Amazon SageMaker - Synthefy Docs

Deploy Nori in your own AWS account from AWS Marketplace. Nori runs as an Amazon SageMaker endpoint, so your data never leaves your environment. It’s in-context: you pass labeled rows as examples and the rows you want scored, and it returns one prediction per row in a single forward pass — no training.

The model runs inside your account. Inputs and predictions stay in your AWS environment; nothing is sent to Synthefy.

Prerequisites

AWS account with SageMaker access

And permission to subscribe to products on AWS Marketplace.

A GPU instance

Nori deploys on GPU instances (e.g. ml.g5.xlarge). Make sure your account has quota for one.

Deploy

Subscribe on AWS Marketplace

Find the Synthefy-Nori listing on AWS Marketplace, choose View purchase options, and Subscribe. It’s free — you pay only for the AWS compute you run.

Create a model

In the SageMaker console, go to Inference → Marketplace model packages (or Create model). Under the container definition, choose Use a model package subscription and select Synthefy-Nori.

Create an endpoint

Select the model and choose Create endpoint. Pick a GPU instance (ml.g5.xlarge) and wait for the endpoint to reach InService (typically a few minutes).

Predict

Python (boto3)
AWS CLI

nori_predict.py

import boto3, json

rt = boto3.client("sagemaker-runtime", region_name="us-east-1")

X_train = [[0.0, 1.0], [1.0, 0.0], [0.5, 0.5], [0.2, 0.8]]
y_train = [0.1, 0.9, 0.5, 0.3]
X_test  = [[0.3, 0.7], [0.8, 0.2]]

resp = rt.invoke_endpoint(
    EndpointName="your-nori-endpoint",
    ContentType="application/json",
    Body=json.dumps({
        "task": "regression",
        "X_train": X_train,
        "y_train": y_train,
        "X_test": X_test,
    }),
)
out = json.loads(resp["Body"].read())
print(out["predictions"])   # [0.37, 0.75]

Invoke

cat > payload.json <<'EOF'
{"task":"regression","X_train":[[0.0,1.0],[1.0,0.0],[0.5,0.5],[0.2,0.8]],"y_train":[0.1,0.9,0.5,0.3],"X_test":[[0.3,0.7],[0.8,0.2]]}
EOF

aws sagemaker-runtime invoke-endpoint \
  --endpoint-name your-nori-endpoint \
  --content-type application/json \
  --body fileb://payload.json \
  out.json && cat out.json

Request / response

Contract

// request
{ "task": "regression", "X_train": [[...]], "y_train": [...], "X_test": [[...]] }

// response
{ "task": "regression", "predictions": [ ... ], "usage": { ... } }

predictions[i] corresponds to X_test[i], in the original target units.

Notes

Numeric features only. Encode categorical columns (one-hot or ordinal) before sending; the same columns and order for X_train and X_test.

Real-time limits. A single invocation is capped at 6 MB and 60 seconds. For large tables, run a batch transform job (or split X_test across requests).

Instances. Supported GPU instances: ml.g5.xlarge, ml.g5.2xlarge, ml.g6.xlarge.

​Deploy

​Predict

​Notes

Deploy

Predict

Notes