repeat it until they are all dead 🙂

Ohh then you do docker sibling:
Basically you map the docker socket into the agent's docker , that lets the agent launch another docker on the host machine.
You cab see an example here:
https://github.com/allegroai/clearml-server/blob/6434f1028e6e7fd2479b22fe553f7bca3f8a716f/docker/docker-compose.yml#L144

https://github.com/googleapis/python-storage/issues/74

Okay, let's take a step back and I'll explain how things work.
When running the code (initially) and calling Task.init
A new experiment is created on the server, it automatically stores the git repo link, commit ID, and the local uncommitted changes . these are all stored on the experiment in the server.
Now assume the trains-agent is running on a different machine (which is always the case even if it is actually on the same machine).
The trains-agent will create a new virtual-environmen...

It is recommended to create a git TOKEN with read only permissions and use it (more secure) 🙂

It should have ....

LittleShrimp86 what do you have in the Configuration Tab of the Cloned Pipeline?
(I think that it has empty configuration -> which means empty DAG, so it does nothing and leaves)

Tested with two sub folders, seems to work.
Could you please test with the latest RC:
pip install clearml==0.17.5rc4

BTW: Can you also please test with the latest clearml version , 1.7.2

Give me a minute, I'll check something

Nice SourOx12 !

JitteryCoyote63 Hmmm in theory, yes.
In practice you need to change this line:
https://github.com/allegroai/clearml/blob/fbbae0b8bc933fbbb9811faeabb9b6d9a0ea8d97/clearml/automation/aws_auto_scaler.py#L78
` python -m clearml_agent --config-file '/root/clearml.conf' daemon --queue '{queue}' {docker} --gpus 0 --detached

python -m clearml_agent --config-file '/root/clearml.conf' daemon --queue '{queue}' {docker} --gpus 1 --detached

python -m clearml_agent --config-file '/root/clearml.conf' d...

while I'm looking to upload local weights

Oh, so this is not "importing uploaded (exiting) model" but manually creating a Model.
The easiest way to do that is actually to create a Task for Model uploading, because the model itself will be uploaded to unique destination path, and this is built on top of the Task.
Does that make sense ?

Could you give an example of such configurations ?
(e.g. what would be diff from one to another)

Okay that look s good, now in the UI start here and then get to the artifacts Tab,
Is it there ?

tell me please, does the agent always create a virtual environment?

Yes, but it inherits from the container preinstalled system environment

is it possible to make the agent run the script in an already prepared docker container without creating a virtual environment in the container?

You can set the CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 environment variable

ReassuredTiger98
Okay, but you should have had the prints ...
uploading artifactand
done uploading artifactSo I suspect something is going on with the agent.
Did you manage to run any experiment on this agent ?

EDIT: Can you try with artifacts example we have on the repo:
https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py

Thanks!

Could you run your code not from the git repository.
I have a theory, you never actually added the entry point file to the git repo, so the agent never actually installed it, and it just did nothing (it should have reported an error, I'll look into it)
WDYT?

Okay there should not be any difference ... 😞

ReassuredTiger98

Can you explain what you meant by 

entropy point file?

There is no need to specify entry point file.
It is automatically detected when you run the Code manually on your machine.
My assumption was that the file "src/run_task.py" (based on your log) is just a test file, and hence was not added top the repository. So the agent failed to actually restore it from the git (files that are not added are not considered part of the git diff, this is usually git behavio...

Thanks for checking @<1545216070686609408:profile|EnthusiasticCow4> stable release will be out soon

Great!
I'll make sure the agent outputs the proper error 🙂

BattyLion34

if I simply clone nntraining stage and run it in default queue - everything goes fine.

When you compare the Task you clone manually and the Task created by the pipeline , what's the difference ?

Could you send me the cosnole log of both tasks, failing and passing one?

So I'm gusseting the cli will be in the folder of python:
import sys from pathlib2 import Path (Path(sys.executable).parent / 'cli-util-here').as_posix()

Hi JitteryCoyote63
Just making sure, the package itself it installed as part of the "Installed packages", and it also installs a command line utility ?

Let's start small. Do you have grafana enabled in your docker compose and can you login to your grafana web ui?
Notice grafana needs to access the prometheus container directly so easiest way is to have everything in the same docker compose

quick video of the search not working

Thank you! this is very helpful, passing along to front-end guys 🙂

and ctrl-f (of the browser) doesn’t work as lines below not loaded (even when you scroll it will remove the other lines not visible, so you can’t ctrl-f them)

Yeah, that's because they are added lazily

BurlyPig26 if this is about Task.init delaying execution, did you check:
Task.init(..., deferred_init=True)it will execute the initialization in the background without disturbing execution.

If this is about Model auto logging, see Task.init(..., auto_connect_frameworks) you can specify per framework a wild card to log the models, or disable completely https://github.com/allegroai/clearml/blob/b24ed1937cf8a685f929aef5ac0625449d29cb69/clearml/task.py#L370

Make sense ?