Hi QuaintPelican38
Can you ssh to {instance_public_ip_address}:10022 (something like ssh -p 10022 user@IP_HERE )?
Basically just getting the password prompt means you are okay.
I suspect that you have some AWS security definition (firewall) that prevents a direct access to the instance, could that be?

MysteriousBee56 when you run the trains-agent with --foreground , before it starts the docker it print the full command line, could you send it please?
I can't figure out where the extra ' came from...
Also could you send the trains.conf file?
(feel free to redact and confidential information)

So what is the difference?!

nice 🙂

We should probably have a section on that (i.e. running two agents on the same GPU, then explain how top use it)

Oh no 😞 I wonder if this is connected to:

Any chance the logger is running (or you have) from a subprocess ?

VexedCat68
delete the uploaded file, or the artifact from the Task ?

Hi @<1661542579272945664:profile|SaltySpider22> I'm not sure I understand the answer to my parallel quesion

Ok i did a pip install -r requirements.txt and NOW it picks them up correctly
So packages have to be installed and not just be mentioned in requirements / imported?

Yes, it looks for them locally so it has all the specific versions you need.
If the "installed packages" is totally empty the agent will revert to looking for requirements.txt inside the repository.

Hmm this is odd, when you press on the parent dataset in the UI, and go to full-details, then the INFO tab. Can you copy here everything ?

(RC will be out in a few days)

I would like to force the usage of those requirements when running any script

How would you force it? Will you just ignore the "Installed Packages" section ?

Hi @<1523701111020589056:profile|DefiantSpider5>
So there are two answers here, I'll start with the open-source version of both

Is there a way in clear ml to interactively view subsets of images based on a lasso of embedding plots

The ClearML Datasets have no "query" capabilities of the data inside the dataset. That means you can see preview images, statistics and download the datasets, but no query capabilities. On the other hand, there is no limitation on the type and format of me...

The agent is using Bash (but when you add command line to the docker run, .bashrc is not executed, hence no conda in PATH)
Maybe add the full path to the conda executable:
ocker_setup_bash_script= [ "export PATH=""/workspace/miniconda/bin:$PATH", "export LOCAL_PYTHON=/workspace/miniconda/bin/python3","/workspace/miniconda/bin/conda activate /PATH_GOES_HERE"])

That's why I want to keep it as separate tasks under a single pipeline.

Hmm Yes, if this is the case then you definitely have to have two Tasks (with execution info on each one).
So you could just create a "draft" pipeline Task and report everything to it? Does that make sense ?
(By design a pipeline is in charge of spinning the Tasks and pulling the data/metric from them if needed, in your case it sounds like you need the Tasks to push the data/metric onto the pipeline Task, this is ...

Hi JitteryCoyote63 ,
The easiest would probably be to list the experiment folder, and delete its content.
I might be missing a few things but the general gist should be:
from trains.storage import StorageHelper h = StorageHelper('s3://my_bucket') files = h.list(prefix='s3://my_bucket/task_project/task_name.task_id') for f in files: h.delete(f)Obviously you should have the right credentials 🙂

Hi ConvolutedChicken69
assuming you are runnign the agent in venv mode you can do something like:
$ CLEARML_AGENT_SKIP_PIP_VENV_INSTALL=1 clearml-agent daemon --queue defaultThis will basically only clone the code and use the default python the clearml-agent itself is using.
Does that help?
BTW:

it gets an error as it can't find it with pip.

What's the error? how come the package cannot be installed ?

The problem is that I currently don't have a way to get them "from outside".

Maybe as a hack (until we add the model object)
` class MyModelCB:
current_args = dict()
@classmethod
def callback(load_save, model_info):
if load_save != "save":
return model_info
model_info.name = "my new name" + str(current_args) # make a name from args
return model_info

WeightsFileHandler.add_pre_callback(MyModelCB.callback)
MyModelCB.current_args = {"args": "value"} `wdyt?

Anyhow if the StorageManager.upload was fast, the upload_artifact is calling that exact function. So I don't think we actually have an issue here. What do you think?

GreasyLeopard35 I think you are on to something, I think UniformParameterRange just misses a min value:
https://github.com/allegroai/clearml/blob/fcad50b6266f445424a1f1fb361f5a4bc5c7f6a3/clearml/automation/parameters.py#L168
Should be:
[self.min_value + v*step_size for v in range(0, int(steps))]

parser.add_argument( "--dataset_mean", type

=

float, nargs

=

"+", default

=

0.5)

I think providing nargs='+ ' assumes the type is a list. nonetheless we should be able to support it. Could you please add a GitHub issue so we do not forget ?

on the side note, is there any way to automatically give more meaningful names to the running docker containers?

What do you mean by that? running where? and where will you see them ?

If possible, can we have a "only one experiment can be given a single tag"

You mean "moving a tag" automatically (i.e. if someone else had the same tag it is removed from it)?

Hi GrievingTurkey78

How can I check the server dashboard to make sure everything is working? I have tried to access the external ip through https but the browser is not able to connect.

What do you mean by the server dashboard ?

regrading (2) see here: https://allegro.ai/docs/faq/faq/#web-auth

I think the reason is that the "original" task is already the right type. I'll make sure we fix it, and always set the system tag

can I mount the s3 bucket as file system on place where

you need to mount it where the file server is storing it's files, correct (notice, not the DBs, just the files server)

Thanks SolidSealion72 !

Also, I found out that adding "pool.join()" after pool.close() seem to solve the issue in the minimal example.

This is interesting, I'm pretty sure it has something to do with the subprocess not "closing" properly (or too fast or something)
Let me see if I can reproduce

That works AND the feature works!

YEY

Quick follow up question, is there any way to abort a pipeline and all of the tasks it ran?

Hmm yes currently if you abort the pipeline is has no "time" to abort the running Tasks (the DAG itself will stop, because the pipeline controller was aborted, bit the running Tasks will continue).
In order to have better support, we need to add a previously requested feature for "abort" callback. This is actually not as straight forward as it sound...

I was having this confusion as well. Did behavior for execute_remote change that it used to be Draft is Aborted now?

Actually it was changed, it used to reset the Task (then push into into execution queue if needed), with clearml v1.0, we now support pushing aborted Tasks back into queues, so execute_remotely aborts the Task (instead of reseting it)
(you can always manually reset it)

Then as you suggested, I would just use sys.path it is probably the easiest and actually very safe (because the subfolders are Always next to the "main" source code)

Are these fields of ClearML Task?

correct