Struct rusoto_sagemaker::InputConfig
source · [−]pub struct InputConfig {
pub data_input_config: String,
pub framework: String,
pub framework_version: Option<String>,
pub s3_uri: 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
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/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
OutputConfig$TargetDevice (ML Model format):
-
shape
: Input shape, for example{"input1": {"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:
{"input1": {"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:
{"input1": {"shape": [[1, 224, 224, 3], [1, 160, 160, 3]]}}
-
-
defaultshape
: Default input shape. You can set a default shape during conversion for both Range Dimension and Enumerated Shapes. For example{"input1": {"shape": ["1..10", 224, 224, 3], "defaultshape": [1, 224, 224, 3]}}
-
type
: Input type. Allowed values:Image
andTensor
. 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 asbias
andscale
. -
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": {"input1": {"shape": [[1,224,224,3], [1,160,160,3]], "defaultshape": [1,224,224,3]}}
-
-
Tensor type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "defaultshape": [1,3,224,224]}]
-
-
Image type input:
-
"DataInputConfig": {"input1": {"shape": [[1,224,224,3], [1,160,160,3]], "defaultshape": [1,224,224,3], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}}
-
"CompilerOptions": {"classlabels": "imagenetlabels1000.txt"}
-
-
Image type input without input name (PyTorch):
-
"DataInputConfig": [{"shape": [[1,3,224,224], [1,3,160,160]], "defaultshape": [1,3,224,224], "type": "Image", "bias": [-1,-1,-1], "scale": 0.007843137255}]
-
"CompilerOptions": {"classlabels": "imagenetlabels1000.txt"}
-
Depending on the model format, DataInputConfig
requires the following parameters for mleia2
<a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/APIOutputConfig.html#sagemaker-Type-OutputConfig-TargetDevice">OutputConfig:TargetDevice.
-
For TensorFlow models saved in the SavedModel format, specify the input names from
signaturedefkey
and the input model shapes forDataInputConfig
. Specify thesignaturedefkey
inOutputConfig:CompilerOptions
if the model does not use TensorFlow's default signature def key. For example:-
"DataInputConfig": {"inputs": [1, 224, 224, 3]}
-
"CompilerOptions": {"signaturedefkey": "servingcustom"}
-
-
For TensorFlow models saved as a frozen graph, specify the input tensor names and shapes in
DataInputConfig
and the output tensor names foroutputnames
inOutputConfig:CompilerOptions
. For example:-
"DataInputConfig": {"inputtensor:0": [1, 224, 224, 3]}
-
"CompilerOptions": {"outputnames": ["output_tensor:0"]}
-
framework: String
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 PyTorch framework versions 1.4
, 1.5
, and 1.6
for cloud instance target devices: ml_c4
, ml_c5
, ml_m4
, ml_m5
, ml_p2
, ml_p3
, and ml_g4dn
.
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
sourceimpl Clone for InputConfig
impl Clone for InputConfig
sourcefn clone(&self) -> InputConfig
fn clone(&self) -> InputConfig
Returns a copy of the value. Read more
1.0.0 · sourcefn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from source
. Read more
sourceimpl Debug for InputConfig
impl Debug for InputConfig
sourceimpl Default for InputConfig
impl Default for InputConfig
sourcefn default() -> InputConfig
fn default() -> InputConfig
Returns the “default value” for a type. Read more
sourceimpl<'de> Deserialize<'de> for InputConfig
impl<'de> Deserialize<'de> for InputConfig
sourcefn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error> where
__D: Deserializer<'de>,
Deserialize this value from the given Serde deserializer. Read more
sourceimpl PartialEq<InputConfig> for InputConfig
impl PartialEq<InputConfig> for InputConfig
sourcefn eq(&self, other: &InputConfig) -> bool
fn eq(&self, other: &InputConfig) -> bool
This method tests for self
and other
values to be equal, and is used
by ==
. Read more
sourcefn ne(&self, other: &InputConfig) -> bool
fn ne(&self, other: &InputConfig) -> bool
This method tests for !=
.
sourceimpl Serialize for InputConfig
impl Serialize for InputConfig
impl StructuralPartialEq for InputConfig
Auto Trait Implementations
impl RefUnwindSafe for InputConfig
impl Send for InputConfig
impl Sync for InputConfig
impl Unpin for InputConfig
impl UnwindSafe for InputConfig
Blanket Implementations
sourceimpl<T> BorrowMut<T> for T where
T: ?Sized,
impl<T> BorrowMut<T> for T where
T: ?Sized,
const: unstable · sourcefn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
sourceimpl<T> Instrument for T
impl<T> Instrument for T
sourcefn instrument(self, span: Span) -> Instrumented<Self>
fn instrument(self, span: Span) -> Instrumented<Self>
sourcefn in_current_span(self) -> Instrumented<Self>
fn in_current_span(self) -> Instrumented<Self>
sourceimpl<T> ToOwned for T where
T: Clone,
impl<T> ToOwned for T where
T: Clone,
type Owned = T
type Owned = T
The resulting type after obtaining ownership.
sourcefn clone_into(&self, target: &mut T)
fn clone_into(&self, target: &mut T)
toowned_clone_into
)Uses borrowed data to replace owned data, usually by cloning. Read more
sourceimpl<T> WithSubscriber for T
impl<T> WithSubscriber for T
sourcefn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
fn with_subscriber<S>(self, subscriber: S) -> WithDispatch<Self> where
S: Into<Dispatch>,
Attaches the provided Subscriber
to this type, returning a
WithDispatch
wrapper. Read more
sourcefn with_current_subscriber(self) -> WithDispatch<Self>
fn with_current_subscriber(self) -> WithDispatch<Self>
Attaches the current default Subscriber
to this type, returning a
WithDispatch
wrapper. Read more