管理模型 - Amazon SageMaker
加载模型卸载模型列出模型描述模型捕获数据获取捕获状态Predict
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本文属于机器翻译版本。若本译文内容与英语原文存在差异，则一律以英文原文为准。

管理模型

SageMaker Edge Manager 代理提供了模型管理 APIs 的列表，可在边缘设备上实施控制层面和数据层面 APIs。除了本文档之外，我们还建议您浏览示例客户端实现，这些实现说明了以下所述的 APIs 的规范用法。

文件作为发布项目的一部分提供（在发布 tarball 内）。proto在此文档中，我们列出并描述此 APIs 文件中列出的 proto 的使用情况。

注意

在 Windows 发行版上，这些 APIs 具有一对一映射，并且在 C# 中实现的应用程序的示例代码与适用于 Windows 的发行版构件共享。以下说明适用于将 代理作为独立进程运行，适用于 Linux 的发布项目。

根据您的操作系统提取存档。其中 VERSION 分为三个组件：<MAJOR_VERSION>.<YYYY-MM-DD>-<SHA-7>。 有关如何获取发行版本 （安装 Edge Manager 代理）、发行构件的时间戳 （<MAJOR_VERSION>） 和存储库提交 ID （<YYYY-MM-DD>） 的信息，请参阅SHA-7

Linux

可以使用以下命令提取 zip 存档：

tar -xvzf <VERSION>.tgz
Windows

可以使用 UI 或命令提取 zip 存档：

unzip <VERSION>.tgz

版本构件层次结构（提取 tar/zip 存档后）如下所示。代理 proto 文件位于 api/ 下。 

0.20201205.7ee4b0b
├── bin
│         ├── sagemaker_edge_agent_binary
│         └── sagemaker_edge_agent_client_example
└── docs
├── api
│         └── agent.proto
├── attributions
│         ├── agent.txt
│         └── core.txt
└── examples
└── ipc_example
├── CMakeLists.txt
├── sagemaker_edge_client.cc
├── sagemaker_edge_client_example.cc
├── sagemaker_edge_client.hh
├── sagemaker_edge.proto
├── README.md
├── shm.cc
├── shm.hh
└── street_small.bmp

加载模型

软件边缘设备支持一次加载一个模型，并对该模型调用推理。Edge Manager agent此 API 验证模型签名并将 EdgePackagingJob 操作生成的所有构件加载到内存中。此步骤要求安装所有必需的证书及其余代理二进制安装。如果无法验证模型的签名，此步骤将失败，并在日志中包含相应的返回代码和错误消息。 

// perform load for a model
// Note:
// 1. currently only local filesystem paths are supported for loading models.
// 2. currently only one model could be loaded at any time, loading of multiple
// models simultaneously shall be implemented in the future.
// 3. users are required to unload any loaded model to load another model.
// Status Codes:
// 1. OK - load is successful
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 4. NOT_FOUND - model doesn't exist at the url
// 5. ALREADY_EXISTS - model with the same name is already loaded
// 6. RESOURCE_EXHAUSTED - memory is not available to load the model
// 7. FAILED_PRECONDITION - model is not compiled for the machine.
//
rpc LoadModel(LoadModelRequest) returns (LoadModelResponse);
Input
//
// request for LoadModel rpc call
//
message LoadModelRequest {
  string url = 1;
  string name = 2;  // Model name needs to match regex "^[a-zA-Z0-9](-*[a-zA-Z0-9])*$"
}
Output
//
//
// response for LoadModel rpc call
//
message LoadModelResponse {
  Model model = 1;
}

//
// Model represents the metadata of a model
//  url - url representing the path of the model
//  name - name of model
//  input_tensor_metadatas - TensorMetadata array for the input tensors
//  output_tensor_metadatas - TensorMetadata array for the output tensors
//
// Note:
//  1. input and output tensor metadata could empty for dynamic models.
//
message Model {
  string url = 1;
  string name = 2;
  repeated TensorMetadata input_tensor_metadatas = 3;
  repeated TensorMetadata output_tensor_metadatas = 4;
}

卸载模型

卸载之前加载的模型。它通过 loadModel 期间提供的模型别名进行标识。 如果未找到别名或找不到加载模型，则返回错误。 

//
// perform unload for a model
// Status Codes:
// 1. OK - unload is successful
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 4. NOT_FOUND - model doesn't exist
//
rpc UnLoadModel(UnLoadModelRequest) returns (UnLoadModelResponse);
Input
//
// request for UnLoadModel rpc call
//
message UnLoadModelRequest {
 string name = 1; // Model name needs to match regex "^[a-zA-Z0-9](-*[a-zA-Z0-9])*$"
}
Output
//
// response for UnLoadModel rpc call
//
message UnLoadModelResponse {}

列出模型

列出所有加载的模型及其别名。

//
// lists the loaded models
// Status Codes:
// 1. OK - unload is successful
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
//
rpc ListModels(ListModelsRequest) returns (ListModelsResponse);
Input
//
// request for ListModels rpc call
//
message ListModelsRequest {}
Output
//
// response for ListModels rpc call
//
message ListModelsResponse {
 repeated Model models = 1;
}

描述模型

描述在代理上加载的模型。

//
// Status Codes:
// 1. OK - load is successful
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 4. NOT_FOUND - model doesn't exist at the url
//
rpc DescribeModel(DescribeModelRequest) returns (DescribeModelResponse);
Input
//
// request for DescribeModel rpc call
//
message DescribeModelRequest {
  string name = 1;
}
Output
//
// response for DescribeModel rpc call
//
message DescribeModelResponse {
  Model model = 1;
}

捕获数据

允许客户端应用程序在 Amazon S3 存储桶中捕获输入和输出张量，并（可选）捕获辅助张量。客户端应用程序应在每次调用此 API 时传递一个唯一的捕获 ID。这稍后可用于查询捕获的状态。

//
// allows users to capture input and output tensors along with auxiliary data.
// Status Codes:
// 1. OK - data capture successfully initiated
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 5. ALREADY_EXISTS - capture initiated for the given capture_id
// 6. RESOURCE_EXHAUSTED - buffer is full cannot accept any more requests.
// 7. OUT_OF_RANGE - timestamp is in the future.
// 8. INVALID_ARGUMENT - capture_id is not of expected format.
//
rpc CaptureData(CaptureDataRequest) returns (CaptureDataResponse);
Input
enum Encoding {
 CSV = 0;
 JSON = 1;
 NONE = 2;
 BASE64 = 3;
}

//
// AuxilaryData represents a payload of extra data to be capture along with inputs and outputs of inference
// encoding - supports the encoding of the data
// data - represents the data of shared memory, this could be passed in two ways:
// a. send across the raw bytes of the multi-dimensional tensor array
// b. send a SharedMemoryHandle which contains the posix shared memory segment id and
// offset in bytes to location of multi-dimensional tensor array.
//
message AuxilaryData {
 string name = 1;
 Encoding encoding = 2;
 oneof data {
 bytes byte_data = 3;
 SharedMemoryHandle shared_memory_handle = 4;
 }
}

//
// Tensor represents a tensor, encoded as contiguous multi-dimensional array.
// tensor_metadata - represents metadata of the shared memory segment
// data_or_handle - represents the data of shared memory, this could be passed in two ways:
// a. send across the raw bytes of the multi-dimensional tensor array
// b. send a SharedMemoryHandle which contains the posix shared memory segment
// id and offset in bytes to location of multi-dimensional tensor array.
//
message Tensor {
 TensorMetadata tensor_metadata = 1; //optional in the predict request
 oneof data {
 bytes byte_data = 4;
 // will only be used for input tensors
 SharedMemoryHandle shared_memory_handle = 5;
 }
}

//
// request for CaptureData rpc call
//
message CaptureDataRequest {
 string model_name = 1;
 string capture_id = 2; //uuid string
 Timestamp inference_timestamp = 3;
 repeated Tensor input_tensors = 4;
 repeated Tensor output_tensors = 5;
 repeated AuxilaryData inputs = 6;
 repeated AuxilaryData outputs = 7;
}
Output
//
// response for CaptureData rpc call
//
message CaptureDataResponse {}

获取捕获状态

根据加载的模型，输入和输出张量可能很大（对于许多边缘设备）。捕获到云中可能需要很长时间。因此，CaptureData() 是作为异步操作实施的。捕获 ID 是客户端在捕获数据调用期间提供的唯一标识符，此 ID 可用于查询异步调用的状态。 

//
// allows users to query status of capture data operation
// Status Codes:
// 1. OK - data capture successfully initiated
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 4. NOT_FOUND - given capture id doesn't exist.
//
rpc GetCaptureDataStatus(GetCaptureDataStatusRequest) returns (GetCaptureDataStatusResponse);
Input
//
// request for GetCaptureDataStatus rpc call
//
message GetCaptureDataStatusRequest {
  string capture_id = 1;
}
Output
enum CaptureDataStatus {
  FAILURE = 0;
  SUCCESS = 1;
  IN_PROGRESS = 2;
  NOT_FOUND = 3;
}

//
// response for GetCaptureDataStatus rpc call
//
message GetCaptureDataStatusResponse {
  CaptureDataStatus status = 1;
}

Predict

API 对之前加载的模型执行推理。predict它以直接注入到神经网络中的张量的形式接受请求。输出是模型中的输出张量（或标量）。这是一个阻止性调用。 

//
// perform inference on a model.
//
// Note:
// 1. users can chose to send the tensor data in the protobuf message or
// through a shared memory segment on a per tensor basis, the Predict
// method with handle the decode transparently.
// 2. serializing large tensors into the protobuf message can be quite expensive,
// based on our measurements it is recommended to use shared memory of
// tenors larger than 256KB.
// 3. SMEdge IPC server will not use shared memory for returning output tensors,
// i.e., the output tensor data will always send in byte form encoded
// in the tensors of PredictResponse.
// 4. currently SMEdge IPC server cannot handle concurrent predict calls, all
// these call will be serialized under the hood. this shall be addressed
// in a later release.
// Status Codes:
// 1. OK - prediction is successful
// 2. UNKNOWN - unknown error has occurred
// 3. INTERNAL - an internal error has occurred
// 4. NOT_FOUND - when model not found
// 5. INVALID_ARGUMENT - when tenors types mismatch
//
rpc Predict(PredictRequest) returns (PredictResponse);
Input
// request for Predict rpc call
//
message PredictRequest {
string name = 1;
repeated Tensor tensors = 2;
}

//
// Tensor represents a tensor, encoded as contiguous multi-dimensional array.
//    tensor_metadata - represents metadata of the shared memory segment
//    data_or_handle - represents the data of shared memory, this could be passed in two ways:
//                        a. send across the raw bytes of the multi-dimensional tensor array
//                        b. send a SharedMemoryHandle which contains the posix shared memory segment
//                            id and offset in bytes to location of multi-dimensional tensor array.
//
message Tensor {
  TensorMetadata tensor_metadata = 1; //optional in the predict request
  oneof data {
    bytes byte_data = 4;
    // will only be used for input tensors
    SharedMemoryHandle shared_memory_handle = 5;
  }
}

//
// Tensor represents a tensor, encoded as contiguous multi-dimensional array.
//    tensor_metadata - represents metadata of the shared memory segment
//    data_or_handle - represents the data of shared memory, this could be passed in two ways:
//                        a. send across the raw bytes of the multi-dimensional tensor array
//                        b. send a SharedMemoryHandle which contains the posix shared memory segment
//                            id and offset in bytes to location of multi-dimensional tensor array.
//
message Tensor {
  TensorMetadata tensor_metadata = 1; //optional in the predict request
  oneof data {
    bytes byte_data = 4;
    // will only be used for input tensors
    SharedMemoryHandle shared_memory_handle = 5;
  }
}

//
// TensorMetadata represents the metadata for a tensor
//    name - name of the tensor
//    data_type  - data type of the tensor
//    shape - array of dimensions of the tensor
//
message TensorMetadata {
  string name = 1;
  DataType data_type = 2;
  repeated int32 shape = 3;
}

//
// SharedMemoryHandle represents a posix shared memory segment
//    offset - offset in bytes from the start of the shared memory segment.
//    segment_id - shared memory segment id corresponding to the posix shared memory segment.
//    size - size in bytes of shared memory segment to use from the offset position.
//
message SharedMemoryHandle {
  uint64 size = 1;
  uint64 offset = 2;
  uint64 segment_id = 3;
}
Output
注意

仅返回 PredictResponse，而不返回 Tensors。SharedMemoryHandle

// response for Predict rpc call
//
message PredictResponse {
   repeated Tensor tensors = 1;
}