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Hi, Is There A General Github Actions Workflow Just To Login Into Your Clearml App (Demo Or Server) So I Can Run Python Files Related To Clearml. I'Ve Seen Clearml-Actions-Train-Model And Clearml-Actions-Get-Stats And They Seem To Be Very Specific. Maybe

Hi, is there a general Github Actions workflow just to login into your clearml app (demo or server) so I can run python files related to clearml. I've seen clearml-actions-train-model and clearml-actions-get-stats and they seem to be very specific. Maybe something that looks like this? :

- name: Login to ClearML id: login-clearml uses: repo/clearml-login@master with: CLEARML_API_ACCESS_KEY: ${{ secrets.ACCESS_KEY }} CLEARML_API_SECRET_KEY: ${{ secrets.SECRET_KEY }} CLEARML_API_HOST: ${{ secrets.CLEARML_API_HOST }} env: GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} run: | # run python files. can be anything related to clearml # get publised models/run a pipeline/train python file.py

  
  
Posted one year ago
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Answers 30


I also tried cloning an individual task, That surprisingly works. Not sure why my pipeline doesn't.

  
  
Posted one year ago

Indeed, running pipelines that were started with pipe.start_locally can not be cloned and ran. We will change this behaviour ASAP such that you can use just 1 queue for your use case.

  
  
Posted one year ago

I dont know why 😢 . Updated to 1.7.2, was staring at the configuration object, there was stuff in it until it reached "Starting Task Execution:" in the logs. It went missing after

  
  
Posted one year ago

All right testing with:
pipe.set_default_execution_queue('agent') pipe.start_locally() pipe.start(queue= 'agent')

  
  
Posted one year ago

Yup, the pipeline stops instantly with "Launching the next 0 steps".

  
  
Posted one year ago

This is very odd...
LittleShrimp86 is this example working for you?
https://github.com/allegroai/clearml/blob/master/examples/pipeline/pipeline_from_tasks.py

  
  
Posted one year ago

LittleShrimp86 did you try to run the pipeline form the UI on remote machines (i.e. with the agents)? Did that work?

  
  
Posted one year ago

Wait. are you saying it is disappearing ? meaning when you cloned the Pipeline (i.e. in draft mode) the configuration was there, then the configuration disappeared ?

  
  
Posted one year ago

BTW: Can you also please test with the latest clearml version , 1.7.2

  
  
Posted one year ago

Sorry for the comments haha. Trying to note down whatever I learn as much as I can

  
  
Posted one year ago

Hi LittleShrimp86 ! Looks like something is broken. We are looking into it

  
  
Posted one year ago

Now I just need to wait for this to finish and clone it later 🤞

  
  
Posted one year ago

This is the cloned one

  
  
Posted one year ago

Still the same problem 😕 . I used pipe.set_default_execution_queue('agent') and pipe.start_locally() for my pipeline.py. When cloning, I enqueue the pipeline to another agent: Task.enqueue(task = clone_task.id, queue_name= 'agent(EC2)')

  
  
Posted one year ago

I mean test with:
pipe.start_locally(run_pipeline_steps_locally=False)This actually creates the steps as Tasks and launches them on remote machines

  
  
Posted one year ago

Can you share the pipeline?

  
  
Posted one year ago

Hi LittleShrimp86

just to login into your clearml app (demo or server) so I can run python files related to clearml.

I think this amounts to creating a Task and enqueueing it, am I understanding correctly ?

  
  
Posted one year ago

Yes the configuration and stages (DAG visualization) were there right until the agent finish cloning the environment. Then it goes missing.

  
  
Posted one year ago

Hi, I found the problem using the example Martin gave. Apparently you cannot use pipe.start_locally() at all when trying to clone the task and work completely remote (I thought it would treat the agent as local instead when I send it to a queue). It works with the combination of pipe.set_default_execution_queue('agent') and pipe.start(queue = 'agent2(EC2)') . However, must I really have two clearml-agents for complete automation? To the best of my knowledge, setting both the function above to the same queue will just cause an infinite queue. Is there no way to use only one worker for everything like start_locally(run_pipeline_steps_locally=True) ? For example, initially I thought if I use Task.enqueue(task = clone_task.id, queue_name= 'agent2(EC2)') (cloning pipeline) and start_locally(run_pipeline_steps_locally=True) (pipeline file), clearml will treat the agent2(EC2) as local instead.

  
  
Posted one year ago

Yes, similar but via Github Actions for automation. Just wanted to know if there is an easier way to connect to clearml instead of creating a new workflow for any CI/CD purpose.

Aside from that, I tried cloning my task (pipeline) and enqueuing it to a clearml-agent.
filter = {'status': ['published'], 'order_by': ['-last_update'], 'type': ['controller']} pipeline_task = Task.get_tasks(project_name='clearmlsample/clearmlsample_pipeline/.pipeline', task_filter = filter) clone_task = Task.clone(source_task=pipeline_task[0], name = "Clone pipeline") Task.enqueue(task = clone_task.id, queue_name= 'agent')I already ran the pipeline a few times locally which works fine. However when I try cloning it, the stages seem to go missing and the pipeline ends instantly.

  
  
Posted one year ago

LittleShrimp86 what do you have in the Configuration Tab of the Cloned Pipeline?
(I think that it has empty configuration -> which means empty DAG, so it does nothing and leaves)

  
  
Posted one year ago

LittleShrimp86 can you post the full log of the pipeline? (something is odd here)

  
  
Posted one year ago

The configuration tab -> configuration objects -> pipeline is empty

  
  
Posted one year ago

Seems to be working, its in the first stage 😮

  
  
Posted one year ago

No worries, you should probably change it to pipe.start(queue= 'queue') not start locally
s it working when you are calling it with start locally ?

  
  
Posted one year ago

Let me test it out. I thought if I run it remotely with pipe.start_locally(run_pipeline_steps_locally=True) , the local becomes remote instead

  
  
Posted one year ago

The configuration tab -> configuration objects -> pipeline is empty

That's the reason it is doing nothing 😞
How come it is empty if you Cloned the local one?

  
  
Posted one year ago

Working on the 1.7.2 right now

  
  
Posted one year ago

` from clearml import PipelineController

pipe = PipelineController(name="clearmlsample_pipeline",
project="clearmlsample",
version="1.0.0")

pipe.add_parameter('seed', 2222, description='random seed to standardize randomness')
pipe.add_parameter('n_trials', 10, description='trials to run during optimization')

pipe.add_step(
name='get_data', # can be named anything
# connect pipeline to task (obtain data from Task.init in python files)
base_task_project='clearmlsample', # project name
base_task_name='get data & preprocess' # task name
)

pipe.add_step(
name='train_and_tune', # name
base_task_project='clearmlsample', # project name
base_task_name='training and tuning', # task name
# connect it to previous task using name not base_task_name
# step cannot run unless parents finish
parents=['get_data'],
# use 'General/parameter' to override parameter from py file
# give value or take value from pipe.add_parameter using '${pipeline.parameter}'
#

# don need any special command to get data from Dataset.create() from previous task
# just use the Dataset.get to get latest version
# maybe easier to store everything except (raw data) in artificats instead
parameter_override={'General/seed': '${pipeline.seed}',
                    'General/n_trials': '${pipeline.n_trials}'}

)

pipe.add_step(
name='evaluate', # name
base_task_project='clearmlsample', # project name
base_task_name='evaluating', # task name
# connect it to previous task use name not base_task_name
parents=['train_and_tune'],
parameter_override={'General/seed': '${pipeline.seed}',
# get task id from previous step to get models
'General/train_task_id': '${train_and_tune.id}'}
)

select default worker to run steps

pipe.set_default_execution_queue('MonashPC')

start the pipeline logic

run this to run EVERYTHING locally

pipe.start_locally(run_pipeline_steps_locally=True)

run this to run logic locally but steps remotely

pipe.start_locally()

run this to run logic remotely

do not start pipeline with same worker as set_default_exceution_queue

e.g. pipe.start(queue='MonashPC')

it will cause the steps to be queued forever because it is occuped by the pipeline logic

pipe.start(queue= 'queue')

print('done') `

  
  
Posted one year ago

Original one

  
  
Posted one year ago
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