SageMaker

Looking for usage documentation? Check out Classification, Regression and KV cache.

`sagemaker.TabPFNClassifier`

TabPFN classifier backed by a SageMaker real-time endpoint.

sagemaker.TabPFNClassifier(
    endpoint_name: str,
    region_name: Optional[str] = None,
    boto_session: Optional[Any] = None,
    model_path: str = "auto",
    n_estimators: int = 8,
    softmax_temperature: float = 0.9,
    balance_probabilities: bool = False,
    average_before_softmax: bool = False,
    ignore_pretraining_limits: bool = True,
    inference_precision: Literal["autocast", "auto"] = "auto",
    random_state: Optional[int] = 0,
    inference_config: Optional[Dict[str, Any]] = None,
    paper_version: bool = False,
    thinking_mode: bool = False,
    thinking_effort: Optional[ThinkingEffort] = None,
    thinking_timeout_s: Optional[float] = None,
    thinking_metric: Optional[str] = None,
    use_kv_cache: bool = False,
    use_async: bool = False,
    s3_bucket: Optional[str] = None,
    s3_prefix: str = "async-io",
    async_poll_interval_s: float = 2.0,
    async_timeout_s: float = 60 * 60,
    invocation_timeout_s: Optional[int] = None,
)

Parameters

Parameter	Type	Default	Description
`endpoint_name`	`str`	Required	—
`region_name`	`Optional[str]`	`None`	—
`boto_session`	`Optional[Any]`	`None`	—
`model_path`	`str`	`"auto"`	—
`n_estimators`	`int`	`8`	—
`softmax_temperature`	`float`	`0.9`	—
`balance_probabilities`	`bool`	`False`	—
`average_before_softmax`	`bool`	`False`	—
`ignore_pretraining_limits`	`bool`	`True`	—
`inference_precision`	`Literal[“autocast”, “auto”]`	`"auto"`	—
`random_state`	`Optional[int]`	`0`	—
`inference_config`	`Optional[Dict[str, Any]]` `InferenceConfig` options	`None`	—
`paper_version`	`bool`	`False`	—
`thinking_mode`	`bool`	`False`	—
`thinking_effort`	`Optional[ThinkingEffort]`	`None`	—
`thinking_timeout_s`	`Optional[float]`	`None`	—
`thinking_metric`	`Optional[str]`	`None`	—
`use_kv_cache`	`bool`	`False`	—
`use_async`	`bool`	`False`	—
`s3_bucket`	`Optional[str]`	`None`	—
`s3_prefix`	`str`	`"async-io"`	—
`async_poll_interval_s`	`float`	`2.0`	—
`async_timeout_s`	`float`	`60 * 60`	—
`invocation_timeout_s`	`Optional[int]`	`None`	—

Example

from tabpfn_client.sagemaker import TabPFNClassifier
clf = TabPFNClassifier(
    endpoint_name="tabpfn-sm-alpha-v3-thinking-001",
    region_name="us-east-1",
)
clf.fit(X_train, y_train)
clf.predict(X_test)
clf.predict_proba(X_test)

`sagemaker.TabPFNClassifier.fit`

View source

Store the training data; the endpoint fits on the next predict* call.

sagemaker.TabPFNClassifier.fit(
    X: Any,
    y: Any,
) -> "_SagemakerBase"

Parameters

Parameter	Type	Default	Description
`X`	`Any`	Required	Training features.
`y`	`Any`	Required	Training targets.

Returns

Type	Description
`_SagemakerBase`	The estimator itself.

Raises ValueError If X and y have different numbers of samples.

`sagemaker.TabPFNClassifier.predict`

View source

Predict class labels for X.

sagemaker.TabPFNClassifier.predict(
    X: Any,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`X`	`Any`	Required	—

Returns

Type	Description
`np.ndarray`	—

`sagemaker.TabPFNClassifier.predict_proba`

View source

Predict class probabilities for X.

sagemaker.TabPFNClassifier.predict_proba(
    X: Any,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`X`	`Any`	Required	—

Returns

Type	Description
`np.ndarray`	—

`sagemaker.TabPFNRegressor`

View source

TabPFN regressor backed by a SageMaker endpoint.

sagemaker.TabPFNRegressor(
    endpoint_name: str,
    region_name: Optional[str] = None,
    boto_session: Optional[Any] = None,
    model_path: str = "auto",
    n_estimators: int = 8,
    softmax_temperature: float = 0.9,
    balance_probabilities: bool = False,
    average_before_softmax: bool = False,
    ignore_pretraining_limits: bool = True,
    inference_precision: Literal["autocast", "auto"] = "auto",
    random_state: Optional[int] = 0,
    inference_config: Optional[Dict[str, Any]] = None,
    paper_version: bool = False,
    thinking_mode: bool = False,
    thinking_effort: Optional[ThinkingEffort] = None,
    thinking_timeout_s: Optional[float] = None,
    thinking_metric: Optional[str] = None,
    use_kv_cache: bool = False,
    use_async: bool = False,
    s3_bucket: Optional[str] = None,
    s3_prefix: str = "async-io",
    async_poll_interval_s: float = 2.0,
    async_timeout_s: float = 60 * 60,
    invocation_timeout_s: Optional[int] = None,
)

Parameters

Parameter	Type	Default	Description
`endpoint_name`	`str`	Required	—
`region_name`	`Optional[str]`	`None`	—
`boto_session`	`Optional[Any]`	`None`	—
`model_path`	`str`	`"auto"`	—
`n_estimators`	`int`	`8`	—
`softmax_temperature`	`float`	`0.9`	—
`balance_probabilities`	`bool`	`False`	—
`average_before_softmax`	`bool`	`False`	—
`ignore_pretraining_limits`	`bool`	`True`	—
`inference_precision`	`Literal[“autocast”, “auto”]`	`"auto"`	—
`random_state`	`Optional[int]`	`0`	—
`inference_config`	`Optional[Dict[str, Any]]` `InferenceConfig` options	`None`	—
`paper_version`	`bool`	`False`	—
`thinking_mode`	`bool`	`False`	—
`thinking_effort`	`Optional[ThinkingEffort]`	`None`	—
`thinking_timeout_s`	`Optional[float]`	`None`	—
`thinking_metric`	`Optional[str]`	`None`	—
`use_kv_cache`	`bool`	`False`	—
`use_async`	`bool`	`False`	—
`s3_bucket`	`Optional[str]`	`None`	—
`s3_prefix`	`str`	`"async-io"`	—
`async_poll_interval_s`	`float`	`2.0`	—
`async_timeout_s`	`float`	`60 * 60`	—
`invocation_timeout_s`	`Optional[int]`	`None`	—

`sagemaker.TabPFNRegressor.fit`

View source

Store the training data; the endpoint fits on the next predict* call.

sagemaker.TabPFNRegressor.fit(
    X: Any,
    y: Any,
) -> "_SagemakerBase"

Parameters

Parameter	Type	Default	Description
`X`	`Any`	Required	Training features.
`y`	`Any`	Required	Training targets.

Returns

Type	Description
`_SagemakerBase`	The estimator itself.

Raises ValueError If X and y have different numbers of samples.

`sagemaker.TabPFNRegressor.predict`

View source

Predict regression targets for X.

sagemaker.TabPFNRegressor.predict(
    X: Any,
    output_type: str = "mean",
    quantiles: Optional[list] = None,
) -> np.ndarray

Parameters

Parameter	Type	Default	Description
`X`	`Any`	Required	Test features.
`output_type`	`str`	`"mean"`	Which prediction to return, e.g. “mean”, “median” or “quantiles”.
`quantiles`	`Optional[list]`	`None`	Quantile levels to compute when `output_type` is “quantiles”.

Returns

Type	Description
`np.ndarray`	The endpoint’s prediction as an array.

Scikit-learn configuration and scoring

The adapters inherit get_params, set_params, and the corresponding estimator mixin’s score method. They do not inherit the main hosted client’s save_model, load_model, or model-version selection methods.

Reference

tabpfn

tabpfn-client

tabpfn-extensions

REST API

`sagemaker.TabPFNClassifier`

`sagemaker.TabPFNClassifier.fit`

`sagemaker.TabPFNClassifier.predict`

`sagemaker.TabPFNClassifier.predict_proba`

`sagemaker.TabPFNRegressor`

`sagemaker.TabPFNRegressor.fit`

`sagemaker.TabPFNRegressor.predict`

Scikit-learn configuration and scoring

​sagemaker.TabPFNClassifier

​sagemaker.TabPFNClassifier.fit

​sagemaker.TabPFNClassifier.predict

​sagemaker.TabPFNClassifier.predict_proba

​sagemaker.TabPFNRegressor

​sagemaker.TabPFNRegressor.fit

​sagemaker.TabPFNRegressor.predict

​Scikit-learn configuration and scoring

`sagemaker.TabPFNClassifier`

`sagemaker.TabPFNClassifier.fit`

`sagemaker.TabPFNClassifier.predict`

`sagemaker.TabPFNClassifier.predict_proba`

`sagemaker.TabPFNRegressor`

`sagemaker.TabPFNRegressor.fit`

`sagemaker.TabPFNRegressor.predict`

Scikit-learn configuration and scoring