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:
Create Version failed. Bad model detected with error: "Error loading the model: Could not load model. "
I am deploying with tensorflow 1.3
. The Experiment
is configured as follows:
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?
It looks like Google Cloud ML Engine only supports serving models produced using tensorflow 1.2.0
and below as of now. See here: https://cloud.google.com/ml-engine/docs/concepts/runtime-version-list
Use --runtime-version 1.2
if possible. 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.
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