Merge branch 'main' into main

hyperparameter_tuning/rapids_bring_your_own/code/Dockerfile

@@ -8,14 +8,18 @@ ENV CV_FOLDS="3"
 # ensure printed output/log-messages retain correct order
 ENV PYTHONUNBUFFERED=True
 
+# delete expired nvidia keys and fetch new ones
+RUN apt-key del 7fa2af80
+RUN rm /etc/apt/sources.list.d/cuda.list
+RUN rm /etc/apt/sources.list.d/nvidia-ml.list
+RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb && dpkg -i cuda-keyring_1.0-1_all.deb 
+
 # add sagemaker-training-toolkit [ requires build tools ], flask [ serving ], and dask-ml
 RUN apt-get update && apt-get install -y --no-install-recommends build-essential \ 
-    && source activate rapids && pip3 install sagemaker-training \
-    && conda install -c anaconda flask \
-    && conda install -c conda-forge dask-ml
+    && source activate rapids && pip3 install sagemaker-training dask-ml flask
 
 # path where SageMaker looks for code when container runs in the cloud
-ENV CLOUD_PATH="/opt/ml/code"
+ENV CLOUD_PATH "/opt/ml/code"
 
 # copy our latest [local] code into the container 
 COPY . $CLOUD_PATH

hyperparameter_tuning/rapids_bring_your_own/rapids_sagemaker_hpo.ipynb

@@ -704,14 +704,18 @@
     "# ensure printed output/log-messages retain correct order\n",
     "ENV PYTHONUNBUFFERED=True\n",
     "\n",
+    "# delete expired nvidia keys and fetch new ones\n",
+    "RUN apt-key del 7fa2af80\n",
+    "RUN rm /etc/apt/sources.list.d/cuda.list\n",
+    "RUN rm /etc/apt/sources.list.d/nvidia-ml.list\n",
+    "RUN wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb && dpkg -i cuda-keyring_1.0-1_all.deb \n",
+    "\n",
     "# add sagemaker-training-toolkit [ requires build tools ], flask [ serving ], and dask-ml\n",
     "RUN apt-get update && apt-get install -y --no-install-recommends build-essential \\ \n",
-    "    && source activate rapids && pip3 install sagemaker-training \\\n",
-    "    && conda install -c anaconda flask \\\n",
-    "    && conda install -c conda-forge dask-ml\n",
+    "    && source activate rapids && pip3 install sagemaker-training dask-ml flask\n",
     "\n",
     "# path where SageMaker looks for code when container runs in the cloud\n",
-    "ENV CLOUD_PATH=\"/opt/ml/code\"\n",
+    "ENV CLOUD_PATH \"/opt/ml/code\"\n",
     "\n",
     "# copy our latest [local] code into the container \n",
     "COPY . $CLOUD_PATH\n",

introduction_to_applying_machine_learning/README.md

@@ -5,6 +5,7 @@
 These examples provide a gentle introduction to machine learning concepts as they are applied in practical use cases across a variety of sectors.
 
 - [Predicting Customer Churn](xgboost_customer_churn) uses customer interaction and service usage data to find those most likely to churn, and then walks through the cost/benefit trade-offs of providing retention incentives.  This uses Amazon SageMaker's implementation of [XGBoost](https://github.com/dmlc/xgboost) to create a highly predictive model.
+- [Predicting Customer Churn](lightgbm_catboost_tabtransformer_autogluon_churn) uses Amazon SageMaker's implementation of [LightGBM](https://lightgbm.readthedocs.io/en/latest/), [CatBoost](https://catboost.ai/), [TabTransformer](https://arxiv.org/abs/2012.06678), and [AutoGluon-Tabular](https://auto.gluon.ai/stable/index.html) with [SageMaker Automatic Model Tuning](https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning.html) to create four predictive models on customer churn dataset, and evaluate their performance on the same test data.
 - [Cancer Prediction](breast_cancer_prediction) predicts Breast Cancer based on features derived from images, using SageMaker's Linear Learner.
 - [Ensembling](ensemble_modeling) predicts income using two Amazon SageMaker models to show the advantages in ensembling.
 - [Video Game Sales](video_game_sales) develops a binary prediction model for the success of video games based on review scores.