SuccessfulKoala55 AgitatedDove14 So I’ve tried the approach and it does work, however, this of course results in the credentials being visible in the ClearML web interface output, which comes close to just hard-coding them in…
Is there any way to send the secrets safely?
Is there any way to access the clearml.conf file contents from within code? (afaik, the file does not get send over to the container - otherwise I could just yml-read it myself…)

Although, some correction here: While the secret is indeed hidden in the logs, it is still visible in the “execution” tab of the experiment, see two screenshots below.
One again I set them with
task.set_base_docker(docker_arguments=["..."])

Hi SuccessfulKoala55 , thanks for getting back to me!
In the docs of Task.set_base_docker() it says “When running remotely the call is ignored”. Does that mean that this function call is executed when running locally to “record” the arguments and then when I duplicate the experiment and clone it remote, the call is ignored and the recorded values are used?

Hey guys, really appreciating the help here!
So what I meant by “it does work” is that the environment variables go through to the container, I can use them there, everything runs.

The remaining problem is that this way, they are visible in the ClearML web UI which is potentially unsafe / bad practice, see screenshot below.

Sorry to ask again, but the values are still showing up in the WebUI console logs this way (see screenshot.)
Here is the config that I paste into the EC2 Autoscaler Setup:
` agent {
extra_docker_arguments: ["-e AWS_ACCESS_KEY_ID=XXXXXX", "-e AWS_SECRET_ACCESS_KEY=XXXXXX"]

hide_docker_command_env_vars {
    enabled: true
    extra_keys: ["AWS_SECRET_ACCESS_KEY"]
    parse_embedded_urls: true
}    

} `Never mind, it came from setting the options wrong, it has to be ...

Ahhh, ok got it! Thanks 👍

Yes totally, but we’ve been having problems of getting these GPUs specifically (even manually in the EC2 console and across regions), so I thought maybe it’s easier to get one big one than many small ones, but I’ve never actually checked if that is true 🙂 Thanks anyhow!

Won’t they be printed out as well in the Web UI? That shows the full Docker command for running the task right…

That was the missing piece - thank you!
Awesome to all the details you have considered in ClearML 😉

Hi AgitatedDove14 , so it took some time but I’ve finally managed to reproduce. The issue seems to be related to writing images via Tensorboard:
` from torch.utils.tensorboard import SummaryWriter
import torch
from clearml import Task, Logger

if name == "main":
task = Task.init(project_name="ClearML-Debug", task_name="[Mac] TB Logger, offline")
tb_logger = SummaryWriter(log_dir="tb_logger/demo/")
image_tensor = torch.rand(256, 256, 3)
for iter in range(10):
t...

It might be broken for me, as I said the program works without the offline mode but gets interrupted and shows the results from above with offline mode. But there might be another issue in between of course - any idea how to debug?
The environment variable is good to know, I will try with that as well and report back.

Hi @<1523701087100473344:profile|SuccessfulKoala55> , sorry there was a mistake on my end - clearml.conf pointed to the wrong URL 🙈

@<1523701070390366208:profile|CostlyOstrich36> thank you, now everything works so far!
Last thing: Is there any way to change all the links in the new ClearML server such that an artifact that was previous under s3://… is now taken from gs://… ? The actual data is already available under the gs:// link of course

Unfortunately not, task.data.output just contains <tasks.Output: { "destination": " s3://some_bucket " }> and when I convert task.data to a string and search for the desired uri, I cannot find it either.
But on the other hand, putting the url together from its name, id, etc. seems to work - it might be a little unsafe if the task gets re-named or something, but otherwise it should be fine.

When running on our bigger research repository which includes saving checkpoints and uploading to S3, the training ends with errors as shown below and a Killed message for the main process (I do not abort the main process manually):

2023-01-26 17:37:17,527 INFO: Save the latest model.
2023-01-26 17:37:19,158 - clearml.storage - INFO - Starting upload: /tmp/.clearml.upload_model_cvqpor8r.tmp => glass-clearml/RealESR/Glass-ClearML Demo/[Lambda] FMEN distributed check, v10 fileserver u...

Hi John, thanks for getting back to me!
So it shows up in the UI like shown below. It happens both when “recording” the local run on Mac and on Linux.

Yes for example, or some other way to get credentials over to the container safely without them showing up in the checked-in code or web UI

I actually wanted to load a specific artifact, but didn’t think of looking through the tasks output models. I have now changed to that approach which feels much safer, so we should be all done here. Thanks!

I meant maybe me activating offline mode, somehow changes something else in the runtime and that in turn leads to the interruption. Let me try to build a minimal reproducible version 🙂

So my own repo I’m launching with either
torchrun --nproc_per_node 2 --standalone --master_addr 127.0.0.1 --master_port 29500 -m http://my_folder.my _script --some_option
or
python3 -m torch.distributed.launch --nproc_per_node 2 --master_addr 127.0.0.1 --master_port 29500 -m http://my_folder.my _script --some_option

More stack trace:

clearml-elastic   | ElasticsearchException[failed to bind service]; nested: AccessDeniedException[/usr/share/elasticsearch/data/nodes];
clearml-elastic   | Likely root cause: java.nio.file.AccessDeniedException: /usr/share/elasticsearch/data/nodes
clearml-elastic   |     at java.base/sun.nio.fs.UnixException.translateToIOException(UnixException.java:90)
clearml-elastic   |     at java.base/sun.nio.fs.UnixException.rethrowAsIOException(UnixException.java:106)
clearml-el...

AgitatedDove14 maybe to come at this from a broader angle:
Is ClearML combined with DataParallel or DistributedDataParallel officially supported / should that work without many adjustments? If so, would it be started via python ... or via torchrun ... ? What about remote runs, how will they support the parallel execution? To go even deeper, what about the machines started via ClearML Autoscaler? Can they either run multiple agents on them and/or start remote distribu...

Sorry that these issues go quite deep and chaotic - we would appreciate any help or ideas you can think of!

To recap, the server started up on GCP as expected before migrating the data over. The migration was done by

• deleting the current data sudo rm -fR /opt/clearml/data/*
• unpacking the backup sudo tar -xzf ~/clearml_backup_data.tgz -C /opt/clearml/data
• setting permissions sudo chown -R 1000:1000 /opt/clearml

By the way, if we don’t wrap other calls in is_offline() we get errors like “DateTime object is not serializable”, but that’s a secondary issue.

Well duh, now it makes total sense! Should have checked docs or examples more closely 🙏
Yes if that works reliably then I think that option could make sense, it would have made things somewhat easier in my case - but this is just as good.

Ok, I re-checked and saw that the data was indeed cached and re-loaded - maybe I waited a little too long last time and it was already a new instance. Awesome implementation guys!

Ok great! I will debug starting with a simpler training script.
Just as a last question, is torchrun also supported rather than the (now deprecated but still usable) torch.distributed.launch ?

SuccessfulKoala55 just in case you have any more thoughts, but we could also continue as is 😊

Yes, when the WebUI prompted me for them. They also seem to work since images in Debug Samples (also in S3) show up after I entered them.
Also, I can see that the plot is also saved in Debug Samples after explicit reporting, even though I don’t set report_interactive=False