TimelyPenguin76 Thanks, it helped me locally, but it doesn't work when I start pipeline task from GUI

Maybe I missed something, whats your flow? Do you have some kind of “template task”? And you clone it?

I discovered that task.set_base_docker() is allowed only locally.
In task.py:

Thank you! 🙂

Draft created successfully, but it doesn't contain property with docker command.
Could you help me?

ApprehensiveFox95 could you test with the latest RC, I think there was a fix
pip install clearml==0.17.5rc5

And in super class, _Task:

This is definitely a but, in the super class it should have the same condition (the issue is checking if you are trying to change the "main" task)
Thanks ApprehensiveFox95
I'll make sure we push a fix 🙂

From the ClearML UI you can just change the value under BASE DOCKER IMAGE section to your image

Thanks, it works!

I wanted to do following:
task = Task.init(
project_name=..., task_name=...,
task_type=Task.TaskTypes.controller) # base pipeline task
after that, I wanted to create steps from scratch, because I have many steps and I hope to avoid manual editing in GUI (commits and other things). I create this tasks:
new_task = Task.create(...)
and finally I added it to pipe:
pipe.add_step(...)
I have problem with some execution properties, like docker and output_uri. I've successfully provided commit, branch and other params from base pipeline task to step-tasks, but I've done it not very legally:
Task.create(...,
commit = task._data._property_script._property_version_num,
branch = task._data._property_script._property_branch,
...)

Hi ApprehensiveFox95 ,

Can you try

task = Task.create(...) task.set_base_docker("docker command")?

Hi ApprehensiveFox95
I think this is what you are looking for:
step1 = Task.create( project_name='examples', task_name='pipeline step 1 dataset artifact', repo=' ` ',
working_directory='examples/pipeline',
script='step1_dataset_artifact.py',
docker='nvcr.io/nvidia/pytorch:20.11-py3'
).id

step2 = Task.create(
project_name='examples', task_name='pipeline step 2 process dataset',
repo=' ',
working_directory='examples/pipeline',
script='step2_data_processing.py',
docker='nvcr.io/nvidia/pytorch:20.11-py3'
).id

step3 = Task.create(
project_name='examples', task_name='pipeline step 3 train model',
repo=' ',
working_directory='examples/pipeline',
script='step3_train_model.py',
docker='nvcr.io/nvidia/pytorch:20.11-py3'
).id

Connecting ClearML with the current process,

from here on everything is logged automatically

task = Task.init(project_name='examples', task_name='pipeline demo',
task_type=Task.TaskTypes.controller, reuse_last_task_id=False)

pipe = PipelineController(default_execution_queue='default', add_pipeline_tags=False)
pipe.add_step(name='stage_data', base_task_project='examples', base_task_id=step1,
clone_base_task=False)
pipe.add_step(name='stage_process', parents=['stage_data', ],
base_task_project='examples', base_task_id=step2,
clone_base_task=False,
parameter_override={'General/dataset_url': '${stage_data.artifacts.dataset.url}',
'General/test_size': 0.25})
pipe.add_step(name='stage_train', parents=['stage_process', ],
base_task_project='examples', base_task_id=step3,
clone_base_task=False,
parameter_override={'General/dataset_task_id': '${stage_process.id}'}) You might need the latest clearml: pip install git+ `

Yes, but I wanted to create task automatically and after that add this task to pipeline for running. I hoped to avoid additional edits in GUI

AgitatedDove14 I use exactly this version

As I understand, providing this param at the Task.init() inside the subtask is too late, because step is already started.

If you are running the task on an agent (with I assume you do), than one way would be to configure the "default_output_uri" on the agnets clearml.conf file.
The other option is to change the task as creation time, task.storage_uri = 's3://...'

after that, I wanted to create steps from scratch, because I have many steps and I hope to avoid manual editing in GUI (commits and other things). I create this tasks:

You can add this to the template task Task.init(project_name=<your project name>, task_name=<your task name>) instead of the Task.create call and it will have all the inputs for you.
After, add task.set_base_docker("docker command") and it will configure the docker for the task.
Once finish configuring the task, add task.execute_remotely() and it wont actually run the task but only register it in the ClearML UI - and you have a template task ready for use (just run it once from your local machine for the registration of the task).

Apparently I don't understand something.
I tried using Task.init() instead of Task.create(), but I got
clearml.errors.UsageError: Current task already created and requested task name 'exp_1.0.31_Main' does not match current task name 'exp_1.0.31'. If you wish to create additional tasks useTask.create
because I wanted to initialize not existing subtask with new unique task_name. If I clone subtask instead of creating new every time, then as I understand, I don't have any opportunities to change commit version and other execution params. Is it right?

Also I have a question about parameter output_uri:
Can I provide this parameter to subtask in Time.create() or after that?
As I understand, providing this param at the Task.init() inside the subtask is too late, because step is already started.