but then why do I have to do task.connect_configuration(read_yaml(conf_path))._to_dict() ?
Why not task.connect_configuration(read_yaml(conf_path)) simply?
I mean what is the benefit of returning ProxyDictPostWrite instead of a dict?

super, thanks a lot 🙂

I see what I described in https://allegroai-trains.slack.com/archives/CTK20V944/p1598522409118300?thread_ts=1598521225.117200&cid=CTK20V944 :
randomly, one of the two experiments is shown for that agent

CostlyOstrich36 How is clearml-session setting the ssh config?

Still the same problem 😞

AgitatedDove14 In theory yes there is no downside, in practice running an app inside docker inside a VM might introduce slowdowns. I guess it’s on me to check whether this slowdown is negligible or not

No space, I will add and test 🙂

yes, the only thing I changed is:
install_requires=[ ... "my-dep @ git+ ]to:
install_requires=[ ... "git+ "]

yes, because it won’t install the local package which has this setup.py with the problem in its install_requires described in my previous message

my agents are all .16 and I install trains 0.16rc2 in each Task being executed by the agent

AgitatedDove14 I was able to redirect the logger by doing so:
clearml_logger = Task.current_task().get_logger().report_text early_stopping = EarlyStopping(...) early_stopping.logger.debug = clearml_logger early_stopping.logger.info = clearml_logger early_stopping.logger.setLevel(logging.DEBUG)

Yes, it is supposed to run for 200 epochs

I followed https://github.com/NVIDIA/nvidia-docker/issues/1034#issuecomment-520282450 and now it seems to be setting up properly

Ok to be fair I get the same curve even when I remove clearml from the snippet, not sure why

correct, you could also use

Task.create

that creates a Task but does not do any automagic.

Yes, I didn't use it so far because I didn't know what to expect since the doc states:
"Create a new, non-reproducible Task (experiment). This is called a sub-task."

Thanks! Corrected both, now its building

Alright, so the steps would be:

trains-agent build --docker nvidia/cuda --id myTaskId --target base_env_services

That would create me a base docker image base_env_services . Then how should I ensure that trains-agent uses that base image for the services queue? My guess is:

trains-agent daemon --services-mode --detached --queue services --create-queue --docker base_env_services --cpu-only

Would that work?

So if all artifacts are logged in the pipeline controller task, I need the last task to access all the artifacts from the pipeline task. I need to execute something like PipelineController.get_artifact() in the last step task

What I put in the clearml.conf is the following:

agent.package_manager.pip_version = "==20.2.3" agent.package_manager.extra_index_url: [" "] agent.python_binary = python3.8

it also happens without hitting F5 after some time (~hours)

The number of documents in the old and the new env are the same though 🤔 I really don’t understand where this extra space used comes from

wow if this works that’s amazing

Ho and also use the colors of the series. That would be a killer feature. Then I simply need to match the color of the series to the name to check the tags

1.1.2

Yes, actually thats what I am doing, because I have a task C depending on tasks A and B. Since a Task cannot have two parents, I retrieve one task id (task A) as the parent id and the other one (ID of task B) as a hyper-parameter, as you described 👍

So I installed docker, added user to group allowed to run docker (not to have to run with sudo, otherwise it fails), then ran these two commands and it worked

Awesome! Thanks! 🙏

Task.get_project_object().default_output_destination = None

Hi TimelyPenguin76 , any chance this was fixed? 🙂

Thanks!3. I don't know, I never used Highcharts 🙂