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# Embeddings Encoder untuk Object2Vec
<a name="object2vec-encoder-embeddings"></a>

Halaman berikut mencantumkan format permintaan input dan respons keluaran untuk mendapatkan inferensi penyematan encoder dari model Amazon SageMaker AI Object2Vec.

## Optimasi GPU: Embeddings Encoder
<a name="object2vec-inference-gpu-optimize-encoder-embeddings"></a>

Embedding adalah pemetaan dari objek diskrit, seperti kata-kata, ke vektor bilangan real.

Karena kelangkaan memori GPU, variabel `INFERENCE_PREFERRED_MODE` lingkungan dapat ditentukan untuk mengoptimalkan apakah jaringan inferensi penyematan encoder [Format Data untuk Inferensi Object2Vec](object2vec-inference-formats.md) atau dimuat ke dalam GPU. Jika sebagian besar inferensi Anda adalah untuk penyematan encoder, tentukan. `INFERENCE_PREFERRED_MODE=embedding` Berikut ini adalah contoh Transformasi Batch menggunakan 4 instance p3.2xlarge yang mengoptimalkan inferensi penyematan encoder:

```
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)
```

## Masukan: Embeddings Encoder
<a name="object2vec-in-encoder-embeddings-data"></a>

<FWD-LENGTH>Tipe konten: aplikasi/json; infer\$1max\$1seqlens=, <BCK-LENGTH>

Dimana <FWD-LENGTH>dan <BCK-LENGTH>merupakan bilangan bulat dalam rentang [1.5000] dan tentukan panjang urutan maksimum untuk encoder maju dan mundur.

```
{
  "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]}
  ]
}
```

<FWD-LENGTH>Tipe konten: aplikasi/jsonlines; infer\$1max\$1seqlens=, <BCK-LENGTH>

Dimana <FWD-LENGTH>dan <BCK-LENGTH>merupakan bilangan bulat dalam rentang [1.5000] dan tentukan panjang urutan maksimum untuk encoder maju dan mundur.

```
{"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]}
```

Dalam kedua format ini, Anda hanya menentukan satu jenis input: `“in0”` atau `“in1.”` Layanan inferensi kemudian memanggil encoder yang sesuai dan mengeluarkan embeddings untuk setiap instance. 

## Keluaran: Embeddings Encoder
<a name="object2vec-out-encoder-embeddings-data"></a>

Tipe konten: aplikasi/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]}
  ]
}
```

Jenis konten: aplikasi/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]}
```

Panjang vektor output embeddings oleh layanan inferensi sama dengan nilai salah satu hyperparameter berikut yang Anda tentukan pada waktu pelatihan:,, atau. `enc0_token_embedding_dim` `enc1_token_embedding_dim` `enc_dim`