and again - feature request - add free text there.

LethalCentipede31 can you add a new https://github.com/allegroai/clearml/issues issue with this request? Just so it won’t get lost

You can always get the running task (the pipeline in your case) with Task.current_task().task_id

Hi ElegantCoyote26 ,

` - cleanup_period_in_days (float): The time period between cleanups. Default: 1.

• run_as_service (bool): The script will be execute remotely (Default queue: "services"). Default: True. so run_as_service will not run the script locally on your machine but just enqueue the script to the services ` queue (you should have clearml-agent in services mode listening to this queue, and the agent will run this service)

If run_as_service is False , the script will start running on your machine, once you will clone and enqueue it, it will run twice (on your machine and by clearml-agent)

i’m guessing the cleanup_period_in_days can only actually run every day or whatever if the script is enqueued to 

services

you can change this value if you like (e.g. 0.5 for every 12 hours)

Not sure getting that, if you are loading the last dataset task in your experiment task code, it should take the most updated one.

Not in your current environment, in the one the clearml-agent creates. In the installed packages you have trains==0.16.4 , so I guess you are using it in your code (if not, you can check in your base task, under installed package, the imports analysis and get the information about where this import is coming from)

About the import issue, did you try adding the missing package and re run the task?

👍

I can not see google package, can you try clone and add it manually? You can always add any package you like to any task with Task.add_requirements('package name', 'package version') ,
Do you have a toy example so I can reproduce it my side (using google.cloud but package is not listed in task)?

SquareFish25 do you have a way trying with access and secret without token? just for the checking

https://clear.ml/docs/latest/docs/references/sdk/automation_controller_pipelinecontroller#pipelinedecoratorcomponent

which clearml version are you using? did you add it and re run it without clearml-agent?

Hi FlatStarfish45 ,

The HPO task will control the HPO process, means it will clone the base task (the one we are optimizing), change the parameters, enqueue it and collect the results.
The base task is the task we want to optimize.
Each one of those two tasks, have different requirements.
You can look at the https://clear.ml/docs/latest/docs/guides/optimization/hyper-parameter-optimization/examples_hyperparam_opt#set-up-the-arguments for how set the base task in the HPO task.
can this be ...

Hi PunyBee36 ,

Thanks for reporting this, the log message will be fixed in the next clearml version, will update here about it 🙂

About the running task, I can read in the logs that a new instance was created (i-02fc8...), can you check if you have a running clearml agent on it? if so, the agent will pull the task from the queue, if not, can you check in this instance logs for errors and share?

I suspect that

I will try to generate a new token for myself and reproduce it with it

WackyRabbit7 you can also use task.execute_remotely() once the task is configure, like in https://github.com/allegroai/clearml/blob/master/examples/pipeline/step1_dataset_artifact.py#L6 example

Hi VivaciousPenguin66 , I’m not aware of such example, but if you have such or creating one, please share 🙂

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...

Hi SarcasticSnake58 ,

The connection to the server is in the api part in your ~/clearml.conf file. I think you don’t have such file in your running container, so you are directed to the demoapp server.

If you are running the container from your local machine, you can start the container with the local ~/clearml.conf , add -v "~/clearml.conf":"/root/clearml.conf" to the docker command. You can also connect the docker and create a new configuration file with ` clearml-init...

The Hyperparameter Optimizer can give you such table, but I’m not sure this is what you are looking for ( https://allegro.ai/clearml/docs/docs/examples/frameworks/pytorch/notebooks/image/hyperparameter_search.html and https://medium.com/pytorch/accelerate-your-hyperparameter-optimization-with-pytorchs-ecosystem-tools-bc17001b9a49 )

Hi VictoriousPenguin97

sdk.storage.direct_access is part of the extended support in the paid version.
But I think its not required since ClearML will simply try to access the path directly as it is, and you don’t need to configure it.

Hi GiganticTurtle0 ,

Uploading artifact is being done async, maybe this is the issue in your case, you can change it with wait_on_upload=True , can you try it?

In the self-hosted we do not have user permissions, so every user sees all the data.

Hi SubstantialElk6 ,

Which server do you use? http://app.community.clear.ml ?

Currently, imshow plots are showing in debug samples section.

I only want to save it as a template so I can later call it in a pipeline

running with task.execute_remotely() it wont really run the task. it will start it and abort it, so you will have it Aborted , and this is your template task

Hi TenseOstrich47 , the StorageManager does use boto3 for those upload (so if its not supported by boto3, same for StorageManager :/ )
Maybe you can use the 'wait_for_upload' and delete the local file after?

yap, that should do the trick

WonderfulArcticwolf3 and CloudySwallow27 are you running it as a service or via the apps? whats the clearml version (not agent)?

Hi TenseOstrich47 ,

Try using aws credentials with region too https://github.com/allegroai/clearml/blob/master/docs/clearml.conf#L88

credentials: [ specifies key/secret credentials to use when handling s3 urls (read or write) { bucket: "my-bucket-name" key: "my-access-key" secret: "my-secret-key" region: "my-region" },

simply changing to 

show

doesn’t work in my case as i am displaying CM.. what about if i use 

matshow

Can you share with me some code you have (just the matplot part)? What about the example? if you run it, do you get some plots in plots section and some in debug?

Hi PompousParrot44 ,

Do you use plt.imshow ? If you run this example, https://github.com/allegroai/trains/blob/master/examples/frameworks/matplotlib/matplotlib_example.py , do you get some outputs in the plots section?

Hi VivaciousPenguin66 ,

This issue should be fixed in the coming version, will be out soon. You can use the Github https://github.com/allegroai/clearml-serving.git version which is already fixed 🙂