Hi UnevenDolphin73 , Do you get any preview run this example - https://github.com/allegroai/clearml/blob/master/examples/reporting/artifacts.py ?

Hi MoodyCentipede68 , do you have the repo as part of the https://clear.ml/docs/latest/docs/references/sdk/automation_controller_pipelinecontroller#pipelinedecoratorcomponent ? you can specify for each step which repo to use

So something like https://github.com/allegroai/clearml/blob/master/examples/reporting/pandas_reporting.py#L28 but multi index?

Was wondering if there are plans to add better support for it

Not currently, can you add a https://github.com/allegroai/clearml/issues/new so we do not forget to add such?

Hi ColossalDeer61 ,

Not from the UI, but you can run a simple script to do that (assuming you can parse your configuration file), here is an example:

` from trains import Task

configuration_file = {
"stage_1": {
"batch_size": 32,
"epochs": 10
},
"stage_2": {
"batch_size": 64,
"epochs": 20
},
}

template_task = Task.get_task(task_id=<YOUR TEMPLATE TASK>)

for name, params in configuration_file.items():
# clone the template task into a new...

ColossalDeer61 we can add to the script Task.init and we will have this service in the UI, with connection to the configuration, users can change the configuration from the UI and send this script to execute (in services queue for example). This way others can change the configuration and run this script.

` from trains import Task

service_task = Task.init(project_name="services", task_name="clone and enqueue by configuration")

configuration_file = {
"stage_1": {
"batch_...

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 ...

Hi WackyRabbit7 ,

It should take the latest one (sorting according to date/time)

Hi SparklingHedgehong28 ,

also, when the AZ spec is left empty (it’s optional), machines fail to start with

Invalid type for parameter LaunchSpecification.Placement.AvailabilityZone, value: None, type: <class 'NoneType'>, valid types: <class 'str'>Checking this issue, when not specifying the AZ it should use an available one, will keep you posted about it

ok, I misread. The launch code runs in the SaaS, but it uses credentials to launch machines in our cloud. What stops it th...

Which one? If the package is located in additional artifact repositories, you can add it

can you try those? Do you have an example for the cloning code?

logger.report_matplotlib_figure(title="some title", series="some series", figure=fig, iteration=1, report_interactive=Fasle)

how do you run it locally? the same?

according to this part

Applying uncommitted changes Executing: ('git', 'apply', '--unidiff-zero'): b"<stdin>:11: trailing whitespace.\n task = Task.init(project_name='MNIST', \n<stdin>:12: trailing whitespace.\n task_name='Pytorch Standard', \nwarning: 2 lines add whitespace errors.\n"I don’t see the requirements change, lets try without the cache, can you clear it (ClearML cache dir is located at ~/.clearml )?

Hi StaleMole4 ,

Hag Sameach 🙂 I just tried with the latest version and didn’t get any issues, can you try with it?

Hi VexedCat68

Is it possible to write text file and see it in results of the experiment?

You can upload any file as an artifact to your task, try:

task.upload_artifact(name="results", artifact_object="text_file.txt")

I want to use it to version data as in keeping a track of what images have been trained on. Or is there a better way of data versioning in ClearML?

You can use https://clear.ml/docs/latest/docs/clearml_data/ for making the data accessible from every machine...

Didnt get that, can you detail a bit?

Hi ElegantDeer55 ,

Are you referring to https://github.com/allegroai/trains-pycharm-plugin ? If so, it should sync you .git folder to the remote machine so the task will log the git.

Basically I am confused if “remote debugging” should work / kick in automatically when running in docker mode and starting a task like this:

 from trains import Task
task = Task.init(project_name=“my project”, task_name=“my task”)
task.execute_remotely()

When you are running this code from you P...

Hi ImmensePenguin78 ,

You can get all the console outputs using task.get_reported_console_output() . can this do the trick?

So for adding a model for serve with endpoint you can use

clearml-serving triton --endpoint "<your endpoint>" --model-project "<your project>" --model-name "<your model name>"
when the model is getting updated, is should use the new one

Hi CheekyToad28 ,

None of the options https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server#deployment works for you?

clearml-agent can listen to one or more queues, once a task is enqueue to those queue, the clearml-agent will pull it and will run the task.

You can allocate you resources to the clearml-agent (like https://clear.ml/docs/latest/docs/clearml_agent#allocating-resources ) and you can prioritize your queues (if you have more than one - https://clear.ml/docs/latest/docs/clearml_agent#queue-prioritization )

ThickDove42 you mean setting the docker init script?

Hi ThickDove42 ,

The SETUP SHELL SCRIPT is the bash script to run at the beginning of the docker before launching the Task itself.

You can just try edit it, for example:

apt update apt-get install -y gcc

HI JitteryCoyote63 ,

can you try increasing the polling_interval_time_min to 5-7 minutes for the check? do you get double machines with it too?

Does it had any errors in the local run up to the task.execute_remotely call?

You can try hack it, in the UI, under EXECUTION tab, add this prefix (-m scalene) to the script path, something like: - scalene my_clearml_task.py , can you try with it? (make sure you install scalene or have it under your installed packages)

There are many ways to do so, this is an example for github action: https://github.com/allegroai/trains-actions-train-model

And you like each series to be in a different plot?

how do you load the data inside each task in PROJ_2?

GrievingTurkey78 all the packages should have a version, do you have some without?

hi DepressedChimpanzee34 . once you change the parameters in the cloned task from the UI, those will be the parameters your task will use when running with the ClearML agent.

The configuration you see in the UI will be the actual running configuration for task