Hi @<1552101458927685632:profile|FreshGoldfish34>
self-hosted, you mean the open source ? if so, then yes totally free 🙂
That said I would recommend to have the server inside your VPN, just in case from a security perspective

what does mount -a return ?

@<1523701079223570432:profile|ReassuredOwl55> did you try adding manually ?

./path/to/package

You can also do that from code:

Task.add_requirements("./path/to/package")
# notice you need to call Task.add_requirements before Task.init
task = Task.init(...)

pywin32 isnt in my requirements file,

CloudySwallow27 whats the OS/env ?
(pywin32 is not in the direct requirements of the agent)

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

Hi @<1523703472304689152:profile|UpsetTurkey67>
You mean https://github.com/Lightning-AI/torchmetrics
?

Where are those stored?

Ohh, if this is the case then it kind of makes sense to store on the Task itself. Which means the Task object will have to store it, and then the UI will display it :(
I think the actual solution is a vault , per user, which would allow users to keep their credentials on the sever, the agent to pass those to the Task when it spins it, based on the user. Unfortunately the vault feature is only available on the paid/enterprise version ( with RBAC etc.).
Does that make sense?

I think they should not 🙂

path: ~/.clearml/venvs-cache

to

# path: ~/.clearml/venvs-cache

Why does my task execution freeze after pip installation (running agent in foreground mode)?

Hi AdventurousButterfly15
Are you running in agent docker mode or venv mode ?
What do you mean freeze? do you see anything on the Taks console log in the UI? what's the host OS ?

SourOx12
Run this example:
https://github.com/allegroai/clearml/blob/master/examples/reporting/scalar_reporting.py
Once, then change line #26 to:
task = Task.init(project_name="examples", task_name="scalar reporting", continue_last_task=True)and run again,

Yes (Mine isn't and it is working 🙂 )

what if the preexisting venv is just the system python? my base image is python:3.10.10 and i just pip install all requirements in that image. Does that not avoid venv still?

it will basically create a new venv inside the container forking the existing preinistalled stuff (i.e. the new venv already has everything the python system has preinstalled)
then it will call "pip install" on all the "installed packages of the Task.
Which should just check everything is there and install nothing...

(second cell)

but I still clearml-agent will raise the same error

which one?

You actually have to login/ssh under said user, have another dedicated mountpoint and spin the agent from that user.

Is it being used to ssh to the instance?

It is used for the SSH client so it "knows" the SSH server (does that make sense) ?

Dynamic GPU option only available with Enterprise version right?

Correct 🙂

... if we have direct access to the Kubernetes worker when we run K8S glue?

Correct, if you have a direct access to the Node (on your k8s cluster) from your laptop (assuming the clearml-session is running from the laptop), everything should work

Seems the apiserver is out of connections, this is odd...
SuccessfulKoala55 do you have an idea ?

Wait, with the Port it does not work?
Notice that since this is external S3 you have to have the port specified so it Knows this is not an AWS S3 but a different compatible service

If this is the case then the easiest is:
from clearml.backend_api.session.client import APIClient client = APIClient() res = client.events.get_task_plots(task="<task-id>")We should defiantly have a nice interface 🙂

BTW: is this on the community server or self-hosted (aka docker-compose)?

Try this one 🙂
HyperParameterOptimizer.start_locally(...)
https://clear.ml/docs/latest/docs/references/sdk/hpo_optimization_hyperparameteroptimizer#start_locally

Hi DisturbedElk70
I think in a few hours 🙂

Hi RobustGoldfish9 Kudos on the mount, and my apologies for forgetting to mention it.
You are absolutely right, I'll make sure we have it in the documentation, there is no way to know that obscure env variable 🙂

You can get a mutable copy of the entire dataset (original version), with get_mutable_copy() Then change the files on the returned directory, then create a new Dataset with the parent dataset as the original verison, then sync the folder.
You can also just update the specific file (without needing to download the entire original version)

What is the specific use case, updating a file on existing dataset and creating a new version?

Make sense ?

Any specific use case for the required "draft" mode?