github-featured/aws--amazon-sagemaker-examples

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Marco Friaz 1c0826e55b Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807 )

**Description**
This change checks the sagemaker session if there is a default_bucket_prefix set. If a default bucket prefix is specified, it is append it to the s3 path. This was done for hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks. This ensures sample notebooks that utilize s3, works in SageMaker Unified Studio.

Co-authored-by: Marco Friaz <marfriaz@amazon.com>

2025-01-21 09:39:05 -08:00

tools

infra: sort imports in all .py files with isort (#2225 )

2021-05-11 16:00:33 -07:00

goldfinch_detections.png

forgot the png file picture that shows a sample output

2019-01-29 14:18:14 -05:00

object_detection_birds.ipynb

Append the default_bucket_prefix to s3 paths if one exists to hyperparameter_tuning, introduction_to_amazon_algorithms and sagemaker-pipelines sample notebooks (#4807 )

2025-01-21 09:39:05 -08:00

README.md

added README.md and two new files in tools/ folder for helping with DeepLens deployments

2019-02-11 09:00:18 -05:00

README.md

Bird Object Detection Sample Notebook

The Amazon SageMaker notebook is object_detection_birds.ipynb. In addition, we have provided files in the tools folder.

patch_ssd.sh - this is a bash script for patching the model artifacts to be suitable for running on AWS DeepLens.
birdsOnEdge.py - this is a Python script that can be deployed as AWS Lambda inference function on AWS DeepLens. It depends on the patched model artifacts and performs the necessary additional step of calling mo.optimize before loading the model.
im2rec.py - this is a copy of a Python script from Apache MXNet that is used by the notebook to create RecordIO files of the bird images.