Hi ProudChicken98
task.connect(input) preserves the types based on the "input" dict types, on the flip side get_parameters returns the string representation (as stored on the clearml-server).
Is there a specific reason for using get_parameters over connect ?

JitteryCoyote63 see if upgrading the packages as they suggest somehow fixes it.
I have the feeling this is the same problem (the first error might be trains masking the original error)

AWS_ACCESS_KEY_ID AWS_SECRET_ACCESS_KEY AWS_DEFAULT_REGION

And the correct region ?

Just a bit of background, the execute)remotely will kill the current process (after the Task is synced) and enqueue the Task that was created for remote execution. What seems to fail is actually killing the current process. You can just pass exit_process=False

sudo curl -L " -s)-$(uname -m)" -o /usr/local/bin/docker-compose

What's the pip verison?

Yes, that sounds like a good start, DilapidatedDucks58 can you open a github issue with the feature request ?
I want to make sure we do not forget

Thank you!

When you install using pip <filename> you should end up with something like:
minerva @ file://... or minerva @ https://...

Hi @<1547028116780617728:profile|TimelyRabbit96>

Trying to do model inference on a video, so first step in

Preprocess

class is to extract frames.

Basically this depends on the RestAPI, usually would will be sending a link to data to be processed and returned Synchronously
What you should have a custom endpoint doing the extraction, send Raw data into another endpoint doing the model inference, basically think "pipeline" end points:
[None](https://github.com/allegro...

BTW: if you feel like pushing forward with integration I'll be more than happy to help PRing new capabilities, even before the "official" release

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 SubstantialElk6
Could you test with the latest RC6 ?
pip install clearml==0.17.5rc6

Hi IrritableGiraffe81
Can you share a code snippet ?
Generally I would try
task = Task.init(..., auto_connect_frameworks={"pytorch': False, 'tensorflow': False)

Won't it be too harsh to have system wide restriction like that ?

@<1523704157695905792:profile|VivaciousBadger56> regrading: None
Is this a discussion or PR ?
(general ranting is saved for our slack channel 🙂 )

👍

Yes, hopefully they have a different exception type so we could differentiate ... :) I'll check

Import Error sounds so out of place it should not be a problem :)

Scheduled training is what I’m looking forward to

I'll try to remember to update here once we pushed into the GitHub repo, feedback is always appropriated 🙂
If in the next two weeks you hear nothing, please ping here to make sure I did not forget 😉

Hi ComfortableHorse5
Yes this is more of a suggestion that you should write them using the platform capabilities, the UI implementation is being worked on, as well as a few helpers classes, I thin you'll be able to see a few in the next release 🙂

JitteryCoyote63 are you calling to:
my_task.output_uri = " s3://my-bucket
in the code itself ?
Why not with Task.init output_uri=...
Also this is running remotely there is no need fo r that, use the Execution -> Output -> Destination and put it there, it will do everything for you 🙂

Hi RipeGoose2
Just to clarify, the issue with the html stuck in cache is a UI, thing, basically the webapp needs to tell the browser not to cache the artifacts, it has nothing to do with how the artifacts are created.
Regardless we love improvements so feel free to mass around with the code and PR once you get something useful 😉
Specifically this is where the html conversion happens
https://github.com/allegroai/clearml/blob/9d108d855f784e1fe7f5691d3b7bf3be64576218/clearml/backend_in...

You mean I can do Epoch001/ and Epoch002/ to split them into groups and make 100 limit per group?

yes then the 100 limit is per "Epoch001" and another 100 limit for "Epoch002" etc. 🙂

This port is odd, it should be 8008
8015

👍

I just set

agent.enable_git_ask_pass: true

in the config of the clearml agent (v1.5.1) and the task is still stuck at asking username when trying to get the private dependency.

Hmm that should not happen, could you delete the cache and retry? maybe?

Is there a helper function option at all that means you can flush the clearml-agent working space automatically, or by command?

Every Task execution the agent clears the venv (packages are cached locally, but the actual venv is cleared). If you want you can turn on the venv cache, but there is no need to manually clear the agent's cache.

Hi NonsensicalSeaanemone47
I'm assuming you mean k8s as compute cluster?
If so, then yes clearml adds priority scheduling on top of your existing kl8s cluster. It also allows you to reuse images as the k8s spins the base container image and then inside the container image the agent sets the environment of the experiment (clones code, apply diff, install missing python packages etc.)
It also gives visibility into the executed pods.
Make sense ?