[英]MultiWorkerMirroredStrategy() not working on Google AI-Platform (CMLE)
[英]ai-platform cloud predict not working but local predict works
我已经从 ai-platform-samples 模板成功地训练和本地预测了 DNNLinearCombinedClassifier。
当我运行pip freeze| grep tensorflow
我本地 PC 上的pip freeze| grep tensorflow
:
tensorflow==1.15.0
tensorflow-datasets==1.2.0
tensorflow-estimator==1.15.1
tensorflow-hub==0.6.0
tensorflow-io==0.8.0
tensorflow-metadata==0.15.1
tensorflow-model-analysis==0.15.4
tensorflow-probability==0.8.0
tensorflow-serving-api==1.15.0
当我为保存的模型运行saved_model_cli show
,我得到以下输出:
The given SavedModel SignatureDef contains the following input(s):
inputs['Sector'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder_2:0
inputs['announcement_type_simple'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder_1:0
inputs['market_cap'] tensor_info:
dtype: DT_FLOAT
shape: (-1)
name: Placeholder_3:0
inputs['sens_content'] tensor_info:
dtype: DT_STRING
shape: (-1)
name: Placeholder:0
The given SavedModel SignatureDef contains the following output(s):
outputs['all_class_ids'] tensor_info:
dtype: DT_INT32
shape: (-1, 3)
name: head/predictions/Tile:0
outputs['all_classes'] tensor_info:
dtype: DT_STRING
shape: (-1, 3)
name: head/predictions/Tile_1:0
outputs['class_ids'] tensor_info:
dtype: DT_INT64
shape: (-1, 1)
name: head/predictions/ExpandDims_2:0
outputs['classes'] tensor_info:
dtype: DT_STRING
shape: (-1, 1)
name: head/predictions/str_classes:0
outputs['logits'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 3)
name: dnn/logits/BiasAdd:0
outputs['probabilities'] tensor_info:
dtype: DT_FLOAT
shape: (-1, 3)
name: head/predictions/probabilities:0
Method name is: tensorflow/serving/predict
输入与我输入到我的 json 文件中的内容一致,如下所示:
{"sens_content": "RFG 201411130005A Trading Statement Rhodes Food Group", "announcement_type_simple": "trade statement", "Sector": "Consumer, Non-cyclical","market_cap": 4377615219.88}
该模型使用gcloud ai-platform local predict
进行gcloud ai-platform local predict
。
当我运行gcloud ai-platform predict --model=${MODEL_NAME} --version=${MODEL_VERSION} --json-instances=data/new-data.json --verbosity debug --log-http
它会创建以下内容邮政 :
==== request start ====
uri: https://ml.googleapis.com/v1/projects/simon-teraflow-project/models/tensorflow_sens1/versions/v3:predict
method: POST
== headers start ==
Authorization: --- Token Redacted ---
Content-Type: application/json
user-agent: gcloud/270.0.0 command/gcloud.ai-platform.predict invocation-id/f01f2f4b8c494082abfc38e19499019b environment/GCE environment-version/None interactive/True from-script/False python/2.7.13 term/xterm (Linux 4.9.0-11-amd64)
== headers end ==
== body start ==
{"instances": [{"Sector": "Consumer, Non-cyclical", "announcement_type_simple": "trade statement", "market_cap": 4377615219.88, "sens_content": "RFG 201411130005A Trading Statement Rhodes Food Group"}]}
== body end ==
==== request end ====
可以看到输入与需要的一致。 以下是回应:
Traceback (most recent call last):
File "/usr/lib/google-cloud-sdk/lib/googlecloudsdk/calliope/cli.py", line 984, in Execute
resources = calliope_command.Run(cli=self, args=args)
File "/usr/lib/google-cloud-sdk/lib/googlecloudsdk/calliope/backend.py", line 798, in Run
resources = command_instance.Run(args)
File "/usr/lib/google-cloud-sdk/lib/surface/ai_platform/predict.py", line 110, in Run
signature_name=args.signature_name)
File "/usr/lib/google-cloud-sdk/lib/googlecloudsdk/api_lib/ml_engine/predict.py", line 77, in Predict
response_body)
HttpRequestFailError: HTTP request failed. Response: {
"error": {
"code": 400,
"message": "Bad Request",
"status": "INVALID_ARGUMENT"
}
}
ERROR: (gcloud.ai-platform.predict) HTTP request failed. Response: {
"error": {
"code": 400,
"message": "Bad Request",
"status": "INVALID_ARGUMENT"
}
}
在 ai 平台上试过同样的事情“测试你的模型”。 相同的结果:
在 AI 平台 gui 上进行预测
我已经检查过运行时是1.15
,这与本地预测一致,也与 Python 版本一致。
我已经搜索过类似的案例,但一无所获。 任何建议将不胜感激。
您可以尝试以下操作:
1)在本地保存您的模型,您可以使用适合您的模式的以下代码段 [1] 示例
2) 使用 Docker 进行测试
3) 将模型部署到 GCP 并向模型 [2](适应您的模型)发出请求,使用 gcloud 命令而不是 GCP UI。
[1]
========Code snippet===============
MODEL_NAME = <MODEL NAME>
VERSION = <MODEL VERSION>
SERVE_PATH = './models/{}/{}'.format(MODEL_NAME, VERSION)
import tensorflow as tf
import tensorflow_hub as hub
use_model = "https://tfhub.dev/google/<MODEL NAME>/<MODEL VERSION>"
with tf.Graph().as_default():
module = hub.Module(use_model, name=MODEL_NAME)
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()
)
========/ Code snippet===============
[2]
Replace <Project_name>, <model_name>, <bucket_name> and <model_version>
$ gcloud ai-platform models create <model_name> --project <Project_name>
$ gcloud beta ai-platform versions create v1 --project <Project_name> --model <model_name> --origin=/location/of/model/dir/<model_name>/<model_version> --staging-bucket gs://<bucket_name> --runtime-version=1.15 --machine-type=n1-standard-8
$ echo '{"text": "cat"}' > instances.json
$ gcloud ai-platform predict --project <Project_name> --model <model_name> --version v1 --json-instances=instances.json
$ curl -X POST -v -k -H "Content-Type: application/json" -d '{"instances": [{"text": "cat"}]}' -H "Authorization: Bearer `gcloud auth print-access-token`" "https://ml.googleapis.com/v1/projects/<Project_name>/models/<model_name>/versions/v1:predict"
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