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‎README.md

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@@ -193,7 +193,8 @@ These examples show you how to use model-packages and algorithms from AWS Market
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- [Using AutoML algorithm](aws_marketplace/using_algorithms/automl) provides a detailed walkthrough on how to use AutoML algorithm from AWS Marketplace.
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- [Using Model Packages](aws_marketplace/using_model_packages)
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- [Using Model Packages From AWS Marketplace](aws_marketplace/using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace.
196+
- [Using Model Packages From AWS Marketplace](aws_marketplace/using_model_packages/generic_sample_notebook) is a generic notebook which provides sample code snippets you can modify and use for performing inference on Model Packages from AWS Marketplace, using Amazon SageMaker.
197+
- [Using Amazon Demo product From AWS Marketplace](aws_marketplace/using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace.
197198
- [Using models for extracting vehicle metadata](aws_marketplace/using_model_packages/auto_insurance) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing.
198199
- [Using models for identifying non-compliance at a workplace](aws_marketplace/using_model_packages/improving_industrial_workplace_safety) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace.
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‎advanced_functionality/distributed_tensorflow_mask_rcnn/container-optimized/Dockerfile

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@@ -37,13 +37,14 @@ RUN pip install Cython==0.28.4
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RUN pip install pycocotools==2.0.0
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RUN pip install matplotlib==3.0.3
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RUN pip install markdown==3.1
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RUN pip install numpy==1.17.5
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RUN git clone https://github.com/aws-samples/mask-rcnn-tensorflow
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RUN cd /mask-rcnn-tensorflow && git fetch origin 153442bc70b06e59f2bbeadc4d359b240f64cbc2
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RUN cd /mask-rcnn-tensorflow && git reset --hard 153442bc70b06e59f2bbeadc4d359b240f64cbc2
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RUN chmod -R +w /mask-rcnn-tensorflow
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RUN pip install --ignore-installed -e /mask-rcnn-tensorflow/
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RUN pip install -e /mask-rcnn-tensorflow/
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##########################################################################################
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# SageMaker requirements

‎advanced_functionality/distributed_tensorflow_mask_rcnn/container/Dockerfile

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@@ -31,11 +31,12 @@ RUN pip install Cython==0.28.4
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RUN pip install pycocotools==2.0.0
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RUN pip install matplotlib==3.0.3
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RUN pip install markdown==3.1
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RUN pip install numpy==1.17.5
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RUN git clone https://github.com/tensorpack/tensorpack.git /tensorpack
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RUN cd /tensorpack && git fetch origin 26664c3f1d58ae029ea6c3ba0af6ae11900b1e55
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RUN cd /tensorpack && git reset --hard 26664c3f1d58ae029ea6c3ba0af6ae11900b1e55
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RUN pip install --ignore-installed -e /tensorpack
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RUN pip install -e /tensorpack
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##########################################################################################
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# SageMaker requirements

‎advanced_functionality/kmeans_bring_your_own_model/kmeans_bring_your_own_model.ipynb

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@@ -53,16 +53,17 @@
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},
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"outputs": [],
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"source": [
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"bucket = '<your_s3_bucket_name_here>'\n",
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"prefix = 'sagemaker/DEMO-kmeans-byom'\n",
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" \n",
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"# Define IAM role\n",
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"import boto3\n",
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"import re\n",
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" \n",
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"import sagemaker\n",
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"from sagemaker import get_execution_role\n",
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"\n",
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"role = get_execution_role()"
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"role = get_execution_role()\n",
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"bucket = sagemaker.Session().default_bucket()\n",
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"prefix = 'sagemaker/DEMO-kmeans-byom'\n",
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" \n"
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]
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},
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{

‎advanced_functionality/pytorch_extending_our_containers/pytorch_extending_our_containers.ipynb

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"A number of files are laid out for your use, under the `/opt/ml` directory:\n",
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"\n",
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" /opt/ml\n",
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" ├── input\n",
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" │   ├── config\n",
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" │   │   ├── hyperparameters.json\n",
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" │   │   └── resourceConfig.json\n",
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" │   └── data\n",
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" │   └── <channel_name>\n",
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" │   └── <input data>\n",
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" ├── model\n",
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" │   └── <model files>\n",
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" └── output\n",
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" └── failure\n",
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" |-- input\n",
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" | |-- config\n",
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" | | |-- hyperparameters.json\n",
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" | | `-- resourceConfig.json\n",
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" | `-- data\n",
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" | `-- <channel_name>\n",
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" | `-- <input data>\n",
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" |-- model\n",
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" | `-- <model files>\n",
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" `-- output\n",
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" `-- failure\n",
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"\n",
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"##### The input\n",
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"\n",
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"The container has the model files in the same place that they were written to during training:\n",
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"\n",
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" /opt/ml\n",
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" └── model\n",
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"    └── <model files>\n",
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" `-- model\n",
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" `-- <model files>\n",
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"\n"
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]
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},
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"The `container` directory has all the components you need to extend the SageMaker PyTorch container to use as an sample algorithm:\n",
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"\n",
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" .\n",
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" ├── Dockerfile\n",
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" ├── build_and_push.sh\n",
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" └── cifar10\n",
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" ├── cifar10.py\n",
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" |-- Dockerfile\n",
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" |-- build_and_push.sh\n",
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" `-- cifar10\n",
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" `-- cifar10.py\n",
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"\n",
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"Let's discuss each of these in turn:\n",
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"\n",

‎advanced_functionality/r_bring_your_own/r_bring_your_own.ipynb

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},
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"outputs": [],
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"source": [
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"bucket = '<your_s3_bucket_name_here>'\n",
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"prefix = 'sagemaker/DEMO-r-byo'\n",
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" \n",
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"# Define IAM role\n",
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"import boto3\n",
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"import re\n",
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"import sagemaker\n",
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"from sagemaker import get_execution_role\n",
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"\n",
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"role = get_execution_role()"
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"role = get_execution_role()\n",
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"bucket = sagemaker.Session().default_bucket()\n",
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"prefix = 'sagemaker/DEMO-r-byo'"
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]
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},
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{

‎advanced_functionality/scikit_bring_your_own/scikit_bring_your_own.ipynb

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@@ -121,17 +121,17 @@
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"When Amazon SageMaker runs training, your `train` script is run just like a regular Python program. A number of files are laid out for your use, under the `/opt/ml` directory:\n",
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"\n",
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" /opt/ml\n",
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" ├── input\n",
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" │   ├── config\n",
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" │   │   ├── hyperparameters.json\n",
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" │   │   └── resourceConfig.json\n",
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" │   └── data\n",
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" │   └── <channel_name>\n",
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" │   └── <input data>\n",
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" ├── model\n",
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" │   └── <model files>\n",
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" └── output\n",
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" └── failure\n",
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" |-- input\n",
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" | |-- config\n",
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" | | |-- hyperparameters.json\n",
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" | | `-- resourceConfig.json\n",
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" | `-- data\n",
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" | `-- <channel_name>\n",
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" | `-- <input data>\n",
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" |-- model\n",
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" | `-- <model files>\n",
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" `-- output\n",
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" `-- failure\n",
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"\n",
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"##### The input\n",
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"\n",
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"The container will have the model files in the same place they were written during training:\n",
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"\n",
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" /opt/ml\n",
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" └── model\n",
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"    └── <model files>\n",
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" `-- model\n",
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" `-- <model files>\n",
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"\n"
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]
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},
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"In the `container` directory are all the components you need to package the sample algorithm for Amazon SageMager:\n",
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"\n",
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" .\n",
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" ├── Dockerfile\n",
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" ├── build_and_push.sh\n",
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" └── decision_trees\n",
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" ├── nginx.conf\n",
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" ├── predictor.py\n",
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" ├── serve\n",
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" ├── train\n",
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" └── wsgi.py\n",
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" |-- Dockerfile\n",
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" |-- build_and_push.sh\n",
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" `-- decision_trees\n",
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" |-- nginx.conf\n",
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" |-- predictor.py\n",
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" |-- serve\n",
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" |-- train\n",
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" `-- wsgi.py\n",
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"\n",
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"Let's discuss each of these in turn:\n",
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"\n",

‎advanced_functionality/tensorflow_bring_your_own/tensorflow_bring_your_own.ipynb

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@@ -116,17 +116,17 @@
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"When Amazon SageMaker runs training, your `train` script is run, as in a regular Python program. A number of files are laid out for your use, under the `/opt/ml` directory:\n",
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"\n",
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" /opt/ml\n",
119-
" ├── input\n",
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" │   ├── config\n",
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" │   │   ├── hyperparameters.json\n",
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" │   │   └── resourceConfig.json\n",
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" │   └── data\n",
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" │   └── <channel_name>\n",
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" │   └── <input data>\n",
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" ├── model\n",
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" │   └── <model files>\n",
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" └── output\n",
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" └── failure\n",
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" |-- input\n",
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" | |-- config\n",
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" | | |-- hyperparameters.json\n",
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" | | `-- resourceConfig.json\n",
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" | `-- data\n",
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" | `-- <channel_name>\n",
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" | `-- <input data>\n",
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" |-- model\n",
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" | `-- <model files>\n",
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" `-- output\n",
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" `-- failure\n",
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"\n",
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"##### The input\n",
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"\n",
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"The container has the model files in the same place that they were written to during training:\n",
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"\n",
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" /opt/ml\n",
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" └── model\n",
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"    └── <model files>\n",
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" `-- model\n",
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" `-- <model files>\n",
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"\n"
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]
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},
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"The `container` directory has all the components you need to package the sample algorithm for Amazon SageMager:\n",
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"\n",
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" .\n",
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" ├── Dockerfile\n",
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" ├── build_and_push.sh\n",
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" └── cifar10\n",
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" ├── cifar10.py\n",
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" ├── resnet_model.py\n",
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" ├── nginx.conf\n",
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" ├── serve\n",
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" ├── train\n",
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" |-- Dockerfile\n",
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" |-- build_and_push.sh\n",
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" `-- cifar10\n",
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" |-- cifar10.py\n",
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" |-- resnet_model.py\n",
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" |-- nginx.conf\n",
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" |-- serve\n",
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" `-- train\n",
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"\n",
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"Let's discuss each of these in turn:\n",
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"\n",

‎aws_marketplace/README.md

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@@ -21,7 +21,8 @@ These examples show you how to use model-packages and algorithms from AWS Market
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- [Using AutoML algorithm](using_algorithms/automl) provides a detailed walkthrough on how to use AutoML algorithm from AWS Marketplace.
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2323
- [Using Model Packages](using_model_packages)
24-
- [Using Model Packages From AWS Marketplace](using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace.
24+
- [Using Model Packages From AWS Marketplace](using_model_packages/generic_sample_notebook) is a generic notebook which provides sample code snippets you can modify and use for performing inference on Model Packages from AWS Marketplace, using Amazon SageMaker.
25+
- [Using Amazon Demo product From AWS Marketplace](using_model_packages/amazon_demo_product) provides a detailed walkthrough on how to use Model Package entities with the enhanced SageMaker Transform/Hosting APIs by choosing a canonical product listed on AWS Marketplace.
2526
- [Using models for extracting vehicle metadata](using_model_packages/auto_insurance) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of auto-insurance claim processing.
2627
- [Using models for identifying non-compliance at a workplace](using_model_packages/improving_industrial_workplace_safety) provides a detailed walkthrough on how to use pre-trained models from AWS Marketplace for extracting metadata for a sample use-case of generating summary reports for identifying non-compliance at a construction/industrial workplace.
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‎aws_marketplace/creating_marketplace_products/Bring_Your_Own-Creating_Algorithm_and_Model_Package.ipynb

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@@ -121,17 +121,17 @@
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"When Amazon SageMaker runs training, your `train` script is run just like a regular Python program. A number of files are laid out for your use, under the `/opt/ml` directory:\n",
122122
"\n",
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" /opt/ml\n",
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" ├── input\n",
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" │   ├── config\n",
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" │   │   ├── hyperparameters.json\n",
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" │   │   └── resourceConfig.json\n",
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" │   └── data\n",
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" │   └── <channel_name>\n",
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" │   └── <input data>\n",
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" ├── model\n",
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" │   └── <model files>\n",
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" └── output\n",
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" └── failure\n",
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" |-- input\n",
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" | |-- config\n",
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" | | |-- hyperparameters.json\n",
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" | | `-- resourceConfig.json\n",
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" | `-- data\n",
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" | `-- <channel_name>\n",
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" | `-- <input data>\n",
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" |-- model\n",
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" | `-- <model files>\n",
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" `-- output\n",
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" `-- failure\n",
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"\n",
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"##### The input\n",
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"\n",
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"The container will have the model files in the same place they were written during training:\n",
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"\n",
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" /opt/ml\n",
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" └── model\n",
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"    └── <model files>\n",
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" `-- model\n",
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" `-- <model files>\n",
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"\n"
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]
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},
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"In the `container` directory are all the components you need to package the sample algorithm for Amazon SageMager:\n",
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"\n",
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" .\n",
177-
" ├── Dockerfile\n",
178-
" ├── build_and_push.sh\n",
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" └── decision_trees\n",
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" ├── nginx.conf\n",
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" ├── predictor.py\n",
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" ├── serve\n",
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" ├── train\n",
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" └── wsgi.py\n",
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" |-- Dockerfile\n",
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" |-- build_and_push.sh\n",
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" `-- decision_trees\n",
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" |-- nginx.conf\n",
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" |-- predictor.py\n",
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" |-- serve\n",
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" |-- train\n",
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" `-- wsgi.py\n",
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"\n",
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"Let's discuss each of these in turn:\n",
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"\n",

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