Will it work retroavtively, or do I need to create the agent with said name?
And if so, how do I create an agent with a predetermined name?

@<1523701070390366208:profile|CostlyOstrich36> Unfortunately I cannot supply anything, as no information is provided. Please see attached screen shot, that is all the information I have.

@<1523701087100473344:profile|SuccessfulKoala55> I disagree. Queues can have multiple workers, and that implies multiple instances of a task can run concurrently.
This is necessary for board farms, or any non-tiny scale of work.

@<1523701087100473344:profile|SuccessfulKoala55> No, I mean a chip. A piece of hardware that cannot, on it's on, run an agent and as such an attached computer - in this case, the server - will have an agent accessing it via ssh. In my case, I want to have a "board farm" - multiple boards for running inferences on them, and I'd like to have them all connected to the same server.

@<1523701070390366208:profile|CostlyOstrich36> Hi!
Thank you very much for the informative answer.
I have a follow-up question on q.1: Is there a pythonic way to retrieve that info mid-run?

@<1523701070390366208:profile|CostlyOstrich36> Will it work? Assume I have 3 workers.
1 takes a task
2 takes a task
1 checks last_worker

Will it get 1 or 2?

Thank you. It says in the doco I can use enviromental to control the ID, but I don't understand how -
None

I think it may be the case - I get that when I don't run through the pipeline but through the IDE option to run a script.

Yes. Sometimes, task (on HIL finish but fails on the HIL and does not produce output. Is it possible to not fail the task and still mark it as uncacheable?

Thank you!

If possible, I would like the second option and invalidate caching that completed task. But I am considreing just failing the task.

I do have 2 initial steps, then one step. However, I abort after the 2 initial steps are done, and I get the "2 experiments aborted."
My third step is ruined because a resource addressed is busy while accessed, which is weird.
Also, when starting the pipeline from a script it starts 2 concurrent runs (experimnt 240 and 241), while it should only start 1.