe it's the nvidia thing I keep thinking about)
Carla makes much more sense 😄

you can also get it flattened with:
task.get_parameters()Type in both cases is string

So that agent on different nodes will probably require different cuda-version images.

That makes sense SarcasticSquirrel56
I would edit the helm chart (or deploy manually) based on a selector that will select the different nodes/gpus and assign the correct containers (i.e. matching CUDA versions to the diff GPUs / drivers)
BTW: you can also playaround with k8s glue, which would dynamically spin pods based on clearml Tasks.
wdyt?

Thanks TroubledJellyfish71 I manged to locate the bug (and indeed it's the new aarach package support)
I'll make sure we push an RC in the next few days, until then as a workaround, you can put the full link (http) to the torch wheel
BTW: 1.11 is the first version to support aarch64, if you request a lower torch version, you will not encounter the bug

GrievingTurkey78

maybe since the package is not directly imported in my code it is possible to get a different version to what I have locally (?).

If these are derivative packages (i.e. imported by other packages) they are not automatically logged when executing the Task manually (in order to keep the "installed packages as lean as possible on the one hand but specify also specify the important packages for you)
That said, when the "trains-agent" executed the task it will store nack...

Oh think I understand you point now.
basically you can:

Create the initial Task, once it is in the system clone it and adjust parameters externally. A simple example here:
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/examples/automation/manual_random_param_search_example.py#L41

wdyt?

Hmm HandsomeGiraffe70
This seem like a bug, let me see what we can do about that 🙂
could it be the parent version was created with an older version of clearml sdk ?

Hi JuicyFox94
I think you are correct, this bug will explain the entire thing.
Basically what happens is that remote_execute stops the local run before the configuration is set on the Task. Then running remotely the code pull the configuration, sees that it is empty and does nothing.
Let me see if I can reproduce it...

they are just neighboring modules to the function I am importing.

So I think that is you specify the repo,, on the remote machine you will end with the code of the component sitting at the root folder of the repo, from there I assume you can import the rest, the root git path should be part of your PYTHONPATH automatically.
wdyt?

PleasantGiraffe85 can you send examples of the different git repo links (one internal one public) ?

Hi GloriousPenguin2 , Sorry this is a bit confusing. Let me expand:
When converting into a plotly object (the default), you cannot really control the dimensions of the plot in the UI programatically, you can however drag the seperator and expand width / height If you pass to report_matplotlib_figure the argument " report_image=True, " it will create a static image from matplotlib figure (as rendered locally) and use that as the figure, this way you get exactly wysiwyg , but the...

Many thanks LazyLeopard18 ! 🙂

These instructions should create the exact chart:
None
What am I missing ?

Sure thing 🙂

you should have a gpu argument there, set it to true

Yeah I think this kind of makes sense to me, any chance you can open a GH issue on this feature request?

what is?

If the manual execution (i.e. pycharm) was working it should have stored it on the Pipeline Task.

@PipelineDecorator.component(repo="..")

The imports are not recognized - they are not on the pythonpath of the task that the agent starts.

RoughTiger69 add the imports inside the functions itself, you cal also specify the, on the component
@PipelineDecorator.component(..., package=["pcakge", "package==1.2.3"])or
@PipelineDecorator.component(...): import pandas as pd # noqa ...

Each user creates a 

.env

 file for their needs or exports them in the shell running the python code. Currently I copy the environment variables to an S3 bucket and download it from there

That is a great hack, but who carries the credentials for the S3 bucket? the reason for asking is I;m thinking maybe the code would directly do that (meaning download the .env file and apply them?!)

Hi DepressedChimpanzee34
I think main issue here is slow response time from the API server, I "think" you can increase the number of API server processes, but considering the 16GB, I'm not sure you have the headroom.
At peak usage, how much free RAM so you have on the machine ?

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

https://techbeacon.com/enterprise-it/5-ways-lose-data-kubernetes-how-avoid-them
see (1) and (4)

This is definitely a but, in the super class it should have the same condition (the issue is checking if you are trying to change the "main" task)
Thanks ApprehensiveFox95
I'll make sure we push a fix 🙂