wpilibsuite / detectcoral Goto Github PK

Is it possible to make the s3 buckets and image public so teams can learn how the data from supervise.ly is ingested into the sagemaker model

Following the training instructions and trying step 6, run the code block.
https://docs.wpilib.org/en/latest/docs/software/examples-tutorials/machine-learning/training.html

We're getting this permission error:
ClientError: An error occurred (AccessDeniedException) when calling the CreateTrainingJob operation: User: arn:aws:sts::308085923257:assumed-role/AmazonSageMaker-ExecutionRole-20200202T132997/SageMaker is not authorized to perform: sagemaker:CreateTrainingJob on resource: arn:aws:sagemaker:us-east-1:308085923257:training-job/wpi-cpu-2020-02-02-21-52-50-104 with an explicit deny

ClientError Traceback (most recent call last)
in ()
27 # Change this bucket if you want to train with your own data. The WPILib bucket contains thousands of high quality labeled images.
28 # s3://wpilib
---> 29 estimator.fit("s3://codepurple5827")

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
459 self._prepare_for_training(job_name=job_name)
460
--> 461 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
462 self.jobs.append(self.latest_training_job)
463 if wait:

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs, experiment_config)
1012 train_args["enable_sagemaker_metrics"] = estimator.enable_sagemaker_metrics
1013
-> 1014 estimator.sagemaker_session.train(**train_args)
1015
1016 return cls(estimator.sagemaker_session, estimator._current_job_name)

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/sagemaker/session.py in train(self, input_mode, input_config, role, job_name, output_config, resource_config, vpc_config, hyperparameters, stop_condition, tags, metric_definitions, enable_network_isolation, image, algorithm_arn, encrypt_inter_container_traffic, train_use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics)
549 LOGGER.info("Creating training-job with name: %s", job_name)
550 LOGGER.debug("train request: %s", json.dumps(train_request, indent=4))
--> 551 self.sagemaker_client.create_training_job(**train_request)
552
553 def process(

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/botocore/client.py in _api_call(self, *args, **kwargs)
274 "%s() only accepts keyword arguments." % py_operation_name)
275 # The "self" in this scope is referring to the BaseClient.
--> 276 return self._make_api_call(operation_name, kwargs)
277
278 _api_call.name = str(py_operation_name)

~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/botocore/client.py in _make_api_call(self, operation_name, api_params)
584 error_code = parsed_response.get("Error", {}).get("Code")
585 error_class = self.exceptions.from_code(error_code)
--> 586 raise error_class(parsed_response, operation_name)
587 else:
588 return parsed_response

ClientError: An error occurred (AccessDeniedException) when calling the CreateTrainingJob operation: User: arn:aws:sts::308085923257:assumed-role/AmazonSageMaker-ExecutionRole-20200202T132997/SageMaker is not authorized to perform: sagemaker:CreateTrainingJob on resource: arn:aws:sagemaker:us-east-1:308085923257:training-job/wpi-cpu-2020-02-02-21-52-50-104 with an explicit deny

Match documentation with README

I have yet to run through the steps for running inference on a Raspberry Pi, so they may be wrong.

Title explains it.

Needs to annotate video with model

Hello all,

Say, I was following the instructions:
https://docs.wpilib.org/en/latest/docs/software/examples-tutorials/machine-learning/training.html

I got my Notebook created, and I clicked on the left hand side to find the training.ipynb, and it was not there.

I then rumaged around and found an older version of the file and uploaded it to my AWS Notebook, then changed the S3 name.

It gave me an error:
ClientError: An error occurred (ValidationException) when calling the CreateTrainingJob operation: Access denied for repository: wpi-cpu in registry ID: 118451457254. Please check if your ECR repository and image exist and role arn:aws:iam::422269586304:role/service-role/AmazonSageMaker-ExecutionRole-20200802T022815 has proper pull permissions for SageMaker: ecr:BatchCheckLayerAvailability, ecr:BatchGetImage, ecr:GetDownloadUrlForLayer

I think it was complaining about this line:
ecr_image = "118451457254.dkr.ecr.us-west-2.amazonaws.com/{}:latest".format(algorithm_name)

What should the line be and more importantly, where can I find this in the AWS tools?

-Jim