[ 
https://issues.apache.org/jira/browse/MADLIB-1363?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
 ]

Frank McQuillan updated MADLIB-1363:
------------------------------------
    Description: 


fit() INFO and CONTEXT messages


(1) no validation_table, metrics_compute_frequency=0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10                    -- num_iterations
                              );

INFO:  Processed 60 images: Fit took 0.567000865936 sec, Total was 
0.757196903229 sec  (seg0 slice1 10.128.0.41:40000 pid=13317)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.55348110199 sec, Total was 
0.741441011429 sec  (seg1 slice1 10.128.0.41:40001 pid=13318)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(2) no validation_table, metrics_compute_frequency!=0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                    -- num_iterations
                                0,                     -- gpus per host
                                NULL,                  -- validation table
                                1                      -- metrics compute 
frequency
                              );

INFO:  Processed 60 images: Fit took 0.534310817719 sec, Total was 
0.712550878525 sec  (seg0 slice1 10.128.0.41:40000 pid=14501)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.564456939697 sec, Total was 
0.751413106918 sec  (seg1 slice1 10.128.0.41:40001 pid=14502)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.28858995438 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.188971996307 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set metric after iteration 1: 0.649999976158.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set loss after iteration 1: 1.1202558279.
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.28858995438 sec
       Time for evaluation in iteration 1: 0.188971996307 sec
       Training set metric after iteration 1: 0.649999976158
       Training set loss after iteration 1: 1.1202558279
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(3) yes validation_table, metrics_compute_frequency=0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                   -- num_iterations
                                0,                    -- GPUs per host
                                'iris_test_packed'   -- validation dataset
                              );

INFO:  Processed 60 images: Fit took 0.552575826645 sec, Total was 
0.734694004059 sec  (seg0 slice1 10.128.0.41:40000 pid=18431)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.549551010132 sec, Total was 
0.734927892685 sec  (seg1 slice1 10.128.0.41:40001 pid=18432)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.36340904236 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(4) yes validation_table, metrics_compute_frequency=!0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                   -- num_iterations
                                0,                    -- GPUs per host
                                'iris_test_packed',   -- validation dataset
                                1                      -- metrics compute 
frequency
                              );

INFO:  Processed 60 images: Fit took 0.57217502594 sec, Total was 
0.817452907562 sec  (seg0 slice1 10.128.0.41:40000 pid=19573)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.554927110672 sec, Total was 
0.800101041794 sec  (seg1 slice1 10.128.0.41:40001 pid=19574)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.43148899078 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.217161893845 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set metric after iteration 1: 0.524999976158.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set loss after iteration 1: 0.984773635864.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.205282926559 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Validation set metric after iteration 1: 0.600000023842.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Validation set loss after iteration 1: 0.940379023552.
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.43148899078 sec
       Time for evaluating training dataset in iteration 1: 0.217161893845 sec
       Training set metric after iteration 1: 0.524999976158
       Training set loss after iteration 1: 0.984773635864
       Time for evaluating validation dataset in iteration 1: 0.205282926559 sec
       Validation set metric after iteration 1: 0.600000023842
       Validation set loss after iteration 1: 0.940379023552
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

Note change in wording ^^^ because there are 2 evaluation times.



  was:


fit() INFO and CONTEXT messages


(1) no validation_table, metrics_compute_frequency=0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10                    -- num_iterations
                              );

INFO:  Processed 60 images: Fit took 0.567000865936 sec, Total was 
0.757196903229 sec  (seg0 slice1 10.128.0.41:40000 pid=13317)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.55348110199 sec, Total was 
0.741441011429 sec  (seg1 slice1 10.128.0.41:40001 pid=13318)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(2) no validation_table, metrics_compute_frequency!=0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                    -- num_iterations
                                0,                     -- gpus per host
                                NULL,                  -- validation table
                                1                      -- metrics compute 
frequency
                              );

INFO:  Processed 60 images: Fit took 0.534310817719 sec, Total was 
0.712550878525 sec  (seg0 slice1 10.128.0.41:40000 pid=14501)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.564456939697 sec, Total was 
0.751413106918 sec  (seg1 slice1 10.128.0.41:40001 pid=14502)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.28858995438 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.188971996307 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set metric after iteration 1: 0.649999976158.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set loss after iteration 1: 1.1202558279.
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.28858995438 sec
       Time for evaluation in iteration 1: 0.188971996307 sec
       Training set metric after iteration 1: 0.649999976158
       Training set loss after iteration 1: 1.1202558279
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(3) yes validation_table, metrics_compute_frequency=0
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                   -- num_iterations
                                0,                    -- GPUs per host
                                'iris_test_packed'   -- validation dataset
                              );

INFO:  Processed 60 images: Fit took 0.552575826645 sec, Total was 
0.734694004059 sec  (seg0 slice1 10.128.0.41:40000 pid=18431)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.549551010132 sec, Total was 
0.734927892685 sec  (seg1 slice1 10.128.0.41:40001 pid=18432)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.36340904236 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.45737695694 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}


(4) yes validation_table, metrics_compute_frequency=!0

{code}
SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
                               'iris_model',          -- model output table
                               'model_arch_library',  -- model arch table
                                1,                    -- model arch id
                                $$ loss='categorical_crossentropy', 
optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
                                $$ batch_size=5, epochs=3 $$,  -- fit_params
                                10,                   -- num_iterations
                                0,                    -- GPUs per host
                                'iris_test_packed',   -- validation dataset
                                1                      -- metrics compute 
frequency
                              );

INFO:  Processed 60 images: Fit took 0.57217502594 sec, Total was 
0.817452907562 sec  (seg0 slice1 10.128.0.41:40000 pid=19573)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Processed 60 images: Fit took 0.554927110672 sec, Total was 
0.800101041794 sec  (seg1 slice1 10.128.0.41:40001 pid=19574)
CONTEXT:  PL/Python function "fit_transition"
INFO:  Time for training in iteration 1: 2.43148899078 sec
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.217161893845 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set metric after iteration 1: 0.524999976158.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Training set loss after iteration 1: 0.984773635864.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Time for evaluation in iteration 1: 0.205282926559 sec.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Validation set metric after iteration 1: 0.600000023842.
CONTEXT:  PL/Python function "madlib_keras_fit"
INFO:  Validation set loss after iteration 1: 0.940379023552.
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

change to

{code}
INFO:  Time for training in iteration 1: 2.43148899078 sec
       Time for evaluating training dataset in iteration 1: 0.217161893845 sec
       Training set metric after iteration 1: 0.524999976158
       Training set loss after iteration 1: 0.984773635864
       Time for evaluating validation dataset in iteration 1: 0.205282926559 sec
       Validation set metric after iteration 1: 0.600000023842
       Validation set loss after iteration 1: 0.940379023552
CONTEXT:  PL/Python function "madlib_keras_fit"
{code}

Note change in wording ^^^ because there are 2 evaluation times.




> Reduce verbose output to console with fit()
> -------------------------------------------
>
>                 Key: MADLIB-1363
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1363
>             Project: Apache MADlib
>          Issue Type: Improvement
>          Components: Deep Learning
>            Reporter: Frank McQuillan
>            Priority: Minor
>             Fix For: v1.16
>
>
> fit() INFO and CONTEXT messages
> (1) no validation_table, metrics_compute_frequency=0
> {code}
> SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
>                                'iris_model',          -- model output table
>                                'model_arch_library',  -- model arch table
>                                 1,                    -- model arch id
>                                 $$ loss='categorical_crossentropy', 
> optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
>                                 $$ batch_size=5, epochs=3 $$,  -- fit_params
>                                 10                    -- num_iterations
>                               );
> INFO:  Processed 60 images: Fit took 0.567000865936 sec, Total was 
> 0.757196903229 sec  (seg0 slice1 10.128.0.41:40000 pid=13317)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Processed 60 images: Fit took 0.55348110199 sec, Total was 
> 0.741441011429 sec  (seg1 slice1 10.128.0.41:40001 pid=13318)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Time for training in iteration 1: 2.45737695694 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> change to
> {code}
> INFO:  Time for training in iteration 1: 2.45737695694 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> (2) no validation_table, metrics_compute_frequency!=0
> {code}
> SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
>                                'iris_model',          -- model output table
>                                'model_arch_library',  -- model arch table
>                                 1,                    -- model arch id
>                                 $$ loss='categorical_crossentropy', 
> optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
>                                 $$ batch_size=5, epochs=3 $$,  -- fit_params
>                                 10,                    -- num_iterations
>                                 0,                     -- gpus per host
>                                 NULL,                  -- validation table
>                                 1                      -- metrics compute 
> frequency
>                               );
> INFO:  Processed 60 images: Fit took 0.534310817719 sec, Total was 
> 0.712550878525 sec  (seg0 slice1 10.128.0.41:40000 pid=14501)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Processed 60 images: Fit took 0.564456939697 sec, Total was 
> 0.751413106918 sec  (seg1 slice1 10.128.0.41:40001 pid=14502)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Time for training in iteration 1: 2.28858995438 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Time for evaluation in iteration 1: 0.188971996307 sec.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Training set metric after iteration 1: 0.649999976158.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Training set loss after iteration 1: 1.1202558279.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> change to
> {code}
> INFO:  Time for training in iteration 1: 2.28858995438 sec
>        Time for evaluation in iteration 1: 0.188971996307 sec
>        Training set metric after iteration 1: 0.649999976158
>        Training set loss after iteration 1: 1.1202558279
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> (3) yes validation_table, metrics_compute_frequency=0
> {code}
> SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
>                                'iris_model',          -- model output table
>                                'model_arch_library',  -- model arch table
>                                 1,                    -- model arch id
>                                 $$ loss='categorical_crossentropy', 
> optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
>                                 $$ batch_size=5, epochs=3 $$,  -- fit_params
>                                 10,                   -- num_iterations
>                                 0,                    -- GPUs per host
>                                 'iris_test_packed'   -- validation dataset
>                               );
> INFO:  Processed 60 images: Fit took 0.552575826645 sec, Total was 
> 0.734694004059 sec  (seg0 slice1 10.128.0.41:40000 pid=18431)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Processed 60 images: Fit took 0.549551010132 sec, Total was 
> 0.734927892685 sec  (seg1 slice1 10.128.0.41:40001 pid=18432)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Time for training in iteration 1: 2.36340904236 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> change to
> {code}
> INFO:  Time for training in iteration 1: 2.45737695694 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> (4) yes validation_table, metrics_compute_frequency=!0
> {code}
> SELECT madlib.madlib_keras_fit('iris_train_packed',   -- source table
>                                'iris_model',          -- model output table
>                                'model_arch_library',  -- model arch table
>                                 1,                    -- model arch id
>                                 $$ loss='categorical_crossentropy', 
> optimizer='adam', metrics=['accuracy'] $$,  -- compile_params
>                                 $$ batch_size=5, epochs=3 $$,  -- fit_params
>                                 10,                   -- num_iterations
>                                 0,                    -- GPUs per host
>                                 'iris_test_packed',   -- validation dataset
>                                 1                      -- metrics compute 
> frequency
>                               );
> INFO:  Processed 60 images: Fit took 0.57217502594 sec, Total was 
> 0.817452907562 sec  (seg0 slice1 10.128.0.41:40000 pid=19573)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Processed 60 images: Fit took 0.554927110672 sec, Total was 
> 0.800101041794 sec  (seg1 slice1 10.128.0.41:40001 pid=19574)
> CONTEXT:  PL/Python function "fit_transition"
> INFO:  Time for training in iteration 1: 2.43148899078 sec
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Time for evaluation in iteration 1: 0.217161893845 sec.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Training set metric after iteration 1: 0.524999976158.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Training set loss after iteration 1: 0.984773635864.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Time for evaluation in iteration 1: 0.205282926559 sec.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Validation set metric after iteration 1: 0.600000023842.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> INFO:  Validation set loss after iteration 1: 0.940379023552.
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> change to
> {code}
> INFO:  Time for training in iteration 1: 2.43148899078 sec
>        Time for evaluating training dataset in iteration 1: 0.217161893845 sec
>        Training set metric after iteration 1: 0.524999976158
>        Training set loss after iteration 1: 0.984773635864
>        Time for evaluating validation dataset in iteration 1: 0.205282926559 
> sec
>        Validation set metric after iteration 1: 0.600000023842
>        Validation set loss after iteration 1: 0.940379023552
> CONTEXT:  PL/Python function "madlib_keras_fit"
> {code}
> Note change in wording ^^^ because there are 2 evaluation times.



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