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)

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...

task._wait_for_repo_detection()You can use the above, to wait until repository & packages are detected
(If this is something users need, we should probably make it a "public function" )

I meant even just a link to a blank comparison and one can then add the experiments from that view

Just making sure you are aware, once you are in comparison you can always add Tasks (any Task):
Notice you can press on the "Add experiments", then select Any experiment (including all projects! as filters)
Notice you need to remove all filters (right side red x on the filter Icon)

now it stopped working locally as well

At least this is consistent 🙂
How so ? Is the "main" Task still running ?

Hi OutrageousSheep60
Do you mean something like:
https://github.com/allegroai/clearml/tree/master/examples/datasets
?

Hi SubstantialElk6
ClearML-Serving is already out with a new version, the ETA for the next ClearML-serving full 1.0 (which is the new redesign version) is the end of May

I guess we should have obfuscated the name better 😄

I still think the issue is getting boto3 credentials

It might be the case
Are you using clearml-agent or are you running it manually ?

Let me check that for a sec

Thanks!

...I'm not sure I follow, the clearml-task is designed to always be used so that at the end the agent will be running the Task. What am I missing?

. However, despite having imported the required types from the 

typing

 library in the script where the function decorated with 

PipelineDecorator.component

 is defined, later in the generated script the 

typing

 library is not imported outside the scope of the function

Actually the typing part is not passed to the "created step" , because there are no global imports, for eexample:
` def step(a: pd.DataFrame):
import pandas as pd
...

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-

Could it be the code is not in a git repository ?
clearml support either a single script or a git repository, but Not a collection of standalone files. wdyt?

Hi PleasantGiraffe85
Did you set git_host to only point to your host ? do you expect all the git clones to use SSH? how does the requirements.txt git link looks like ?
https://github.com/allegroai/clearml-agent/blob/bf07b7f76d3236c1118b81730c6d9718705a795a/docs/clearml.conf#L22

It's the pip cache

well cudnn is actually missing from the base image...

do I still need to specify a OutputModel

No need, only if you want to upload a local model file (but I assume in this case, no new model is created)

JitteryCoyote63 I think that without specifically adding torch to the requirements, the agent will not be able to automatically resolve the correct cuda/torch version. Basically you should add torch to the requirements.txt file, and provide it to Task create, or use Task.force_requirements_env_freeze

GentleSwallow91 how come it does not already find the correct pytorch version inside the docker ? whats the clearml-agent version you are using ?

it means it should work in

~/clearml.conf

no?

Yes exactly

I was hoping to be able to set the default server-wide

I think this type of server-side wide defaults is not supported in the open-source version.
But in most cases, setting it up on the clearml-agents is probably the important thing. btw: you can also set it in an OS environment CLEARML_DEFAULT_OUTPUT_URI

Sure:
task.get_project_name()

if project_name is None and Task.current_task() is not None: project_name = Task.current_task().get_project_name()This should have fixed it, no?

This would be my only improvement, otherwise awesome!!!
output_model.update_weights(weights_filename=os.path.join(training_data_path, 'runs', 'train', 'yolov5s6_results', 'weights', 'best.onnx'))

Probably less secure though :)