so most likely one hard requirement installs the version 2 of pyjwt while setting up the experiment

I am trying to upload an artifact during the execution

I mean, inside a parent, do not show the project [parent] if there is nothing inside

and then call task.connect_configuration probably

But according the the example, this syntax should be supported right?

What is this cleanup service? where is it available?

I added the pass_hashed and restarted the server, still get the same problem

so what worked for me was the following startup userscript:
` #!/bin/bash
sleep 120
while sudo fuser /var/{lib/{dpkg,apt/lists},cache/apt/archives}/lock >/dev/null 2>&1; do echo 'Waiting for other instances of apt to complete...'; sleep 5; done
sudo apt-get update
while sudo fuser /var/{lib/{dpkg,apt/lists},cache/apt/archives}/lock >/dev/null 2>&1; do echo 'Waiting for other instances of apt to complete...'; sleep 5; done
sudo apt-get install -y python3-dev python3-pip gcc git build-essential...

So the problem comes when I do
my_task.output_uri = " s3://my-bucket , trains in the background checks if it has access to this bucket and it is not able to find/ read the creds

Hi AgitatedDove14 , sorry somehow this message got lost 😄
clearml version is the latest at the time, 1.7.1 Yes, I always see the "model uploaded completed" for such stuck tasks I am using python 3.8.10

This is what I get, when I am connected and when I am logged out (by clearing cache/cookies)

AgitatedDove14 According to the dependency order you shared, the original message of this thread isn't solved: the agent mentionned used output from nvcc (2) before checking the nvidia driver version (1)

Yes it would be very valuable to be able to tweak that param, currently it's quite annoying because it's set to 30 mins, so when a worker is killed by the autoscaler, I have to wait 30 mins before the autoscaler spins up a new machine because the autoscaler thinks there is already enough agents available, while in reality the agent is down

but it is set here, right?

AgitatedDove14 Yes that might work, also the first one (with conda) might work as well, I will give it a try, thanks!

They indeed do auto-rotate when you limit the size of the logs

Hi AgitatedDove14 , coming by after a few experiments this morning:
Indeed torch 1.3.1 does not support cuda, I tried with 1.7.0 and it worked, BUT trains was not able to pick the right wheel when I updated the torch req from 1.3.1 to 1.7.0: It downloaded wheel for cuda version 101. But in the experiment log, the agent correctly reported the cuda version (111). I then replaced the torch==1.7.0 with the direct https link to the torch wheel for cuda 110, and that worked (I also tried specifyin...

Ok yes, I get it, this info is also available at the very beginning of the logs, where the agent logs the full docker run command, this docker_cmd is a shorter version?

thanks for clarifying! Maybe this could be clarified in the agent logs of the experiments with something like the following?
agent.cuda_driver_version = ... agent.cuda_runtime_version = ...

Now I'm curious, what did you end up doing ?

in my repo I maintain a bash script to setup a separate python env. then in my task I spawn a subprocess and I don't pass the env variables, so that the subprocess properly picks up the separate python env

The only thing that changed is the new auth.fixed_users.pass_hashed field, that I don’t have in my config file

Ping CostlyOstrich36 AgitatedDove14 SuccessfulKoala55 Just making sure this wasn't missed 🙂

Exactly

Thanks TimelyPenguin76 and AgitatedDove14 ! I would like to delete artifacts/models related to the old archived experiments, but they are stored on s3. Would that be possible?

Both ^^, I already adapted the code for GCP and I was planning to adapt to Azure now

This is not the case, I downloaded it and I got a cuda error at runtime

Hi AnxiousSeal95 , I hope you had nice holidays! Thanks for the update! I discovered h2o when looking for ways to deploy dashboards with apps like streamlit. Most likely I will use either streamlit deployed through clearml or h2o as standalone if ClearML won't support deploying apps (which is totally fine, no offense there 🙂 )

now how to adapt to do it from extra_vm_bash_script ?

That’s why I said “not really” 😄

btw task._get_task_property('hyperparams') also gives me ValueError: Task has no hyperparams section defined