Error
Failed to get Scalar Charts

this is probably what I need, thanks. I'll check if it works

agent.hide_docker_command_env_vars.extra_keys: ["DB_PASSWORD=password"]

like this? or ["DB_PASSWORD", "password"]

ah, I see, I still keep it in agent.extra_docker_arguments

nice, thanks

fantastic, everything is working perfectly
thanks guys

thanks! this bug and cloning problem seem to be fixed

okay, I will try it with port

maybe I should use explicit reporting instead of Tensorboard

yeah, I am aware of trains-agent, we are planning to start using it soon, but still, copying original training command would be useful

okay, I will open an issue

I guess I could manually explore different containers and their content 😃 as far as I remember, I had to update Elastic records when we moved to the new cloud provider in order to update model URLs

same here, no live logs

give me a sec

task = Task.get_task(task_id = args.task_id)
task.mark_started()
task.set_parameters_as_dict(
{
"General": {
"checkpoint_file": model.url,
"restart_optimizer": False,
}
}
)
task.set_initial_iteration(0)
task.mark_stopped()
Task.enqueue(task = task, queue_name = task.data.execution.queue)

yes. we upload artifacts to Yandex.Cloud S3 using ClearML. we set " s3://storage.yandexcloud.net/clearml-models " as output uri parameter and add this section to the config:
{
host: " http://storage.yandexcloud.net "
key: "KEY"
secret:"SECRET_KEY",
secure: true
}

this works like a charm. but download button in UI is not working

not sure what you mean. I used to do task.set_initial_iteration(task.get_last_iteration()) in the task resuming script, but in the training code I explicitly pass global_step=epoch to the TensorBoard writer

yeah, we've used pipelines in other scenarios. might be a good fit here. thanks!

I added the link just in case anyway 😃

also, is there any way to install a repo that we clone as a package. we often use absolute imports and do "pip install -e ." to utilize it
sorry there are so many questions, we just really want to migrate to trains-agent)

yeah, server (1.0.0) and client (1.0.1)

exactly

yes

do you have any idea why cleanup task keeps failing then (it used to work before the update)

for me, increasing shm-size usually helps. what does this RC fix?

does this mean that setting initial iteration to 0 should help?

perfect!

okay, what do I do if it IS installed?

in the docker image

on the side note, is there any way to automatically give more meaningful names to the running docker containers?

this is how the interface looks