What is this repository

Description

This is an example for Amazon SageMaker with not Deep learning but simpler algorithm. And this example is based on scikit_bring_your_own example in https://github.com/awslabs/amazon-sagemaker-examples.

Algorithm

Closest avg classification : I think this is a simplest single feature classification in the world.

Training : Calculating avg from a series of feature values for each label.

Prediction : Finding closest avg value from trained avgs.

Other changes

• Using pickled pandas.DataFrame as input of training.
• Using pickled pandas.DataFrame as payload for prediction.
• Using pickled pandas.DataFrame as response format.
• Showing error message in response when error has occurred.

Known problems

• local_test/test_dir/output/success is not created automatically while testing.
• I couldn't find how to test trainingParams so I couldn't test feature to specify label/feature column names.
• I updated build_and_push.sh because original command uses --no-include-email with bad way...

Original ReadMe.md

Bring-your-own Algorithm Sample

This example shows how to package an algorithm for use with SageMaker. We have chosen a simple scikit-learn implementation of decision trees to illustrate the procedure.

SageMaker supports two execution modes: training where the algorithm uses input data to train a new model and serving where the algorithm accepts HTTP requests and uses the previously trained model to do an inference (also called "scoring", "prediction", or "transformation").

The algorithm that we have built here supports both training and scoring in IM with the same container image. It is perfectly reasonable to build an algorithm that supports only training or scoring as well as to build an algorithm that has separate container images for training and scoring.v

In order to build a production grade inference server into the container, we use the following stack to make the implementer's job simple:

1. nginx is a light-weight layer that handles the incoming HTTP requests and manages the I/O in and out of the container efficiently.
2. gunicorn is a WSGI pre-forking worker server that runs multiple copies of your application and load balances between them.
3. flask is a simple web framework used in the inference app that you write. It lets you respond to call on the /ping and /invocations endpoints without having to write much code.

The Structure of the Sample Code

The components are as follows:

• Dockerfile: The Dockerfile describes how the image is built and what it contains. It is a recipe for your container and gives you tremendous flexibility to construct almost any execution environment you can imagine. Here. we use the Dockerfile to describe a pretty standard python science stack and the simple scripts that we're going to add to it. See the Dockerfile reference for what's possible here.

• build_and_push.sh: The script to build the Docker image (using the Dockerfile above) and push it to the Amazon EC2 Container Registry (ECR) so that it can be deployed to IM. Specify the name of the image as the argument to this script. The script will generate a full name for the repository in your account and your configured AWS region. If this ECR repository doesn't exist, the script will create it.

• im-decision-trees: The directory that contains the application to run in the container. See the next session for details about each of the files.

• local-test: A directory containing scripts and a setup for running a simple training and inference jobs locally so that you can test that everything is set up correctly. See below for details.

The application run inside the container

When IM starts a container, it will invoke the container with an argument of either train or serve. We have set this container up so that the argument in treated as the command that the container executes. When training, it will run the train program included and, when serving, it will run the serve program.

• train: The main program for training the model. When you build your own algorithm, you'll edit this to include your training code.
• serve: The wrapper that starts the inference server. In most cases, you can use this file as-is.
• wsgi.py: The start up shell for the individual server workers. This only needs to be changed if you changed where predictor.py is located or is named.
• predictor.py: The algorithm-specific inference server. This is the file that you modify with your own algorithm's code.
• nginx.conf: The configuration for the nginx master server that manages the multiple workers.

Setup for local testing

The subdirectory local-test contains scripts and sample data for testing the built container image on the local machine. When building your own algorithm, you'll want to modify it appropriately.

• train-local.sh: Instantiate the container configured for training.
• serve-local.sh: Instantiate the container configured for serving.
• predict.sh: Run predictions against a locally instantiated server.
• test-dir: The directory that gets mounted into the container with test data mounted in all the places that match the container schema.
• payload.csv: Sample data for used by predict.sh for testing the server.

The directory tree mounted into the container

The tree under test-dir is mounted into the container and mimics the directory structure that IM would create for the running container during training or hosting.

• input/config/hyperparameters.json: The hyperparameters for the training job.
• input/data/training/leaf_train.csv: The training data.
• model: The directory where the algorithm writes the model file.
• output: The directory where the algorithm can write its success or failure file.

Environment variables

When you create an inference server, you can control some of Gunicorn's options via environment variables. These can be supplied as part of the CreateModel API call.

Parameter                Environment Variable              Default Value
---------                --------------------              -------------
number of workers        MODEL_SERVER_WORKERS              the number of CPU cores
timeout                  MODEL_SERVER_TIMEOUT              60 seconds