Can you please tell me how to return the folder where the script should run?

add it to the python path

PYTHONPATH="/src/project"

could you send the entire log here?
i.e. from the "docker-compose" command line and onward

Docker cmd is basically docker image name but you can add parameters as well.
For example "Nvidia/cuda" or "Nvidia/cuda -v /mnt/data:/mnt/data"

Ohh! I see now
@<1526371965655322624:profile|NuttyCamel41> the "backend: "pytorch" is not really supported because it does not use the optimized Triron engine (which is the reason to run Triron server)
In order to use pytorch you need to convert it to torchscript and then deploy, see example here:
None
[None](https://github.com/allegroai/clearml-serving/blob/7ba356efc97a6ae2159283d198d981b3c1ab85e6/examples/pytor...

Hi HappyDove3
task.set_script is a great way to add the info (assuming the .git is missing)
Are you running it using PyCharm? (If so use the clearml pycharm plugin, it basically passes the info from your local git to the remote machine via OS environment)

RoundMosquito25 do notice the agent is pulling the code from the remote repo, so you do need to push the local commits, but the uncommitted changes clearml will do for you. Make sense?

It runs directly but leads to the above error with clearml

Both manually (i.e. calling Task.init and running it without agent, and with agent ? same exact behavior ?

You should manually remove the cudatoolkit from the installed packages section in the UI, then try to send it to the agent and see if it works. The question is how it ended there in the first place

The problem is not really for the agents to wait (this is easily solved by additional high priority queue) the problem is will you have a "free" agent... you see my point ?

SweetGiraffe8 Task.init will autolog everything (git/python packages/console etc), for your existing process.
Task.create purely creates a new Task in the system, and lets' you manually fill in all the details on that Task
Make sense ?

Building the pipeline in runtime from external configuration is very cool!!
I think nested components is exactly the correct solution, and it is a great use case.

I see now.
Let's assume you know which snapshot that was:
` prev_task = Task.get_task(task_id='the_first_training_task_id')

get the second from last checkpoint

task.models['output'][-2].url
prev_scalars = prev_task.get_reported_scalars()
new_task = Task.init('example', 'new task')
logger = new_task.get_logger()

do some fpr loop and report the prev_scalars with logger.report_scalars

new_task.flush(wait_for_uploads=True)
new_task.set_initial_iteration(22000)

start the train `

task = Task.init(...) if task.running_locally(): # wait for the repo detection and requirements update task._wait_for_repo_detection() # reset requirements task._update_requirements(None)🙂

Hi PunyGoose16 ,
I think the website is probably the easiest 🙂
https://clear.ml/contact-us/
I think they get back to quite quickly

Hi NastyFox63
This seems like most of the reports are converted to pngs (which is what the automagic does if it fails to convert the matplotlib into interactive plot).

no more than 114 plots are shown in the plots tab.

Are you saying we have 114 limit on plots ?
Is this true for "full screen" mode (i.e. not in the experiments table but switch to full detailed view)

I have to assume that I do not know the dataset ID

Sorry I mean:

datasets = Dataset.list_datasets(dataset_project="some_project") 
for d in datasets:
  d["version"] = Dataset.get(dataset_id=d["id"]).version

wdyt?

@<1545216070686609408:profile|EnthusiasticCow4>

Is there currently a way to bind the same GPU to multiple queues? I believe the agent complains last time I tried (which was a bit ago)

run multiple agents on the same GPU,

CLEARML_WORKER_NAME=host-gpu0a clearml-agent daemon --gpus 0
CLEARML_WORKER_NAME=host-gpu0b clearml-agent daemon --gpus 0

GiganticTurtle0 this one worked for me 🙂
` from clearml import Task
from clearml.automation.controller import PipelineDecorator

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue1")
def step_1(msg: str):
msg += "\nI've survived step 1!"
return msg

@PipelineDecorator.component(return_values=["msg"], execution_queue="myqueue2")
def step_2(msg: str):
msg += "\nI've also survived step 2!"
return msg

@PipelineDecorator.component(return_values=["m...

GrumpyPenguin23 could you help and point us to an overview/getting-started video?

one can containerise the whole pipeline and run it pretty much anywhere.

Does that mean the entire pipeline will be running on the instance spinning the container ?
From here: this is what I understand:
https://kedro.readthedocs.io/en/stable/10_deployment/06_kubeflow.html

My thinking was I can use one command and run all steps locally while still registering all "nodes/functions/inputs/outputs etc" with clearml such that I could also then later go into the interface and clone an...

Sorry if it's something trivial. I recently started working with ClearML.

No worries, this has actually more to do with how you work with Dask
The Task ID is the unique id of the any Task in the system (task.id will return the UID str)
Can you post a toy Dash code here, I'll explain how to make it compatible with clearml 🙂

So is there any tutorial on this topic

Dude, we just invented it 🙂
Any chance you feel like writing something in a github issue, so other users know how to do this ?

Guess I’ll need to implement job schedule myself

You have a scheduler, it will pull jobs from the queue by order, then run them one after the other (one at a time)

Hmm I guess that now that you mention it, not that obvious when I'm on a Mac as well, maybe we should have the archive button at the bottom as well..
SteadyFox10 What do you think?

correct (i.e. not frontend)

Oh, did you try task.connect_configuration
?
https://allegro.ai/docs/examples/reporting/model_config/#using-a-configuration-file

from task pick-up to "git clone" is now ~30s, much better.

This is "spent" calling apt update && update install && pip install clearml-agent
if you have those preinstalled it should be quick

though as far as I understand, the recommendation is still to not run workers-in-docker like this:

if you do not want it to install anything and just use existing venv (leaving the venv as is) and if something is missing then so be it, then yes sure that the way to go

That might be me, let me check...

LOL, if this is important we probably could add some support (meaning you will be able to specify it in the "installed packages" section, per Task).
If you find an actual scenario where it is needed, I'll make sure we support it 🙂

You mean to add these two to the model when deploying?

    │   ├── model_NVIDIA_GeForce_RTX_3080.plan
    │   └── model_Tesla_T4.plan

Notice the preprocess.py is Not running on the GPU instance, it is running on a CPU instance (technically not the same machine)

Task.get_task(..).system_tags