will my datasets be stored on the same machine that hosts the clearml server?

By default yes, they will be stored to the files-server (but you can change it, this is an argument for both the CLI and the python interface)

Hi ExuberantParrot61
Is the pipeline logic code running from inside the repo?

AttractiveCockroach17 could it be Hydra actually kills these processes?
(I'm trying to figure out if we can fix something with the hydra integration so that it marks them as aborted)

SubstantialElk6 I just executed it , and everything seems okay on my machine.
Could you pull the latest clearml-agent from the github and try again ?

EDIT:
just try to run:
git clone cd clearml-agent python examples/k8s_glue_example.py

Let me know if there is an issue 🙂

The issue only arises upon sending Images. (Both numpy, mpl and PIL)

BTW: they should appear under debug-samples Tab in the results

The reasoning is that most likely simultaneous processes will fail on GPU due to memory limit

this?
ids = [t.id for t in top_task]

(Venv mode makes sense if running inside a container, if you need docker support you will need to mount the docker socket inside)
What is exactly the error you re getting from clearml? And what do you have in the configuration file?

I see if this is the case try to set
'output_uri="file:///full/path/to/dir"'
Notice it has to have the full path there and the file:// prefix

. However, despite having imported the required types from the 

typing

 library in the script where the function decorated with 

PipelineDecorator.component

 is defined, later in the generated script the 

typing

 library is not imported outside the scope of the function

Actually the typing part is not passed to the "created step" , because there are no global imports, for eexample:
` def step(a: pd.DataFrame):
import pandas as pd
...

Oh then this should just work
cp -R --link b a/You can achieve the same symbol link link from python as well

Just wanted to know how many people are actively working on clearml.

probably 30+ 🙂
ReassuredTiger98 are you afraid from lack of support? or are you offering some (it is always welcomed) ?

A Lot 😄

(I'll make sure it is added to the docstring because apparently it was not there

Hi @<1784754456546512896:profile|ConfusedSealion46>

clear ml server took so much memory usage, especially for elastic search

Yeah that depends on how many metrics/logs you have there, but you really have to have at least 8GB RAM
delete old experiments ?

Well from the error it seems there is no layer called "dense" , hence triton failing to find the layer returning the reult. Does that make sense?

Fixing that would make this feature great.

Hmm, I guess that is doable, this is a good point, search for the GUID is not always trivial (or maybe at least we can put in the description the project/dataset/version )

We are always looking for additional talented people 😉 DM me...

Hi CostlyElephant1
What do you mean by "delete raw data"? Data is always fetched to cached folders and clearml takes care of cache cleanup
That said notice that get mutable copy is a target you specify, in this case you should definetly delete after usage. Wdyt ?

Thanks Martin, so does it mean I won’t be able to see the data hosted on S3 bucket in ClearMl dashboard under datasets tab after registering it?

Sure you can, let's assume you have everything in your local /mnt/my/data you can just add this folder with add_files then upload to your S3 bucket with upload(output_uri=" None ",...)
make sense ?

Hi AbruptWorm50

I am currently using the repo cache,

What do you mean by "using the repo cache" ? This is transparent, the agent does that, users should not access that folder?

I also looked at the log you send, why do you think it is re-downloading the repo?

Hi UnevenDolphin73
You mean this part?
https://github.com/allegroai/clearml-agent/blob/5afb604e3d53d3f09dd6de81fe0a494dacb2e94d/docs/clearml.conf#L212

(In other words, the 

the Task's Environment section

 is a bit unclear)

Yes we should expand, but generally you are correct it should work as you described 🙂

what do you mean? the same env for all components ? if they are using/importing exactly the same packages, and using the same container, then yes it could

While I'll look into it, you can do:
from clearml import OutputModel output_model = OutputModel() output_model.update_weights("best_model.onnx")

Oh!
I see this is using the colab as remote agent (i.e. to launch jobs on it),

[ColabKernelApp] CRITICAL | Bad config encountered during initialization: The 'kernel_class' trait of <main.ColabKernelApp object at 0x7fa41b29e5c0> instance must be a type, but 'google.colab._kernel.Kernel' could not be imported

Can you send the full log?

why are there indefinitely growing anonymous tasks, even after i've closed the main schedulers.

The anonymous Tasks are The Dataset you are creating (a Dataset version is also a Task of a certain type with artifacts, the idea is usually Datasets are created from code, hence the need to combine the two).
Make sense ?

Nice SubstantialElk6 !
BTW: you can configure your cleaml client to store the changes from the latest Pushed commit (and not the default which is latest local commit)
see store_code_diff_from_remote: in clearml.conf:
https://github.com/allegroai/clearml/blob/9b962bae4b1ccc448e1807e1688fe193454c1da1/docs/clearml.conf#L150

Hmm I think the easiest is using the helm chart:
https://github.com/allegroai/clearml-server-helm-cloud-ready
I know there is work on a teraform template, not sure about instio.
Is helm okay for you ?

Hi ApprehensiveFox95
You mean from code remove the argparse arguments ?
Or post execution in the UI?