you can put task.execute_remotely() to create it in draft mode. I've taken to configuring defaults to run things very quickly just in case i forget though (e.g. placeholder string for dataset, bail out early if not changed… or just do one epoch on a small subset of samples, etc).

For reproducibility, it kind of makes sense though. The existence of the file is contingent on the worker cloning the source code. I'm sure things can be done to maintain state differently but I personally adapted to the git-based workflow for managing files pretty quickly.

though yes I will admit I had the same thought first: why must I run it each time?

Beware: squash merges will ruin the ability to reproduce the experiment at that time since the git commit will be lost (presuming th...

I'm guessing this is done through code-server?

I'm currently rolling a JupyterHub instance (multiuser, with codeserver inside) on the same machine as clearml-server. That’s where tasks are executed etc. so, all browser dev env.

It sounds like there’s an option to basically bypass this latter step and just use clearml’s credentialing to accomplish much the same thing? Am I understanding clearml-session correctly?

Yup if you scroll through the logs in the console, near the top (post config dump), you’ll see a git clone and checkout to the specific hash.

PS You can actually change this parameter in an experiment’s configuration if it is in draft mode.

probably, but the syntax would be in that of a git diff, so it’d be a touch clunky if you asked me
Are you trying to avoid local development?

Oh neat! I want to take a look at this. Only a few more weeks at the client but it’d be nice to reduce the complexity of the software stack if I can before handoff.

Can you please elaborate on the latter point? My jupyterhub’s fully containerized and allows users to select their own containers (from a list i built) at launch, and launch multiple containers at the same time, not sure I follow how toes are stepped on.

Oh yes. I see. Yeah, no ML here actually (doing the testing infra of endpoints), but certainly when there is its an issue.

How does clearml session avoid it? I guess only if autoscaling is used (one worker one machine)?

I ran into something similar during deployment. Hopefully this helps with your debugging: if the agent was launched separately from the rest of the stack, it may not have proper docker-DNS resolution to None . (e.g. if in the same docker-compose, perhaps you didnt add the backend network field, or if it was launched separately through docker run without an explicit external network defined)

if the agent's on the same machine, try docker network connect to add...

oh i see. you're talking about the agent-services, not a separate agent in a container.
yup, I've got the same thing going there.
fwiw...
for me, HOST_IP is 0.0.0.0 and the other "HOSTS" env vars don't contain "http" in them.
and my server is publicly reachable, not sure if that matter either.

👀 following.
I have much the same issue, and it's mission-critical that I resolve it soon.

I believe pipe.connect_configuration is what you're looking for?

Might be under examples

the clearml github, search for a file named cleanup service dot py (or something to that effect)

credentials for the server to do things with s3 will be in /opt/clearml/apiserver.conf.

tasks that create pipelines feels like a hack and i found they dont show up in the UI (have to use the link in the console).

I've found that sometimes i need to right click "Run" a couple of times before the parameters are filled in properly.

I think you’d have to run the cleanup service. That’s what seems to be what is controlling deletion based on archived status and some other temporal filters

hm, you should be able to hit None if docker networking is working properly. it shouldn't need to go through the internet to get back to your machine.

If you can hit the endpoint with curl, you for sure can hook it up to many frontend frameworks.

Personal recs: gradio, streamlit

Abstract the interaction into a function call, and wrap it all in some UI elements using python.

if you commit but do not push, the metadata tells clearml that it needs to pull a non-existant commit. any changes you made on top may be saved as a diff, but they'd fail to apply.

for clearml to work on un-pushed commits, it'd have to wait for a push to register a new diff target, which can become a problem (what if you have multiple remotes? which one will it wait for?) so rather, it assumes it can access the most recent commit from your remote repo, and records this as the "base" upon whi...

Can vouch, this works well. Had my server hard reboot (maybe bc of clearml? maybe bc of hardware, maybe both… haven’t figured it out), and busy remote workers still managed to update the backend once it came back up.

Re: backups… what would happen if zipped while running but no work was being performed? Still an issue potentially?

and what happens if docker compose down is run while there’s work in the services queue? Will it be restored? What are the implications if a backup is perform...

my approach was to spin up an EC2 and run the deployment there from within the EBS volume mount.

I symlinked /opt/clearml to /mnt/xvda/clearml to minimize docker-compose changes. been working out fine so far.

with aws-cdk, the deployment steps can be automated (format the volume, clone a repo with the config, etc). I can link you to a resource that may help with that if you're interested.

@<1541954607595393024:profile|BattyCrocodile47> put together None

dug deeper. if i'm to make a guess...
/root/clearml.conf -> used on startup of agent-services as a template of sorts to create .clearml_agent.<id>.cfg on demand -> this task-specific file is used to mount to /tmp/clearml_default.conf in a new container (docker in docker bc of the socket mounted to the agent-services) -> used to execute the task

I tried mounting a config file (in the structure of the one on github but with just the relevant s3 section) into the agent-services container at /root/clearml.conf and after restarting the container, it seems to have had an impact. thank you!

When I inspect the console of the task I'm trying to run, I see there's a call to cp /tmp/clearml.conf ~/default_clearml.conf in the docker command and that the volume /tmp/clearml.conf is picked up from the host at some custom-named file ...

thank you!
I'll add a volume mount to the services-agent container, and from what I understand that will become the template it uses?

is this the structure of the file?
None

or is it the "dot" syntax (like what shows up in the console when the task executes / your snippet)?

yeah let's step through this, i'm having her execute these steps as we speak.

create a task with the new project name. its created as a draft. can see it in the UI under the new project.

pipeline script is updated with new project name for. execute script to create pipeline. now see in UI under this new project name. nothing hidden.

the pipeline is running. when the queue is default (only serviced by one container with agent in it ( clearml-agent==1.5.2 ). abort it. everything is still ...

I opened github.com/allegroai/clearml/pull/1083 as an attempt to help catch this.

waiting now to see if they disappear.
any problems you may have spotted with the versions used?

project hasn't disappeared just yet. but it's happened twice now

i will attempt to start that now.

the dataset, task, and pipeline were under the same project name. i'm seeing what happens if the dataset project name was different ( f"{project_name}_data" ). which project would get deleted... the dataset one or the project of the task that kicked it off?

and the answer is...
the project is preserved, the dataset's project hidden.

so ... empty dataset names due to a small typo in parameter override + the choice for the dataset to have the same project name as the task that created it (...