This repository contains source code and a jupyter notebook that triggers the cross validation training pipeline using Sagemaker Pipeline.
This main goal of this project is to provide a reference implementation for building a cross validation training pipeline with Amazon Sagemaker. By running the supplement juypter notebook provided in this project, you would have built a cross validation training pipeline that integrates with Sagemaker Pipelines and Sagemaker Automatic Model Tuning. Typically, training cross validation models involve k fold models trained in sequence, usually performed on the same server. In this project, we implement a technique that leverages Sagemaker SDK, Sagemaker Hyperparameter Tuner and Sagemaker Training Jobs to train k fold models in parallel to improve scalability and performance.
This project leverages a custom docker container. The specific steps on integrating a custom docker container are detailed in the next few sections below.
This library is licensed under the MIT-0 License. See the LICENSE file.
External Dependencies
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** sklearn; version 0.22.1 -- https://scikit-learn.org
- Sagemaker Studio
- CLI Terminal that supports building docker images. (e.g. AWS Cloud9)
In a CLI terminal that supports building docker image, run the following commands: (replace aws_acct_id and aws_region to reflect the target environment where the training jobs should be run)
git clone https://github.com/aws-samples/sagemaker-cross-validation-pipeline
cd sagemaker-cross-validation-pipeline/code
./build-and-push-docker.sh [aws_acct_id] [aws_region]
Note: Sagemaker Studio Terminal does not support building docker images today. Therefore, you would need to run the docker image building script outside of Sagemaker Studio that supports docker. For example, AWS Cloud9.
Capture the ECR repository URI from the script after a successful run. You'll need to provide the image URI at pipeline execution time. Here's a sample format of a ECR repo UR: ############.dkr.ecr.us-east-2.amazonaws.com/sagemaker-cross-validation-pipeline:latest. The following screenshots showed the sample output from the script executedin an AWS Cloud9 IDE. The docker image URI is highlighted in RED box for your reference.
- Launch Sagemaker Studio with a valid user
- Clone this repo into a Sagemaker studio workspace, as followed:
- Navigate to the repository folder cloned in previous step, open the jupyter notebook. Choose Python3 (Data Science) kernel, and ml.t3.medium instance type if not selected by default, as shown in the following:
Following items are a list of variables used in pipeline definition. These values can be overwritten at pipeline execution time for different results.
- ProcessingInstanceCount - number of instances for a Sagemaker Processing job in prepropcessing step.
- ProcessingInstanceType - instance type used for a Sagemaker Processing job in prepropcessing step.
- TrainingInstanceType - instance type used for Sagemaker Training job.
- TrainingInstanceCount - number of instances for a Sagemaker Training job.
- InferenceInstanceType - instance type for hosting the deployment of the Sagemaker trained model.
- HPOTunerScriptInstanceType - instance type for the script processor that triggers the hyperparameter tuning job
- ModelApprovalStatus - the initial approval status for the trained model in Sagemaker Model Registry
- ExecutionRole - IAM role to use throughout the specific pipeline execution.
- DefaultS3Bucket - default S3 bucket name as the object storage for the target pipeline execution.
- BaselineModelObjectiveValue - the minimum objective metrics used for model evaluation.
- S3BucketPrefix - bucket prefix for the pipeline execution.
- ImageURI - docker image URI (ECR) for triggering cross validation model training with HyperparameterTuner.
- KFold - the value of k to be used in k fold cross validation
- MaxTrainingJobs - maximum number of model training jobs to trigger in a single hyperparameter tuner job.
- MaxParallelTrainingJobs - maximum number of parallel model training jobs to trigger in a single hyperparameter tuner job.
- MinimumC, MaximumC - Hyperparameter ranges for SVM 'c' parameter.
- MimimumGamma, MaximumGamma - Hyperparameter ranges for SVM 'gamma' parameter.
To update any variables, navigate towards the bottom of the notebook where the pipeline execution is triggered and update the parameters with the desired values:
execution = pipeline.start(
parameters=dict(
BaselineModelObjectiveValue=0.8,
MinimumC=0,
MaximumC=1,
ImageURI="############.dkr.ecr.us-east-2.amazonaws.com/sagemaker-cross-validation-pipeline:latest"
))
Important Note You must replace the ImageURI with the docker image URI that was built and pushed to ECR as described in Step 1
Execute all cells in the notebook from the beginning. As prerequistes, select Python 3 (Data Science) jupyter kernel if not selected by default. This notebook has been tested successfully in Sagemaker Studio with 2vCPU + 4GiB instance type. Additionally, you can visualize the pipeline execution in realtime using Sagemaker Pipelines user interface available in the left hand panel in Sagemaker Studio console.
You can track the status of pipeline execution directly from the pipeline dashboard:
To drill down to specific job, double click the execution from the dashboard:
