src/ports/postgres/modules/deep_learning/madlib_keras_gpu_info.sql_in - madlib - Git at Google

 /* ----------------------------------------------------------------------- *//**
  *
  * Licensed to the Apache Software Foundation (ASF) under one
  * or more contributor license agreements.  See the NOTICE file
  * distributed with this work for additional information
  * regarding copyright ownership.  The ASF licenses this file
  * to you under the Apache License, Version 2.0 (the
  * "License"); you may not use this file except in compliance
  * with the License.  You may obtain a copy of the License at
  *
  *   http://www.apache.org/licenses/LICENSE-2.0
  *
  * Unless required by applicable law or agreed to in writing,
  * software distributed under the License is distributed on an
  * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
  * KIND, either express or implied.  See the License for the
  * specific language governing permissions and limitations
  * under the License.
  *
  *
  * @file madlib_keras_gpu_info.sql_in
  *
  * @brief SQL functions for GPU configuration.
  * @date Nov 2019
  *
  *
  *//* ----------------------------------------------------------------------- */

 m4_include(`SQLCommon.m4')

 /**
 @addtogroup grp_gpu_configuration

 @brief Utility function to report number and type of GPUs in the database cluster.

 \warning <em> This MADlib method is still in early stage development.
 Interface and implementation are subject to change. </em>

 <div class="toc"><b>Contents</b><ul>
 <li class="level1"><a href="#get_gpu_config">GPU Configuration</a></li>
 <li class="level1"><a href="#example">Examples</a></li>
 <li class="level1"><a href="#references">References</a></li>
 <li class="level1"><a href="#related">Related Topics</a></li>
 </ul></div>

 This utility function reports the number and type of GPUs
 attached to hosts on the database cluster.
 This can be useful when determining which segments to use for
 training deep neural nets.  For example, for economic reasons
 you may wish to set up a heterogeneous clusters with GPUs only
 on some of the hosts, not all of them. This utility
 can help you identify where the GPUS are and direct the compute
 to those locations only in subsequent training and evaluation steps.

 @anchor get_gpu_config
 @par GPU Confuguration

 <pre class="syntax">
 gpu_configuration( output_table,
                    source
 	               )
 </pre>
 \b Arguments
 <dl class="arglist">
   <dt>output_table</dt>
   <dd>TEXT. Name of the output table to write out the GPU information.
   </dd>

   <dt>source (optional)</dt>
   <dd>TEXT, default: 'tensorflow'. Source for determining GPU configuration.
   Using 'tensorflow' returns a description based on what TensorFlow reports.
   Using 'nvidia' returns a description based on what the Nvidia Systems
   Management Interface (nvidia-smi) reports [1].  Note that MADlib and Keras will use the
   TensorFlow information; the lower level nvidia-smi info is provided for convenience.

   @note
   If the command 'nvidia-smi -L' returns an error, we do not pass through
   the error message, but instead will show no GPUs for that host in the output table.
   You may want to run nvidia-smi from the command line to see error
   and informational messages.

   </dd>
 </dl>

 <b>Output</b>
 <br>
     The output table contains the following:
     <table class="output">
       <tr>
         <th>hostname</th>
         <td>TEXT. Name of the host machine in the cluster.
         Does not include master or mirrors.  For PostgreSQL
         this will always return 'localhost'.
         </td>
       </tr>
       <tr>
         <th>gpu_descr</th>
         <td>TEXT. String reported by TensorFlow or nvidia-smi.
         The formats are different and shown in the examples below.
         </td>
       </tr>
     </table>
 </br>

 @anchor example
 @par Examples

 -# Get GPU configuration as per TensorFlow:
 <pre class="example">
 DROP TABLE IF EXISTS host_gpu_mapping_tf;
 SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');
 SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;
 </pre>
 <pre class="result">
  hostname |                                        gpu_descr
 ----------+------------------------------------------------------------------------------------------
  phoenix0 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
  phoenix0 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
  phoenix0 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
  phoenix0 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
  phoenix1 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
  phoenix1 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
  phoenix3 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
  phoenix3 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
  phoenix3 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
  phoenix3 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
  phoenix4 | device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
  phoenix4 | device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
  phoenix4 | device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
  phoenix4 | device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
 (14 rows)
 </pre>
 In this heterogeneous cluster there are 4 GPUs attached to hosts 0, 3 and 4.  There are 2 GPUs
 attached to host 1 and no GPUs attached to host 2.

 -# Get GPU configuration as per nvidia-smi:
 <pre class="example">
 DROP TABLE IF EXISTS host_gpu_mapping_nvidia;
 SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_nvidia', -- output table
                                        'nvidia'                   -- source for GPU info
                                        );
 SELECT * FROM host_gpu_mapping_nvidia ORDER BY hostname, gpu_descr;
 </pre>
 <pre class="result">
  hostname |                                  gpu_descr
 ----------+------------------------------------------------------------------------------
  phoenix0 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-f2ccc77e-2501-f6ee-4754-069dda256fb2)
  phoenix0 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-b1fc40ca-c7c6-bc86-f20f-6e9a62cda3f8)
  phoenix0 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-d93bb21b-96f9-7c1d-3bab-cdd92b7bbc9d)
  phoenix0 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-2d79c4a8-479e-2f33-39f8-3ba80b63f830)
  phoenix1 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-0af6bb1e-5b5b-4988-ad3a-a917e9584702)
  phoenix1 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d824c976-a8aa-ef26-a13c-9a9a7fe86bfd)
  phoenix3 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-3681d0b6-1ec6-0453-fd81-29d88e549cd9)
  phoenix3 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d4b1f2e7-b238-ac9a-bbfe-918adeb69472)
  phoenix3 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-42a32ef1-a60c-e599-c8cf-0e669111ab6f)
  phoenix3 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-1cce09c4-6856-8031-be0b-8e8bbf9a10f3)
  phoenix4 | GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-a71bdc18-fdd5-ba25-617e-19b23cc8e827)
  phoenix4 | GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-f9d13688-7fe6-a029-24d1-985a5659f18f)
  phoenix4 | GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-06a7f54b-c07a-e87a-20d6-09bd99b19531)
  phoenix4 | GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-af3b32f3-8bd9-cb75-a8fb-25253b9da926)
 (14 rows)
 </pre>

 -# To get a fuller picture at the segment level, combine with the Greenplum catalog
 table 'gp_segment_configuration' which contains information about
 segment instance configuration [2].  Here is an example of this table
 filtering out master and mirrors:
 <pre class="example">
 SELECT * FROM gp_segment_configuration WHERE role='p' AND content>=0 ORDER BY hostname, dbid;
 </pre>
 <pre class="result">
  dbid | content | role | preferred_role | mode | status | port  | hostname | address  | replication_port
 ------+---------+------+----------------+------+--------+-------+----------+----------+------------------
     2 |       0 | p    | p              | c    | u      | 40000 | phoenix0 | phoenix0 |            70000
     3 |       1 | p    | p              | c    | u      | 40001 | phoenix0 | phoenix0 |            70001
     4 |       2 | p    | p              | c    | u      | 40002 | phoenix0 | phoenix0 |            70002
     5 |       3 | p    | p              | c    | u      | 40003 | phoenix0 | phoenix0 |            70003
     6 |       4 | p    | p              | c    | u      | 40000 | phoenix1 | phoenix1 |            70000
     7 |       5 | p    | p              | c    | u      | 40001 | phoenix1 | phoenix1 |            70001
     8 |       6 | p    | p              | c    | u      | 40002 | phoenix1 | phoenix1 |            70002
     9 |       7 | p    | p              | c    | u      | 40003 | phoenix1 | phoenix1 |            70003
    10 |       8 | p    | p              | c    | u      | 40000 | phoenix2 | phoenix2 |            70000
    11 |       9 | p    | p              | c    | u      | 40001 | phoenix2 | phoenix2 |            70001
    12 |      10 | p    | p              | c    | u      | 40002 | phoenix2 | phoenix2 |            70002
    13 |      11 | p    | p              | c    | u      | 40003 | phoenix2 | phoenix2 |            70003
    14 |      12 | p    | p              | c    | u      | 40000 | phoenix3 | phoenix3 |            70000
    15 |      13 | p    | p              | c    | u      | 40001 | phoenix3 | phoenix3 |            70001
    16 |      14 | p    | p              | c    | u      | 40002 | phoenix3 | phoenix3 |            70002
    17 |      15 | p    | p              | c    | u      | 40003 | phoenix3 | phoenix3 |            70003
    18 |      16 | p    | p              | c    | u      | 40000 | phoenix4 | phoenix4 |            70000
    19 |      17 | p    | p              | c    | u      | 40001 | phoenix4 | phoenix4 |            70001
    20 |      18 | p    | p              | c    | u      | 40002 | phoenix4 | phoenix4 |            70002
    21 |      19 | p    | p              | c    | u      | 40003 | phoenix4 | phoenix4 |            70003
 (20 rows)
 </pre>
 Now join this table with the GPU resources table to create a table containing a
 list of all segments on hosts with GPUs attached:
 <pre class="example">
 DROP TABLE IF EXISTS segments_to_use;
 CREATE TABLE segments_to_use AS
   SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)
   WHERE role='p' AND content>=0;
 SELECT * FROM segments_to_use ORDER BY hostname, dbid;
 </pre>
 <pre class="result">
  dbid | hostname
 ------+----------
     2 | phoenix0
     3 | phoenix0
     4 | phoenix0
     5 | phoenix0
     6 | phoenix1
     7 | phoenix1
     8 | phoenix1
     9 | phoenix1
    14 | phoenix3
    15 | phoenix3
    16 | phoenix3
    17 | phoenix3
    18 | phoenix4
    19 | phoenix4
    20 | phoenix4
    21 | phoenix4
 (16 rows)
 </pre>

 @anchor references
 @par References

 [1] Nvidia Systems Management Interface (nvidia-smi) https://developer.nvidia.com/nvidia-system-management-interface

 [2] Greenplum 'gp_segment_configuration' table https://gpdb.docs.pivotal.io/latest/ref_guide/system_catalogs/gp_segment_configuration.html

 @anchor related
 @par Related Topics

 See madlib_keras_gpu_info.sql_in

 */

 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_tensorflow() RETURNS TEXT[] as
 $$
     PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
     return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_tensorflow()
 $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_nvidia() RETURNS TEXT[] as
 $$
     PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
     return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_nvidia()
 $$ LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text, source text)
 RETURNS VOID AS
 $$
     PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
     from utilities.control import MinWarning
     with AOControl(False) and MinWarning("error"):
         madlib_keras_gpu_info.gpu_configuration(schema_madlib, output_table, source)
 $$
 LANGUAGE plpythonu
 m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text)
 RETURNS VOID AS
 $$
     SELECT MADLIB_SCHEMA.gpu_configuration($1, NULL);
 $$
 LANGUAGE sql;


 CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration()
     RETURNS VARCHAR AS $$
     PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
     return madlib_keras_gpu_info.gpu_configuration_help(schema_madlib)
 $$ LANGUAGE plpythonu IMMUTABLE
     m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');
	/* ----------------------------------------------------------------------- //*
	*
	* Licensed to the Apache Software Foundation (ASF) under one
	* or more contributor license agreements. See the NOTICE file
	* distributed with this work for additional information
	* regarding copyright ownership. The ASF licenses this file
	* to you under the Apache License, Version 2.0 (the
	* "License"); you may not use this file except in compliance
	* with the License. You may obtain a copy of the License at
	*
	* http://www.apache.org/licenses/LICENSE-2.0
	*
	* Unless required by applicable law or agreed to in writing,
	* software distributed under the License is distributed on an
	* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
	* KIND, either express or implied. See the License for the
	* specific language governing permissions and limitations
	* under the License.
	*
	*
	* @file madlib_keras_gpu_info.sql_in
	*
	* @brief SQL functions for GPU configuration.
	* @date Nov 2019
	*
	*
	// ----------------------------------------------------------------------- */

	m4_include(`SQLCommon.m4')

	/**
	@addtogroup grp_gpu_configuration

	@brief Utility function to report number and type of GPUs in the database cluster.

	\warning <em> This MADlib method is still in early stage development.
	Interface and implementation are subject to change. </em>

	<div class="toc"><b>Contents</b><ul>
	<li class="level1"><a href="#get_gpu_config">GPU Configuration</a></li>
	<li class="level1"><a href="#example">Examples</a></li>
	<li class="level1"><a href="#references">References</a></li>
	<li class="level1"><a href="#related">Related Topics</a></li>
	</ul></div>

	This utility function reports the number and type of GPUs
	attached to hosts on the database cluster.
	This can be useful when determining which segments to use for
	training deep neural nets. For example, for economic reasons
	you may wish to set up a heterogeneous clusters with GPUs only
	on some of the hosts, not all of them. This utility
	can help you identify where the GPUS are and direct the compute
	to those locations only in subsequent training and evaluation steps.

	@anchor get_gpu_config
	@par GPU Confuguration

	<pre class="syntax">
	gpu_configuration( output_table,
	source
	)
	</pre>
	\b Arguments
	<dl class="arglist">
	<dt>output_table</dt>
	<dd>TEXT. Name of the output table to write out the GPU information.
	</dd>

	<dt>source (optional)</dt>
	<dd>TEXT, default: 'tensorflow'. Source for determining GPU configuration.
	Using 'tensorflow' returns a description based on what TensorFlow reports.
	Using 'nvidia' returns a description based on what the Nvidia Systems
	Management Interface (nvidia-smi) reports [1]. Note that MADlib and Keras will use the
	TensorFlow information; the lower level nvidia-smi info is provided for convenience.

	@note
	If the command 'nvidia-smi -L' returns an error, we do not pass through
	the error message, but instead will show no GPUs for that host in the output table.
	You may want to run nvidia-smi from the command line to see error
	and informational messages.

	</dd>
	</dl>

	<b>Output</b>
	<br>
	The output table contains the following:
	<table class="output">
	<tr>
	<th>hostname</th>
	<td>TEXT. Name of the host machine in the cluster.
	Does not include master or mirrors. For PostgreSQL
	this will always return 'localhost'.
	</td>
	</tr>
	<tr>
	<th>gpu_descr</th>
	<td>TEXT. String reported by TensorFlow or nvidia-smi.
	The formats are different and shown in the examples below.
	</td>
	</tr>
	</table>
	</br>

	@anchor example
	@par Examples

	-# Get GPU configuration as per TensorFlow:
	<pre class="example">
	DROP TABLE IF EXISTS host_gpu_mapping_tf;
	SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_tf');
	SELECT * FROM host_gpu_mapping_tf ORDER BY hostname, gpu_descr;
	</pre>
	<pre class="result">
	hostname \| gpu_descr
	----------+------------------------------------------------------------------------------------------
	phoenix0 \| device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
	phoenix0 \| device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
	phoenix0 \| device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
	phoenix0 \| device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
	phoenix1 \| device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
	phoenix1 \| device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
	phoenix3 \| device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
	phoenix3 \| device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
	phoenix3 \| device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
	phoenix3 \| device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
	phoenix4 \| device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0
	phoenix4 \| device: 1, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:05.0, compute capability: 6.0
	phoenix4 \| device: 2, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:06.0, compute capability: 6.0
	phoenix4 \| device: 3, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:07.0, compute capability: 6.0
	(14 rows)
	</pre>
	In this heterogeneous cluster there are 4 GPUs attached to hosts 0, 3 and 4. There are 2 GPUs
	attached to host 1 and no GPUs attached to host 2.

	-# Get GPU configuration as per nvidia-smi:
	<pre class="example">
	DROP TABLE IF EXISTS host_gpu_mapping_nvidia;
	SELECT * FROM madlib.gpu_configuration('host_gpu_mapping_nvidia', -- output table
	'nvidia' -- source for GPU info
	);
	SELECT * FROM host_gpu_mapping_nvidia ORDER BY hostname, gpu_descr;
	</pre>
	<pre class="result">
	hostname \| gpu_descr
	----------+------------------------------------------------------------------------------
	phoenix0 \| GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-f2ccc77e-2501-f6ee-4754-069dda256fb2)
	phoenix0 \| GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-b1fc40ca-c7c6-bc86-f20f-6e9a62cda3f8)
	phoenix0 \| GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-d93bb21b-96f9-7c1d-3bab-cdd92b7bbc9d)
	phoenix0 \| GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-2d79c4a8-479e-2f33-39f8-3ba80b63f830)
	phoenix1 \| GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-0af6bb1e-5b5b-4988-ad3a-a917e9584702)
	phoenix1 \| GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d824c976-a8aa-ef26-a13c-9a9a7fe86bfd)
	phoenix3 \| GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-3681d0b6-1ec6-0453-fd81-29d88e549cd9)
	phoenix3 \| GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-d4b1f2e7-b238-ac9a-bbfe-918adeb69472)
	phoenix3 \| GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-42a32ef1-a60c-e599-c8cf-0e669111ab6f)
	phoenix3 \| GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-1cce09c4-6856-8031-be0b-8e8bbf9a10f3)
	phoenix4 \| GPU 0: Tesla P100-PCIE-16GB (UUID: GPU-a71bdc18-fdd5-ba25-617e-19b23cc8e827)
	phoenix4 \| GPU 1: Tesla P100-PCIE-16GB (UUID: GPU-f9d13688-7fe6-a029-24d1-985a5659f18f)
	phoenix4 \| GPU 2: Tesla P100-PCIE-16GB (UUID: GPU-06a7f54b-c07a-e87a-20d6-09bd99b19531)
	phoenix4 \| GPU 3: Tesla P100-PCIE-16GB (UUID: GPU-af3b32f3-8bd9-cb75-a8fb-25253b9da926)
	(14 rows)
	</pre>

	-# To get a fuller picture at the segment level, combine with the Greenplum catalog
	table 'gp_segment_configuration' which contains information about
	segment instance configuration [2]. Here is an example of this table
	filtering out master and mirrors:
	<pre class="example">
	SELECT * FROM gp_segment_configuration WHERE role='p' AND content>=0 ORDER BY hostname, dbid;
	</pre>
	<pre class="result">
	dbid \| content \| role \| preferred_role \| mode \| status \| port \| hostname \| address \| replication_port
	------+---------+------+----------------+------+--------+-------+----------+----------+------------------
	2 \| 0 \| p \| p \| c \| u \| 40000 \| phoenix0 \| phoenix0 \| 70000
	3 \| 1 \| p \| p \| c \| u \| 40001 \| phoenix0 \| phoenix0 \| 70001
	4 \| 2 \| p \| p \| c \| u \| 40002 \| phoenix0 \| phoenix0 \| 70002
	5 \| 3 \| p \| p \| c \| u \| 40003 \| phoenix0 \| phoenix0 \| 70003
	6 \| 4 \| p \| p \| c \| u \| 40000 \| phoenix1 \| phoenix1 \| 70000
	7 \| 5 \| p \| p \| c \| u \| 40001 \| phoenix1 \| phoenix1 \| 70001
	8 \| 6 \| p \| p \| c \| u \| 40002 \| phoenix1 \| phoenix1 \| 70002
	9 \| 7 \| p \| p \| c \| u \| 40003 \| phoenix1 \| phoenix1 \| 70003
	10 \| 8 \| p \| p \| c \| u \| 40000 \| phoenix2 \| phoenix2 \| 70000
	11 \| 9 \| p \| p \| c \| u \| 40001 \| phoenix2 \| phoenix2 \| 70001
	12 \| 10 \| p \| p \| c \| u \| 40002 \| phoenix2 \| phoenix2 \| 70002
	13 \| 11 \| p \| p \| c \| u \| 40003 \| phoenix2 \| phoenix2 \| 70003
	14 \| 12 \| p \| p \| c \| u \| 40000 \| phoenix3 \| phoenix3 \| 70000
	15 \| 13 \| p \| p \| c \| u \| 40001 \| phoenix3 \| phoenix3 \| 70001
	16 \| 14 \| p \| p \| c \| u \| 40002 \| phoenix3 \| phoenix3 \| 70002
	17 \| 15 \| p \| p \| c \| u \| 40003 \| phoenix3 \| phoenix3 \| 70003
	18 \| 16 \| p \| p \| c \| u \| 40000 \| phoenix4 \| phoenix4 \| 70000
	19 \| 17 \| p \| p \| c \| u \| 40001 \| phoenix4 \| phoenix4 \| 70001
	20 \| 18 \| p \| p \| c \| u \| 40002 \| phoenix4 \| phoenix4 \| 70002
	21 \| 19 \| p \| p \| c \| u \| 40003 \| phoenix4 \| phoenix4 \| 70003
	(20 rows)
	</pre>
	Now join this table with the GPU resources table to create a table containing a
	list of all segments on hosts with GPUs attached:
	<pre class="example">
	DROP TABLE IF EXISTS segments_to_use;
	CREATE TABLE segments_to_use AS
	SELECT DISTINCT dbid, hostname FROM gp_segment_configuration JOIN host_gpu_mapping_tf USING (hostname)
	WHERE role='p' AND content>=0;
	SELECT * FROM segments_to_use ORDER BY hostname, dbid;
	</pre>
	<pre class="result">
	dbid \| hostname
	------+----------
	2 \| phoenix0
	3 \| phoenix0
	4 \| phoenix0
	5 \| phoenix0
	6 \| phoenix1
	7 \| phoenix1
	8 \| phoenix1
	9 \| phoenix1
	14 \| phoenix3
	15 \| phoenix3
	16 \| phoenix3
	17 \| phoenix3
	18 \| phoenix4
	19 \| phoenix4
	20 \| phoenix4
	21 \| phoenix4
	(16 rows)
	</pre>

	@anchor references
	@par References

	[1] Nvidia Systems Management Interface (nvidia-smi) https://developer.nvidia.com/nvidia-system-management-interface

	[2] Greenplum 'gp_segment_configuration' table https://gpdb.docs.pivotal.io/latest/ref_guide/system_catalogs/gp_segment_configuration.html

	@anchor related
	@par Related Topics

	See madlib_keras_gpu_info.sql_in

	*/

	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_tensorflow() RETURNS TEXT[] as
	$$
	PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
	return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_tensorflow()
	$$ LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_info_nvidia() RETURNS TEXT[] as
	$$
	PythonFunctionBodyOnlyNoSchema(`deep_learning', `madlib_keras_gpu_info')
	return madlib_keras_gpu_info.GPUInfoFunctions.get_gpu_info_from_nvidia()
	$$ LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text, source text)
	RETURNS VOID AS
	$$
	PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
	from utilities.control import MinWarning
	with AOControl(False) and MinWarning("error"):
	madlib_keras_gpu_info.gpu_configuration(schema_madlib, output_table, source)
	$$
	LANGUAGE plpythonu
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `NO SQL', `');

	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration(output_table text)
	RETURNS VOID AS
	$$
	SELECT MADLIB_SCHEMA.gpu_configuration($1, NULL);
	$$
	LANGUAGE sql;


	CREATE OR REPLACE FUNCTION MADLIB_SCHEMA.gpu_configuration()
	RETURNS VARCHAR AS $$
	PythonFunctionBodyOnly(`deep_learning', `madlib_keras_gpu_info')
	return madlib_keras_gpu_info.gpu_configuration_help(schema_madlib)
	$$ LANGUAGE plpythonu IMMUTABLE
	m4_ifdef(`__HAS_FUNCTION_PROPERTIES__', `CONTAINS SQL', `');