[英]Google Cloud ML Engine: Create model version failed
I have successfully trained a TensorForestEstimator
on Google Cloud's ML Engine, but when I try to create a model version I get the following error: 我已经在Google Cloud的ML Engine上成功训练了
TensorForestEstimator
,但是当我尝试创建模型版本时,出现以下错误:
Create Version failed.
创建版本失败。 Bad model detected with error: "Error loading the model: Could not load model. "
检测到错误的模型,并显示以下错误:“加载模型时出错:无法加载模型。”
I am deploying with tensorflow 1.3
. 我正在使用
tensorflow 1.3
部署。 The Experiment
is configured as follows: Experiment
配置如下:
def get_experiment_fn(args):
def _experiment(run_config, hparams):
return Experiment(
estimator=TensorForestEstimator(
params=ForestHParams(
num_trees=args.num_trees,
max_nodes=10000,
min_split_samples=2,
num_features=8,
num_classes=args.num_projections,
regression=True
),
model_dir=args.job_dir,
graph_builder_class=RandomForestGraphs,
config=run_config,
keys_name=None,
report_feature_importances=True
),
train_input_fn=get_input_fn(
project_name=args.project,
data_location=args.train_data,
dataset_size=args.train_size,
batch_size=args.train_batch_size
),
train_steps=args.train_steps,
eval_input_fn=get_input_fn(
project_name=args.project,
data_location=args.eval_data,
dataset_size=args.eval_size,
batch_size=args.eval_batch_size
),
eval_steps=args.eval_steps,
eval_metrics=get_eval_metrics(),
export_strategies=[
make_export_strategy(
serving_input_fn,
default_output_alternative_key=None,
exports_to_keep=1
)
]
)
return _experiment
What is the issue? 有什么问题
It looks like Google Cloud ML Engine only supports serving models produced using tensorflow 1.2.0
and below as of now. tensorflow 1.2.0
Google Cloud ML Engine似乎仅支持使用tensorflow 1.2.0
及更低版本生成的服务模型。 See here: https://cloud.google.com/ml-engine/docs/concepts/runtime-version-list 参见此处: https : //cloud.google.com/ml-engine/docs/concepts/runtime-version-list
Use --runtime-version 1.2
if possible. 如果可能,请使用
--runtime-version 1.2
。 If you are using a feature specific to tensorflow 1.3
, you will need to host your model using Flask
on Google App Engine until ML Engine support for tensorflow 1.3
arrives. 如果您正在使用特定于
tensorflow 1.3
的功能,则需要在Google App Engine上使用Flask
托管模型,直到ML Engine对tensorflow 1.3
支持到达为止。
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