sagify

Sagify

A command-line utility to train and deploy Machine Learning/Deep Learning models on AWS SageMaker in a few simple steps!

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Installation

Prerequisites

sagify requires the following:

  1. Python (2.7, 3.5, 3.6)
  2. Docker installed and running
  3. Configured awscli

Install sagify

At the command line:

pip install sagify

Getting started

Step 1: Clone Deep Learning Calculator repository

You're going to clone and train a Deep Learning codebase to evaluate arithmetic additions on up to 3 digit integers.

Clone repository:

git clone https://github.com/Kenza-AI/deep-learning-addition.git

Optionally, if you want to use Python 2.7 replace the value of REQUIRED_PYTHON and PYTHON_INTERPRETER in test_environment.py and Makefile, respectively, to python2.

Create environment:

make create_environment

Don't forget to activate the virtualenv after the creation of environment by executing workon deep-learning-addition.

Install dependencies:

make requirements

Generate training and validation data:

make data

Step 2: Initialize sagify

sagify init

Type in deep-learning-addition for SageMaker app name and make sure to choose your preferred Python version, AWS profile and region.

A module called sagify is created under the directory you provided. The structure is:

sagify/
    local_test/
        test_dir/
            input/
                config/
                    hyperparameters.json
                data/
                    training/
            model/
            output/
        deploy_local.sh
        train_local.sh
    prediction/
        __init__.py
        nginx.conf
        predict.py
        predictor.py
        serve
        wsgi.py
    training/
        __init__.py
        train
    __init__.py
    build.sh
    Dockerfile
    executor.sh
    push.sh

Step 3: Integrate sagify

As a Data Scientist, you only need to conduct a few actions. Sagify takes care of the rest:

  1. Copy a subset of training data under sagify/local_test/test_dir/input/data/training/ to test that training works locally
  2. Implement train(...) function in sagify/training/train
  3. Implement predict(...) function in sagify/prediction/predict.py
  4. Optionally, specify hyperparameters in sagify/local_test/test_dir/input/config/hyperparameters.json

Hence,

  1. Copy .npy files from data/processed/ to sagify/local_test/test_dir/input/data/training/

  2. Replace the TODOs in the try..except of train(...) function in sagify/training/train file with:

    train_model.train(input_path=input_data_path, output_path=model_save_path)
    

    and after the import traceback at the top of the file, add:

    from src.models import train_model
    

    The body of try..except should look like:

    try:
        train_model.train(input_path=input_data_path, output_path=model_save_path)
        print('Training complete.')
    except Exception as e:
    
  3. Replace the body of predict(...) function in sagify/prediction/predict.py with:

    def _format_addition(input_str):
        def _format(input_str_num, part_one):
            required_spaces_num = 3 - len(input_str_num)
            spaces = ''
            for _ in range(required_spaces_num):
                spaces += ' '
    
            return spaces + input_str_num if part_one else input_str_num + spaces
    
        two_parts = input_str.split('+')
        formatted_part_one = _format(two_parts[0], True)
        formatted_part_two = _format(two_parts[1], False)
    
        return '{}+{}'.format(formatted_part_one, formatted_part_two)
    
    addition_str = _format_addition(json_input['addition'])
    
    from src.encoding_utils import decode_prediction, encode_query
    input_model = encode_query(addition_str)
    
    prediction = ModelService.predict(input_model)
    
    result = {
        'result': decode_prediction(prediction)
    }
    
    return result
    

    and replace the body of get_model() function in ModelService class in the same file with:

    if cls.model is None:
        import keras
        cls.model = keras.models.load_model(os.path.join(_MODEL_PATH, 'model.h5'))
    return cls.model
    

Step 4: Build Docker image

It's time to build the Docker image that will contain the Deep Learning Addition codebase:

sagify build -r requirements.txt

The path to requirements.txt is necessary to be specified so that all the required dependencies are installed in Docker image.

If you run docker images | grep deep-learning-addition-img in your terminal, you'll see the created Deep Learning Addition image.

Step 5: Train Deep Learning model

Time to train the Deep Learning model in the newly built Docker image:

sagify local train

This step takes ~5 minutes in a MacBook Pro Early 2015 3.1 GHz Intel Core i7.

Step 6: Deploy Deep Learning model

Finally, serve the model as a REST Service:

sagify local deploy

Run the following curl command on your terminal to verify that the REST Service works:

curl -X POST \
http://localhost:8080/invocations \
-H 'Cache-Control: no-cache' \
-H 'Content-Type: application/json' \
-d '{
    "addition": "943+604"
 }'

It will be slow in the first couple of calls as it loads the model in a lazy manner.

Voila! That's a proof that this Deep Learning model is going to be trained and deployed on AWS SageMaker successfully. Now, go to the Usage section in Sagify Docs to see how to train and deploy this Deep Learning model to AWS SageMaker!

Usage

Configure AWS Account

  • Sign in to the AWS Management Console as an IAM user and open the IAM console at https://console.aws.amazon.com/iam/
  • Select Roles from the list in the left-hand side, and click on Create role
  • Then, select SageMaker as the image shows:

Create Role 1st Step

  • Click Next: Review on the following page:

Create Role 2nd Step

  • Type a name for the SageMaker role, and click on Create role:

Create Role 3rd Step

  • Click on the created role:

Successful Role Creation

  • Click on Attach policy and search for AmazonEC2ContainerRegistryFullAccess. Attach the corresponding policy:

Attach Policy

  • Do the same to attach the AmazonS3FullAccess and IAMReadOnlyAccess policies, and end up with the following:

Policies

  • Now, go to Users page by clicking on Users on the left-hand side.

  • Click on your IAM user that you want to use for AWS SageMaker:

Users

  • Copy the ARN of that user:

ARN

  • Then, go back the page of the Role you created and click on the Trust relationships tab:

Trust Relationship

  • Click on Edit trust relationship and add the following:

    {
        "Version": "2012-10-17",
        "Statement": [
            {
                "Sid": "",
                "Effect": "Allow",
                "Principal": {
                    "AWS": "PASTE_THE_ARN_YOU_COPIED_EARLIER",
                    "Service": "sagemaker.amazonaws.com"
                },
                "Action": "sts:AssumeRole"
            }
        ]
    }
    
  • You're almost there! Make sure that you have added the IAM user in your ~/.aws/credentials file. For example:

    [test-sagemaker]
    aws_access_key_id = ...
    aws_secret_access_key = ...
    
  • And, finally, add the following in the ~/.aws/config file:

    [profile test-sagemaker]
    region = us-east-1 <-- USE YOUR PREFERRED REGION
    role_arn = COPY_PASTE_THE_ARN_OF_THE_CREATED_ROLE_NOT_USER! for example: arn:aws:iam::...:role/TestSageMakerRole
    source_profile = test-sagemaker
    
  • That's it! From now on, choose the created AWS profile when initializing sagify.

  • You can change the AWS profile in an already initialized sagify module by changing the value of aws_profile and profile in sagify/config.json and sagify/push.sh, respectively.

Push Docker Image to AWS ECS

If you have followed all the steps of Getting Started, run sagify push src to push the Docker image to AWS ECS. This step may take some time depending on your internet connection upload speed.

Create S3 Bucket

Make sure to create an S3 bucket with a name of your choice, for example: my-dl-addition

Upload Training Data

Execute sagify cloud upload-data -i data/processed/ -s s3://my-dl-addition/training-data to upload training data to S3

Train on AWS SageMaker

Execute sagify cloud train -i s3://my-dl-addition/training-data/ -o s3://my-dl-addition/output/ -e ml.m4.xlarge to train the Deep Learning model on SageMaker. This command will use the pushed Docker image.

Copy the displayed Model S3 location after the command is executed (example: s3://my-dl-addition/output/deep-learning-addition-img-2018-04-29-15-04-14-483/output/model.tar.gz)

Deploy on AWS SageMaker

Execute sagify cloud deploy -m s3://my-dl-addition/output/.../output/model.tar.gz -n 3 -e ml.m4.xlarge to deploy the model on SageMaker.

Call SageMaker REST Endpoint

Find the endpoint URL under Endpoints in AWS SageMaker service on AWS console. Please, refer to https://docs.aws.amazon.com/apigateway/latest/developerguide/how-to-use-postman-to-call-api.html on how to call it from Postman as authorization is required.

Remember that it's a POST HTTP request with Content-Type application/json, and the request JSON body is of the form:

    {
        "addition": "112+143"
    }

Hyperparameter Optimization

Given that you have configured your AWS Account as described in the previous section, you're now ready to perform Bayesian Hyperparameter Optimization on AWS SageMaker! The process is similar to training step.

Step 1: Define Hyperparameter Configuration File

Define the Hyperparameter Configuration File. More specifically, you need to specify in a local JSON file the ranges for the hyperparameters, the name of the objective metric and its type (i.e. Maximize or Minimize). For example:

{
    "ParameterRanges": {
        "CategoricalParameterRanges": [
            {
                "Name": "kernel",
                "Values": ["linear", "rbf"]
            }
        ],
        "ContinuousParameterRanges": [
        {
          "MinValue": 0.001,
          "MaxValue": 10,
          "Name": "gamma"
        }
        ],
        "IntegerParameterRanges": [
            {
                "Name": "C",
                "MinValue": 1,
                "MaxValue": 10
            }
        ]
    },
    "ObjectiveMetric": {
        "Name": "Precision",
        "Type": "Maximize"
    }
}

Step 2: Implement Train function

Replace the TODOs in the try..except of train(...) function in sagify/training/train file with your logic. For example:

    from sklearn import datasets
    iris = datasets.load_iris()

    # Read the hyperparameter config json file
    import json
    with open(hyperparams_path) as _in_file:
        hyperparams_dict = json.load(_in_file)

    from sklearn import svm
    clf = svm.SVC(
        gamma=float(hyperparams_dict['gamma']),  # Values will be read as strings, so make sure to convert them to the right data type
        C=float(hyperparams_dict['C']),
        kernel=hyperparams_dict['kernel']
    )

    from sklearn.model_selection import train_test_split
    X_train, X_test, y_train, y_test = train_test_split(
        iris.data, iris.target, test_size=0.3, random_state=42)

    clf.fit(X_train, y_train)

    from sklearn.metrics import precision_score

    predictions = clf.predict(X_test)

    precision = precision_score(y_test, predictions, average='weighted')

    # Log the objective metric name with its calculated value. In tis example is Precision.
    # The objective name should be exactly the same with the one specified in the hyperparams congig json file.
    # The value must be a numeric (float or int).
    from sagify.api.hyperparameter_tuning import log_metric
    name = "Precision"
    log_metric(name, precision)

    from joblib import dump
    dump(clf, os.path.join(model_save_path, 'model.pkl'))

    print('Training complete.')

Step 3: Build and Push Docker image

  1. sagify build -r requirements.txt Make sure sagify is in your requirements.txt file.
  2. sagify push

Step 4: Call The CLI Command

And, finally, call the hyperparameter-optimization CLI command. For example:

 sagify cloud hyperparameter_optimization -i s3://my-bucket/training-data/ -o s3://my-bucket/output/ -e ml.m4.xlarge -h local/path/to/hyperparam_ranges.json

Step 5: Monitor Progress

You can monitor the progress via the SageMaker UI console. Here is an example of a finished Hyperparameter Optimization job:

Hyperparameter Optimization Results

Commands

Initialize

Name

Initializes a sagify module

Synopsis

sagify init

Description

This command initializes a sagify module in the directory you provide when asked after you invoke the init command.

Example

sagify init

Configure

Description

Updates an existing configuration value e.g. python version or AWS region.

Synopsis

sagify configure [--aws-region AWS_REGION] [--aws-profile AWS_PROFILE] [--image-name IMAGE_NAME] [--python-version PYTHON_VERSION]

Optional Flags

--aws-region AWS_REGION: AWS region where Docker images are pushed and SageMaker operations (train, deploy) are performed.

--aws-profile AWS_PROFILE: AWS profile to use when interacting with AWS.

--image-name IMAGE_NAME: Docker image name used when building for use with SageMaker. This shows up as an AWS ECR repository on your AWS account.

--python-version PYTHON_VERSION: Python version used when building SageMaker's Docker images. Curently supported versions: 2.7 , 3.6.

Example

sagify configure --aws-region us-east-2 --aws-profile default --image-name sage-docker-image-name --python-version 3.6

Build

Name

Builds a Docker image

Synopsis

sagify build --requirements-dir REQUIREMENTS_FILE

Description

This command builds a Docker image from code under the directory sagify is installed in. A REQUIREMENTS_FILE needs to be specified in order to install all required dependencies in the Docker image.

Required Flags

--requirements-dir REQUIREMENTS_FILE or -r REQUIREMENTS_FILE: Path to REQUIREMENTS_FILE

Example

sagify build -r requirements.txt

Local Train

Name

Executes a Docker image in train mode

Synopsis

sagify local train

Description

This command executes a Docker image in train mode. More specifically, it executes the train(...) function in sagify/training/train inside an already built Docker image (see Build command section).

Example

sagify local train

Local Deploy

Name

Executes a Docker image in serve mode

Synopsis

sagify local deploy

Description

This command executes a Docker image in serve mode. More specifically, it runs a Flask REST app in Docker image and directs HTTP requests to /invocations endpoint. Then, the /invocations endpoint calls the predict(...) function in sagify/prediction/predict.py (see Build command section on how to build a Docker image).

Example

sagify local deploy

Push

Name

Pushes a Docker image to AWS Elastic Container Service

Synopsis

sagify push [--aws-profile PROFILE_NAME] [--aws-region AWS_REGION] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID]

Description

This command pushes an already built Docker image to AWS Elastic Container Service. Later on, AWS SageMaker will consume that image from AWS Elastic Container Service for train and serve mode.

Only one of iam-role-arn and aws_profile can be provided. external-id is ignored when no iam-role-arn is provided.

Optional Flags

--iam-role-arn IAM_ROLE or -i IAM_ROLE: AWS IAM role to use for pushing to ECR

--aws-region AWS_REGION or -r AWS_REGION: The AWS region to push the image to

--aws-profile PROFILE_NAME or -p PROFILE_NAME: AWS profile to use for pushing to ECR

--external-id EXTERNAL_ID or -e EXTERNAL_ID: Optional external id used when using an IAM role

Example

sagify push

Cloud Upload Data

Name

Uploads data to AWS S3

Synopsis

sagify cloud upload-data --input-dir LOCAL_INPUT_DATA_DIR --s3-dir S3_TARGET_DATA_LOCATION

Description

This command uploads content under LOCAL_INPUT_DATA_DIR to S3 under S3_TARGET_DATA_LOCATION

Required Flags

--input-dir LOCAL_INPUT_DATA_DIR or -i LOCAL_INPUT_DATA_DIR: Local input directory

--s3-dir S3_TARGET_DATA_LOCATION or -s S3_TARGET_DATA_LOCATION: S3 target location

Example

sagify cloud upload-data -i ./training_data/ -s s3://my-bucket/training-data/

Cloud Train

Name

Trains your ML/DL model using a Docker image on AWS SageMaker with input from S3

Synopsis

sagify cloud train --input-s3-dir INPUT_DATA_S3_LOCATION --output-s3-dir S3_LOCATION_TO_SAVE_OUTPUT --ec2-type EC2_TYPE [--hyperparams-file HYPERPARAMS_JSON_FILE] [--volume-size EBS_SIZE_IN_GB] [--time-out TIME_OUT_IN_SECS] [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID] [--base-job-name BASE_JOB_NAME] [--job-name JOB_NAME] [--metric-names COMMA_SEPARATED_METRIC_NAMES]

Description

This command retrieves a Docker image from AWS Elastic Container Service and executes it on AWS SageMaker in train mode

Required Flags

--input-s3-dir INPUT_DATA_S3_LOCATION or -i INPUT_DATA_S3_LOCATION: S3 location to input data

--output-s3-dir S3_LOCATION_TO_SAVE_OUTPUT or -o S3_LOCATION_TO_SAVE_OUTPUT: S3 location to save output (models, reports, etc). Make sure that the output bucket already exists. Any not existing key prefix will be created by sagify.

--ec2-type EC2_TYPE or -e EC2_TYPE: ec2 type. Refer to https://aws.amazon.com/sagemaker/pricing/instance-types/

Optional Flags

--hyperparams-file HYPERPARAMS_JSON_FILE or -h HYPERPARAMS_JSON_FILE: Path to hyperparams JSON file

--volume-size EBS_SIZE_IN_GB or -v EBS_SIZE_IN_GB: Size in GB of the EBS volume (default: 30)

--time-out TIME_OUT_IN_SECS or -s TIME_OUT_IN_SECS: Time-out in seconds (default: 24 * 60 * 60)

--aws-tags TAGS or -a TAGS: Tags for labeling a training job of the form tag1=value1;tag2=value2. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

--iam-role-arn IAM_ROLE or -r IAM_ROLE: AWS IAM role to use for training with SageMaker

--external-id EXTERNAL_ID or -x EXTERNAL_ID: Optional external id used when using an IAM role

--base-job-name BASE_JOB_NAME or -n BASE_JOB_NAME: Optional prefix for the SageMaker training job

--job-name JOB_NAME: Optional name for the SageMaker training job. NOTE: if a --base-job-name is passed along with this option, it will be ignored.

--metric-names COMMA_SEPARATED_METRIC_NAMES: Optional comma-separated metric names for tracking performance of training jobs. Example: Precision,Recall,AUC. Then, make sure you log these metric values using the log_metric function in the train function:

```
...
from sagify.api.hyperparameter_tuning import log_metric
log_metric("Precision:, precision)
log_metric("Accuracy", accuracy)
...
```

When the training jobs finishes, they will be stored in the CloudWatch algorithm metrics logs of the SageMaker training job:

Algorithm Metrics

Example

sagify cloud train -i s3://my-bucket/training-data/ -o s3://my-bucket/output/ -e ml.m4.xlarge -h local/path/to/hyperparams.json -v 60 -t 86400 --metric-names Accuracy,Precision

Cloud Hyperparameter Optimization

Name

Executes a Docker image in hyperparameter-optimization mode on AWS SageMaker

Synopsis

sagify cloud hyperparameter_optimization --input-s3-dir INPUT_DATA_S3_LOCATION --output-s3-dir S3_LOCATION_TO_SAVE_MULTIPLE_TRAINED_MODELS --ec2-type EC2_TYPE [--hyperparams-config-file HYPERPARAM_RANGES_JSON_FILE] [--max-jobs MAX_NUMBER_OF_TRAINING_JOBS] [--max-parallel-jobs MAX_NUMBER_OF_PARALLEL_TRAINING_JOBS] [--volume-size EBS_SIZE_IN_GB] [--time-out TIME_OUT_IN_SECS] [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID] [--base-job-name BASE_JOB_NAME] [--job-name JOB_NAME] [--wait WAIT_UNTIL_HYPERPARAM_JOB_IS_FINISHED]

Description

This command retrieves a Docker image from AWS Elastic Container Service and executes it on AWS SageMaker in hyperparameter-optimization mode

Required Flags

--input-s3-dir INPUT_DATA_S3_LOCATION or -i INPUT_DATA_S3_LOCATION: S3 location to input data

--output-s3-dir S3_LOCATION_TO_SAVE_OUTPUT or -o S3_LOCATION_TO_SAVE_OUTPUT: S3 location to save output (models, reports, etc). Make sure that the output bucket already exists. Any not existing key prefix will be created by sagify.

--ec2-type EC2_TYPE or -e EC2_TYPE: ec2 type. Refer to https://aws.amazon.com/sagemaker/pricing/instance-types/

--hyperparams-config-file HYPERPARAM_RANGES_JSON_FILE or -h HYPERPARAM_RANGES_JSON_FILE: Local path to hyperparameters configuration file. Example:

{
    "ParameterRanges": {
        "CategoricalParameterRanges": [
            {
                "Name": "kernel",
                "Values": ["linear", "rbf"]
            }
        ],
        "ContinuousParameterRanges": [
        {
          "MinValue": 0.001,
          "MaxValue": 10,
          "Name": "gamma"
        }
        ],
        "IntegerParameterRanges": [
            {
                "Name": "C",
                "MinValue": 1,
                "MaxValue": 10
            }
        ]
    },
    "ObjectiveMetric": {
        "Name": "Precision",
        "Type": "Maximize"
    }
}

Optional Flags

--max-jobs MAX_NUMBER_OF_TRAINING_JOBS or -m MAX_NUMBER_OF_TRAINING_JOBS: Maximum total number of training jobs to start for the hyperparameter tuning job (default: 3)

--max-parallel-jobs MAX_NUMBER_OF_PARALLEL_TRAINING_JOBS or -p MAX_NUMBER_OF_PARALLEL_TRAINING_JOBS: Maximum number of parallel training jobs to start (default: 1)

--volume-size EBS_SIZE_IN_GB or -v EBS_SIZE_IN_GB: Size in GB of the EBS volume (default: 30)

--time-out TIME_OUT_IN_SECS or -s TIME_OUT_IN_SECS: Time-out in seconds (default: 24 * 60 * 60)

--aws-tags TAGS or -a TAGS: Tags for labeling a training job of the form tag1=value1;tag2=value2. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

--iam-role-arn IAM_ROLE or -r IAM_ROLE: AWS IAM role to use for training with SageMaker

--external-id EXTERNAL_ID or -x EXTERNAL_ID: Optional external id used when using an IAM role

--base-job-name BASE_JOB_NAME or -n BASE_JOB_NAME: Optional prefix for the SageMaker training job

--job-name JOB_NAME: Optional name for the SageMaker training job. NOTE: if a --base-job-name is passed along with this option, it will be ignored.

--wait WAIT_UNTIL_HYPERPARAM_JOB_IS_FINISHED or -w WAIT_UNTIL_HYPERPARAM_JOB_IS_FINISHED: Optional flag to wait until Hyperparameter Tuning is finished. (default: don't wait)

Example

sagify cloud hyperparameter_optimization -i s3://my-bucket/training-data/ -o s3://my-bucket/output/ -e ml.m4.xlarge -h local/path/to/hyperparam_ranges.json -v 60 -t 86400

Cloud Deploy

Name

Executes a Docker image in serve mode on AWS SageMaker

Synopsis

sagify cloud deploy --s3-model-location S3_LOCATION_TO_MODEL_TAR_GZ --num-instance NUMBER_OF_EC2_INSTANCES --ec2-type EC2_TYPE [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID]

Description

This command retrieves a Docker image from AWS Elastic Container Service and executes it on AWS SageMaker in serve mode

Required Flags

--s3-model-location S3_LOCATION_TO_MODEL_TAR_GZ or -m S3_LOCATION_TO_MODEL_TAR_GZ: S3 location to to model tar.gz

--num-instances NUMBER_OF_EC2_INSTANCES or n NUMBER_OF_EC2_INSTANCES: Number of ec2 instances

--ec2-type EC2_TYPE or e EC2_TYPE: ec2 type. Refer to https://aws.amazon.com/sagemaker/pricing/instance-types/

Optional Flags

--aws-tags TAGS or -a TAGS: Tags for labeling a training job of the form tag1=value1;tag2=value2. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

--iam-role-arn IAM_ROLE or -r IAM_ROLE: AWS IAM role to use for deploying with SageMaker

--external-id EXTERNAL_ID or -x EXTERNAL_ID: Optional external id used when using an IAM role

Example

sagify cloud deploy -m s3://my-bucket/output/model.tar.gz -n 3 -e ml.m4.xlarge

Cloud Batch Transform

Name

Executes a Docker image in batch transform mode on AWS SageMaker, i.e. runs batch predictions on user defined S3 data

Synopsis

sagify cloud batch_transform --s3-model-location S3_LOCATION_TO_MODEL_TAR_GZ --s3-input-location S3_INPUT_LOCATION --s3-output-location S3_OUTPUT_LOCATION --num-instance NUMBER_OF_EC2_INSTANCES --ec2-type EC2_TYPE [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID]

Description

This command retrieves a Docker image from AWS Elastic Container Service and executes it on AWS SageMaker in batch transform mode, i.e. runs batch predictions on user defined S3 data. SageMaker will spin up REST container(s) and call it/them with input data(features) from a user defined S3 path.

Things to do: - You should implement the predict function that expects a JSON containing the required feature values. It's the same predict function used for deploying the model as a REST service. Example of a JSON:

{
    "features": [5.1,3.5,1.4,0.2]
}
  • The input S3 path should contain a file or multiple files where each line is a JSON, the same JSON format as the one expected in the predict function. Example of a file:
{"features": [5.1,3.5,1.4,0.2]}
{"features": [4.9,3.0,1.4,0.2]}
{"features": [4.7,3.2,1.3,0.2]}
{"features": [4.6,3.1,1.5,0.2]}

Required Flags

--s3-model-location S3_LOCATION_TO_MODEL_TAR_GZ or -m S3_LOCATION_TO_MODEL_TAR_GZ: S3 location to to model tar.gz

--s3-input-location S3_INPUT_LOCATION or -i S3_INPUT_LOCATION: s3 input data location

--s3-output-location S3_OUTPUT_LOCATION or -o S3_OUTPUT_LOCATION: s3 location to save predictions

--num-instances NUMBER_OF_EC2_INSTANCES or n NUMBER_OF_EC2_INSTANCES: Number of ec2 instances

--ec2-type EC2_TYPE or e EC2_TYPE: ec2 type. Refer to https://aws.amazon.com/sagemaker/pricing/instance-types/

Optional Flags

--aws-tags TAGS or -a TAGS: Tags for labeling a training job of the form tag1=value1;tag2=value2. For more, see https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.

--iam-role-arn IAM_ROLE or -r IAM_ROLE: AWS IAM role to use for deploying with SageMaker

--external-id EXTERNAL_ID or -x EXTERNAL_ID: Optional external id used when using an IAM role

Example

sagify cloud batch_transform -m s3://my-bucket/output/model.tar.gz -i s3://my-bucket/input_features -o s3://my-bucket/predictions -n 3 -e ml.m4.xlarge