named as 

venv_update

 (I believe it's still in beta). Do you think enabling this parameter significantly helps to build environments faster?

This is deprecated... it was a test to use the a package that can update pip venvs, but it was never stable, we will remove it in the next version

Yes, I guess. Since pipelines are designed to be executed remotely it may be pointless to enable an 

output_uri

 parameter in the 

PipelineDecorator.componen...

Many thanks!

If the same Task is run with different parameters...

ShinyWhale52 sorry, I kind of missed that in the explanation
The pipeline will always* create a new copy (clone) of the original Task (step), then modify the step's inputs etc.
The idea is that you have the experiment management (read execution management) to create full transparancy into the pipelines and steps. Think of it as the missing part in a lot of pipelines platforms where after you executed the pipeline you need to furthe...

Shout-out to Emilio for quickly stumbling on this rare bug and letting us know. If you have a feeling your process is stuck on exit, just upgrade to 1.0.1 😉

Hi ComfortableHorse5
Yes this is more of a suggestion that you should write them using the platform capabilities, the UI implementation is being worked on, as well as a few helpers classes, I thin you'll be able to see a few in the next release 🙂

ClearML seems to store stuff that's relevant to script execution outside of clearml.Task

Outside of the cleaml.Task?

Thank you StrangePelican34 !!!

Hi JitteryCoyote63 , is there a callback for that?

Hi WhimsicalLion91
https://allegro.ai/docs/logger.html#trains.logger.Logger.report_line_plot

https://github.com/allegroai/trains/blob/master/examples/reporting/scatter_hist_confusion_mat_reporting.py

a task of queue B if the next task is of type A it will have to wait,

It seems you imply there are two types of Tasks and they need to be executed one after the other ?

Hmm, you are correct
Which means this is some conda issue, basically when installing from env file, conda is not resolving the correct pytorch version 😞
Not sure why... Could you try to upgrade conda ?

Amazing! 🎉
Let me know how we can help 🙂

Hmm and you are getting empty list for thi one:

server_info['url'] = f"http://{server_info['hostname']}:{server_info['port']}/"

print(os.environ)

Notice the args will be set on the connect call, so the check on whether they are empty should come after

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

YEY

With pleasure 🙂

If this is the case, then we do not change the maptplotlib backend
Also

I've attempted converting the 

mpl

 image to 

PIL

 and use 

report_image

  to push the image, to no avail.

What are you getting? error / exception ?

Do people use ClearML with huggingface transformers? The code is std transformers code.

I believe they do 🙂
There is no real way to differentiate between, "storing model" using torch.save and storing configuration ...

A few implementation / design details:
When you run code with Trains (and call init) it will record your environment (python packages, git code, uncommitted changes etc) Everything is stored on the Task object in the trains-server, when you clone a task you literally create a copy of the Task object (i.e. a second experiment). on the cloned experiment, you can edit everything (parameters, git, base docker image etc) When you enqueue a Task you add its ID to the execution queue list a trains-a...

Is it possibe to launch a task from Machine C to the queue that Machine B's agent is listening to?

Yes, that's the idea

Do I have to have anything installed (aside from the 

trains

 PIP package) on Machine C to do so?

Nothing, pure magic 🙂

Wait, why aren't you just calling Popen? (or os.system), I'm not sure how it relates to the torch multiprocess example. What am I missing ?

Hi @<1587615463670550528:profile|DepravedDolphin12>

Is there anyway to get the id of the pipeline using pipeline name?

In the UI top right "details" panel should have the Pipeline ID
Is this what you are looking for ?

Are you sure trains-server not trains package (i.e. backend)

How can i find queue name

You can generate as many as you like, the default one is called "default" but you can add new queues in the UI (goto workers & queus page, then Queues, and click "+ New Queue"

Would I be able to add customized columns like I am able to in 

task.connect

 ? Same question applies for parallel coordinates and all kinds of comparisons

No to both 😞

For now I come to the conclusion, that keeping a 

requirements.txt

 and making clearml parse

Maybe we could just have that as another option?

Hi @<1600661428556009472:profile|HighCoyote66>

However, we need to allocate resources to ourselves manually, using an

srun

command or

sbatch

Long story short, there is a full SLURM integration, basically you push a job into the ClearML queue and it produces a slurm job that uses the agent to setup the venv/container and run your Task, but this is only part of the enterprise version 😞
You can however do the following (notice this is ...

or shall I call the Task.init even from the agent

WorriedParrot51 I think something is lost here.
Task.init() is always called, even when the agent is executing the code. The difference is in what happens inside the Task.init() call. When the codebase itself is executed by the trains-agent, it signals through OS environment to the task.init() that instead of a new created task, it should use the already created one. from this point all data flows from the trains-server back into the c...