Disable automatic model uploads

Disable the auto upload
task = Task.init(..., auto_connect_frameworks{'pytorch': False})

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

Ohh wow

Hi SteadySeagull18

However, it seems to be entirely hanging here in the "Running" state.

Did you set a an agent to listen to the "services" queue ?
Someone needs to run the pipeline logic itself, it is sometimes part of the clearml-server deployment but not a mist

Hi UptightBeetle98
The hyper parameter example assumes you have agents ( trains-agent ) connected to your account. These agents will pull the jobs from the queue (which they are now, aka pending) setup the environment for the jobs (venv or docker+venv) and execute the job with the specific arguments the optimizer chose.

Make sense ?

👍

Hmm so yes that is true, if you are changing the bucket values you will have to manually also adjust it in grafana. I wonder if there is a shortcut here, the data is stored in Prometheus, and I would rather try to avoid deleting old data, Wdyt?

LOL

UnevenDolphin73 go to the profile page, I think at the bottom right corner you should see it
(Also ctrl-F5 to reload the web application, if you upgraded the server 🙂 )

make sense ?

You might be able to also find out exactly what needs to be pickled using the 

f_code

 of the function (but that's limited to C implementation of python).

Nice!

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 ?

Hi @<1585078763312386048:profile|ArrogantButterfly10>

Now i want to clone the pipeline and change the hyperparameters of train task, is it possible? If so, how??

the pipeline arguments are for the pipeline DAG/logic, you need to pass one of the arguments as an argument for the training step/task. Make sense ?

in my repo I maintain a bash script to setup a separate python env.

Hmm interesting, now I have to wonder what is the difference ? meaning why doesn't the agent build a similar one based on the requirements ?

Hurray!

CooperativeSealion8

when it first asks me to enter my full name

Where? in the Web?

Let me check if we can reproduce it

I appended python path with /code/app/flair in my base image and execute

the python path is changing since it installs a new venv into the system.
Let me check what's going on with the pythonpath, because it is definitely is changed when running the code (the code base root folder is added to it). Maybe we need to make sure that if you had PYTHON PATH pre-defined we restore it.

Yes
Are you trying to upload_artifact to a Task that is already completed ?

FrustratingWalrus87 If you need active one, I think there is currently no alternative to TB tSNE 🙂 it is truly great 🙂
That said you can use plotly for the graph:
https://plotly.com/python/t-sne-and-umap-projections/#project-data-into-3d-with-tsne-and-pxscatter3d
and report it to ClearML with Logger report_plotly :
https://github.com/allegroai/clearml/blob/e9f8fc949db7f82b6a6f1c1ca64f94347196f4c0/examples/reporting/plotly_reporting.py#L20

but now serving is not able to locate the model file itself,

from your screen shot the file seems to be in local folder somewhere "file://" it should be in the file server or in object storage, how did it get there? how is the file server configured

regrading the artifact, yes that make sense, I guess this is why there is "input" type for an artifact, the actual use case was never found (I guess until now?! what are you point there?)
Regrading the configuration

It's very useful for us to be able to see the contents of the configuration and understand

Wouldn't that just do exactly what you are looking for:
` local_config_file_that_i_can_always_open = task.connect_configuration("important", "/path/to/config/I/only/have/on/my/machi...

task.set_script(working_dir=dir, entry_point="my_script.py")Why do you have this part? isn't it the same code, the script entry point is auto detected ?

... or when I run my_script.py locally (in order to create and enqueue the task)?

the latter, When the script is running locally

So something like

os.path.join(os.path.dirname(file), "requirements.txt")

is the right way?

Sure this will work 🙂

BroadSeaturtle49 btw: RC with a fix should be out after the weekend

Hi @<1559711593736966144:profile|SoggyCow20>
How did you configure the clerml.conf ? see here an example:
None

Hi @<1773158043551272960:profile|PungentRobin32>

1732496915556 lab03:gpuall DEBUG docker: invalid reference format.

So seems like the docker command is incorrect?! the error you are seeing is the agent failing to spin the docker, what's the OS of the host machine ?

Hmm I just noticed:

'--rm', '', 'bash'

This is odd this is an extra argument passed as "empty text" how did that end up there? could it be you did not provide any docker image or default docker container?

Also, I did also accidentally create multiple services via

clearml-serving create --name <> --project <>

, and cannot get rid of them.

find them in the UI (you can go to All Projects, then in the search bar put their UIs) and archive / delete them

So the part that is confusing to me is: None
who / how is this Task running? did you also setup a "service" queue (as part of the clear...

Hi, is there a way to force the requirements.txt?

You mean to ignore the "Installed Packages" ?

My model files are also there, just placed in some usual non-shared linux directory.

So this is the issue, How would the container Get to these models? you either need to mount the folder to the container,
or you push them to ClearML model repo with the OutputModel class , does that make sense ?