results of `gcloud ml-engine local predict`attempts with different `tf.estimator.export.ServingInputReceiver` args

gcloud-ml-engine-local-predict.log

# --------------------------------------------------------
# result of a `json_serving_input_fn` that returns
# tf.estimator.export.ServingInputReceiver(inputs, inputs)
# where 'inputs' contains the instance key
# --------------------------------------------------------

export MODE=local_single
export TRAIN_STEPS=2000
export NUM_EPOCHS=1
DATE=`date '+%Y%m%d_%H%M%S'`
export TRAINER_DIR=prop_car_gen_model/trainer
export PARENT_DIR=/Users/$USER/dev/anaconda2/envs/repos
export DATA_DIR=$PARENT_DIR/prop_car_gen_data
export OUTPUT_DIR=$PARENT_DIR/prop_car_gen_${MODE}_${DATE}
rm -rf $OUTPUT_DIR

TRAIN_FILE_LOCAL=$DATA_DIR/tier_3_train_201712.csv
EVAL_FILE_LOCAL=$DATA_DIR/tier_3_eval_201712.csv

echo $TRAIN_STEPS
echo $NUM_EPOCHS
echo $DATE
echo $TRAINER_DIR
echo $PARENT_DIR
echo $OUTPUT_DIR
echo $TRAIN_FILE_LOCAL
echo $EVAL_FILE_LOCAL

# $ echo $DATE
# 20180104_111756

# first train the model
gcloud ml-engine local train --package-path $TRAINER_DIR \
  --module-name trainer.task \
  -- \
  --train-files $TRAIN_FILE_LOCAL \
  --eval-files $EVAL_FILE_LOCAL \
  --train-steps $TRAIN_STEPS \
  --job-dir $OUTPUT_DIR \
  --eval-steps 100

model_export_id=`'ls' $OUTPUT_DIR/export/prop-car-gen`

# 1515103301
echo $model_export_id
ls $OUTPUT_DIR/export/prop-car-gen/$model_export_id/

# Result of `tf.estimator.export.ServingInputReceiver(inputs, inputs),
# where the 'inputs' dict contains the instance key
#  1. the instance key does _not_ appear among the 'predict' & 'serving_default' 
#     signature inputs
#  2. the instance key _does_ appear among the 'predict' & 'serving_default' 
#     signature outputs

# inspect the saved model's signature definition
saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_140037/export/prop-car-gen/1515103301/ \
--tag_set serve

# The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:
# SignatureDef key: "predict"
# SignatureDef key: "serving_default"

saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_140037/export/prop-car-gen/1515103301/ \
--tag_set serve \
--signature_def serving_default

# The given SavedModel SignatureDef contains the following inputs(s):
# # ...
# # <no inputs['user_id'] tensor_info entry>

# The given SavedModel SignatureDef contains the following output(s):
# # ...
# outputs['user_id'] tensor_info:
#     dtype: DT_INT64
#     shape: (-1)
#     name: Placeholder:0
# Method name is: tensorflow/serving/predict

# predict with the trained model
gcloud ml-engine local predict \
--model-dir=$OUTPUT_DIR/export/prop-car-gen/$model_export_id/ \
--json-instances=$DATA_DIR/tier_3_test_sample_v1_201712.json

# --- ERROR BEGIN ----

# prediction_lib.PredictionError: 
# Invalid inputs: Unexpected tensor name: user_id (Error code: 1)

# --- ERROR END ----

# ----------------------------------------------------------
# result of a `json_serving_input_fn` that returns
# tf.estimator.export.ServingInputReceiver(features, inputs)
# where 'features' does NOT contain the instance key while
# 'inputs' does
# ----------------------------------------------------------

# CMLE run in local single-node mode
export MODE=local_single
export TRAIN_STEPS=2000
export NUM_EPOCHS=1
DATE=`date '+%Y%m%d_%H%M%S'`
export TRAINER_DIR=prop_car_gen_model/trainer
export PARENT_DIR=/Users/$USER/dev/anaconda2/envs/repos
export DATA_DIR=$PARENT_DIR/prop_car_gen_data
export OUTPUT_DIR=$PARENT_DIR/prop_car_gen_${MODE}_${DATE}
rm -rf $OUTPUT_DIR

TRAIN_FILE_LOCAL=$DATA_DIR/tier_3_train_201712.csv
EVAL_FILE_LOCAL=$DATA_DIR/tier_3_eval_201712.csv

# first train the model
gcloud ml-engine local train --package-path $TRAINER_DIR \
  --module-name trainer.task \
  -- \
  --train-files $TRAIN_FILE_LOCAL \
  --eval-files $EVAL_FILE_LOCAL \
  --train-steps $TRAIN_STEPS \
  --job-dir $OUTPUT_DIR \
  --eval-steps 100

model_export_id=`'ls' $OUTPUT_DIR/export/prop-car-gen`

# 1515092673
echo $model_export_id
ls $OUTPUT_DIR/export/prop-car-gen/$model_export_id/

# Result of `tf.estimator.export.ServingInputReceiver(features, inputs)`, where
# 'features' dict does _not_ contain the instance key while 'inputs' dict does:
#  1. the instance key _does_ appear among the 'predict' & 'serving_default' 
#     signature inputs
#  2. the instance key does _not_ appear among the 'predict' & 'serving_default' 
#     signature outputs

# inspect the saved model's signature definition
saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_100210/export/prop-car-gen/1515092673/ \
--tag_set serve

# The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:
# SignatureDef key: "predict"
# SignatureDef key: "serving_default"

# same results for both 'signature_def'
saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_100210/export/prop-car-gen/1515092673/ \
--tag_set serve \
--signature_def serving_default

saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_100210/export/prop-car-gen/1515092673/ \
--tag_set serve \
--signature_def predict

# The given SavedModel SignatureDef contains the following inputs(s): 
# ...
# inputs['user_id'] tensor_info:
#     dtype: DT_INT64
#     shape: (-1)
#     name: Placeholder:0

# The given SavedModel SignatureDef contains the following output(s): 
# ...
# <no outputs['user_id'] tensor_info entry>

# predict with the trained model
gcloud ml-engine local predict \
--model-dir=$OUTPUT_DIR/export/prop-car-gen/$model_export_id/ \
--json-instances=$DATA_DIR/tier_3_test_sample_v1_201712.json

# --- ERROR BEGIN ----

# InvalidArgumentError (see above for traceback): You must feed a value for placeholder  
# tensor 'Placeholder_52' with dtype int8 and shape [?]
# [[Node: Placeholder_52 = Placeholder[dtype=DT_INT8, shape=[?], 
# _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
# (Error code: 2)

# --- ERROR END ----

# -------------------------------------------------
# result of a `json_serving_input_fn` that returns
# tf.estimator.export.ServingInputReceiver(
# features, inputs, {'serving_default': inputs}),
# where 'features' does NOT contain the instance key
# while 'inputs' does
# -------------------------------------------------

export MODE=local_single
export TRAIN_STEPS=2000
export NUM_EPOCHS=1
DATE=`date '+%Y%m%d_%H%M%S'`
export TRAINER_DIR=prop_car_gen_model/trainer
export PARENT_DIR=/Users/$USER/dev/anaconda2/envs/repos
export DATA_DIR=$PARENT_DIR/prop_car_gen_data
export OUTPUT_DIR=$PARENT_DIR/prop_car_gen_${MODE}_${DATE}
rm -rf $OUTPUT_DIR

TRAIN_FILE_LOCAL=$DATA_DIR/tier_3_train_201712.csv
EVAL_FILE_LOCAL=$DATA_DIR/tier_3_eval_201712.csv

echo $TRAIN_STEPS
echo $NUM_EPOCHS
echo $DATE
echo $TRAINER_DIR
echo $PARENT_DIR
echo $OUTPUT_DIR
echo $TRAIN_FILE_LOCAL
echo $EVAL_FILE_LOCAL

# first train the model
gcloud ml-engine local train --package-path $TRAINER_DIR \
  --module-name trainer.task \
  -- \
  --train-files $TRAIN_FILE_LOCAL \
  --eval-files $EVAL_FILE_LOCAL \
  --train-steps $TRAIN_STEPS \
  --job-dir $OUTPUT_DIR \
  --eval-steps 100

model_export_id=`'ls' $OUTPUT_DIR/export/prop-car-gen`

# 1515093102
echo $model_export_id
ls $OUTPUT_DIR/export/prop-car-gen/$model_export_id/

# Result of `tf.estimator.export.ServingInputReceiver(features, inputs, 
# {'serving_default': inputs})`, where 'features' dict does _not_ contain the instance
# key while 'inputs' dict does:
#  1. the instance key _does_ appear among the 'predict' & 'serving_default' 
#     signature inputs
#  2. the instance key does _not_ appear among the 'predict' & 'serving_default' 
#     signature outputs

saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_110943/export/prop-car-gen/1515093102/ \
--tag_set serve

# The given SavedModel MetaGraphDef contains SignatureDefs with the following keys:
# SignatureDef key: "predict"
# SignatureDef key: "serving_default"
# SignatureDef key: "serving_default:predict"
# SignatureDef key: "serving_default:serving_default"

# inspect the saved model's signature definition
# same results for all of these `signature_def` settings
saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_110943/export/prop-car-gen/1515093102/ \
--tag_set serve \
--signature_def predict

saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_110943/export/prop-car-gen/1515093102/ \
--tag_set serve \
--signature_def serving_default

saved_model_cli show  \
--dir prop_car_gen_local_single_20180104_110943/export/prop-car-gen/1515093102/ \
--tag_set serve \
--signature_def serving_default:serving_default

# The given SavedModel SignatureDef contains the following inputs(s): 
# ...
# inputs['user_id'] tensor_info:
#     dtype: DT_INT64
#     shape: (-1)
#     name: Placeholder:0

# The given SavedModel SignatureDef contains the following output(s): 
# ...
# <no outputs['user_id'] tensor_info entry>

# predict with the trained model
gcloud ml-engine local predict \
--model-dir=$OUTPUT_DIR/export/prop-car-gen/$model_export_id/ \
--json-instances=$DATA_DIR/tier_3_test_sample_v1_201712.json

# --- ERROR BEGIN ----

# InvalidArgumentError (see above for traceback): You must feed a value for placeholder 
# tensor 'Placeholder_52' with dtype int8 and shape [?]
# [[Node: Placeholder_52 = Placeholder[dtype=DT_INT8, shape=[?], 
# _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
# (Error code: 2)

# --- ERROR END ----