RC you can see on the main readme, (for some reason the Conda badge will show RC and the PyPi won't)
https://github.com/allegroai/clearml/

Let me know if it solved it, if it did I'll make sure we push the RC

Hi MagnificentPig49 I suspect it's the ResnetCIFAR10Model wrapper, is it your module ?

Wait, so the pipeline step only runs if the pre execute callback returns True? It'll stop if it doesn't run?

Only if you have a Callback function, and that callback function returns False, then it will skip it (otherwise it will process it)

Another question, in the parents sequence in pipe.add_step, we have to pass in the name of the step right?

Correct, the step name is a unique identifier for the pipeline

how would I access the artifact of a previous step within the pre ...

I see,
@<1571308003204796416:profile|HollowPeacock58> can you please send the full log?
(The odd thing is it is trying to install the python 3.10 version of torch, when your command line suggest it is running python 3.8)

Nice!

regrading the actual artifact access, this is the usual Task.artifacts access: see example here:
https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts_retrieval.py

Okay yes, that's exactly the reason!! Cross origin blocks the file link

, but what I really want to achieve is to share this code:

You mean to share the code between them, unless this is a "preinstalled" package in the container, each endpoint has it's own separate set of modules / files
(this is on purpose, so you could actually change them, just image diff versions of the same common.py file)

Just making sure, after the pipe object is created, you can call Task.current_task() , is that correct?

Hi MistakenDragonfly51

Hello everyone! First, thanks a lot to everyone that made ClearML possible,

❤
To your questions 🙂
long story short, no unless you really want to compile the dockers, which I can't see the real upside here Yes, add the following /opt/clearml.conf:/root/clearml.conf herehttps://github.com/allegroai/clearml-server/blob/5de7c120621c2831730e01a864cc892c1702099a/docker/docker-compose.yml#L154
and configure your hosts " /opt/clearml.conf" with ...

... transformed to 'str' when passed to a function decorated with 

PipelineDecorator.component

 at the time of calling it in the pipeline itself. Again, is this something intentional?

Are you sure about that? Notice the example code specifies, int as well...

Hi VexedCat68
(sorry I just saw the message)

I wanted to ask, how to run pipeline steps conditionally? E.g if step returns a specific value, exit the pipeline or run another step instead of the sequential step

So do do so you can do:
` def pre_execute_callback_example(a_pipeline, a_node, current_param_override):
# if we want to skip this node (and subtree of this node) we return False
...
# ew decided to skip so we return False
return False

pipe.add_step(name='...

What is the link you are seeing there?

GiganticTurtle0

That definitely makes sense. Where can I specify callbacks in the 

PipelineDecorator

 API?

Hmm there isn't one actually... (the interface I was thinking about was PipelineConroller ...)
Would it make sense to throw an exception in the pipeline execution code?

BTW: I just verified, if the pipeline step fails an exception is raised (ValueError)

GiganticTurtle0 quick update, a fix will be pushed, so that casting is based on the Actual value passed not even type hints 🙂
(this is only in case there is no default value, otherwise the default value type is used for casting)

Hi @<1544853721739956224:profile|QuizzicalFox36>

http:/34.67.35.46:8081/...

notice there is a / missing in the link, how is that possible? it should be http://

I have also tried with type hints and it still broadcasts to string. Very weird...

Type hints are ignored, it's the actual value you pass that is important:
` @PipelineDecorator.component(return_values=['data_frame'], cache=True, task_type=TaskTypes.data_processing)
def step_one(pickle_data_url: str, extra: int = 43):
...

@PipelineDecorator.pipeline(name='custom pipeline logic', project='examples', version='0.0.5')
def executing_pipeline(pickle_url, mock_parameter='mock'):
da...

Hi FranticCormorant35
So Tasks have parent field, that would link one to another.
Unfortunately there is no visual representation for it.
What we did with the hyper-parameter for example, was also to add a tag with the ID of the "parent" Task. This would make sense if you have multiple tasks all generated from the same "parent", like in hyper-parameter optimization.
What's your use case ? Is it a single evaluation Task per training, or multiple or con job alike ?

What do you have in the artifacts of this task id: 4a80b274007d4e969b71dd03c69d504c

Hi VexedCat68
So if I understand correctly, the issue is this argument:
parameter_override={'Args/dataset_id': '${split_dataset.split_dataset_id}', 'Args/model_id': '${get_latest_model_id.clearml_model_id}'},I think that what is missing is telling it this an artifact:
parameter_override={'Args/dataset_id': '${split_dataset.artifacts.split_dataset_id.url}', 'Args/model_id': '${get_latest_model_id.clearml_model_id}'},You can see the example here:
https://clear.ml/docs/latest/docs/ref...

There was an issue in some versions where seeborn plots were blank. Is that the case?

Can you do the following
Clone the Task you previously sent me the installed packages of, then enqueue the cloned task to the queue the agent with the conda.
Then send me the full log of the task that the agent run

Also there was a truck that worked in the previous big, could you zoom out in the browser, and see if you suddenly get the plot?

However, when 'extra' is a positional argument then it is transformed to 'str'

Hmm... okay let me check something

BTW if the plots are too complicated to convert to interactive plotly graphs, they will be rendered to images and the server will show them. This is usually the case with seaborn plots

VexedCat68 both are valid. In case the step was cached (i.e. already executed) the node.job will be None, so it is probably safer to get the Task based on the "executed" field which stores the Task ID used.

This is a part of a bigger process which times quite some time and resources, I hope I can try this soon if this will help get to the bottom of this

No worries, if you have another handle on how/why/when we loose the current Task, please share 🙂

So I checked the code, and the Pipeline constructor internally calls Task.init, that means that after you constructs the pipeline object, Task.current_task() should return a valid object....
let me know what you find out

This is strange... Could you send the browser console log, maybe there is an exception there