Hi guys, sorry, this thread was some reason forgotten,

pipeline I ran but during its execution the overrides (which is filled) is not taken into account and I have my default params.

Could this be the same issue as this one?
None

is there a built in programmatic way to adjust 

development.default_output_uri

?

How about: In your Task.init(output_uri='...')

And maybe adding idle time spent without a job to API is not that a bad idea 😉
yes, adding that to the feature list 🙂

What if I write the last active state in an instance tag? This could be a solution…

I love this hack, yes this should just work.
BTW: if you lambda is a for loop that is constantly checking there is no need to actually store "last idle timestamp check as tag", no?

@<1523701079223570432:profile|ReassuredOwl55>

Hey, here’s a quickie – is it possible to specify different “types” of input parameters (“Args/…“) such that they are handled nicely on the front end?

You me cast / checked in the UI ?

LudicrousParrot69 this is implementation issue, this entire page is based on "task comparison" single Task means totally different interface for querying the data 🙂

OddAlligator72 quick question:

suggest that you implement a simple entry-point API

How would the system get the correct packages / git repo / arguments if you are only passing a single function entrypoint ?

How are you building your pipeline?
None
None

You might be able to write a script to override the links ... wdyt?

Make sense ?

Hi NutritiousBear41 , asking questions here is exactly the reason we open the Slack channel :)
Regrading the error, it might be that you are stubbled on a bug , do you get the git repo on the UI?

UnsightlyShark53 See if this one solves the problem :)
BTW: the reasoning for the message is that when running the task with "trains-agent" if the parsing of the argparser happens before the the Task is initialized, the patching code doesn't know if it supposed to override the values. But this scenario was fixed a long time ago, and I think the error was mistakenly left behind...

There is a version coming out next week, the one after it (probably 2/3 weeks later) will have this feature

And the agent section on this machine is:
api_server:  web_server:  files_server: 
Is that correct?

Ohh then YES!
the Task will be closed by the process, and since the process is inside the Jupyter and the notebook kernel is running, it is still running

Oh think I understand you point now.
basically you can:

Create the initial Task, once it is in the system clone it and adjust parameters externally. A simple example here:
https://github.com/allegroai/clearml/blob/0397f2b41e41325db2a191070e01b218251bc8b2/examples/automation/manual_random_param_search_example.py#L41

wdyt?

Hi DeliciousBluewhale87
I think you are correct, there is no way to pass it.
As TimelyPenguin76 mentioned you can either set a default output_uri on the agent's config file, or edit the created Task in the UI.
What is the specific use case ? Maybe we should add this ability, wdyt?

task = Task.get_task('task_id_here') task.mark_started(force=True) task.upload_artifact(..., wait_on_upload=True) task.mark_completed()

Okay, let me quickly run a test

if executed remotely...

You mean cloning the local execution, sending to the agent, then when running on the agent the Args/command is updated to a list ?

Hi EnchantingOstrich20
You how doe s clearml get it there?
In runtime it analyzes the code you are running looking for imports then checks the version you have actively used (i.e. active venv / python) and lists it there.
You can also override those in code, or edit them after you clone the ask and before you enqueue it for remote execution

trains-agent RC (which they tell me will be out tomorrow) will have a switch to do that, just so it is easier 🙂

Hi JuicyFox94
you pointed to exactly the issue 🙂
In your trains.conf
https://github.com/allegroai/trains/blob/f27aed767cb3aa3ea83d8f273e48460dd79a90df/docs/trains.conf#L94

I assume every fit starts reporting from step 0 , so they override one another. Could it be?

Metadata might be expensive, it's a RestAPI call, and we have found users putting hundreds of artifacts, with preview entries ...

Hi MysteriousBee56 ,
what do you mean by:

Can we upload our project repository to trains server?

Hi UnsightlySeagull42
Basically you can get the agent to always add additional arguments for the docker run, such as -v for mounting:
https://github.com/allegroai/clearml-agent/blob/948fc4c6ce1ecf33a74619ad570d69b8188f6db9/docs/clearml.conf#L133

What probably happens is first torch is installed via "trains-agent", then it installs the other packages and they require a different version, so pip automatically replaces it.

BTW:
TrickySheep9 what's the jupyter version / python version / OS ?

You can see in the log it tries to download an artifact from a specific IP:URL is that link a valid one?
(this seems like the main cause of the error, first line in the screenshot)

BTW: seems like conda doesn't support git+git:// packages
How about switching to pip ? you can still run the entire thing from conda env, it will just use pip & venv to install everything, other than that it should work as expected.