Train, tune, and deploy a custom ML model using For Seller to update: Title_of_your_ML Algorithm Algorithm from AWS Marketplace

For Seller to update: Add overview of the algorithm here

For Seller to update: Add link to the research paper or a detailed description document of the algorithm here

This sample notebook shows you how to train a custom ML model using For Seller to update:Title_of_your_Algorithm from AWS Marketplace.

Note: This is a reference notebook and it cannot run unless you make changes suggested in the notebook.


  1. Note: This notebook contains elements which render correctly in Jupyter interface. Open this notebook from an Amazon SageMaker Notebook Instance or Amazon SageMaker Studio.

  2. Ensure that IAM role used has AmazonSageMakerFullAccess

  3. Some hands-on experience using Amazon SageMaker.

  4. To use this algorithm successfully, ensure that:

    1. Either your IAM role has these three permissions and you have authority to make AWS Marketplace subscriptions in the AWS account used:

      1. aws-marketplace:ViewSubscriptions

      2. aws-marketplace:Unsubscribe

      3. aws-marketplace:Subscribe

    2. or your AWS account has a subscription to For Seller to update:Title_of_your_algorithm.


  1. Subscribe to the algorithm

  2. Prepare dataset

    1. Dataset format expected by the algorithm

    2. Configure and visualize train and test dataset

    3. Upload datasets to Amazon S3

  3. Train a machine learning model

    1. Set up environment

    2. Train a model

  4. Deploy model and verify results

    1. Deploy trained model

    2. Create input payload

    3. Perform real-time inference

    4. Visualize output

    5. Calculate relevant metrics

    6. Delete the endpoint

  5. Tune your model! (optional)

    1. Tuning Guidelines

    2. Define Tuning configuration

    3. Run a model tuning job

  6. Perform Batch inference

  7. Clean-up

    1. Delete the model

    2. Unsubscribe to the listing (optional)

Usage instructions

You can run this notebook one cell at a time (By using Shift+Enter for running a cell).

1. Subscribe to the algorithm

To subscribe to the algorithm: 1. Open the algorithm listing page For Seller to update:Title_of_your_product. 1. On the AWS Marketplace listing, click on Continue to subscribe button. 1. On the Subscribe to this software page, review and click on “Accept Offer” if you agree with EULA, pricing, and support terms. 1. Once you click on Continue to configuration button and then choose a region, you will see a Product Arn. This is the algorithm ARN that you need to specify while training a custom ML model. Copy the ARN corresponding to your region and specify the same in the following cell.

[ ]:
algo_arn = "<Customer to specify algorithm ARN corresponding to their AWS region>"

2. Prepare dataset

For Seller to update: Add all necessary imports in following cell. If you need specific packages to be installed, try to provide them in this section, in a separate cell.

[ ]:
import base64
import json
import uuid
from sagemaker import ModelPackage
import sagemaker as sage
from sagemaker import get_execution_role
from sagemaker import ModelPackage
from urllib.parse import urlparse
import boto3
from IPython.display import Image
from PIL import Image as ImageEdit
import urllib.request
import numpy as np

A. Dataset format expected by the algorithm

For Seller to update: In following cell, provide a description of the dataset accepted by the training job. This will help customer understand what the dataset fed to the algorithm needs to look like.

You can also find more information about dataset format in Usage Information section of For Seller to update:Title_of_your_product.

B. Configure and visualize train and test dataset

For Seller to update: upload the sample training dataset into data/train directory and update the training_dataset parameter value in following cell. You are strongly recommended to either upload the dataset into data/train directory or download it from a reliable source at runtime. If you intend to download it at run-time, add relevant code in following cell. Do not hardcode your bucket name.

[ ]:
training_dataset = "data/train/<FileName.ext>"

For Seller to update/read: We recommend that you support a test channel and accept a test dataset to calculate your algorithm metrics on. Emit both - training as well as test metrics.

For Seller to update: upload a test dataset into data/test directory. Alternately, you may want to download the test dataset on-the-fly. If you intend to download it at run-time, add relevant code in following cell. Update the test_dataset parameter value in following cell.

[ ]:
test_dataset = "data/test/<FileName.ext>"

For Seller to update: Add code that displays a few rows from the training dataset. Also explain how the training dataset provided as part of the notebook was created.

[ ]:

C. Upload datasets to Amazon S3

For Seller to read: Do not change bucket parameter value. Do not hardcode your S3 bucket name.

[ ]:
sagemaker_session = sage.Session()
bucket = sagemaker_session.default_bucket()

For Seller to update: Update prefix with a unique S3 prefix for your algorithm.

[ ]:
training_data = sagemaker_session.upload_data(
    test_dataset, bucket=bucket, key_prefix="<For Seller to update:S3 Prefix>"
test_data = sagemaker_session.upload_data(
    test_dataset, bucket=bucket, key_prefix="<For Seller to update:S3 Prefix>"

3: Train a machine learning model

Now that dataset is available in an accessible Amazon S3 bucket, we are ready to train a machine learning model.

For Seller to update: Initialize required variables in following cell.

[ ]:
role = get_execution_role()

For Seller to update: update algorithm sepcific unique prefix in following cell.

[ ]:
output_location = "s3://{}/<For seller to Update:Update a unique prefix>/{}".format(
    bucket, "output"

For Seller to update: Update following cell with appropriate hyperparameter values to be passed to the training job

You can also find more information about dataset format in Hyperparameters section of For Seller to update:Title_of_your_product.

[ ]:
# Define hyperparameters
hyperparameters = {}

For Seller to update: Update appropriate values in estimator definition and ensure that fit call works as expected.

For information on creating an Estimator object, see documentation

[ ]:
# Create an estimator object for running a training job
estimator = sage.algorithm.AlgorithmEstimator(
    base_job_name="<For Seller to update: Specify base job name>",
    train_instance_type="<For Seller to update: Specify an instance-type recommended for training>",
# Run the training job.{"training": training_dataset, "test": test_dataset})

See this blog-post for more information how to visualize metrics during the process. You can also open the training job from Amazon SageMaker console and monitor the metrics/logs in Monitor section.

Now you can deploy the model for performing real-time inference.

For seller to update: Update appropriate values in following cell.

[ ]:
model_name = "For Seller to update:<specify-model_or_endpoint-name>"

content_type = "For Seller to update:<specify_content_type_accepted_by_trained_model>"

real_time_inference_instance_type = (
    "For Seller to update:<Update recommended_real-time_inference instance_type>"
batch_transform_inference_instance_type = (
    "For Seller to update:<Update recommended_batch_transform_job_inference instance_type>"

A. Deploy trained model

[ ]:
predictor = estimator.deploy(
    1, real_time_inference_instance_type, serializer="<For seller to update>"

Once endpoint is created, you can perform real-time inference.

B. Create input payload

For Seller to update: Add code snippet that reads the input from ‘data/inference/input/real-time/’ directory and converts it into format expected by the endpoint in the following cell

[ ]:

For Seller to update: Ensure that the file_name variable points to the payload you created. Ensure that the output_file_name variable points to a file-name in which output of real-time inference needs to be stored

[ ]:

C. Perform real-time inference

For Seller to update: review/update file_name, output_file name, and custom attributes in the following AWS CLI example to perform a real-time inference using the payload file you created from 2.B

[ ]:
!aws sagemaker-runtime invoke-endpoint \
    --endpoint-name $predictor.endpoint \
    --body fileb://$file_name \
    --content-type $content_type \
    --region $sagemaker_session.boto_region_name \

D. Visualize output

For Seller to update: Write code in the following cell to display the output generated by real-time inference. This output must match with output available in data/inference/output/real-time folder.

[ ]:

E. Calculate relevant metrics

For seller to update: write code to calculate metrics such as accuracy or any other metrics relevant to the business problem, using the test dataset. This is highly recommended if your algorithm does not support and calculate metrics on test channel. For information on how to configure metrics for your algorithm, see Step 4 of this blog post.

[ ]:

If Amazon SageMaker Model Monitor supports the type of problem you are trying to solve using this algorithm, use the following examples to add Model Monitor support to your product: For sample code to enable and monitor the model, see following notebooks: 1. Enable Amazon SageMaker Model Monitor 2. Amazon SageMaker Model Monitor - visualizing monitoring results

F. Delete the endpoint

Now that you have successfully performed a real-time inference, you do not need the endpoint any more. you can terminate the same to avoid being charged.

[ ]:
predictor = sage.RealTimePredictor(model_name, sagemaker_session, content_type)

Since this is an experiment, you do not need to run a hyperparameter tuning job. However, if you would like to see how to tune a model trained using a third-party algorithm with Amazon SageMaker’s hyperparameter tuning functionality, you can run the optional tuning step.

5: Tune your model! (optional)

For Seller to update/read: It is important to provide hyperparameter tuning functionality as part of your algorithm. Users of algorithms range from new developers, to data scientists and ML practitioners. As an algorithm maker, you need to make your algorithm usable in production. To be able to do so, you need to give tools such as capability to tune a custom ML model using Amazon SageMaker Automatic Model Tuning(HPO) SDK. Enabling your algorithm for automatic model tuning functionality is really easy. You need to mark appropriate hyperparameters as Tunable=True and emit multiple metrics that customers can choose to tune an ML model on.

We recommend that you provide notes on how your customer can scale usage of your algorithm for really large datasets.

You are strongly recommended to provide this section with tuning guidelines and code for running an automatic tuning job.

For information about Automatic model tuning, see Perform Automatic Model Tuning

A. Tuning Guidelines

For Seller to update: Provide guidelines on how customer can tune their ML model effectively using your algorithm in following cell. Provide details such as which parameter can be tuned for best results.

B. Define Tuning configuration

For seller to update: Provide a recommended hyperparameter range configuration in the following cell. This configuration would be used for running an HPO job. For More information, see Define Hyperparameters

[ ]:
hyperparameter_ranges = {}

For seller to update: As part of your algorithm, provide multiple objective metrics so that customer can choose a metric for tuning a custom ML model. Update the following variable with a most suitable/popular metric that your algorithm emits. For more information, see Define Metrics

[ ]:
objective_metric_name = (
    "<For seller to update : Provide an appropriate objective metric emitted by the algorithm>"

For seller to update: Specify whether to maximize or minimize the objective metric, in following cell.

[ ]:
tuning_direction = "<For seller to update: Provide tuning direction for objective metric specified>"

C. Run a model tuning job

For seller to update: Review/update the tuner configuration including but not limited to base_tuning_job_name, max_jobs, and max_parallel_jobs.

[ ]:
tuner = HyperparameterTuner(
    base_tuning_job_name="<For Seller to update: Specify base job name>",

For seller to update: Uncomment following lines, specify appropriate channels, and run the tuner to test it out.

[ ]:
# Uncomment following two lines to run Hyperparameter optimization job.
#{'training':  data})
# tuner.wait()

For seller to update: Once you have tested the code written in the preceding cell, comment three lines in the preceding cell so that customers who choose to simply run entire notebook do not end up triggering a tuning job.

Once you have completed a tuning job, (or even while the job is still running) you can clone and use this notebook to analyze the results to understand how each hyperparameter effects the quality of the model.

6. Perform Batch inference

In this section, you will perform batch inference using multiple input payloads together.

[ ]:
# upload the batch-transform job input files to S3
transform_input_folder = "data/inference/input/batch"
transform_input = sagemaker_session.upload_data(transform_input_folder, key_prefix=model_name)
print("Transform input uploaded to " + transform_input)
[ ]:
# Run the batch-transform job
transformer = model.transformer(1, batch_transform_inference_instance_type)
transformer.transform(transform_input, content_type=content_type)
[ ]:
# output is available on following path

For Seller to update: Add code that displays output generated by the batch transform job available in S3. This output must match the output available in data/inference/output/batch folder.

7. Clean-up

A. Delete the model

[ ]:

B. Unsubscribe to the listing (optional)

If you would like to unsubscribe to the algorithm, follow these steps. Before you cancel the subscription, ensure that you do not have any deployable model created from the model package or using the algorithm. Note - You can find this information by looking at the container name associated with the model.

Steps to unsubscribe to product from AWS Marketplace: 1. Navigate to Machine Learning tab on **Your Software subscriptions page** 2. Locate the listing that you want to cancel the subscription for, and then choose Cancel Subscription to cancel the subscription.