I am trying to follow this tutorial for connecting aws to a jupyter notebook for local development (I am running jupyter inside of vscode which I don't think matters but just noting it in case).
I have this ipynb file from the tutorial up and have gotten it to run successfully all the way until the cell where you try to fit the tensorflow estimator on the data:
code looks like this:
# Train! This will pull (once) the SageMaker CPU/GPU container for TensorFlow to your local machine.
# Make sure that Docker is running and that docker-compose is installed
tf_estimator.fit({'training': training_input_path, 'validation': validation_input_path})
I have docker installed but I don't really know what I'm doing with it since I've never used it before. I can't tell if this is a docker issue or a sagemaker issue but this is the error that is thrown when I try to run that cell:
Failed to import yaml. Local mode features will be impaired or broken. Please run "pip install 'sagemaker[local]'" to install all required dependencies.
---------------------------------------------------------------------------
ModuleNotFoundError Traceback (most recent call last)
/var/folders/cf/ft88j_856fv5rk3whgs12d9w0000gq/T/ipykernel_97461/84733352.py in <module>
2 # Make sure that Docker is running and that docker-compose is installed
3
----> 4 tf_estimator.fit({'training': training_input_path, 'validation': validation_input_path})
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/workflow/pipeline_context.py in wrapper(*args, **kwargs)
207 return self_instance.sagemaker_session.context
208
--> 209 return run_func(*args, **kwargs)
210
211 return wrapper
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
976 self._prepare_for_training(job_name=job_name)
977
--> 978 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
979 self.jobs.append(self.latest_training_job)
980 if wait:
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs, experiment_config)
1806 train_args = cls._get_train_args(estimator, inputs, experiment_config)
1807
-> 1808 estimator.sagemaker_session.train(**train_args)
1809
1810 return cls(estimator.sagemaker_session, estimator._current_job_name)
~/opt/anaconda3/envs/localsm/lib/python3.7/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_uri, algorithm_arn, encrypt_inter_container_traffic, use_spot_instances, checkpoint_s3_uri, checkpoint_local_path, experiment_config, debugger_rule_configs, debugger_hook_config, tensorboard_output_config, enable_sagemaker_metrics, profiler_rule_configs, profiler_config, environment, retry_strategy)
592 self.sagemaker_client.create_training_job(**request)
593
--> 594 self._intercept_create_request(train_request, submit, self.train.__name__)
595
596 def _get_train_request( # noqa: C901
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/session.py in _intercept_create_request(self, request, create, func_name)
4201 func_name (str): the name of the function needed intercepting
4202 """
-> 4203 return create(request)
4204
4205
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/session.py in submit(request)
590 LOGGER.info("Creating training-job with name: %s", job_name)
591 LOGGER.debug("train request: %s", json.dumps(request, indent=4))
--> 592 self.sagemaker_client.create_training_job(**request)
593
594 self._intercept_create_request(train_request, submit, self.train.__name__)
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/local/local_session.py in create_training_job(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig, ResourceConfig, InputDataConfig, Environment, **kwargs)
190 logger.info("Starting training job")
191 training_job.start(
--> 192 InputDataConfig, OutputDataConfig, hyperparameters, Environment, TrainingJobName
193 )
194
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/local/entities.py in start(self, input_data_config, output_data_config, hyperparameters, environment, job_name)
235
236 self.model_artifacts = self.container.train(
--> 237 input_data_config, output_data_config, hyperparameters, environment, job_name
238 )
239 self.end_time = datetime.datetime.now()
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/local/image.py in train(self, input_data_config, output_data_config, hyperparameters, environment, job_name)
234
235 compose_data = self._generate_compose_file(
--> 236 "train", additional_volumes=volumes, additional_env_vars=training_env_vars
237 )
238 compose_command = self._compose()
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/local/image.py in _generate_compose_file(self, command, additional_volumes, additional_env_vars)
687 except ImportError as e:
688 logger.error(sagemaker.utils._module_import_error("yaml", "Local mode", "local"))
--> 689 raise e
690
691 yaml_content = yaml.dump(content, default_flow_style=False)
~/opt/anaconda3/envs/localsm/lib/python3.7/site-packages/sagemaker/local/image.py in _generate_compose_file(self, command, additional_volumes, additional_env_vars)
684
685 try:
--> 686 import yaml
687 except ImportError as e:
688 logger.error(sagemaker.utils._module_import_error("yaml", "Local mode", "local"))
ModuleNotFoundError: No module named 'yaml'
Wondering if anyone else has had this happen or if someone can point me in the right direction to try and figure out what the issue is and how to solve it. Thanks!
CodePudding user response：
You are just missing the pyyaml module
Install it by:
pip install pyyaml