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69 × Eureka!MelancholyElk85 So I need to change the paths each time?
Hi CostlyOstrich36 CrookedWalrus33 AgitatedDove14
When I init an agent and run a task it works, but when I run the same task again it does not map the keys..
I know, but I run a scheduler on the script that downloads a dataset, and if there is no new dataset to download, I try to figure out what it will do
No, Its stuck here:
Collecting botocore<1.23.0,>=1.22.9
Using cached botocore-1.22.12-py3-none-any.whl (8.1 MB)
I need to change to pipe.set_default_execution_queue('services')?
or leave it defult?
CostlyOstrich36
The pipline demo is still stack on running,
The first step still on pending, and belong to services queue
Hey,
Thanks!
The pipline demo is running but the first step stack on 'Pending'
CostlyOstrich36 tensorflow reporting it, ClearML capture it, and I get it with that function.
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 I get the last iteration by task.get_last_iteration()
I want to report each iteration..
I have nowhere else to bring iteration number ..
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
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?