I managed to do it by using logger.report_scalar, thanks!

I tried removing type=str but I got same problem 😕

Hi DilapidatedDucks58 , I did that already, but I am reusing the same experiment instead of merging two experiments. Step 4 can be seen as:
Update the experiment status to stopped (if it is failed, you won’t be able to re-enqueue it) Set a parameter of that task to point to the latest checkpoint and load it (you can also infer it directy: I simply add a tag to the task resume , and check at runtime if this tag exists, if yes, I fetch the latest checkpoint of the task) Use https://clea...

Not sure about that, I think you guys solved it with your PipelineController implementation. I would need to test it before giving any feedback 🙂

I did change the replica setting on the same index yes, I reverted it back from 1 to 0 afterwards

Here is the minimal reproducable example.
Run test_task_a.py - It will register a dummy artifact, create a new task, set a parameter in that task and enqueue it test_task_b will try to retrieve parameter from parent task and fail

Hi SuccessfulKoala55 , super that’s what I was looking for

ok, now I actually remember why I used _update_requirements instead of add_requirements: The first overwrites all the other, the later only add to the already detected packages. Since my deps are listed in the dependencies of my setup.py, I don't want clearml to list the dependencies of the current environment

I should also rename /opt/trains/data/elastic_migrated_2020-08-11_15-27-05 folder to /opt/trains/data/elastic before running the migration tool right?

Will the from clearml import Task raise an error if no clearml.conf exists? Or only when actual features requiring to define the server (such as Task.init ) will be called

Yes, it did spin two instances for the same task

Thanks for your inputs, I will try that! For completion, here is how I retrieve the parameters:
` from trains import Task

task = Task.init("test", "test")
parent_task = Task.get_task(task.parent)
task.get_logger().report_text(task.get_parameters())
artifact_name = task.get_parameter("General/artifact_name")
artifact = parent_task.artifacts[artifact_name].get() `

https://github.com/allegroai/clearml-agent.git@f019905720529acbd316bd39b67c5ab0c02fcd55 to be exact

the first problem I had, that didn’t gave useful infos, was that docker was not installed in the agent machine x)

the instances takes so much time to start, like 5 mins

meaning the RestAPI returns nothing, is that correct

Yes exactly, this is the response from the api server when I try to scroll down on the console to get more logs

This is no coincidence - Any data versioning tool you will find are somehow close to how git works (dvc, etc.) since they aim to solve a similar problem. In the end, datasets are just files.
Where clearml-data stands out imo is the straightfoward CLI combined with the Pythonic API that allows you to register/retrieve datasets very easily

Nevermind, i was able to make it work, but no idea how

thanks for your help!

Hi TimelyPenguin76 , I guess it tries to spin them down a second time, hence the double print

That was also my feeling! But I though that spawning the trains-agent from a conda env would isolate me from cuda drivers on the system

--- /data ---------- 48.4 GiB [##########] /elastic_7 1.8 GiB [ ] /shared 879.1 MiB [ ] /fileserver . 163.5 MiB [ ] /clearml_cache . 38.6 MiB [ ] /mongo 8.0 KiB [ ] /redis

BTW, is there any specific reason for not upgrading to clearml?

I just didn't have time so far 🙂

trains-agent-1: runs an experiment for a long time (>12h). Picks a new experiment on top of the long one running trains-agent-2: runs only one experiment at a time, normal trains-agent-3: runs only one experiment at a time, normalIn total: 4 experiments running for 3 agents

awesome! Unfortunately, calling artifact["foo"].get() gave me:
Could not retrieve a local copy of artifact foo, failed downloading file:///checkpoints/test_task/test_2.fgjeo3b9f5b44ca193a68011c62841bf/artifacts/foo/foo.json
It tries to get it from the local storage, but the json is stored in s3 (it does exists) and I did create both tasks specifying the correct output_uri (to s3)

from the ClearML UI

Thanks SuccessfulKoala55 😁

and in the logs:
`
agent.worker_name = worker1
agent.force_git_ssh_protocol = false
agent.python_binary =
agent.package_manager.type = pip
agent.package_manager.pip_version = ==20.2.3
agent.package_manager.system_site_packages = true
agent.package_manager.force_upgrade = false
agent.package_manager.conda_channels.0 = pytorch
agent.package_manager.conda_channels.1 = conda-forge
agent.package_manager.conda_channels.2 = defaults
agent.package_manager.torch_nightly = false
agent.venvs_dir = /...

I’m not too fond of many user configurations, it’s confusing.

100% agree, nevertheless, how much is too many? Currently, there are only two settings in the user preferences category, so one more wouldn’t hurt?

however, clearml is open source, nothing stops you from adding the code and sending a PR

I’d be super happy to contribute yes! Nevertheless, I am not sure where to start: clearml-server repo? clearml-web repo?

In the comparison the problem will be the same, right? If I choose last/min/max values, it won’t tell me the corresponding values for others metrics. I could switch to graphs, group by metric and look manually for the corresponding values, but that becomes quickly cumbersome as the number of experiments compared grow