SmarmySeaurchin8 I might be missing something in your description. The way the pipeline works,
the Tasks in the DAG are pre-executed (either with "execute_remotely" or actually fully executed once").
The DAG nodes themselves are executed on the trains-agent , which means they reproduce the code / env for every cloned Task in the DAG (not on the original Tasks).
WDYT?

Can you do it manually, i.e. checkout the same commit id, then take the uncommitted changes (you can copy paste it to diff.txt) then call git apply diff.txt ?

I've seen that the file location of a task is saved

What do you mean by that? is it the execution section "entry point" ?

@<1558986821491232768:profile|FunnyAlligator17> can you provide the full log ?

just got the pipeline to run 

Nice!

using the default queue okay?

Using the default queue is fine. The different queue is the "services" queue that by default the "trains-server" is running an agent the will pull jobs from there.
With "services" mode, an agent will pull jobs right after the other (not waiting for the previous job to finish), as opposed to regular queue (any other) that the trains-agent will pull a job only after the previous one completed .

BTW: seems like conda doesn't support git+git:// packages
How about switching to pip ? you can still run the entire thing from conda env, it will just use pip & venv to install everything, other than that it should work as expected.

Hi DilapidatedDucks58 ,
I'm not aware of anything of this nature, but I'd like to get a bit more information so we could check it.
Could you send the web-server logs ? either from the docker or the browser itself.

Let me know :)

Hi @<1541954607595393024:profile|BattyCrocodile47>
Did you check None ?
You are not supposed to do 2,3,4
After (1) you should just do

ssh root@localhost -p 8022

and provide the password that is written in the CLI

(Notice to pass --public-ip if your remote machine is using a public IP you can access)

Hi SoreDragonfly16
Sadly no, the idea is to create full visibility to all users in the system (basically saying share everything with your colleagues) .
That said, I know the enterprise version have permission / security features, I'm sure it covers this scenario as well.

see here the docker_setup_bash_script argument
None
It will be executed (no need for the #!/bin/bash btw) before starting to setup the env inside the container, so apt-get and the like can be executed if needed. Notice that if this is something that Always needs to be executed, you can put the same list of commands here: [None](https://github.com/allegroai/clearml-agen...

This code will give you one graph titled "loss" with two series: (1) trains (2) loss

just want to be very precise an concise about them

Always appreciated 🙂

Also, don't be shy, we love questions 🙂

And you want all of them to log into the same experiment ? or do you want an experiment per 60sec (i.e. like the scheduler)

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...

You added it to the deprecated function 🙂
https://github.com/allegroai/clearml/blob/8496b99888deda45955bdd95f22dc929a88ec67b/clearml/backend_config/config.py#L400
See this line
https://github.com/allegroai/clearml/blob/8496b99888deda45955bdd95f22dc929a88ec67b/clearml/backend_config/config.py#L338

Let me chech

Hi TeenyFly97

Can I super-impose the graphs while comparing experiments?

Hmm not at the moment, I think someone asked for the option to control it, in both comparison mode and "standalone" mode.
There is a long discussion on this feature here:
https://github.com/allegroai/trains/issues/81#issuecomment-645425450
Feel free to chime in 🙂
I think that the latest agreement is a switch in the UI, separating or collecting (super-imposing) those graphs.

MuddyCrab47 Try this one 🙂

DistressedGoat23 check this example:
https://github.com/allegroai/clearml/blob/master/examples/optimization/hyper-parameter-optimization/hyper_parameter_optimizer.py
aSearchStrategy = RandomSearchIt will collect everything on the main Task

This is a curial point for using clearml HPO since comparing dozens of experiments in the UI and searching for the best is just not manageable.

You can of course do that (notice you can actually order them by scalars they report, and even do ...

I will take any suggestion 🙂
git remote -v could be a good start but I'm not familiar with the output structure, is there a template for parsing ?

SmarmySeaurchin8
Something like this one:
vector_series = np.random.randint(10, size=10).reshape(2,5) logger.report_vector(title='vector example', series='vector series', values=vector_series, iteration=0, labels=['A','B'], xaxis='X axis label', yaxis='Y axis label')

I am providing a helper to run a task in queue after running it locally in the notebook

Is this part of a pipeline process or just part of the workflow ?
(reason for asking is that if this is a pipeline thing we might be able to support it in v2)

DistressedGoat23

We are running a hyperparameter tuning (using some cv) which might take a long time and might be even aborted unexpectedly due to machine resources.
We therefore want to see the progress

On the HPO Task itself (not the individual experiments the one controlling it all) there is the global progress of the optimization metric, is this what you are looking for ? Am I missing something?

Are all the docker running? it looks like the mongo docker is down

I think I found something, let me see if I can reproduce it

Yey! MysteriousBee56 kudos on keep trying!
I'll make sure we report those errors, because this debug process should have much shorter 🙂

Hi @<1523704198338711552:profile|RoughTiger69>
From this scenario can we assume the "selection" will be tagging the model manually?
Also, how would an human operator decide on the best model, that is what is the input to base the decision on?

Hover near the edge of the plot, the you should get a "bar" you can click on to resize