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# Object2Vec 的编码器嵌入
<a name="object2vec-encoder-embeddings"></a>

下一页列出了用于从 Amazon A SageMaker I Object2Vec 模型中获取编码器嵌入推理的输入请求和输出响应格式。

## GPU 优化：编码器嵌入
<a name="object2vec-inference-gpu-optimize-encoder-embeddings"></a>

嵌入是从离散对象（例如单词）到实数向量的映射。

由于 GPU 内存稀缺，可以指定 `INFERENCE_PREFERRED_MODE` 环境变量来优化是将[用于 Object2Vec 推理的数据格式](object2vec-inference-formats.md)还是将编码器嵌入推理网络加载到 GPU 中。如果您的绝大多数推理用于进行编码器嵌入，则指定 `INFERENCE_PREFERRED_MODE=embedding`。下面是一个批量转换示例，用于说明如何使用 4 个针对编码器嵌入推理进行优化的 p3.2xlarge 实例：

```
transformer = o2v.transformer(instance_count=4,
                              instance_type="ml.p2.xlarge",
                              max_concurrent_transforms=2,
                              max_payload=1,  # 1MB
                              strategy='MultiRecord',
                              env={'INFERENCE_PREFERRED_MODE': 'embedding'},  # only useful with GPU
                              output_path=output_s3_path)
```

## 输入：编码器嵌入
<a name="object2vec-in-encoder-embeddings-data"></a>

Content-type: application/json; infer\$1max\$1seqlens=<FWD-LENGTH>,<BCK-LENGTH>

其中 <FWD-LENGTH> 和 <BCK-LENGTH> 是 [1,5000] 范围内的整数，并定义向前和向后编码器的最大序列长度。

```
{
  "instances" : [
    {"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4]},
    {"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4]},
    {"in0": [774, 14, 21, 206]}
  ]
}
```

Content-type: application/jsonlines; infer\$1max\$1seqlens=<FWD-LENGTH>,<BCK-LENGTH>

其中 <FWD-LENGTH> 和 <BCK-LENGTH> 是 [1,5000] 范围内的整数，并定义向前和向后编码器的最大序列长度。

```
{"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4]}
{"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4]}
{"in0": [774, 14, 21, 206]}
```

在这两种格式中，您可以仅指定一种输入类型：`“in0”` 或 `“in1.”`。然后，推理服务调用相应的编码器，并输出每个实例的嵌入。

## 输出：编码器嵌入
<a name="object2vec-out-encoder-embeddings-data"></a>

Content-type：application/json

```
{
  "predictions": [
    {"embeddings":[0.057368703186511,0.030703511089086,0.099890425801277,0.063688032329082,0.026327300816774,0.003637571120634,0.021305780857801,0.004316598642617,0.0,0.003397724591195,0.0,0.000378780066967,0.0,0.0,0.0,0.007419463712722]},
    {"embeddings":[0.150190666317939,0.05145975202322,0.098204270005226,0.064249359071254,0.056249320507049,0.01513972133398,0.047553978860378,0.0,0.0,0.011533712036907,0.011472506448626,0.010696629062294,0.0,0.0,0.0,0.008508535102009]}
  ]
}
```

Content-type：application/jsonlines

```
{"embeddings":[0.057368703186511,0.030703511089086,0.099890425801277,0.063688032329082,0.026327300816774,0.003637571120634,0.021305780857801,0.004316598642617,0.0,0.003397724591195,0.0,0.000378780066967,0.0,0.0,0.0,0.007419463712722]}
{"embeddings":[0.150190666317939,0.05145975202322,0.098204270005226,0.064249359071254,0.056249320507049,0.01513972133398,0.047553978860378,0.0,0.0,0.011533712036907,0.011472506448626,0.010696629062294,0.0,0.0,0.0,0.008508535102009]}
```

推理服务输出的嵌入的向量长度等于在训练时指定的以下超参数之一的值：`enc0_token_embedding_dim`、`enc1_token_embedding_dim` 或 `enc_dim`。