本文為英文版的機器翻譯版本,如內容有任何歧義或不一致之處,概以英文版為準。
下頁說明從 Amazon SageMaker AI Object2Vec 模型取得評分推論的輸入請求和輸出回應格式。
GPU 最佳化:分類或廻歸
由於 GPU 記憶體不足,無論分類/廻歸或 輸出:編碼器內嵌 推論網路是否載入 GPU,都會指定要最佳化 INFERENCE_PREFERRED_MODE
環境變數。如果大部分的推論是用於分類或廻歸,請指定 INFERENCE_PREFERRED_MODE=classification
。以下是使用 4 個 p3.2xlarge 執行個體,最佳化分類/廻歸推論的批次轉換範例:
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': 'classification'}, # only useful with GPU
output_path=output_s3_path)
輸入:分類或迴歸請求格式
Content-type: application/json
{
"instances" : [
{"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4], "in1": [16, 21, 13, 45, 14, 9, 80, 59, 164, 4]},
{"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4], "in1": [22, 32, 13, 25, 1016, 573, 3252, 4]},
{"in0": [774, 14, 21, 206], "in1": [21, 366, 125]}
]
}
Content-type: application/jsonlines
{"in0": [6, 17, 606, 19, 53, 67, 52, 12, 5, 10, 15, 10178, 7, 33, 652, 80, 15, 69, 821, 4], "in1": [16, 21, 13, 45, 14, 9, 80, 59, 164, 4]}
{"in0": [22, 1016, 32, 13, 25, 11, 5, 64, 573, 45, 5, 80, 15, 67, 21, 7, 9, 107, 4], "in1": [22, 32, 13, 25, 1016, 573, 3252, 4]}
{"in0": [774, 14, 21, 206], "in1": [21, 366, 125]}
若是分類問題,分數向量的長度與 num_classes
對應。若是迴歸問題,長度為 1。
輸出:分類或迴歸回應格式
Accept: application/json
{
"predictions": [
{
"scores": [
0.6533935070037842,
0.07582679390907288,
0.2707797586917877
]
},
{
"scores": [
0.026291321963071823,
0.6577019095420837,
0.31600672006607056
]
}
]
}
Accept: application/jsonlines
{"scores":[0.195667684078216,0.395351558923721,0.408980727195739]}
{"scores":[0.251988261938095,0.258233487606048,0.489778339862823]}
{"scores":[0.280087798833847,0.368331134319305,0.351581096649169]}
在這兩種分類和迴歸格式中,分數會套用到個別標籤。