Amazon SageMaker

• TabPFN-3.5-Plus - Public, free for non-commercial use, recommended version.
• TabPFN-3-Plus - Public, free for non-commercial use.
• TabPFN-2.5 - Public, free for non-commercial use.

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TabPFN-3.5-Plus

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What’s bundled in the listing

• State-of-the-art classification and regression in a single forward pass — tops the public TabArena benchmark for classification and regression.
• Supports up to 1M training rows or 20,000 columns, with up to 6,000 columns recommended. Row and column limits trade off; see model and text preprocessing limits.
• Native handling of mixed feature types — numerical, categorical, and free-text (string-valued) columns — plus missing values, outliers, and uninformative columns. No bespoke preprocessing required.
• Native many-class classification (up to 160 classes).
• Thinking mode — additional inference-time compute for higher prediction quality — is also available via a separate enterprise listing (see below).

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Subscribe and deploy

1. Open the TabPFN-3.5-Plus Marketplace listing and click View Purchase Options → Subscribe.
2. Open the Amazon SageMaker AI console, switch to the AWS region where you want to deploy, and open Deployments & inference → Deployable models → Create model. Under Container definition, choose Use a model package subscription, and select TabPFN-3.5-Plus. Create the model.
3. Select the model just created, and choose Create endpoint. Pick an endpoint name, and create or pick an existing endpoint configuration. Optionally, when creating the endpoint configuration, we recommend configuring Async invocation config in the same step — this enables use of use_async=True in the Python SDK for larger payloads or longer-running requests (see below).
4. Under Variants, you can set the instance type (Actions → Edit). You must use a GPU instance such as ml.g5.xlarge.
5. After clicking Submit, SageMaker provisions the endpoint. The endpoint is ready when its status transitions to InService (typically 6–10 minutes).

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Using your endpoint

pip install --upgrade 'tabpfn-client[sagemaker]'

from tabpfn_client.sagemaker import TabPFNClassifier
# For regression, use:
# from tabpfn_client.sagemaker import TabPFNRegressor
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

clf = TabPFNClassifier(
    endpoint_name="your-tabpfn-3.5-plus-endpoint",
    region_name="us-east-1",
)
clf.fit(X_train, y_train)

y_pred = clf.predict(X_test)
proba = clf.predict_proba(X_test)

import boto3, json

rt = boto3.client("sagemaker-runtime", region_name="us-east-1")
resp = rt.invoke_endpoint(
    EndpointName="your-tabpfn-3.5-plus-endpoint",
    ContentType="application/json",
    Accept="application/json",
    Body=json.dumps({
        "task_config": {
            "task": "classification",
            "tabpfn_config": {"n_estimators": 4},
            "predict_params": {"output_type": "probas"},
        },
        "X_train": X_train.tolist(),
        "y_train": y_train.tolist(),
        "X_test":  X_test.tolist(),
    }),
)
out = json.loads(resp["Body"].read())
proba = out["prediction"]              # 2D list, shape (n_test, n_classes)
metadata = out["metadata"]             # echoed task / package_version / dataset shape

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Async inference for larger datasets

from tabpfn_client.sagemaker import TabPFNClassifier

clf = TabPFNClassifier(
    endpoint_name="your-tabpfn-3.5-plus-async-endpoint",
    region_name="us-east-1",
    use_async=True,
    s3_bucket="your-async-io-bucket",        # bucket the endpoint role can read+write
)
clf.fit(X_train, y_train)
proba = clf.predict_proba(X_test)

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Thinking mode

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Limitations

• Payload caps. Real-time invocations are capped by AWS at 6 MB payload / 60 s processing. Use async inference (see above) for anything bigger or slower.
• GPU-only. The model package only declares GPU instance types as supported. The SageMaker console default (ml.m4.xlarge, CPU) is rejected with a ValidationException; see the Marketplace listing’s Recommended instance types section for the GPU-only allowlist.

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TabPFN-2.5

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Subscribe and deploy

1. Open the TabPFN-2.5 Marketplace listing and click View Purchase Options → Subscribe.
2. In the SageMaker AI console, navigate to AWS Marketplace resources → AWS Marketplace subscriptions → TabPFN-2.5, then Actions → Create endpoint. TabPFN-2.5 requires at least one NVIDIA T4 or P4 GPU instance; see the listing’s Recommended instance types for the full list.

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Limitations

• application/json — a JSON-encoded request body.
• multipart/form-data — containing the dataset as Parquet files.