如何提供张量流模块，特别是通用句子编码器？

Question

我花了几个小时尝试设置 Tensorflow-hub 模块的 Tensorflow 服务，"Universal Sentence Encoder."这里有一个类似的问题：

How to make the tensorflow hub embeddings servable using tensorflow serving?

我一直在 Windows 机器上这样做。

这是我用来构建模型的代码：

import tensorflow as tf
import tensorflow_hub as hub

MODEL_NAME = 'test'
VERSION = 1
SERVE_PATH = './models/{}/{}'.format(MODEL_NAME, VERSION)

with tf.Graph().as_default():
  module = hub.Module("https://tfhub.dev/google/universal-sentence- 
  encoder/1")
  text = tf.placeholder(tf.string, [None])
  embedding = module(text)

  init_op = tf.group([tf.global_variables_initializer(), 
  tf.tables_initializer()])
  with tf.Session() as session:
  session.run(init_op)
    tf.saved_model.simple_save(
    session,
    SERVE_PATH,
    inputs = {"text": text},
    outputs = {"embedding": embedding},
    legacy_init_op = tf.tables_initializer()        
   )

我已经到了运行以下行的地步：

saved_model_cli show --dir ${PWD}/models/test/1 --tag_set serve --signature_def serving_default

给我以下结果：

The given SavedModel SignatureDef contains the following input(s):
inputs['text'] tensor_info:
  dtype: DT_STRING
  shape: (-1)
  name: Placeholder:0
The given SavedModel SignatureDef contains the following output(s):
 outputs['embedding'] tensor_info:
  dtype: DT_FLOAT
  shape: (-1, 512)
  name: module_apply_default/Encoder_en/hidden_layers/l2_normalize:0

然后我尝试了运行:

saved_model_cli run --dir ${PWD}/models/test/1 --tag_set serve --signature_def serving_default --input_exprs 'text=["what this is"]'

给出错误：

  File "<string>", line 1
[what this is]
         ^
SyntaxError: invalid syntax

我试过更改 'text=["what this is"]' 部分的格式，但对我没有任何效果。

不管这部分是否有效，主要目标是设置服务模块并创建可调用 API。

我试过 docker，下面一行：

docker run -p 8501:8501 --name tf-serve -v ${PWD}/models/:/models -t tensorflow/serving --model_base_path=/models/test

似乎设置正确：

Building single TensorFlow model file config:  model_name: model model_base_path: /models/test
2018-10-09 07:05:08.692140: I tensorflow_serving/model_servers/server_core.cc:462] Adding/updating models.
2018-10-09 07:05:08.692301: I tensorflow_serving/model_servers/server_core.cc:517]  (Re-)adding model: model
2018-10-09 07:05:08.798733: I tensorflow_serving/core/basic_manager.cc:739] Successfully reserved resources to load servable {name: model version: 1}
2018-10-09 07:05:08.798841: I tensorflow_serving/core/loader_harness.cc:66] Approving load for servable version {name: model version: 1}
2018-10-09 07:05:08.798870: I tensorflow_serving/core/loader_harness.cc:74] Loading servable version {name: model version: 1}
2018-10-09 07:05:08.798904: I external/org_tensorflow/tensorflow/contrib/session_bundle/bundle_shim.cc:360] Attempting to load native SavedModelBundle in bundle-shim from: /models/test/1
2018-10-09 07:05:08.798947: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:31] Reading SavedModel from: /models/test/1
2018-10-09 07:05:09.055822: I external/org_tensorflow/tensorflow/cc/saved_model/reader.cc:54] Reading meta graph with tags { serve }
2018-10-09 07:05:09.338142: I external/org_tensorflow/tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-10-09 07:05:09.576751: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:162] Restoring SavedModel bundle.
2018-10-09 07:05:28.975611: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:138] Running MainOp with key saved_model_main_op on SavedModel bundle.
2018-10-09 07:06:30.941577: I external/org_tensorflow/tensorflow/cc/saved_model/loader.cc:259] SavedModel load for tags { serve }; Status: success. Took 82120946 microseconds.
2018-10-09 07:06:30.990252: I tensorflow_serving/servables/tensorflow/saved_model_warmup.cc:83] No warmup data file found at /models/test/1/assets.extra/tf_serving_warmup_requests
2018-10-09 07:06:31.046262: I tensorflow_serving/core/loader_harness.cc:86] Successfully loaded servable version {name: model version: 1}
2018-10-09 07:06:31.184541: I tensorflow_serving/model_servers/server.cc:285] Running gRPC ModelServer at 0.0.0.0:8500 ...
[warn] getaddrinfo: address family for nodename not supported
2018-10-09 07:06:31.221644: I tensorflow_serving/model_servers/server.cc:301] Exporting HTTP/REST API at:localhost:8501 ...
[evhttp_server.cc : 235] RAW: Entering the event loop ...

我试过了

curl http://localhost:8501/v1/models/test

这给出了

{ "error": "Malformed request: GET /v1/models/test:predict" }

和

curl -d '{"text": "Hello"}' -X POST http://localhost:8501/v1/models/test:predict

这给出

{ "error": "JSON Parse error: Invalid value. at offset: 0" }

这里有一个类似的问题

正在寻找使该模块可用的方法。谢谢。

Answer 1

我终于想通了。我会 post 我在这里所做的，以防其他人试图做同样的事情。

我对 saved_model_cli 运行命令的问题是引号（使用 Windows 命令提示符）。将 'text=["what this is"]' 更改为 "text=['what this is']"

POST 请求的问题有两方面。第一，我注意到模特的名字是模特，所以应该是 http://localhost:8501/v1/models/model:predict

其次，输入格式不正确。我使用了 Postman，请求的正文如下所示： {"inputs": {"text": ["Hello"]}}

如何提供张量流模块，特别是通用句子编码器？

How to serve a tensorflow-module, specifically Universal Sentence Encoder?

python

tensorflow

tensorflow-serving

tensorflow-hub