sagify

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

Why Sagify?

"Why should I use Sagify" you may ask.

We'll provide you with some examples of how Sagify can simplify and expedite your ML pipelines. You can train, tune and deploy a Machine Learning on the same day by using Sagify!

No More Configuring Cloud Instances for Training a Machine Learning Model

We have all been in a situation where a Machine Learning team is formed without planning for a team responsible to implement ML tools for ML Scientists. Here's a timeline of what is happening in this case:

1. All ML Scientists in the team have great ideas to solve problems using ML.
2. The team has built a feature engineering pipeline (This is painful, too, and it's in Sagify's roadmap).
3. You probably have downloaded on your laptop a subset of the features data set and there's evidence that your model will work.
4. The moment of experimentation comes and you want to run 10s of training jobs on the cloud using the entire features data set.
5. Everybody in the team has some idea on how to do it but nobody wants to do it :-).
6. ML Scientists should focus on ML and not on engineering tasks!
7. Just use Sagify! You just need to implement a train function!

No More Infrastructure Pain to Run Hyperparameter Jobs on the Cloud

Yet another similar situation to the one before but this time even more challenging! It's exactly the same story line.

Just use Sagify! Implement a train function and provide a path to json file that contains ranges for your hyperparameters!

No More Need to Hand Over Models to a Software Engineer to Deploy them

This is probably the most classic one! You need to run a batch prediction pipeline or deploy your model as a RESTful endpoint. Here's a real-world storyline:

1. The ML team somehow managed to train a fraud detection model and it's time to A/B test it against an existing heuristic.
2. The fraud detection backend team wants you to deploy the model behind a RESTful endpoint.
3. Nobody wants to do it in the ML team even if some people know how to do it.
4. Either way, tt's a waste of ML team's time to ask them to take care of the model deployment infrastructure.
5. The fraud detection backend team has other priorities and they have no time to help you in this task.
6. Just use Sagify! You just need to implement a predict function!

Installation

Prerequisites

sagify requires the following:

1. Python (3.7, 3.8)
2. Docker installed and running
3. Configured awscli

Install sagify

At the command line:

pip install sagify

Getting started - No code deployment

1. Create a file with name huggingface_config.json with the following content:

   {
     "transformers_version": "4.6.1",
     "pytorch_version": "1.7.1",
     "hub": {
       "HF_MODEL_ID": "gpt2",
       "HF_TASK": "text-generation"
     }
   }
   

2. Then, make sure to configure your AWS account by following the instructions at section Configure AWS Account

3. Finally, run the following command:

   sagify cloud lightning-deploy --framework huggingface -n 1 -e ml.c4.2xlarge --extra-config-file huggingface_config.json --aws-region us-east-1 --aws-profile sagemaker-dev
   

You can change the values for ec2 type (-e), aws region and aws profile with your preferred ones.

1. Once the Hugging Face model is deployed, you can go to https://console.aws.amazon.com/sagemaker/home?region=us-east-1#/endpoints (make sure you're on your preferred region) and find your deployed endpoint. For example:

1. Then, you can click on your deployed endpoint and copy the endpoint url. For example:

1. Postman is a good app to call the deployed endpoint. Here's an example on how to set up the AWS signature in order to call the endpoint:

1. Finally, you can call the endpoint from Postman:

Getting started - Custom Training and Deployment

Step 1: Clone Machine Learning demo repository

You're going to clone and train a Machine Learning codebase to train a classifier for the Iris data set.

Clone repository:

git clone https://github.com/Kenza-AI/sagify-demo.git

Create environment:

mkvirtualenv -p python3.7 sagify-demo

or

mkvirtualenv -p python3.8 sagify-demo

Don't forget to activate the virtualenv after the creation of environment by executing workon sagify-demo.

Install dependencies:

make requirements

Step 2: Initialize sagify

sagify init

Type in sagify-demo for SageMaker app name, N in question Are you starting a new project?, src for question Type in the directory where your code lives and make sure to choose your preferred Python version, AWS profile and region. Finally, type requirements.txt in question Type in the path to requirements.txt.

A module called sagify_base is created under the src directory. The structure is:

sagify_base/
    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
        prediction.py
        predictor.py
        serve
        wsgi.py
    training/
        __init__.py
        train
        training.py
    __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_base/local_test/test_dir/input/data/training/ to test that training works locally
2. Implement train(...) function in sagify_base/training/training.py
3. Implement predict(...) function in sagify_base/prediction/prediction.py
4. Optionally, specify hyperparameters in sagify_base/local_test/test_dir/input/config/hyperparameters.json

Hence,

1. Copy iris.data files from data to sagify_base/local_test/test_dir/input/data/training/

2. Replace the TODOs in the train(...) function in sagify_base/training/training.py file with:

       input_file_path = os.path.join(input_data_path, 'iris.data')
       clf, accuracy = training_logic(input_file_path=input_file_path)
   
       output_model_file_path = os.path.join(model_save_path, 'model.pkl')
       joblib.dump(clf, output_model_file_path)
   
       accuracy_report_file_path = os.path.join(model_save_path, 'report.txt')
       with open(accuracy_report_file_path, 'w') as _out:
           _out.write(str(accuracy))
   

   and at the top of the file, add:

   import os
   
   import joblib
   
   from iris_training import train as training_logic
   

3. Replace the body of predict(...) function in sagify_base/prediction/prediction.py with:

   model_input = json_input['features']
   prediction = ModelService.predict(model_input)
   
   return {
       "prediction": prediction.item()
   }
   

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

   if cls.model is None:
       import joblib
       cls.model = joblib.load(os.path.join(_MODEL_PATH, 'model.pkl'))
   return cls.model
   

Step 4: Build Docker image

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

sagify build

If you run docker images | grep sagify-demo in your terminal, you'll see the created Sagify-Demo image.

Step 5: Train model

Time to train the model for the Iris data set in the newly built Docker image:

sagify local train

Model file model.pkl and report file report.txt are now under sagify_base/local_test/test_dir/model/

Step 6: Deploy 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 '{
    "features":[[0.34, 0.45, 0.45, 0.3]]
}'

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 Machine 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 Machine 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:

• Click Next: Review on the following page:

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

• Click on the created role:

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

• Do the same to attach the AmazonS3FullAccess, IAMReadOnlyAccess, AmazonSQSFullAccess, AWSLambdaFullAccess, AmazonEC2ContainerRegistryFullAccess and AmazonSageMakerFullAccess policies, and end up with the following:

• 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 and attach the AmazonSageMakerFullAccess permission policy:

• Copy the ARN of that user:

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

• 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/region in an already initialized sagify module by changing the value of aws_profile/aws_region in .sagify.json.

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: sagify-demo

Upload Training Data

Execute sagify cloud upload-data -i data/ -s s3://sagify-demo/training-data to upload training data to S3

Train on AWS SageMaker

Execute sagify cloud train -i s3://sagify-demo/training-data/ -o s3://sagify-demo/output/ -e ml.m4.xlarge to train the Machine 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://sagify-demo/output/sagify-demo-2018-04-29-15-04-14-483/output/model.tar.gz)

Deploy on AWS SageMaker

Execute sagify cloud deploy -m s3://sagify-demo/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:

    {
        "features":[[0.34, 0.45, 0.45, 0.3]]
    }

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 train(...) function in sagify_base/training/training.py 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 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:

Monitor ML Models in Production

Superwise

Superwise provides organizations with the ability to streamline model observability and the monitoring process 🚀. The solution is platform-agnostic and provides an extensive freemium tier.

The steps below explain how you can integrate with Superwise to gain full visibility into your deployed models. Once you complete the integration, you can use the Superwise platform to define workflows that automatically monitor: data drift, performance degradation, data integrity, model activity, or any other customized monitoring use case.

Step 1: Create a Superwise Account

Go to Superwise and click the signup button to create an account. Using the free tier, you can monitor up to three models.

Step 2: Add your model

You can use the Superwise SDK to create the model. To use the SDK you should generate tokens.

Step 3: Initialize sagify

To initialize sagify, type the following command sagify init

Enter iris-model for the SageMaker app name, and answer y to the prompt asking Are you starting a new project?

Next, make sure to choose Python version 3 and the AWS profile and region you wish to use. Type requirements.txt in answer to the prompt Type in the path to requirements.txt.

A module called sagify_base is created under the src directory. The module’s structure is as follows:

sagify_base/
    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
        prediction.py
        predictor.py
        serve
        wsgi.py
    training/
        __init__.py
        train
        training.py
    __init__.py
    build.sh
    Dockerfile
    executor.sh
    push.sh

Step 4: Initialize the requirements.txt

Make sure the 'requirements.txt' at the root of the project has the following content:

    awscli
    flake8
    Flask
    joblib
    pandas
    s3transfer
    sagify>=0.18.0
    scikit-learn
    superwise

Step 5: Download the Iris data set

Download the Iris data set from https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data and save it in a file named "iris.data" under src/sagify_base/local_test/test_dir/input/data/training/.

Step 6: Implement the training logic

In the src/sagify_base/training/training.py file, replace the TODOs in the train(...) function with the following text:

input_file_path = os.path.join(input_data_path, 'iris.data')

df = pd.read_csv(
    input_file_path,
    header=None,
    names=['feature1', 'feature2', 'feature3', 'feature4', 'label']
)
df['date_time'] = pd.to_datetime('now')
df["id"] = df.apply(lambda _: uuid.uuid4(), axis=1)
df_train, df_test = train_test_split(df, test_size=0.3, random_state=42)

features_train_df = df_train[['feature1', 'feature2', 'feature3', 'feature4']]
labels_train_df = df_train[['label']]

features_train = features_train_df.values
labels_train = labels_train_df.values.ravel()

features_test_df = df_test[['feature1', 'feature2', 'feature3', 'feature4']]
labels_test_df = df_test[['label']]

features_test = features_test_df.values
labels_test = labels_test_df.values.ravel()

clf = SVC(gamma='auto', kernel="linear")
clf.fit(features_train, labels_train)

###### Report Testing Data ######
test_predictions = clf.predict(features_test)

accuracy = accuracy_score(labels_test, test_predictions)
output_model_file_path = os.path.join(model_save_path, 'model.pkl')
joblib.dump(clf, output_model_file_path)

accuracy_report_file_path = os.path.join(model_save_path, 'report.txt')
with open(accuracy_report_file_path, 'w') as _out:
    _out.write(str(accuracy))

## create superwise model
model = sw.model.create(Model(name=MODEL_NAME,
                              description="Iris Model Demo")
                        )
df["prediction"] = clf.predict(df[['feature1', 'feature2', 'feature3', 'feature4']])
## summarize the data, use infer_dtype for auto detect features
entities = sw.data_entity.summarise(
    data=df,
    entities_dtypes=infer_dtype(df),
    specific_roles={
        "date_time" : DataEntityRole.TIMESTAMP,
        "id" : DataEntityRole.ID,
        "label" : DataEntityRole.LABEL,
        "prediction" : DataEntityRole.PREDICTION_VALUE
    },
    importance=dict(zip(clf.coef_[0], ['feature1', 'feature2', 'feature3', 'feature4']))
)

## create and activate superwise version
version = Version(model_id=model.id, name="V1", data_entities=entities)
active_version = sw.version.create(version)
sw.version.activate(active_version.id)

And, at the top of the file, add the following:

import uuid

import joblib
import os

import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from superwise import Superwise
from superwise.models.model import Model
from superwise.models.version import Version
from superwise.resources.superwise_enums import DataEntityRole
from superwise.controller.infer import infer_dtype
MODEL_NAME = "Iris Model"

sw = Superwise(
    client_id="<my client_id>",
    secret="<my secret>",
)

Step 7: Implement the prediction logic

In the file src/sagify_base/prediction/prediction.py replace the body of the predict(...) function with the following:

model_input = json_input['features']
prediction = ModelService.predict(model_input)
model = ModelService.get_superwise_model(MODEL_NAME)

for m in model_input:
    records = {
        "date_time" : str(datetime.utcnow()),
        "id" : str(uuid.uuid4()),
        "prediction": prediction,
        "feature1": m[0],
        "feature2": m[1],
        "feature3": m[2],
        "feature4": m[3]
    }
    transaction_id = sw.transaction.log_records(
        model_id=model[0].id,
        version_id=model[0].active_version_id,
        records=records
    )
    print(f"Created transaction:  {transaction_id}")
return {
    "prediction": prediction.item()
}

Within the ModelService class in the same file, replace the body of the get_model() function with the following:

    if cls.model is None:
        cls.model = joblib.load(os.path.join(_MODEL_PATH, 'model.pkl'))
    return cls.model

Then, add a new function called get_superwise_model() to the ‘ModelService’ class, using the following:

@classmethod
def get_superwise_model(cls,model_name):
    """ Get superwise model using superwise SDK """
    return sw.model.get_by_name(model_name)

Now, add the following text to the top of the file:

from superwise import Superwise
import joblib
import os
import pandas as pd
from datetime import datetime
import uuid

sw = Superwise(
    client_id="<my client_id>",
    secret="<my secret>",
)
MODEL_NAME = "Iris Model"
_MODEL_PATH = os.path.join('/opt/ml/', 'model')  # Path where all your model(s) live in

Step 8: Build and train the ML model

If you’re ready to build and train the ML model, run the command sagify build and then run sagify local train

Step 9: Call the inference REST API

To use the REST API, run the command sagify local deploy

Once that’s done, call the inference endpoint by running the following curl command:

curl -X POST \
http://localhost:8080/invocations \
-H 'Cache-Control: no-cache' \
-H 'Content-Type: application/json' \
-d '{
    "features":[[0.34, 0.45, 0.45, 0.3]]
}'

You should now be able to see data coming in on the Superwise dashboards.

Aporia

In this part, you'll integrate with Aporia in order to monitor deployed ML models. More specifically, you'll monitor:

1. data drifting
2. model degradation
3. data integrity

Step 1: Create Aporia Account

Go to Aporia and create an account. There's a generous free tier.

Step 2: Create model at Aporia

Add your model to Aporia's console. Click the Add Model button in the Models page and name it "Iris Model"

Step 3: Initialize sagify

sagify init

Type in iris-model for SageMaker app name, y in question Are you starting a new project?, make sure to choose Python version 3 and your preferred AWS profile and region. Finally, type requirements.txt in question Type in the path to requirements.txt.

A module called sagify_base is created under the src directory. The structure is:

sagify_base/
    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
        prediction.py
        predictor.py
        serve
        wsgi.py
    training/
        __init__.py
        train
        training.py
    __init__.py
    build.sh
    Dockerfile
    executor.sh
    push.sh

Step 4: Initialize the requirements.txt

The requirements.txt at the root of the project must have the following content:

    awscli
    flake8
    Flask
    joblib
    pandas
    s3transfer
    sagify>=0.18.0
    scikit-learn
    aporia[all]

Step 5: Download Iris data set

Download the Iris data set from https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data and save it in a file named "iris.data" under src/sagify_base/local_test/test_dir/input/data/training/.

Step 6: Implement Training logic

Replace the TODOs in the train(...) function in src/sagify_base/training/training.py file with the following. Remember to use the model_id that Aporia gave you in step 2:

    input_file_path = os.path.join(input_data_path, 'iris.data')

    df = pd.read_csv(
        input_file_path,
        header=None,
        names=['feature1', 'feature2', 'feature3', 'feature4', 'label']
    )

    df_train, df_test = train_test_split(df, test_size=0.3, random_state=42)

    features_train_df = df_train[['feature1', 'feature2', 'feature3', 'feature4']]
    labels_train_df = df_train[['label']]

    features_train = features_train_df.values
    labels_train = labels_train_df.values.ravel()

    features_test_df = df_test[['feature1', 'feature2', 'feature3', 'feature4']]
    labels_test_df = df_test[['label']]

    features_test = features_test_df.values
    labels_test = labels_test_df.values.ravel()

    ###### Report Version Schema ######
    apr_model_version = 'v1'
    apr_model_type = 'multiclass'  # Select model type: "binary" or "regression"

    ###### Create a model version ######
    apr_model = aporia.create_model_version(
        model_id="REPLACE WITH YOUR APORIA MODEL ID",
        model_version=apr_model_version,
        model_type=apr_model_type,
        features=aporia.pandas.infer_schema_from_dataframe(features_train_df),
        predictions=aporia.pandas.infer_schema_from_dataframe(labels_train_df)
    )

    ###### Report Training Data ######
    apr_model.log_training_set(
        features=features_train_df,
        labels=labels_train_df
    )

    clf = SVC(gamma='auto')
    clf.fit(features_train, labels_train)

    ###### Report Testing Data ######
    test_predictions = clf.predict(features_test)
    apr_model.log_test_set(
        features=features_test_df,
        labels=labels_test_df,
        predictions=pd.DataFrame({'label': test_predictions})
    )

    accuracy = accuracy_score(labels_test, test_predictions)

    output_model_file_path = os.path.join(model_save_path, 'model.pkl')
    joblib.dump(clf, output_model_file_path)

    accuracy_report_file_path = os.path.join(model_save_path, 'report.txt')
    with open(accuracy_report_file_path, 'w') as _out:
        _out.write(str(accuracy))

and at the top of the file, add:

    import joblib
    import os

    import aporia
    import pandas as pd
    from sklearn.metrics import accuracy_score
    from sklearn.model_selection import train_test_split
    from sklearn.svm import SVC


    ###### Initiate Aporia ######
    aporia.init(token="TOKEN PROVIDED BY APORIA", environment="YOUR CHOSEN ENV VALUE")

Step 7: Implement Prediction logic

Replace the body of predict(...) function in src/sagify_base/prediction/prediction.py with. Remember to use the model_id that Aporia gave you in step 2:

    model_input = json_input['features']
    prediction = ModelService.predict(model_input)

    ###### Report Inference ######
    apr_prediction_id = str(uuid.uuid4())

    apr_model = aporia.Model("REPLACE WITH YOUR APORIA MODEL ID", "v1")
    apr_model.log_prediction(
        id=apr_prediction_id,
        features={
            'feature1': model_input[0][0],
            'feature2': model_input[0][1],
            'feature3': model_input[0][2],
            'feature4': model_input[0][3],
        },
        predictions={
            'label': prediction.item()
        },
    )

    apr_model.flush()

    return {
        "prediction": prediction.item()
    }

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

    if cls.model is None:
        from sklearn.externals import joblib
        cls.model = joblib.load(os.path.join(_MODEL_PATH, 'model.pkl'))
    return cls.model

and the top of the file must look like:

    import aporia
    import os
    import uuid


    ###### Initiate Aporia ######
    aporia.init(token="TOKEN PROVIDED BY APORIA", environment="YOUR CHOSEN ENV VALUE")

    _MODEL_PATH = os.path.join('/opt/ml/', 'model')  # Path where all your model(s) live in

Step 8: Build and Train the ML model

Run sagify build and after that sagify local train

Step 9: Call inference REST API

Run sagify local deploy and then run the following curl command to call the inference endpoint:

curl -X POST \
http://localhost:8080/invocations \
-H 'Cache-Control: no-cache' \
-H 'Content-Type: application/json' \
-d '{
    "features":[[0.34, 0.45, 0.45, 0.3]]
}'

Now you should be able to see data coming in on Aporia dashboards.

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. Currently supported versions: 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

Description

This command builds a Docker image from code under the directory sagify is installed in. A REQUIREMENTS_FILE needs to be specified during sagify init or later via sagify configure --requirements-dir for all required dependencies to be installed in the Docker image.

Example

sagify build

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_base/training/training.py 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_base/prediction/prediction.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] [--use-spot-instances FLAG_TO_USE_SPOT_INSTANCES]

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.

--use-spot-instances FLAG_TO_USE_SPOT_INSTANCES: Optional flag that specifies whether to use SageMaker Managed Spot instances for training. It should be used only for training jobs that take less than 1 hour. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html (default: False).

--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:

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] [--use-spot-instances FLAG_TO_USE_SPOT_INSTANCES]

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)

--use-spot-instances FLAG_TO_USE_SPOT_INSTANCES: Optional flag that specifies whether to use SageMaker Managed Spot instances for training. It should be used only for training jobs that take less than 1 hour. More information: https://docs.aws.amazon.com/sagemaker/latest/dg/model-managed-spot-training.html (default: False).

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-instances NUMBER_OF_EC2_INSTANCES --ec2-type EC2_TYPE [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID] [--endpoint-name ENDPOINT_NAME]

Description

This command retrieves a Docker image from AWS Elastic Container Service and executes it on AWS SageMaker in serve mode. You can update an endpoint (model or number of instances) by specifying the endpoint-name.

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

--endpoint-name ENDPOINT_NAME: Optional name for the SageMaker endpoint

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] [--wait WAIT_UNTIL_BATCH_TRANSFORM_JOB_IS_FINISHED] [--job-name JOB_NAME]

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

--wait WAIT_UNTIL_BATCH_TRANSFORM_JOB_IS_FINISHED or -w WAIT_UNTIL_BATCH_TRANSFORM_JOB_IS_FINISHED: Optional flag to wait until Batch Transform is finished. (default: don't wait)

--job-name JOB_NAME: Optional name for the SageMaker batch transform job

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

Cloud Create Streaming Inference

NOTE: THIS IS AN EXPERIMENTAL FEATURE

Make sure that the following 2 policies are attached to the role you created in section "Configure AWS Account":

Name

Creates streaming inference pipelines

Synopsis

sagify cloud create-streaming-inference --name WORKER_NAME --endpoint-name ENDPOINT_NAME --input-topic-name FEATURES_INPUT_TOPIC_NAME --output-topic-name PREDICTIONS_OUTPUT_TOPIC_NAME --type STREAMING_INFERENCE_TYPE

Description

This command creates a worker as a Lambda function that listens to features in the FEATURES_INPUT_TOPIC_NAME, calls the the endpoint ENDPOINT_NAME and, finally, forwards predictions to PREDICTIONS_OUTPUT_TOPIC_NAME.

Required Flags

--name WORKER_NAME: The name of the Lambda function

--endpoint-name ENDPOINT_NAME: The name of the endpoint of the deployed model

--input-topic-name FEATURES_INPUT_TOPIC_NAME: Topic name where features will be landed

--output-topic-name PREDICTIONS_OUTPUT_TOPIC_NAME: Topic name where model predictions will be forwarded

--type STREAMING_INFERENCE_TYPE: The type of streaming inference. At the moment, only SQS is supported!

Example

sagify cloud create-streaming-inference --name recommender-worker --endpoint-name my-recommender-endpoint-1 --input-topic-name features --output-topic-name model-predictions --type SQS

Cloud Delete Streaming Inference

NOTE: THIS IS AN EXPERIMENTAL FEATURE

Make sure that the following 2 policies are attached to the role you created in section "Configure AWS Account":

Name

Deletes streaming inference pipelines

Synopsis

sagify cloud delete-streaming-inference --name WORKER_NAME --input-topic-name FEATURES_INPUT_TOPIC_NAME --output-topic-name PREDICTIONS_OUTPUT_TOPIC_NAME --type STREAMING_INFERENCE_TYPE

Description

This command deletes the worker (i.e. Lambda function), input topic FEATURES_INPUT_TOPIC_NAME and output topic PREDICTIONS_OUTPUT_TOPIC_NAME.

Required Flags

--name WORKER_NAME: The name of the Lambda function

--input-topic-name FEATURES_INPUT_TOPIC_NAME: Topic name where features will be landed

--output-topic-name PREDICTIONS_OUTPUT_TOPIC_NAME: Topic name where model predictions will be forwarded

--type STREAMING_INFERENCE_TYPE: The type of streaming inference. At the moment, only SQS is supported!

Example

sagify cloud delete-streaming-inference --name recommender-worker --input-topic-name features --output-topic-name model-predictions --type SQS

Cloud Lightning Deploy

Name

Command for lightning deployment of pre-trained ML model(s) on AWS SageMaker without code

Synopsis

sagify cloud lightning-deploy --framework FRAMEWORK --num-instances NUMBER_OF_EC2_INSTANCES --ec2-type EC2_TYPE --aws-profile AWS_PROFILE --aws-region AWS_REGION --extra-config-file EXTRA_CONFIG_FILE [--model-server-workers MODEL_SERVER_WORKERS] [--s3-model-location S3_LOCATION_TO_MODEL_TAR_GZ] [--aws-tags TAGS] [--iam-role-arn IAM_ROLE] [--external-id EXTERNAL_ID] [--endpoint-name ENDPOINT_NAME]

Description

This command deploys a pre-trained ML model without code.

Required Flags

--framework FRAMEWORK: Name of the ML framework. Valid values: sklearn, huggingface, xgboost

--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/

--aws-profile AWS_PROFILE: The AWS profile to use for the lightning deploy command

--aws-region AWS_REGION: The AWS region to use for the lightning deploy command

--extra-config-file EXTRA_CONFIG_FILE: Json file with ML framework specific arguments

For SKLearn, you have to specify the framework_version in the EXTRA_CONFIG_FILE and specify the S3 location to model tar.gz (i.e. tar gzip your sklearn pickled file

Optional Flags

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

--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

--endpoint-name ENDPOINT_NAME: Optional name for the SageMaker endpoint

Example for SKLearn

Compress your pre-trained sklearn model to a GZIP tar archive with command !tar czvf model.tar.gz $your_sklearn_model_name.

sagify cloud lightning-deploy --framework sklearn -n 1 -e ml.c4.2xlarge --extra-config-file sklearn_config.json --aws-region us-east-1 --aws-profile sagemaker-dev -m s3://my-bucket/output/model.tar.gz

The sklearn_config.json must contain the following flag framework_version. Supported sklearn version(s): 0.20.0, 0.23-1.

Example of sklearn_config.json:

    {
      "framework_version": "0.23-1"
    }

Example for HuggingFace by specifying the S3_LOCATION_TO_MODEL_TAR_GZ

Compress your pre-trained HuggingFace model to a GZIP tar archive with command !tar czvf model.tar.gz $your_hg_model_name.

sagify cloud lightning-deploy --framework huggingface -n 1 -e ml.c4.2xlarge --extra-config-file huggingface_config.json --aws-region us-east-1 --aws-profile sagemaker-dev -m s3://my-bucket/output/model.tar.gz

The huggingface_config.json must contain the following flags pytorch_version or tensorflow_version (not both), and transformers_version. For more info: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model.

Example of huggingface_config.json:

    {
      "transformers_version": "4.6.1",
      "pytorch_version": "1.7.1"
    }

Example for HuggingFace without specifying the S3_LOCATION_TO_MODEL_TAR_GZ

sagify cloud lightning-deploy --framework huggingface -n 1 -e ml.c4.2xlarge --extra-config-file huggingface_config.json --aws-region us-east-1 --aws-profile sagemaker-dev

The huggingface_config.json must contain the following flags pytorch_version or tensorflow_version (not both), transformers_version and hub. For more info: https://sagemaker.readthedocs.io/en/stable/frameworks/huggingface/sagemaker.huggingface.html#hugging-face-model.

Example of huggingface_config.json:

    {
      "transformers_version": "4.6.1",
      "pytorch_version": "1.7.1",
      "hub": {
        "HF_MODEL_ID": "gpt2",
        "HF_TASK": "text-generation"
      }
    }

Example for XGBoost

Compress your pre-trained XGBoost model to a GZIP tar archive with command !tar czvf model.tar.gz $your_xgboost_model_name.

sagify cloud lightning-deploy --framework xgboost -n 1 -e ml.c4.2xlarge --extra-config-file xgboost_config.json --aws-region us-east-1 --aws-profile sagemaker-dev -m s3://my-bucket/output/model.tar.gz

The xgboost_config.json must contain the following flag framework_version. Supported xgboost version(s): 0.90-2, 1.0-1, and later.

Example of xgboost_config.json:

    {
      "framework_version": "0.23-1"
    }