amazon-sagemaker-examples
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aws
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
**Link to the notebook** [Add the link to the notebook.](https://github.com/aws/amazon-sagemaker-examples/blob/main/ground_truth_labeling_jobs/ground_truth_streaming_labeling_jobs/ground_truth_create_chained_streaming_labeling_job.ipynb) **Describe the bug** I am running a example notebook above, using the DEFAULT = True. I have replaced the WORKTEAM)ARN...
*Issue #, if available:* *Description of changes:* Initial draft for guide on differentiating between AWS forecasting services. *Testing done:* Ran in SageMaker notebook instance. ## Merge Checklist _Put an `x`...
**Describe the bug** Even though the base_job_name argument is set in the Processor definition, for instance sagemaker.sklearn.processing.SKLearnProcessor, the resulting processing job created has a totally different name. **To reproduce** To...
**Link to the notebook** [pytorch_smdataparallel_mnist_demo](https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/data_parallel/mnist/pytorch_smdataparallel_mnist_demo.ipynb) **What aspects of the notebook can be improved?** This notebook is not working anymore with `ml.p3dn.24xlarge` instances : `botocore.exceptions.ClientError: An error occurred (ValidationException) when calling...
**Describe the bug** After I trained my model I try to make predictions using my y_test data and eventually the notebook restarts.: Here are the errors shown on CloudWatch: 2022-07-04T15:53:56.528+02:00Copy[I...
I have trained an XGBoost model, fine-tuned it, evaluated it and registered it using aws sagemaker pipeline. Now I want to deploy the model which performs the best. However, the...
*Issue #, if available:* *Description of changes:* *Testing done:* ## Merge Checklist _Put an `x` in the boxes that apply. You can also fill these out after creating the PR....
**Link to the notebook** (https://github.com/awslabs/amazon-sagemaker-examples/blob/master/advanced_functionality/inference_pipeline_sparkml_xgboost_abalone/inference_pipeline_sparkml_xgboost_abalone.ipynb) **Describe the bug** The notebooks for building inference pipeline mentioned in [https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines](https://sagemaker.readthedocs.io/en/stable/overview.html#inference-pipelines) are not available in the example repo.
**Describe the use case example you want to see** A notebook example describing how to tune hyper-parameters while training with SM Training Compiler on TensorFlow 2.9. SM Training Compiler recently...
*Issue #, if available:* *Description of changes:* Reorganized pipeline notebooks and updated pipeline code as per the new version of the sdk *Testing done:* ## Merge Checklist _Put an `x`...
