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69 × Eureka!I need to change to pipe.set_default_execution_queue('services')?
or leave it defult?
SweetBadger76
There's something I can do to help you check? (task id, project name etc.)
CostlyOstrich36 tensorflow reporting it, ClearML capture it, and I get it with that function.
CostlyOstrich36 I have my own function that gives an estimate of performance, and I want to display it in the graph of each iteration.
And I am using tensorflow
Haa,
How can I change it?
The pipline is on services
and the first task on defult
Hey,
Thanks!
The pipline demo is running but the first step stack on 'Pending'
Hi SweetBadger76
It worked great yesterday and also a week ago ..
I'm trying to figure out what happened because I did not make any change to the code
SuccessfulKoala55
Hi,
Using task.upload_artifact
logger = task.get_logger()
train(model_dir=trained_model_dst, pipeline_config_path=pipeline_config_path, save_checkpoints_steps=args.checkpoints)
logger.report_scalar(title='evaluate', series='score', value=5, iteration=task.get_last_iteration())
CostlyOstrich36 Not sure I understood, the current iterations come from the function
task.get_last_iteration() ...
CostlyOstrich36 When I do
logger = task.get_logger()
logger.report_scalar(title='evaluate', series='score', value=5, iteration=task.get_last_iteration())
train(model_dir=trained_model_dst, pipeline_config_path=pipeline_config_path, save_checkpoints_steps=args.checkpoints)
It only captures the first iteration...
CostlyOstrich36 More precisely, My function only calculates the accuracy as I defined it.
I passing the accuracy to logger.report_scalar by
logger.report_scalar(title='evaluate', series='score', value=my_acc, iteration=task.get_last_iteration())
CostlyOstrich36 I get the last iteration by task.get_last_iteration()
I want to report each iteration..
Yes that's exactly what i do, But I'm trying to figure out if I can write down the line of code
logger.report_scalar(title='evaluate', series='score', value=5, iteration=task.get_last_iteration())
anywhere in the code?
Does the line of code open up another process parallel to training?
Or do I have to dive into the code in train function and write the code there?
SuccessfulKoala55
The pipline demo is still stack on running,
The first step still on pending, and belong to services queue