Paws::SageMaker::InputConfig
This class represents one of two things:
Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object.
As an example, if Att1 is expected to be a Paws::SageMaker::InputConfig object:
$service_obj->Method(Att1 => { DataInputConfig => $value, ..., S3Uri => $value });
Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::InputConfig object:
$result = $service_obj->Method(...); $result->Att1->DataInputConfig
Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$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.
TensorFlow
Examples for one input:
If using the console, {"input":[1,1024,1024,3]}
{"input":[1,1024,1024,3]}
If using the CLI, {\"input\":[1,1024,1024,3]}
{\"input\":[1,1024,1024,3]}
Examples for two inputs:
If using the console, {"data1": [1,28,28,1], "data2":[1,28,28,1]}
{"data1": [1,28,28,1], "data2":[1,28,28,1]}
If using the CLI, {\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
{\"data1\": [1,28,28,1], \"data2\":[1,28,28,1]}
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.
MXNET/ONNX
If using the console, {"data":[1,3,1024,1024]}
{"data":[1,3,1024,1024]}
If using the CLI, {\"data\":[1,3,1024,1024]}
{\"data\":[1,3,1024,1024]}
If using the console, {"var1": [1,1,28,28], "var2":[1,1,28,28]}
{"var1": [1,1,28,28], "var2":[1,1,28,28]}
If using the CLI, {\"var1\": [1,1,28,28], \"var2\":[1,1,28,28]}
{\"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.
PyTorch
Examples for one input in dictionary format:
If using the console, {"input0":[1,3,224,224]}
{"input0":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224]}
{\"input0\":[1,3,224,224]}
Example for one input in list format: [[1,3,224,224]]
[[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]}
{"input0":[1,3,224,224], "input1":[1,3,224,224]}
If using the CLI, {\"input0\":[1,3,224,224], \"input1\":[1,3,224,224]}
{\"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]]
[[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
XGBOOST
Identifies the framework in which the model was trained. For example: TENSORFLOW.
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).
This class forms part of Paws, describing an object used in Paws::SageMaker
The source code is located here: https://github.com/pplu/aws-sdk-perl
Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues
To install Paws::SDK::Config, copy and paste the appropriate command in to your terminal.
cpanm
cpanm Paws::SDK::Config
CPAN shell
perl -MCPAN -e shell install Paws::SDK::Config
For more information on module installation, please visit the detailed CPAN module installation guide.