I aborted the task because of a bug on my side

🙂

Following this one, is treating abort as failed a must feature for the pipeline (in your case) or is it sort of a bug in your opinion ?

One more question, in the second log, trains agent is configured with Conda, on the first it is configured with pip, or at least this is what it looks like, can you confirm?

What are you seeing?

Hi EnchantingWorm39
Great question!
Regrading the data management, I know the enterprise edition has full support for unstructured data, and we plan to soon have a solution for structured data as part of the open source (soon= hopefully in a month time)
Regrading model serving, I know you can integrate with TFServing or seldon with very little effort (usually the challenge is creating triggers etc, but but in most cases this is custom code anyhow 🙂 )
I do not have experience with Cortex/B...

Any chance you can zip the entire folder? I can't figure out what's missing, specifically "from config_files" , i.e. I have no packages nor file named config_files

AntsySeagull45 kudos on sorting it out 🙂
quick note, trains-agent will try to run the python version specified by the original Task. i.e. if you were running python3.7 it will first try to look for python 3.7 then if it is not there it will run the default python3. This allows a system with multiple python versions to run exactly the python version you had on your original machine. The fact that it was trying to run python2 is quite odd, one explanation I can think of is if the original e...

DepressedChimpanzee34
I might have an idea , based on the log you are getting LazyCompletionHelp in stead of str
Could it be you installed hyrda bash completion ?
https://github.com/facebookresearch/hydra/blob/3f74e8fced2ae62f2098b701e7fdabc1eed3cbb6/hydra/_internal/utils.py#L483

@<1546303293918023680:profile|MiniatureRobin9>

, not the pipeline itself. And that's the last part I'm looking for.

Good point, any chance you want to PR this code snippet ?

    def add_tags(self, tags):
        # type: (Union[Sequence[str], str]) -> None
        """
        Add Tags to this pipeline. Old tags are not deleted. 
        When executing a Pipeline remotely (i.e. launching the pipeline from the UI/enqueuing it), this method has no effect.

        :param tags: A li...

You mean to design the entire pipeline from YAML?
(this assumes your Tasks know how to process links to artifacts)
Is this what you are after?
(BTW: any reason for working with YAML files instead of coding it?)

Hi ShinyWhale52
This is just a suggestion, but this is what I would do:

1. use clearml-data and create a dataset from the local CSV file
   clearml-data create ... clearml-data sync --folder (where the csv file is)2. Write a python code that takes the csv file from the dataset and creates a new dataset of the preprocessed data
   ` from clearml import Dataset

original_csv_folder = Dataset.get(dataset_id=args.dataset).get_local_copy()

process csv file -> generate a new csv

preproces...

What's the clearml-server you are running ?
Is this reproducible with the hydra example?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py

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)

Quick update Nexus supports direct http upload, which means that as CostlyOstrich36 mentioned, just pointing to the Nexus http upload endpoint would work:
output_uri="http://<nexus>:<port>/repository/something/"See docs:
https://support.sonatype.com/hc/en-us/articles/115006744008-How-can-I-programmatically-upload-files-into-Nexus-3-

Only those components that are imported in the script where the pipeline is defined would be included in the DAG plot, is that right?

Actually the way it works currently (and we might change it if there is a better way), every time you call PipelineDecorator.component a new component is stored on the Pipeline Task, which is later translated into DaG graph and Table (next version will have a very nice UI to display / edit them).
The idea is first to have a representation of the p...

GleamingGrasshopper63 what do you have configured in the "package manager" section?
https://github.com/allegroai/clearml-agent/blob/5446aed9cf6217f876d3b62226e38f21d88374f7/docs/clearml.conf#L64

Python3.8 I can quickly check, give me a minute

time.sleep(time_sleep)

You should not call time.sleep in async functions, it should be asyncio.sleep,
None

See if that makes a difference

Hi ClumsyElephant70

Any idea how to get the credentials in there?

How about to map it into the docker with -v you can set it here:
https://github.com/allegroai/clearml-agent/blob/0e7546f248d7b72f762f981f8d9033c1a60acd28/docs/clearml.conf#L137
extra_docker_arguments: ["-v", "/host/folder/cred.json:/gcs/cred.json"]

This will fix it, the issue is the "no default value" that breaks the casting
@PipelineDecorator.component(cache=False) def step_one(my_arg=""):

DilapidatedDucks58 You might be able to, check the links, they might be embedded into the docker, so you can map diff png file from the host 😛
BTW: what would you change the icons to?

ScantWorm7
Tensorboard is automatically captured and sent to the trains server. This is in addition to the local copy of your TB files. Actually in most cases the local copy is redundant

Thanks @<1523702652678967296:profile|DeliciousKoala34> I think I know what the issue is!
The container has 1.3.0a and you need 1.3.0 this is why it is re-downloading (I'll make sure the agent can sort it out, becuase this is Nvidia's version in reality it should be a perfect match)

I update my-private-dep to 1.8.0

Not sure how this is connected with the venv, could you expand ?

@<1564422644407734272:profile|DistressedCoyote60> could you open a GitHub issue on it in clearml-agent, just so we know of the problem and fix it for next version ?

WickedGoat98
The webUI will look like the demo server 🙂https://demoapp.trains.allegro.ai/
2. curl http://server-ip:8008 should return something like:
{"meta":{"id":"78a9dc77081348e2930d1f429fd7e092","trx":"78a9dc77081348e2930d1f429fd7e092","endpoint":{"name":"","requested_version":1.0,"actual_version":null},"result_code":400,"result_subcode":0,"result_msg":"Invalid request path /","error_stack":null},"data":{}}%3. curl http://server-ip:8080 should return something like:
` <!d...

Could it be someone deleted the file? this is inside the temp venv folder but it should not get there

I mean the caching will work, but it will reinstall this repository on top of the cached copy.
make sense ?

You can also see the code there, could you run a quick test against the demo-server, this might be a UI issue that was fixed in the last version.

agent.package_manager.system_site_packages

 can be used to inherit packages

Correct, it is basically venv with --system-site-packages

I do not think virtualenv nesting is support, if it was then in theory you could have executed the clearml-agent from virtual environment with system_site_packages turned on and then it would inherit from it. But again I'm not sure virtualenv supports it.
BTW: the latest clearml-agent RC already have venv caching (both pip/conda) 🙂

Yes, albeit not actually "intercept" as the user will be able to directly put Task sin queues B_machine_a/B_machine_b , but any time the user is pushing Tasks into queue B, this service will pull it and push to the individual machines queue.
what do you think?