LazyFish41 just making sure, you built a container from the docker file, and used it as base docker image for the Task, is that correct ?
Also notice the cleaml-agent will not change the entry point of the docker meaning if the entry point does not end with plain bash, it will not actually run anything

Is this the same?
https://github.com/allegroai/clearml/issues/411

We workaround the issue by downloading the file with a request and unzipping only when needed.

We have located the issue, it seems the file-server is changing the header when sending back the file (basically saying CSV with gzip compression, which in turn will cause any http download client to automatically unzip the content). Working on a hot fix for it 🙂

Only as "default docker + argument" , if you need the "extra_docker_arguments" (which I think a mount point is a good example for), then you have to add it in the conf file

https://github.com/allegroai/clearml-agent/blob/60a58f6fadf755cb91b39a30c6462cc8788f3c9f/docs/clearml.conf#L133

I prepared my own image and want use this venv

No worries, it creates a "transparent" venv, it uses everything from the docker (the penalty of create a new venv is negligible 🙂 , you end up with the exact same set of packages)

others from the local environment and this causes a conflict when importing the attr module

Inside the docker ? " local environment" ?
This is all under "root" no?

ResponsiveCamel97
BTW: any reason not to allow this flexibility ?

Hi ShinyWhale52
Every execution of the pipeline (by definition) will create a new job based on the pipeline steps
This is the reason you see all the steps twice (the default assumption is you wish to re-run the step, as this is part of the processing workflow (e.g. training a model)

the model has been overwritten. I guess this is due to this instruction:

This is because you are storing it locally to the same path, it just reflects the fact you just overwrote your model.
To create a...

os.system

Yes that's the culprit, it actually runs a new process and clearml assumes that there are no other scripts in the repository that are used, so it does not analyze them
A few options:
Manually add the missing requirement Task.add_requirements('package_name')make sure you call it before the Task.init
2. import the second script from the first script. This will tell clearml to analyze it as well.
3. Force the entire clearml to analyze the whole repository: https://g...

Hi SmarmyDolphin68

I see this in between my training epochs, what could be causing this?

This is basically saying we are saving a second model on the same Task and even though both are logged, only the last is stored on the Task itself.
This will change as in the next version a Task will be able to hold reference to multiple models in the artifactory 🙂

ReassuredTiger98
(for some reason it kind of jumps over PyTorch, but then installs torchvision?!)
Could you run with the latest with --debug
We just added but you will have to install from git:
pip3 install git+Then run with --debug:
clearml-agent --debug daemon ...

nice 🙂

Yey!

I think there was an issue with the entire .ml domain name (at least for some dns providers)

3.a
Regarding the model query, sure from Python or restapi you can query based on any metadata
https://clear.ml/docs/latest/docs/references/sdk/model_model/#modelquery_modelsmodels

3.b
If you are using clearml-serving then check the docs / readme, but in a nutshell yes you can.
If the inference code is batchprocessing, which means a Task, then of course you can and lauch it, check the clearml agent f...

This is sitting on top of the serving engine itself, acting a s a control plane.
Integration with GKE is being worked on (basically KFServing as the serving engine)

and I install the tar

I think the only way to do that is add it into the docker bash setup script (this is a bash script executed before Task)

TrickySheep9 is this a conda package or a wheel you are installing manually ?

Ohh is this a conda installed ?

If i were to push the private package to, say artifactory, is it possible to use that do the install?

Yes that's the recommended way 🙂
You add the private repo here, for the agent to use:
https://github.com/allegroai/clearml-agent/blob/e93384b99bdfd72a54cf2b68b3991b145b504b79/docs/clearml.conf#L65

Hi SmallDeer34
ClearML automagical logging will work on the current python process. But in your example yyour Bash is running another python script (that has nothing to do with the original notebook), hence clearml automagic is not aware of it (i.e. it cannot "patch" the tensorboard calls).
In order to make it work.
you should do something like:
from joeynmt import train train.main(...)Or something similar 🙂
Make sense ?

Ohh, sure then editing git config will solve it.
btw: why would you need to do that, the agent knows how to do this conversion on the fly

Hi JealousParrot68
This is the same as:
https://clearml.slack.com/archives/CTK20V944/p1627819701055200
and,
https://github.com/allegroai/clearml/issues/411

There is something odd happening in the files-server as it replaces the header (i.e. guessing the content o fthe stream) and this breaks the download (what happens is the clients automatically ungzip the csv).
We are working on a hit fix to he issue (BTW: if you are using object-storage / shared folders, this will not happen)

or do you mean agent can convert https url to ssh??

Yep it does that automatically if you set: force_git_ssh_protocol: true
https://github.com/allegroai/clearml-agent/blob/42606d9247afbbd510dc93eeee966ddf34bb0312/docs/clearml.conf#L25

task.update({'script': {'version_num': 'my_new_commit_id'}})
This will update to a specific commit id, you can pass empty string '' to make the agent pull the latest from the branch

Notice that in your execute_remotely() you did not specify a queue to put the current Task into
What it does is it stops the current running code and it puts the newly created task into the specified queue, if you do not specify a queue , it will just abort it, and wait for you to Manually enqueue it.
To solve it:
task.execute_remotely(queue_name='my_queue')

Hi DeliciousBluewhale87
You can achieve the same results programmatically with Task.create
https://github.com/allegroai/clearml/blob/d531b508cbe4f460fac71b4a9a1701086e7b6329/clearml/task.py#L619

DeliciousBluewhale87

Upon ssh-ing into the folders in the both the physical node (/opt/clearml/agent) and the pod (/root/.clearml), it seems there are some files there..

Hmm that means it is working...
Do you see there a *.conf files? What do they contain? (it point to the correct clearml-server config)

Hi RipeGoose2
Can you try with the latest from git ?
pip install -U git+

Hi UnsightlyShark53 apologies for this delayed reply, slack doesn't alert users unless you add @ , so things sometimes get lost :(
I think you pointed at the correct culprit...
Did you manage to overcome the circular include?
BTW , how could I reproduce it? It will be nice if we could solve it