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533 × Eureka!So once I enqueue it is up? Docs says I can configure the queues that the auto scaler listens to in order to spin up instances, inside the auto scale task - I wanted to make sure that this config has nothing to do to where the auto scale task was enqueued to
how do I run this wizard? is this wizard train's or aws's?
I mean I don't get how all the pieces add up
but the task pending says its in the queue
I was refering to what is the returned object of Task.artifacts['...'] - when I call .get I understand what I get, I'm asking because I want to see how the object I'm calling .get on behaves
What about permissions to the machines that are being spun up? For exampel if I want the instances to have specific permissions to read/write to S3 for example, how do I mange those?
it's double weird, because also a task that the pipeline says is "in progress" is actually completed
First of all I wasn't aware that was an option - but I think it's preferable to be able to do it through the command line. Because I'm developing the pipeline to be executed remotely, but for debugging I run it locally.
Using what you showed I can obviously write it, and delete it once it is ready, and rewrite it when I'm debugging or adding features - but I think DX-wise it would be nicer to be able to trigger this functionality through the command line
thx! i was looking in the docs and was looking for something like URL/URI now i know why i didnt find it 😅
I don't know, I'm the one asking the question 😄
the link to manual model registry doesn't work
Yes, I have a metric I want to monitor so I will be able to sort my experiments by it. It is logged in this manner
logger.report_scalar(title='Mean Top 4 Accuracy', series=ARGS.model, iteration=0, value=results['top_4_acc'].mean())
When looking at my dashboard this is how it looks
my current version of the images used:
By the examples I figured out this ould appear as a scatter plot with X and Y axis and one point only.. Does it avoid that?
I guess what I want is a way to define environment variables in agents
I suspect that it has something to do with remote execution / local execution of pipelines, because we play with this , so sometimes the pipeline task itself executes on the client, and sometimes on the host (where the agent is also)
I believe that is why MetaFlow chose conda as their package manager, because it can take care of these kind of dependencies (even though I hate conda 😄 )