> ## Documentation Index
> Fetch the complete documentation index at: https://docs.synthefy.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Amazon SageMaker
> Subscribe to Nori in AWS Marketplace, deploy one GPU endpoint, and call all three models with the Synthefy client.
Deploy Nori in your AWS account from one AWS Marketplace product. One endpoint
serves all three models; select the model in each client request.
## Included models
| `model` | Weights | Outputs | Memory policy |
| -------------------------- | --------------------------------- | ------------------------------------------ | ------------- |
| `nori-6m` | 6M | mean, median, quantiles, full distribution | supported |
| `nori-30m` | 30M | mean, median, quantiles, full distribution | supported |
| `nori-30m-thinking-medium` | 30M with a medium thinking budget | mean | not supported |
There is no primary or default model. Keep the same endpoint and change
`model=` to select another model.
## Before you deploy
* Your AWS account must be allowed to subscribe to Marketplace products.
* Request SageMaker real-time endpoint quota for `ml.g5.xlarge` in the region
where you will deploy Nori.
* Use an AWS role or profile that can create SageMaker models and endpoints.
## Deploy from AWS Marketplace
Open the Synthefy Nori listing in AWS Marketplace and choose **View purchase
options**.
Review the pricing and terms, then choose **Subscribe**.
**Option 1 — AWS Marketplace:** after subscribing, choose **Launch your
software**.
**Option 2 — SageMaker console:** open **Inference → Marketplace model
packages**, select the subscribed Synthefy Nori package version, choose
**Create model**, and select a SageMaker execution role.
From the Marketplace launch page, select **Amazon SageMaker AI console**,
choose your region and **Create real-time inference endpoint**, then choose
**Create endpoint**.
If you used option two, create the endpoint from the SageMaker model. Choose
`ml.g5.xlarge`, set the initial instance count, and wait until the endpoint
status is `InService`.
## Invoke the endpoint
Install the Synthefy client with AWS support:
```bash theme={null}
pip install "synthefy[aws]>=6.3.0"
```
Use the endpoint name from step three:
```python nori_sagemaker.py theme={null}
from synthefy import SynthefyNoriClient
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]]
with SynthefyNoriClient(
mode="sagemaker",
model="nori-30m",
endpoint_name="your-nori-endpoint",
region_name="us-east-1",
) as client:
predictions = client.predict(X_train, y_train, X_test)
print(predictions)
```
The client uses your configured AWS role or profile. To run another included
model, change `model=` and keep the same endpoint name.
For DataFrames, categorical encoding, output types, and retries, see the
[Python Client guide](/nori/client).
Requests are limited to 25 MB.
## Next steps
DataFrames, categorical encoding, retries, and distribution outputs.
Learn the in-context regression request and model sizes.