Domino Valdano created MADLIB-1426:
--------------------------------------
Summary: Without GPU's, FitMultipleModel fails in evaluate()
Key: MADLIB-1426
URL: https://issues.apache.org/jira/browse/MADLIB-1426
Project: Apache MADlib
Issue Type: Bug
Components: Deep Learning
Reporter: Domino Valdano
Whenever I try to run `madlib_keras_fit_multiple_model()` on a system without
GPU's, it always fails in evaluate complaining that device `gpu0` is not
available. This happens regardless of whether use_gpus=False or use_gpus=True.
My platform is OSX 10.14.1 with latest version of madlib (1.17.0) and gpdb5. I
think I've also seen this happen on CentOS in gpdb6, so I believe this is a bug
that affects all platforms, but not entirely sure of that. Possibly specific
to OSX or gpdb5.
The problem happens in `internal_keras_eval_transition()` in
`madlib_keras.py_in`.
With `use_gpus=False`, it calls:
```
with K.tf.device(device_name):
res = segment_model.evaluate(x_val, y_val)
```
with `device_name='/gpu0'`
```
I know this because I added a plpy.info statement to print `device_name` at the
beginning of this function. I also printed the value of `use_gpus` on master
before training begins:
```
INFO: 00000: use_gpus = False
```
This is what the error looks like:
```
INFO: 00000: device_name = /gpu:0 (seg1 slice1 127.0.0.1:25433 pid=90300)
CONTEXT: PL/Python function "internal_keras_eval_transition"
LOCATION: PLy_output, plpython.c:4773
psql:../run_fit_mult_iris.sql:1: ERROR: XX000: plpy.SPIError:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot assign a
device for operation group_deps: Operation was explicitly assigned to
/device:GPU:0 but available devices are [
/job:localhost/replica:0/task:0/device:CPU:0 ]. Make sure the device
specification refers to a valid device. (plpython.c:5038) (seg0 slice1
127.0.0.1:25432 pid=90299) (plpython.c:5038)
DETAIL:
[[{{node group_deps}} = NoOp[_device="/device:GPU:0"](^loss/mul,
^metrics/acc/Mean)]]
Traceback (most recent call last):
PL/Python function "internal_keras_eval_transition", line 6, in <module>
return madlib_keras.internal_keras_eval_transition(**globals())
PL/Python function "internal_keras_eval_transition", line 782, in
internal_keras_eval_transition
PL/Python function "internal_keras_eval_transition", line 1112, in evaluate
PL/Python function "internal_keras_eval_transition", line 391, in test_loop
PL/Python function "internal_keras_eval_transition", line 2714, in __call__
PL/Python function "internal_keras_eval_transition", line 2670, in _call
PL/Python function "internal_keras_eval_transition", line 2622, in
_make_callable
PL/Python function "internal_keras_eval_transition", line 1469, in
_make_callable_from_options
PL/Python function "internal_keras_eval_transition", line 1351, in
_extend_graph
PL/Python function "internal_keras_eval_transition"
CONTEXT: Traceback (most recent call last):
PL/Python function "madlib_keras_fit_multiple_model", line 23, in <module>
fit_obj = madlib_keras_fit_multiple_model.FitMultipleModel(**globals())
PL/Python function "madlib_keras_fit_multiple_model", line 42, in wrapper
PL/Python function "madlib_keras_fit_multiple_model", line 216, in __init__
PL/Python function "madlib_keras_fit_multiple_model", line 230, in
fit_multiple_model
PL/Python function "madlib_keras_fit_multiple_model", line 270, in
train_multiple_model
PL/Python function "madlib_keras_fit_multiple_model", line 302, in
evaluate_model
PL/Python function "madlib_keras_fit_multiple_model", line 417, in
compute_loss_and_metrics
PL/Python function "madlib_keras_fit_multiple_model", line 739, in
get_loss_metric_from_keras_eval
PL/Python function "madlib_keras_fit_multiple_model"
LOCATION: PLy_elog, plpython.c:5038
```
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