A few examples:
https://medium.com/@Sushil_Kumar/readwritemany-persistent-volumes-in-google-kubernetes-engine-a0b93e203180
https://docs.openshift.com/enterprise/3.1/install_config/storage_examples/shared_storage.html

Is there a way to do this all elegantly?

Of yes there is, this is how TaskB code will look:

` task = Task.init(..., 'task b')
param = {'TaskA' :'TaskAs ID HERE'}
task.connect(param)
taska_model = Task.get_task(param['TaskA']).models['output''][-1]
torch.load(taska_model.get_local_copy())

train

torch.save('modelb') `I might have missed something there, but generally speaking this will let you:
Select TASKA as a parameter of TaskB training process Will register automagically Tasks'A...

Yes you have to spin the server in order to generate the access/secret key...

First let's verify with the manual change, but yes

This is odd it says 1.0.0 but then, it was updated t weeks ago ...

Thanks!
In the conf file, I guess this will be where ppl will look for it.

p.s. you should remove this line 🙂
extra_index_url: ["git@github.com:salimmj/xxxx"]

basically use the template 🙂 we will deprecate the override option soon

it overwrites the previous run?

It will overwrite the previous if
Under 72h from last execution no artifact/model was createdYou can control it with "reuse_last_task_id=False" passed to Task.init
Task name itself is Not unique in the system, think of it as short description
Make sense ?

In the "installed packages" section you should have "nvidia-dali-cuda110" In the agent's clearml.conf you should add:extra_index_url: [" ", ]https://github.com/allegroai/clearml-agent/blob/master/docs/clearml.conf#L78
Should solve the issue

Just fixed, will be merged later, basically some field you are not supposed to change post execution (but system tags should be exempt from that). The SDK checks before the backend does, so you get a nice error 🙂 anyhow the backend will obviously allow it

ConvolutedSealion94 if you do bash:
cd ~/work/repo/code/ git statuswhat are you getting ?

BeefyCow3 see this https://allegroai-trains.slack.com/archives/CTK20V944/p1593077204051100 :)

SmarmySeaurchin8 yes, the package containing the Controller is only RC, plan is to release the stable one in a couple of days. In the meantime:
pip install git+

kubectl get pods -n {namespace} -o=JSONWhat are you getting when running the above on your cluster ?

Hi JoyousElephant80

Another possibility would be to run a process somewhere that periodically polls ClearML Server for tasks that have recently finished

this is the easiest way to implement what you are after, and have full control over the logic itself.
Basically you inherit from the Monitor class
And implement the callback function:
https://github.com/allegroa...

BTW: the new documentation should contain a full search over the docstring

I wonder if using our own containers which should have most the deps will work better than a simpler container.

Why not, it's transparent, just run in --docker mode and provide a default docker image if the Task doesn't specify one.

I am providing a helper to run a task in queue after running it locally in the notebook

Is this part of a pipeline process or just part of the workflow ?
(reason for asking is that if this is a pipeline thing we might be able to support it in v2)

how come the previous gitdiff passed ?

Can you let me know if i can override the docker image using template.yaml?

No, you cannot.
But you can pass OS environment "CLEARML_DOCKER_IMAGE" to set a diff default one

And you have the exact same folder structure / content, and server A/B give a different set of experiments ?
(is serverB empty, meaning no experiments at all?)

Hi SarcasticSparrow10

which database services are used to...

Mongo & Elastic
You can query everything using ClearML interface, or talk directly with the databases.
Full RestAPI is here:
https://clear.ml/docs/latest/docs/references/api/endpoints
You can use the APIClient for easier pythonic interface:
See example here
https://github.com/allegroai/clearml/blob/master/examples/services/cleanup/cleanup_service.py

What is the exact use case you have in mind?

If you use this one for example, will the component have pandas as part of the requirement
None

def step_two(...):
    import pandas as pd
    # do stuff

If so (and it should), what's the difference, where is "internal.repo " different from pandas ?

I think this one is on us, I don't think a search would have led you to the correct answer ...
I'll try to make sure they add something regrading the configuration 🙂

quick video of the search not working

Thank you! this is very helpful, passing along to front-end guys 🙂

and ctrl-f (of the browser) doesn’t work as lines below not loaded (even when you scroll it will remove the other lines not visible, so you can’t ctrl-f them)

Yeah, that's because they are added lazily

Hi LovelyHamster1
You mean when as a section name or a variable?
Could you change this example to include a variable that breaks the support ?
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py

are you referring to 

extra_docker_shell_

scrip

t

Correct

the thing is that this runs before you create the virtual environment, so then in the new environment those settings are no longer there

Actually that is better, because this is what we need to setup the pip before it is used. So instead of passing --trusted-host just do:
` extra_docker_shell_script: ["echo "[global] \n trusted-host = pypi.python.org pypi.org files.pythonhosted.org YOUR_S...

@<1539780258050347008:profile|CheerfulKoala77> make sure the AMI id matches the zone of the EC2 machine

For example, could you test if this one works:
https://github.com/allegroai/clearml/blob/master/examples/frameworks/hydra/hydra_example.py