support specifying gpus for tuner and advisor (#1556)

* support specifying gpu for tuner and advisor

docs/en_US/Tutorial/ExperimentConfig.md

@@ -35,7 +35,7 @@ tuner:
   classArgs:
     #choice: maximize, minimize
     optimize_mode:
-  gpuNum:
+  gpuIndices:
 trial:
   command:
   codeDir:
@@ -71,14 +71,13 @@ tuner:
   classArgs:
     #choice: maximize, minimize
     optimize_mode:
-  gpuNum:
+  gpuIndices:
 assessor:
   #choice: Medianstop
   builtinAssessorName:
   classArgs:
     #choice: maximize, minimize
     optimize_mode:
-  gpuNum:
 trial:
   command:
   codeDir:
@@ -113,14 +112,13 @@ tuner:
   classArgs:
     #choice: maximize, minimize
     optimize_mode:
-  gpuNum:
+  gpuIndices:
 assessor:
   #choice: Medianstop
   builtinAssessorName:
   classArgs:
     #choice: maximize, minimize
     optimize_mode:
-  gpuNum:
 trial:
   command:
   codeDir:
@@ -245,11 +243,11 @@ machineList:
   * __builtinTunerName__ and __classArgs__
     * __builtinTunerName__
 
-      __builtinTunerName__ specifies the name of system tuner, NNI sdk provides four kinds of tuner, including {__TPE__, __Random__, __Anneal__, __Evolution__, __BatchTuner__, __GridSearch__}
+      __builtinTunerName__ specifies the name of system tuner, NNI sdk provides different tuners introduced [here](../Tuner/BuiltinTuner.md).
 
     * __classArgs__
 
-      __classArgs__ specifies the arguments of tuner algorithm. If the __builtinTunerName__ is in {__TPE__, __Random__, __Anneal__, __Evolution__}, user should set __optimize_mode__.
+      __classArgs__ specifies the arguments of tuner algorithm. Please refer to [this file](../Tuner/BuiltinTuner.md) for the configurable arguments of each built-in tuner.
   * __codeDir__, __classFileName__, __className__ and __classArgs__
     * __codeDir__
 
@@ -264,16 +262,16 @@ machineList:
 
       __classArgs__ specifies the arguments of tuner algorithm.
 
-  * __gpuNum__
-
-      __gpuNum__ specifies the gpu number to run the tuner process. The value of this field should be a positive number. If the field is not set, NNI will not set `CUDA_VISIBLE_DEVICES` in script (that is, will not control the visibility of GPUs on trial command through `CUDA_VISIBLE_DEVICES`), and will not manage gpu resource.
+  * __gpuIndices__
 
-      Note: users could only specify one way to set tuner, for example, set {tunerName, optimizationMode} or {tunerCommand, tunerCwd}, and could not set them both.
+      __gpuIndices__ specifies the gpus that can be used by the tuner process. Single or multiple GPU indices can be specified, multiple GPU indices are seperated by comma(,), such as `1` or `0,1,3`. If the field is not set, `CUDA_VISIBLE_DEVICES` will be '' in script, that is, no GPU is visible to tuner.
 
   * __includeIntermediateResults__
 
       If __includeIntermediateResults__ is true, the last intermediate result of the trial that is early stopped by assessor is sent to tuner as final result. The default value of __includeIntermediateResults__ is false.
 
+  Note: users could only use one way to specify tuner, either specifying `builtinTunerName` and `classArgs`, or specifying `codeDir`, `classFileName`, `className` and `classArgs`.
+
 * __assessor__
 
   * Description
@@ -282,7 +280,7 @@ machineList:
   * __builtinAssessorName__ and __classArgs__
     * __builtinAssessorName__
 
-      __builtinAssessorName__ specifies the name of system assessor, NNI sdk provides one kind of assessor {__Medianstop__}
+      __builtinAssessorName__ specifies the name of built-in assessor, NNI sdk provides different assessors introducted [here](../Assessor/BuiltinAssessor.md).
     * __classArgs__
 
       __classArgs__ specifies the arguments of assessor algorithm
@@ -305,11 +303,39 @@ machineList:
 
       __classArgs__ specifies the arguments of assessor algorithm.
 
-  * __gpuNum__
+  Note: users could only use one way to specify assessor, either specifying `builtinAssessorName` and `classArgs`, or specifying `codeDir`, `classFileName`, `className` and `classArgs`. If users do not want to use assessor, assessor fileld should leave to empty.
+
+* __advisor__
+  * Description
 
-    __gpuNum__ specifies the gpu number to run the assessor process. The value of this field should be a positive number.
+    __advisor__ specifies the advisor algorithm in the experiment, there are two kinds of ways to specify advisor. One way is to use advisor provided by NNI sdk, need to set __builtinAdvisorName__ and __classArgs__. Another way is to use users' own advisor file, and need to set __codeDirectory__, __classFileName__, __className__ and __classArgs__.
+  * __builtinAdvisorName__ and __classArgs__
+    * __builtinAdvisorName__
 
-    Note: users' could only specify one way to set assessor, for example,set {assessorName, optimizationMode} or {assessorCommand, assessorCwd}, and users could not set them both.If users do not want to use assessor, assessor fileld should leave to empty.
+      __builtinAdvisorName__ specifies the name of a built-in advisor, NNI sdk provides [different advisors](../Tuner/BuiltinTuner.md).
+
+    * __classArgs__
+
+      __classArgs__ specifies the arguments of the advisor algorithm. Please refer to [this file](../Tuner/BuiltinTuner.md) for the configurable arguments of each built-in advisor.
+  * __codeDir__, __classFileName__, __className__ and __classArgs__
+    * __codeDir__
+
+      __codeDir__ specifies the directory of advisor code.
+    * __classFileName__
+
+      __classFileName__ specifies the name of advisor file.
+    * __className__
+
+      __className__ specifies the name of advisor class.
+    * __classArgs__
+
+      __classArgs__ specifies the arguments of advisor algorithm.
+
+  * __gpuIndices__
+
+      __gpuIndices__ specifies the gpus that can be used by the tuner process. Single or multiple GPU indices can be specified, multiple GPU indices are seperated by comma(,), such as `1` or `0,1,3`. If the field is not set, `CUDA_VISIBLE_DEVICES` will be '' in script, that is, no GPU is visible to tuner.
+
+  Note: users could only use one way to specify advisor, either specifying `builtinAdvisorName` and `classArgs`, or specifying `codeDir`, `classFileName`, `className` and `classArgs`.
 
 * __trial(local, remote)__
 
@@ -560,7 +586,6 @@ machineList:
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   trial:
     command: python3 mnist.py
     codeDir: /nni/mnist
@@ -586,14 +611,12 @@ machineList:
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   assessor:
     #choice: Medianstop
     builtinAssessorName: Medianstop
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   trial:
     command: python3 mnist.py
     codeDir: /nni/mnist
@@ -620,15 +643,13 @@ machineList:
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   assessor:
     codeDir: /nni/assessor
     classFileName: myassessor.py
     className: MyAssessor
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   trial:
     command: python3 mnist.py
     codeDir: /nni/mnist
@@ -656,7 +677,6 @@ machineList:
     classArgs:
       #choice: maximize, minimize
       optimize_mode: maximize
-    gpuNum: 0
   trial:
     command: python3 mnist.py
     codeDir: /nni/mnist
@@ -780,7 +800,6 @@ machineList:
     builtinAssessorName: Medianstop
     classArgs:
       optimize_mode: maximize
-    gpuNum: 0
   trial:
     codeDir: .
     worker:

examples/trials/nas_cifar10/config_ppo.yml

@@ -15,6 +15,9 @@ tuner:
     trials_per_update: 60
     epochs_per_update: 12
     minibatch_size: 10
+  #could use the No. 0 gpu for this tuner
+  #if want to specify multiple gpus, here is an example of specifying three gpus: 0,1,2
+  gpuIndices: 0
 trial:
   command: sh ./macro_cifar10.sh
   codeDir: ./

src/nni_manager/common/utils.ts

@@ -219,6 +219,11 @@ function getMsgDispatcherCommand(tuner: any, assessor: any, advisor: any, multiP
         if (advisor.classFileName !== undefined && advisor.classFileName.length > 1) {
             command += ` --advisor_class_filename ${advisor.classFileName}`;
         }
+        if (advisor.gpuIndices !== undefined) {
+            command = `CUDA_VISIBLE_DEVICES=${advisor.gpuIndices} ` + command;
+        } else {
+            command = `CUDA_VISIBLE_DEVICES='' ` + command;
+        }
     } else {
         command += ` --tuner_class_name ${tuner.className}`;
         if (tuner.classArgs !== undefined) {
@@ -243,6 +248,12 @@ function getMsgDispatcherCommand(tuner: any, assessor: any, advisor: any, multiP
                 command += ` --assessor_class_filename ${assessor.classFileName}`;
             }
         }
+
+        if (tuner.gpuIndices !== undefined) {
+            command = `CUDA_VISIBLE_DEVICES=${tuner.gpuIndices} ` + command;
+        } else {
+            command = `CUDA_VISIBLE_DEVICES='' ` + command;
+        }
     }
 
     return command;

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -170,26 +170,25 @@ export namespace ValidationSchemas {
                 classFileName: joi.string(),
                 className: joi.string(),
                 classArgs: joi.any(),
-                gpuNum: joi.number().min(0),
-                checkpointDir: joi.string().allow('')
+                checkpointDir: joi.string().allow(''),
+                gpuIndices: joi.string()
             }),
             tuner: joi.object({
                 builtinTunerName: joi.string().valid('TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'BatchTuner', 'GridSearch', 'NetworkMorphism', 'MetisTuner', 'GPTuner', 'PPOTuner'),
                 codeDir: joi.string(),
                 classFileName: joi.string(),
                 className: joi.string(),
                 classArgs: joi.any(),
-                gpuNum: joi.number().min(0),
                 checkpointDir: joi.string().allow(''),
-                includeIntermediateResults: joi.boolean()
+                includeIntermediateResults: joi.boolean(),
+                gpuIndices: joi.string()
             }),
             assessor: joi.object({
                 builtinAssessorName: joi.string().valid('Medianstop', 'Curvefitting'),
                 codeDir: joi.string(),
                 classFileName: joi.string(),
                 className: joi.string(),
                 classArgs: joi.any(),
-                gpuNum: joi.number().min(0),
                 checkpointDir: joi.string().allow('')
             }),
             clusterMetaData: joi.array().items(joi.object({

src/sdk/pynni/nni/ppo_tuner/ppo_tuner.py

@@ -23,7 +23,6 @@ class PPOTuner
 """
 
 import os
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
 import copy
 import logging
 import numpy as np

test/naive_test/local.yml

@@ -14,14 +14,12 @@ tuner:
     className: NaiveTuner
     classArgs:
         optimize_mode: maximize
-    gpuNum: 0
 assessor:
     codeDir: .
     classFileName: naive_assessor.py
     className: NaiveAssessor
     classArgs:
         optimize_mode: maximize
-    gpuNum: 0
 trial:
     command: python3 naive_trial.py
     codeDir: .

tools/nni_cmd/config_schema.py

@@ -76,7 +76,7 @@ def setPathCheck(key):
             'optimize_mode': setChoice('optimize_mode', 'maximize', 'minimize'),
         },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     ('Evolution'): {
         'builtinTunerName': setChoice('builtinTunerName', 'Evolution'),
@@ -85,12 +85,12 @@ def setPathCheck(key):
             Optional('population_size'): setNumberRange('population_size', int, 0, 99999),
         },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     ('BatchTuner', 'GridSearch', 'Random'): {
         'builtinTunerName': setChoice('builtinTunerName', 'BatchTuner', 'GridSearch', 'Random'),
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'TPE': {
         'builtinTunerName': 'TPE',
@@ -100,7 +100,7 @@ def setPathCheck(key):
             Optional('constant_liar_type'): setChoice('constant_liar_type', 'min', 'max', 'mean')
         },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'NetworkMorphism': {
         'builtinTunerName': 'NetworkMorphism',
@@ -112,7 +112,7 @@ def setPathCheck(key):
             Optional('n_output_node'): setType('n_output_node', int),
             },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'MetisTuner': {
         'builtinTunerName': 'MetisTuner',
@@ -124,7 +124,7 @@ def setPathCheck(key):
             Optional('cold_start_num'): setType('cold_start_num', int),
             },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'GPTuner': {
         'builtinTunerName': 'GPTuner',
@@ -140,7 +140,7 @@ def setPathCheck(key):
             Optional('selection_num_starting_points'):  setType('selection_num_starting_points', int),
             },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool), 
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'PPOTuner': {
         'builtinTunerName': 'PPOTuner',
@@ -158,15 +158,15 @@ def setPathCheck(key):
             Optional('cliprange'): setType('cliprange', float),
         },
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'customized': {
         'codeDir': setPathCheck('codeDir'),
         'classFileName': setType('classFileName', str),
         'className': setType('className', str),
         Optional('classArgs'): dict,
         Optional('includeIntermediateResults'): setType('includeIntermediateResults', bool),
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     }
 }
 
@@ -178,7 +178,7 @@ def setPathCheck(key):
             Optional('R'): setType('R', int),
             Optional('eta'): setType('eta', int)
         },
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'BOHB':{
         'builtinAdvisorName': Or('BOHB'),
@@ -194,14 +194,14 @@ def setPathCheck(key):
             Optional('bandwidth_factor'): setNumberRange('bandwidth_factor', float, 0, 9999),
             Optional('min_bandwidth'): setNumberRange('min_bandwidth', float, 0, 9999),
         },
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     },
     'customized':{
         'codeDir': setPathCheck('codeDir'),
         'classFileName': setType('classFileName', str),
         'className': setType('className', str),
         Optional('classArgs'): dict,
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
+        Optional('gpuIndices'): Or(int, And(str, lambda x: len([int(i) for i in x.split(',')]) > 0), error='gpuIndex format error!'),
     }
 }
 
@@ -212,7 +212,6 @@ def setPathCheck(key):
             Optional('optimize_mode'): setChoice('optimize_mode', 'maximize', 'minimize'),
             Optional('start_step'): setNumberRange('start_step', int, 0, 9999),
         },
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
     },
     'Curvefitting': {
         'builtinAssessorName': 'Curvefitting',
@@ -223,14 +222,12 @@ def setPathCheck(key):
             Optional('threshold'): setNumberRange('threshold', float, 0, 9999),
             Optional('gap'): setNumberRange('gap', int, 1, 9999),
         },
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999),
     },
     'customized': {
         'codeDir': setPathCheck('codeDir'),
         'classFileName': setType('classFileName', str),
         'className': setType('className', str),
         Optional('classArgs'): dict,
-        Optional('gpuNum'): setNumberRange('gpuNum', int, 0, 99999)
     }
 }