Little update here: It happened again after an update to ClearML SDK 1.0.4, but this time it happened immediately after I lost HTTP connection. This makes sense with your explanations. Can I suppress this by setting sdk.development.support_stopping in the config to false ?
After some investigation, this might be related to an issue in ClearML SDK 1.0.2 with the subprocesses support - I suggest upgrading to ClearML SDK 1.0.4 🙂
So that doesn't explain why the task's status was changed...
It seems like I regained connection. At least I can see all values until the task got terminated and after the HTTPTimeOut warning in my logs, the training runs for another 200 Iterations (~1.5 hours)
I mean, assuming you lost connection to the server and stopped reporting
Well, there's a watchdog on the server that automatically stops tasks that haven't reported for a long time - I guess that's what happened...
It seems like I lost connection during the run of my experiment. But this happened like 200 Epochs before the process got terminated
Yes, also on my machine, where I store the Tensorboard logs together with additional results (Meshes and Model checkpoints) of all experiments, I only use like 1 GB
It's on the way, but not yet possible 🙂
Is there a way to check how much storage I am using on the community server?
but updating to the latest version is always a good idea 🙂
I am using the community server at https://app.community.clear.ml . In my environment I use clearml==1.0.2 , so I probably should update to the latest version
There are literally only two things that can cause that specific message to be printed 🙂
What you're seeing is basically the SDK's response to the Task's status being change mid-run, or to someone clicking "Stop" in the UI
I use tensorboard and rely on automatic logging for all of my scalar reporting. However, I periodically log some scatter plots using clearml.Logger.report_plotly . And I use report_text to log some information about training progress to the console.
ShallowKitten67 are you relying on the automatic reporting (so just creating a task and doing nothing clearml-related afterwards), or are you explicitly calling any clearml methods in your code?
Oh, and I do not change the task’s status in my code. I just create it at the beginning of my training.
` configuration = parser.parse(config_path)
task = clearml.Task.init(project_name='Foo',
task_name=configuration.name) `
Since I do not manage the cluster, I do not have permission to access system logs. In the docker logs, the last thing that gets printed is the clearml.Task WARNING .
Here are the machine monitoring scalars. Seems fine to me. I am currently trying to reproduce results from a paper, thus I do not tune batch_size etc to use all available resources.
ShallowKitten67 this could happen if you're changing your task's status somewhere in your code - are you?
Hi ShallowKitten67 .
Can you send the logs? can you share the machine monitoring (from scalars section)?
