aws_sdk_sagemaker::types

Struct InputConfig

Source 
#[non_exhaustive]
pub struct InputConfig {
    pub s3_uri: Option<String>,
    pub data_input_config: Option<String>,
    pub framework: Option<Framework>,
    pub framework_version: Option<String>,
}
Expand description

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

Fields (Non-exhaustive)§

This struct is marked as non-exhaustive
Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.
§s3_uri: Option<String>

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

§data_input_config: Option<String>

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.

  • TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input":\[1,1024,1024,3\]}

      • If using the CLI, {\"input\":\[1,1024,1024,3\]}

    • Examples for two inputs:

      • If using the console, {"data1": \[1,28,28,1\], "data2":\[1,28,28,1\]}

      • If using the CLI, {\"data1\": \[1,28,28,1\], \"data2\":\[1,28,28,1\]}

  • KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input_1":\[1,3,224,224\]}

      • If using the CLI, {\"input_1\":\[1,3,224,224\]}

    • Examples for two inputs:

      • If using the console, {"input_1": \[1,3,224,224\], "input_2":\[1,3,224,224\]}

      • If using the CLI, {\"input_1\": \[1,3,224,224\], \"input_2\":\[1,3,224,224\]}

  • MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"data":\[1,3,1024,1024\]}

      • If using the CLI, {\"data\":\[1,3,1024,1024\]}

    • Examples for two inputs:

      • If using the console, {"var1": \[1,1,28,28\], "var2":\[1,1,28,28\]}

      • If using the CLI, {\"var1\": \[1,1,28,28\], \"var2\":\[1,1,28,28\]}

  • PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, {"input0":\[1,3,224,224\]}

      • If using the CLI, {\"input0\":\[1,3,224,224\]}

    • Example for one input in list format: \[\[1,3,224,224\]\]

    • Examples for two inputs in dictionary format:

      • If using the console, {"input0":\[1,3,224,224\], "input1":\[1,3,224,224\]}

      • If using the CLI, {\"input0\":\[1,3,224,224\], \"input1\":\[1,3,224,224\]}

    • Example for two inputs in list format: \[\[1,3,224,224\], \[1,3,224,224\]\]

  • XGBOOST: input data name and shape are not needed.

DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):

  • shape: Input shape, for example {"input_1": {"shape": \[1,224,224,3\]}}. In addition to static input shapes, CoreML converter supports Flexible input shapes:

    • Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": \["1..10", 224, 224, 3\]}}

    • Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": \[\[1, 224, 224, 3\], \[1, 160, 160, 3\]\]}}

  • default_shape: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": \["1..10", 224, 224, 3\], "default_shape": \[1, 224, 224, 3\]}}

  • type: Input type. Allowed values: Image and Tensor. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale.

  • bias: If the input type is an Image, you need to provide the bias vector.

  • scale: If the input type is an Image, you need to provide a scale factor.

CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:

  • Tensor type input:

    • "DataInputConfig": {"input_1": {"shape": \[\[1,224,224,3\], \[1,160,160,3\]\], "default_shape": \[1,224,224,3\]}}

  • Tensor type input without input name (PyTorch):

    • "DataInputConfig": \[{"shape": \[\[1,3,224,224\], \[1,3,160,160\]\], "default_shape": \[1,3,224,224\]}\]

  • Image type input:

    • "DataInputConfig": {"input_1": {"shape": \[\[1,224,224,3\], \[1,160,160,3\]\], "default_shape": \[1,224,224,3\], "type": "Image", "bias": \[-1,-1,-1\], "scale": 0.007843137255}}

    • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

  • Image type input without input name (PyTorch):

    • "DataInputConfig": \[{"shape": \[\[1,3,224,224\], \[1,3,160,160\]\], "default_shape": \[1,3,224,224\], "type": "Image", "bias": \[-1,-1,-1\], "scale": 0.007843137255}\]

    • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice.

  • For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def key. For example:

    • "DataInputConfig": {"inputs": \[1, 224, 224, 3\]}

    • "CompilerOptions": {"signature_def_key": "serving_custom"}

  • For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:

    • "DataInputConfig": {"input_tensor:0": \[1, 224, 224, 3\]}

    • "CompilerOptions": {"output_names": \["output_tensor:0"\]}

§framework: Option<Framework>

Identifies the framework in which the model was trained. For example: TENSORFLOW.

§framework_version: Option<String>

Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.

Implementations§

Source§

impl InputConfig

Source

pub fn s3_uri(&self) -> Option<&str>

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

Source

pub fn data_input_config(&self) -> Option<&str>

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are Framework specific.

  • TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input":\[1,1024,1024,3\]}

      • If using the CLI, {\"input\":\[1,1024,1024,3\]}

    • Examples for two inputs:

      • If using the console, {"data1": \[1,28,28,1\], "data2":\[1,28,28,1\]}

      • If using the CLI, {\"data1\": \[1,28,28,1\], \"data2\":\[1,28,28,1\]}

  • KERAS: You must specify the name and shape (NCHW format) of expected data inputs using a dictionary format for your trained model. Note that while Keras model artifacts should be uploaded in NHWC (channel-last) format, DataInputConfig should be specified in NCHW (channel-first) format. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"input_1":\[1,3,224,224\]}

      • If using the CLI, {\"input_1\":\[1,3,224,224\]}

    • Examples for two inputs:

      • If using the console, {"input_1": \[1,3,224,224\], "input_2":\[1,3,224,224\]}

      • If using the CLI, {\"input_1\": \[1,3,224,224\], \"input_2\":\[1,3,224,224\]}

  • MXNET/ONNX/DARKNET: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, {"data":\[1,3,1024,1024\]}

      • If using the CLI, {\"data\":\[1,3,1024,1024\]}

    • Examples for two inputs:

      • If using the console, {"var1": \[1,1,28,28\], "var2":\[1,1,28,28\]}

      • If using the CLI, {\"var1\": \[1,1,28,28\], \"var2\":\[1,1,28,28\]}

  • PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, {"input0":\[1,3,224,224\]}

      • If using the CLI, {\"input0\":\[1,3,224,224\]}

    • Example for one input in list format: \[\[1,3,224,224\]\]

    • Examples for two inputs in dictionary format:

      • If using the console, {"input0":\[1,3,224,224\], "input1":\[1,3,224,224\]}

      • If using the CLI, {\"input0\":\[1,3,224,224\], \"input1\":\[1,3,224,224\]}

    • Example for two inputs in list format: \[\[1,3,224,224\], \[1,3,224,224\]\]

  • XGBOOST: input data name and shape are not needed.

DataInputConfig supports the following parameters for CoreML TargetDevice (ML Model format):

  • shape: Input shape, for example {"input_1": {"shape": \[1,224,224,3\]}}. In addition to static input shapes, CoreML converter supports Flexible input shapes:

    • Range Dimension. You can use the Range Dimension feature if you know the input shape will be within some specific interval in that dimension, for example: {"input_1": {"shape": \["1..10", 224, 224, 3\]}}

    • Enumerated shapes. Sometimes, the models are trained to work only on a select set of inputs. You can enumerate all supported input shapes, for example: {"input_1": {"shape": \[\[1, 224, 224, 3\], \[1, 160, 160, 3\]\]}}

  • default_shape: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example {"input_1": {"shape": \["1..10", 224, 224, 3\], "default_shape": \[1, 224, 224, 3\]}}

  • type: Input type. Allowed values: Image and Tensor. By default, the converter generates an ML Model with inputs of type Tensor (MultiArray). User can set input type to be Image. Image input type requires additional input parameters such as bias and scale.

  • bias: If the input type is an Image, you need to provide the bias vector.

  • scale: If the input type is an Image, you need to provide a scale factor.

CoreML ClassifierConfig parameters can be specified using OutputConfig CompilerOptions. CoreML converter supports Tensorflow and PyTorch models. CoreML conversion examples:

  • Tensor type input:

    • "DataInputConfig": {"input_1": {"shape": \[\[1,224,224,3\], \[1,160,160,3\]\], "default_shape": \[1,224,224,3\]}}

  • Tensor type input without input name (PyTorch):

    • "DataInputConfig": \[{"shape": \[\[1,3,224,224\], \[1,3,160,160\]\], "default_shape": \[1,3,224,224\]}\]

  • Image type input:

    • "DataInputConfig": {"input_1": {"shape": \[\[1,224,224,3\], \[1,160,160,3\]\], "default_shape": \[1,224,224,3\], "type": "Image", "bias": \[-1,-1,-1\], "scale": 0.007843137255}}

    • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

  • Image type input without input name (PyTorch):

    • "DataInputConfig": \[{"shape": \[\[1,3,224,224\], \[1,3,160,160\]\], "default_shape": \[1,3,224,224\], "type": "Image", "bias": \[-1,-1,-1\], "scale": 0.007843137255}\]

    • "CompilerOptions": {"class_labels": "imagenet_labels_1000.txt"}

Depending on the model format, DataInputConfig requires the following parameters for ml_eia2 OutputConfig:TargetDevice.

  • For TensorFlow models saved in the SavedModel format, specify the input names from signature_def_key and the input model shapes for DataInputConfig. Specify the signature_def_key in OutputConfig:CompilerOptions if the model does not use TensorFlow's default signature def key. For example:

    • "DataInputConfig": {"inputs": \[1, 224, 224, 3\]}

    • "CompilerOptions": {"signature_def_key": "serving_custom"}

  • For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in DataInputConfig and the output tensor names for output_names in OutputConfig:CompilerOptions . For example:

    • "DataInputConfig": {"input_tensor:0": \[1, 224, 224, 3\]}

    • "CompilerOptions": {"output_names": \["output_tensor:0"\]}

Source

pub fn framework(&self) -> Option<&Framework>

Identifies the framework in which the model was trained. For example: TENSORFLOW.

Source

pub fn framework_version(&self) -> Option<&str>

Specifies the framework version to use. This API field is only supported for the MXNet, PyTorch, TensorFlow and TensorFlow Lite frameworks.

For information about framework versions supported for cloud targets and edge devices, see Cloud Supported Instance Types and Frameworks and Edge Supported Frameworks.

Source§

impl InputConfig

Source

pub fn builder() -> InputConfigBuilder

Creates a new builder-style object to manufacture InputConfig.

Trait Implementations§

Source§

impl Clone for InputConfig

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fn clone(&self) -> InputConfig

Returns a duplicate of the value. Read more
1.0.0 · Source§

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for InputConfig

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fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter. Read more
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impl PartialEq for InputConfig

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fn eq(&self, other: &InputConfig) -> bool

Tests for self and other values to be equal, and is used by ==.
1.0.0 · Source§

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.
Source§

impl StructuralPartialEq for InputConfig

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👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

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