InputConfig - Amazon SageMaker Service
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InputConfig

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.

Contents

DataInputConfig

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, {"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: 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.

Type: String

Length Constraints: Minimum length of 1. Maximum length of 1024.

Pattern: [\S\s]+

Required: Yes

Framework

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

Type: String

Valid Values: TENSORFLOW | KERAS | MXNET | ONNX | PYTORCH | XGBOOST | TFLITE

Required: Yes

S3Uri

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).

Type: String

Length Constraints: Maximum length of 1024.

Pattern: ^(https|s3)://([^/]+)/?(.*)$

Required: Yes

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: