amazon-sagemaker-examples
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Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
*Issue #, if available:* n/a *Description of changes:* Add notebook output file for XGBoost customer churn notebook. *Testing done:* n/a ## Merge Checklist _Put an `x` in the boxes that...
**Link to the notebook** [All the PyTorch NEO compilation jobs in this directory](https://github.com/aws/amazon-sagemaker-examples/tree/main/sagemaker_neo_compilation_jobs) **Describe the bug** Running the pytorch example notebooks: - [inf1_bert_compile_and_deploy](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_neo_compilation_jobs/deploy_huggingface_model_on_Inf1_instance/inf1_bert_compile_and_deploy.ipynb) - [pytorch_torchvision_neo_on_Inf1](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_neo_compilation_jobs/deploy_pytorch_model_on_Inf1_instance/pytorch_torchvision_neo_on_Inf1.ipynb) - [pytorch_torchvision_neo](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_neo_compilation_jobs/pytorch_torchvision/pytorch_torchvision_neo.ipynb) unchanged, on ml.c5.xlarge,...
Hi, I have created a Sagemaker Pipeline Model using an Sklearn model followed by an xgboost model. I followed the instructions [here](https://github.com/aws/amazon-sagemaker-examples/issues/729) to set the 'SAGEMAKER_DEFAULT_INVOCATIONS_ACCEPT' environment variable but I'm...
*Issue #, if available:* *Description of changes:* This PR introduces an example of using RStudio on SageMaker to create a SageMaker Pipeline and deploy an R model, using R *Testing...
**Link to the notebook** [Add the link to the notebook.](https://github.com/aws/amazon-sagemaker-examples/blob/main/sagemaker_model_monitor/model_quality/model_quality_churn_sdk.ipynb) **Describe the bug** I have scheduled an hourly model-quality-monitoring job both the jobs, ground-truth-merge and model-quality-monitoring completes successfully without any...
*Issue #, if available:* *Description of changes:* Splitting the processing script to 2 separate; one used for model building and one for inference. Reason being that this is more clean...
Stumbled upon this while trying to evaluate my xgboost model ` model = pickle.load(open("./data/xgboost-model", "rb")) UnpicklingError: unpickling stack underflow` I get this same error while doing evaluation with a script...
Got a custom inference container on sagemaker. Addresses sagemaker /ping and /invocations on port 8080 as per spec. However internally custom container redirects URI /invocations to a different /original_predict_uri. ===================================...
**Link to the notebook** [Add the link to the notebook.](https://github.com/aws-samples/amazon-sagemaker-r-kernel-access-data-sources/blob/master/notebooks/aurora_connect.ipynb) **Describe the bug** The lifecycle configuration shown [here](https://github.com/aws-samples/amazon-sagemaker-r-kernel-access-data-sources/blob/master/lc_scripts/SageMaker_LC_Script.sh) does not have the dependencies required to load the `RJDBC` package. The...
*Issue #, if available:* *Description of changes:* Adds a standalone notebook to demonstrate BYO LightGBM model. It registers the model, and then uses the registered model for doing inference with...
