aws / aws/amazon-sagemaker-examples

tensorflow2_smdataparallel_maskrcnn_demo.ipynb failed CI

Open
#2,260 0 comments 0 reactions 0 assignees View on GitHub
Dominant language
Jupyter Notebook
Stars
11k
Forks
7k
Avg merge
8h 29m
Merged PRs (30d)
8

Description

Link to the notebook:
https://github.com/aws/amazon-sagemaker-examples/blob/master/training/distributed_training/tensorflow/data_parallel/maskrcnn/tensorflow2_smdataparallel_maskrcnn_demo.ipynb

Error:

---------------------------------------------------------------------------
Exception encountered at "In [12]":
---------------------------------------------------------------------------
ParamValidationError Traceback (most recent call last)
in
1 # Submit SageMaker training job
----> 2 estimator.fit(inputs=data_channels, job_name=job_name)

/usr/local/lib/python3.7/site-packages/sagemaker/estimator.py in fit(self, inputs, wait, logs, job_name, experiment_config)
667 self._prepare_for_training(job_name=job_name)
668
--> 669 self.latest_training_job = _TrainingJob.start_new(self, inputs, experiment_config)
670 self.jobs.append(self.latest_training_job)
671 if wait:

/usr/local/lib/python3.7/site-packages/sagemaker/estimator.py in start_new(cls, estimator, inputs,

[...]

Invalid length for parameter InputDataConfig[0].DataSource.FileSystemDataSource.FileSystemId, value: 8, valid min length: 11

Contributor guide

Open the contributing guide

Research direction

Open training/distributed_training/tensorflow/data_parallel/maskrcnn/tensorflow2_smdataparallel_maskrcnn_demo.ipynb and start at In [12], where estimator.fit(inputs=data_channels, job_name=job_name) fails. Reproduce the CI run and inspect the filesystem input configuration. Done means the notebook completes CI without the FileSystemId validation error.

Written by the indexing model from the issue text.

Assessment

Tech stack
aws, jupyter-notebook, tensorflow
Domain
cloud, machine-learning, testing
Issue type
Bug
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.