[
https://issues.apache.org/jira/browse/MADLIB-1426?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]
Domino Valdano updated MADLIB-1426:
-----------------------------------
Description:
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 runs:
{{with K.tf.device(device_name):}}
{{ res = segment_model.evaluate(x_val, y_val)}}
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. While {{use_gpus}} is set to false, the {{device_name}} on the
segments is set to {{/gpu:0}}. This is the bug (it should be set to {{/cpu:0}}).
This is the error message that happens:
{{INFO: 00000: use_gpus = False}}
{{ ...}}
{{ 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}}
was:
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 runs:
{{{{with K.tf.device(device_name):}}}}
{{ res = segment_model.evaluate(x_val, y_val)}}
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. While {{use_gpus}} is set to false, the {{device_name}} on the
segments is set to {{/gpu:0}}. This is the bug (it should be set to {{/cpu:0}}).
This is the error message that happens:
{{INFO: 00000: use_gpus = False}}
{{ ...}}
{{ 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}}
> 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
> Priority: Major
>
> 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 runs:
> {{with K.tf.device(device_name):}}
> {{ res = segment_model.evaluate(x_val, y_val)}}
> 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. While {{use_gpus}} is set to false, the {{device_name}} on
> the segments is set to {{/gpu:0}}. This is the bug (it should be set to
> {{/cpu:0}}).
> This is the error message that happens:
> {{INFO: 00000: use_gpus = False}}
> {{ ...}}
> {{ 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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