Marco Friaz
4534bff4b5
**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 inference sample notebooks. This ensures sample notebooks that utilize s3, works in SageMaker Unified Studio. Co-authored-by: Marco Friaz <marfriaz@amazon.com> Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com>
Amazon SageMaker Examples
Amazon SageMaker Pre-Built Framework Containers and the Python SDK
Pre-Built Deep Learning Framework Containers
These examples focus on the Amazon SageMaker Python SDK which allows you to write idiomatic TensorFlow or MXNet and then train or host in pre-built containers.
- CIFAR-10 with Chainer and ChainerMN
- Sentiment Analysis with Chainer
- MNIST with Chainer
- Sentiment Analysis with MXNet Gluon
- IRIS with Scikit-learn
- Visualize Amazon SageMaker Training Jobs with TensorBoard (CIFAR-10, TensorFlow 2.2)
- Managed Spot Training on TensorFlow
Pre-Built Machine Learning Framework Containers
These examples focus on building standard Machine Learning models powered by frameworks like Apache Spark or Scikit-learn using SageMaker Python SDK.