sagemaker-101-workshop
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aws-samples
Hands-on demonstrations for data scientists exploring Amazon SageMaker
Perhaps some Pandas behaviour changed? But currently the following line in notebook *1 Autopilot and XGBoost.ipynb*: ```python df_model_data = pd.get_dummies(df_model_data) # Convert categorical variables to sets of indicators ``` ...is...
### Feature request We already provide example shell commands to invoke and test training scripts for all variants of the [migration challenge](https://github.com/aws-samples/sagemaker-101-workshop/tree/main/migration_challenge). For example [from SKLearn](https://github.com/aws-samples/sagemaker-101-workshop/blob/58ad6f83eeabf978b72f5ca85c0a11b776fc116e/migration_challenge/sklearn_cls/Instructions.ipynb): ``` !python3 src/main.py \...
Not sure yet whether it's worth it for the use-cases shown in this workshop, but we could consider adding: ```python import sagemaker_datawrangler ``` To enable the [built-in notebook data preparation...
Per the [torchtext README](https://github.com/pytorch/text#installation), our current pinned torchtext version (0.6) is a long way out of sync with our PyTorch version (PTv1.8=TTv0.9, PTv1.10=TTv0.11). I explored pinning the PT version to...
The current `conda_tensorflow2_p38` kernel on the latest SageMaker Notebook Instance platform (`notebook-al2-v2`, as used in the CFn template) seems to break local CPU-only training for the MNIST migration challenge. In...
In the first lab (SageMaker XGBoost HPO.ipynb), [generate_classification_report()](https://github.com/aws-samples/sagemaker-101-workshop/blob/d49b827f647c9f050879ca60e81afc890d0e7a22/builtin_algorithm_hpo_tabular/util/classification_report.py#L168) is called a couple of times. There does not appear to be a final call to `plt.show()` (from matplotlib.pyplot), and so the...
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Hands-on demonstrations for data scientists exploring Amazon SageMaker