See here:
https://pip.pypa.io/en/stable/user_guide/#environment-variables

Pass these environment variables as part of the YAML template you are using with the k8s.
Should work for both 🙂

Hi @<1564785037834981376:profile|FrustratingBee69>

It's the previous container I've used for the task.

Notice that what you are configuring is the Default container, i.e. if the Task does not "request" a specific container, then this is what the agent will use.
On the Task itself (see Execution Tab, down below Container Image) you set the specific container for the Task. After you execute the Task on an Agent, the agent will put there the container it ended up using. This means that ...

If I call explicitly 

task.get_logger().report_scalar("test", str(parse_args.local_rank), 1., 0)

 , this will log as expected one value per process, so reporting works

JitteryCoyote63 and do prints get logged as well (from all processes) ?

or even different task types

Yes there are:
https://clear.ml/docs/latest/docs/fundamentals/task#task-types
https://github.com/allegroai/clearml/blob/b3176a223b192fdedb78713dbe34ea60ccbf6dfa/clearml/backend_interface/task/task.py#L81

Right now I dun see differences, is this a deliberated design?

You mean on how to use them? I.e. best practice ?
https://clear.ml/docs/latest/docs/fundamentals/task#task-states

Hmm do you host it somewhere? Is it pre-installed on the container?

Hi HandsomeGiraffe70

First:
# During pipeline initialisation pipeline_params is empty and we need to use default values. # When pipeline start the run, params are lunched again, and then pipeline_params can be used.Hmm that should probably be fixed, maybe a function on the pipeline to deal with it ?

When I reduce tune_optime value to just 'recall'. Pipeline execution failed with msg:

ValueError: Node 'tune_et_for_Precision', base_task_id is empty

.

I would...

Yey!!!!!

Hi QuaintPelican38
Can you ssh to {instance_public_ip_address}:10022 (something like ssh -p 10022 user@IP_HERE )?
Basically just getting the password prompt means you are okay.
I suspect that you have some AWS security definition (firewall) that prevents a direct access to the instance, could that be?

Ohh I see, so basically the ASG should check if the agent is Idle, rather than the Task is running ?

Do we have it on the git issue ?

Is it possible to make a connection to a S3 bucket via this authentication method with the open source version on EKS?

Hi BoredBluewhale23
In your setup, are we talking about agents running inside the Kubernetes cluster, or clients connecting from their own machine ?

It's always the details... Is the new Task running inside a new subprocess ?
basically there is a difference between
remote task spawning new tasks (as subprocesses, or as jobs on remote machine), remote task still running remote task, is being replaced by a spawned task (same process?!)UnevenDolphin73 am I missing a 3rd option? which of these is your case?
p,s. I have a suspicion that there might be a misuse of "Task" here?! What are you considering a Task? (from clearml perspective a Task...

Hi @<1523715429694967808:profile|ThickCrow29>

I am using the PipelineController with abort_on_failure set to False.

Is this a pipeline from code or from Tasks?
What is the clearml version?
Lastly, if a component fails, and another components is dependent on it's output, how would it run? if it is not dependent, why is it a child component?

Hi GrotesqueMonkey62 any chance you can be a bit more specific? Maybe a screen grab?
Here is how it works, if you look at an individual experiment scalars are grouped by title (i.e. multiple series on the same graph if they have the same title)
When comparing experiments, any unique combination of title/series will get its own graph, then the different series on the graph are the experiments themselves.
Where do you think the problem lays ?

Hi @<1523709807092043776:profile|GrittyKangaroo27>

some of my completed datasets,

This only has an effect on the dataset when it is being uploaded, if completed it is there for logging purposes only. What is exactly the use case? (just to be verify, once a Task/Dataset is completed you cannot edit it)

As I installed ClearML using pip,

Where is the clearml-serving runs ? usually your configuration file is in ~/clearml.conf
Notice if it is not there it means it is using the defaults so just create a new one and add that line

Hmm make sense, then I would call the export_task once (kind of the easiest to get the entire Task object description pre-filled for you) with that, you can just create as many as needed by calling import_task.
Would that help?

Hi EnchantingWorm39
Great question!
Regrading the data management, I know the enterprise edition has full support for unstructured data, and we plan to soon have a solution for structured data as part of the open source (soon= hopefully in a month time)
Regrading model serving, I know you can integrate with TFServing or seldon with very little effort (usually the challenge is creating triggers etc, but but in most cases this is custom code anyhow 🙂 )
I do not have experience with Cortex/B...

@<1541954607595393024:profile|BattyCrocodile47> not restarting the docker, restarting the Docker service (on Mac it's an app, I think there is an option on the Docker app to do that)

think perhaps it came across as way more passive aggressive than I was intending.

Dude, you are awesome for saying that! no worries 🙂 we try to assume people have the best intention at heart (the other option is quite depressing 😉 )

I've been working on a Azure load balancer example, ...

This sounds exciting, let me know if we can help in any way

additionally, I found is that clearml==1.0.5 package is able to find these partial changes, newer versions find nothing at all, maybe it's because it's always comparing against remote

Hmm it was always from remote...
it is actually doing the following:
git rev-parse --abbrev-ref --symbolic-full-name @{u}Then with the branch name output,
git diff --submodule=diff <add_branch_name_here>

My bad I meant the output of mount

BTW: Can you also please test with the latest clearml version , 1.7.2

they are just neighboring modules to the function I am importing.

So I think that is you specify the repo,, on the remote machine you will end with the code of the component sitting at the root folder of the repo, from there I assume you can import the rest, the root git path should be part of your PYTHONPATH automatically.
wdyt?

Hi @<1657918706052763648:profile|SillyRobin38>

I have included some print statements

you should see those under the Task of the inference instance.
You can also do:

import clearml
...
def preprocess(...):
  clearml.Logger.current_logger().report_text(...)
  clearml.Logger.current_logger().report_scalar(...)

, specifically within the containers where the inferencing occurs.

it might be that fastapi is capturing the prints...
[None](https://github.com/tiangolo/uvicor...

Hi @<1523701868901961728:profile|ReassuredTiger98>
This should have worked, and seems like conda is not fetching the correct pytorch version (even though the conda env contains the cuda version they specify)
Let's try something, reset the Task, then edit the "Installed packages" and add:

cudatoolkit==11.1.1

Then try again.
Let's see what we get.
(The idea, is that I think conda forgets it just install cudatoolkit and assumes the env is for CPU)

And same behavior if I make the dependance explicty via the retunr of the first one

Wait, are you saying that in the code above, when you abort "step_a" , then "step_b" is executed ?

In that case, no the helm chart does not spin a default agent (You should however spin a service mode agent for running pipelines logic)

Hi WickedBee96

Queue1 will take 3GPUs, Queue2 will take another 3GPUs, so in Queue3 can I put 2-4 GPUs??

Yes exactly !

if there are idle GPUs so take them to process the task? o

Correct, basically you are saying, this queue needs a minimum of 2 GPUs, but if you have more allocate them to the Task it pulled (with a maximum of 45 GPUs)
Make sense ?

VivaciousWalrus21 I took a look at your example from the github issue:
https://github.com/allegroai/clearml/issues/762#issuecomment-1237353476
It seems to do exactly what you expect. and stores its own last iteration as part of the checkpoint. When running the example with continue_last_task=int(0) you get exactly what you expect
(Do notice that TB visualizes these graphs in a very odd way, and it took me a few clicks to verify it...)