Struct InputConfig

pub struct InputConfig {
    pub data_input_config: String,
    pub framework: String,
    pub s3_uri: String,
}

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

data_input_config: String

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$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, {"input1":[1,3,224,224]}

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

  • Examples for two inputs:

    • If using the console, {"input1": [1,3,224,224], "input2":[1,3,224,224]}

    • If using the CLI, {"input1": [1,3,224,224], "input2":[1,3,224,224]}

• MXNET/ONNX: 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.

framework: String

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

s3_uri: 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).

Trait Implementations

impl Clone for InputConfig

fn clone(&self) -> InputConfig

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for InputConfig

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

Formats the value using the given formatter.

impl Default for InputConfig

fn default() -> InputConfig

Returns the “default value” for a type.

impl<'de> Deserialize<'de> for InputConfig

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for InputConfig

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl Serialize for InputConfig

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for InputConfig