Hi RobustRat47 , the volumes should auto deleted

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" },

For this you don’t really need the output_uri , you can just do it as is.

SpotlessFish46 It should work 🙂

Hi TenseOstrich47 ,

If you want to get all the scalars. you can use task.get_last_scalar_metrics() , can this help?

Hi GiddyTurkey39 , it should be released next week, I will update you on this thread once out 🙂

So running the docker with ‘“device=0,1”’ works? We will check that

exactly

max_spin_up_time_min - the maximum time for an instance to spin up
max_idle_time_min - the maximum time for an instance to stay up with worker on it (the time the ec2 instance is finished running a task with the agent and still up with an worker running on it listening to the queue)

I will check the aws token, just to verify, you imported the StorageManager after the os.environ calls?

Did you get the same error message? What do you have in the error under ‘device=XXX’?

What about this? Do you get the same as the first one ( device=0,1 )? or with quote ( device="0,1" )?

Hi CleanPigeon16 ,

Currently, only argparse arguments are supported (list of arg=val ).
How do you use the args in your script?

It should create task B with the same commit as task A in this scenario, do you have different commits?

Hi JitteryCoyote63 , what commit and branch do you see in the UI?

How did you create task B? cloned from other task?

The agent will run the task according to the commit in the execution tab (you can choose commit, branch or tag)

Great, thanks

Great 🙂

If you do wanna change the commit/branch/tag, you can change it from the data.script section in the cloned_task object

Hi HelpfulHare30 , can you try upgrade to the latest ClearML agent?

pip install clearml-agent==1.0.0

Where did you add the task.execute_remotely command? do you have a sample code I can run?

try to do it with clearml-data, https://clear.ml/docs/latest/docs/clearml_data/ , you can access and share the data with it and can configure it to work with minio

You can send "yet_another_property_name": 1 too, or you can do
"another_property_name": {"description": "This is another user property", "value": "1", "type": "int"}

Hi WackyRabbit7

When calling Task.init() , you can provide output_uri parameter. This allows you to specify the location in which model snapshots will be stored.
Allegro-Trains supports shared folders, S3 buckets, Google Cloud Storage and Azure Storage.

For example (with S3):

Task.init(project_name="My project", task_name="S3 storage", output_uri="s3://bucket/folder")
You will need to add storage credentials in ~/trains.conf file (you will need to add your aws in thi...

Hi BrightElephant64 , can you add an example? Also, the ClearML AWS autoscaler know how to work with ClearML-agent queues

CrookedWalrus33 can you share the log?

The fileserver will store the debug samples (if you have any).

You'll have cache too.

CrookedWalrus33 clearml agent wont install conda on the container, but you can add such with https://clear.ml/docs/latest/docs/references/sdk/task#set_base_docker -> docker_setup_bash_script

Hi TrickySheep9 ,

ClearML does analyze your packages, but you always can add any package you like with Task.add_requirements('xlrd', '') or if its a package that you want the ClearML agent to install always (not per task), you can add it to the agent’s configuration file https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L82

Can this do the trick?

Hi MinuteWalrus85 .

Good news about fastai , the integration in almost done and a version will be release in the coming days :)

The AWS autoscaler is running as a https://github.com/allegroai/trains-server#trains-agent-services-- .
For services, we use a trains-agent configure to be in services mode. Now, each “service task” you insert to the queue should run until you stop it.

So, you need the AWS autoscaler keep running, which the services agent provide you. The “service task” is still a task, so it should have a project and a task name (just one for ALL your projects).

You can always run things locally wit...

Hi DefeatedCrab47 ,

You can set the HP with a dict, like:

Task.current_task().set_user_properties( { "property_name": {"description": "This is a user property", "value": "property value"}, "another_property_name": {"description": "This is another user property", "value": "another value"}, "yet_another_property_name": "some value" } )or list of dicts, like:

` Task.current_task().set_user_properties(
[
{
"name": "prop...