Merge branch 'master' of https://github.com/SparkSnail/nni

src/sdk/pynni/nni/metis_tuner/Regression_GP/Prediction.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge,
+# to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import os
+import sys
+
+import numpy
+
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+
+
+def predict(parameters_value, regressor_gp):
+    '''
+    Predict by Gaussian Process Model
+    '''
+    parameters_value = numpy.array(parameters_value).reshape(-1, len(parameters_value))
+    mu, sigma = regressor_gp.predict(parameters_value, return_std=True)
+
+    return mu[0], sigma[0]
+    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/Regression_GP/Selection.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge,
+# to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import os
+import random
+import sys
+
+import nni.metis_tuner.lib_acquisition_function as lib_acquisition_function
+import nni.metis_tuner.lib_constraint_summation as lib_constraint_summation
+import nni.metis_tuner.lib_data as lib_data
+import nni.metis_tuner.Regression_GP.Prediction as gp_prediction
+
+sys.path.insert(1, os.path.join(sys.path[0], '..'))
+
+CONSTRAINT_LOWERBOUND = None
+CONSTRAINT_UPPERBOUND = None
+CONSTRAINT_PARAMS_IDX = []
+
+
+def selection_r(acquisition_function,
+                samples_y_aggregation,
+                x_bounds,
+                x_types,
+                regressor_gp,
+                num_starting_points=100,
+                minimize_constraints_fun=None):
+    '''
+    Selecte R value
+    '''
+    minimize_starting_points = [lib_data.rand(x_bounds, x_types) \
+                                    for i in range(0, num_starting_points)]
+    outputs = selection(acquisition_function, samples_y_aggregation,
+                        x_bounds, x_types, regressor_gp,
+                        minimize_starting_points,
+                        minimize_constraints_fun=minimize_constraints_fun)
+
+    return outputs
+
+def selection(acquisition_function,
+              samples_y_aggregation,
+              x_bounds, x_types,
+              regressor_gp,
+              minimize_starting_points,
+              minimize_constraints_fun=None):
+    '''
+    selection
+    '''
+    outputs = None
+
+    sys.stderr.write("[%s] Exercise \"%s\" acquisition function\n" \
+                        % (os.path.basename(__file__), acquisition_function))
+
+    if acquisition_function == "ei":
+        outputs = lib_acquisition_function.next_hyperparameter_expected_improvement(\
+                        gp_prediction.predict, [regressor_gp], x_bounds, x_types, \
+                        samples_y_aggregation, minimize_starting_points, \
+                        minimize_constraints_fun=minimize_constraints_fun)
+    elif acquisition_function == "lc":
+        outputs = lib_acquisition_function.next_hyperparameter_lowest_confidence(\
+                        gp_prediction.predict, [regressor_gp], x_bounds, x_types,\
+                        minimize_starting_points, minimize_constraints_fun=minimize_constraints_fun)
+    elif acquisition_function == "lm":
+        outputs = lib_acquisition_function.next_hyperparameter_lowest_mu(\
+                        gp_prediction.predict, [regressor_gp], x_bounds, x_types,\
+                        minimize_starting_points, minimize_constraints_fun=minimize_constraints_fun)
+    return outputs
+
+def _rand_with_constraints(x_bounds, x_types):
+    '''
+    Random generate with constraints
+    '''
+    outputs = None
+
+    x_bounds_withconstraints = [x_bounds[i] for i in CONSTRAINT_PARAMS_IDX]
+    x_types_withconstraints = [x_types[i] for i in CONSTRAINT_PARAMS_IDX]
+    x_val_withconstraints = lib_constraint_summation.rand(x_bounds_withconstraints,
+                                                          x_types_withconstraints,
+                                                          CONSTRAINT_LOWERBOUND,
+                                                          CONSTRAINT_UPPERBOUND)
+    if x_val_withconstraints is not None:
+        outputs = [None] * len(x_bounds)
+
+        for i, _ in enumerate(CONSTRAINT_PARAMS_IDX):
+            outputs[CONSTRAINT_PARAMS_IDX[i]] = x_val_withconstraints[i]
+
+        for i, _ in enumerate(outputs):
+            if outputs[i] is None:
+                outputs[i] = random.randint(x_bounds[i][0], x_bounds[i][1])
+    return outputs
+
+
+def _minimize_constraints_fun_summation(x):
+    '''
+    Minimize the constraints fun summation
+    '''
+    summation = sum([x[i] for i in CONSTRAINT_PARAMS_IDX])
+    return CONSTRAINT_UPPERBOUND >= summation >= CONSTRAINT_LOWERBOUND

src/sdk/pynni/nni/metis_tuner/lib_acquisition_function.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge,
+# to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import sys
+import numpy
+
+from scipy.stats import norm
+from scipy.optimize import minimize
+
+import nni.metis_tuner.lib_data as lib_data
+
+
+def next_hyperparameter_expected_improvement(fun_prediction,   
+                                             fun_prediction_args,
+                                             x_bounds, x_types,
+                                             samples_y_aggregation,
+                                             minimize_starting_points,
+                                             minimize_constraints_fun=None):
+    '''
+    "Expected Improvement" acquisition function
+    '''
+    best_x = None
+    best_acquisition_value = None
+    x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
+    x_bounds_minmax = numpy.array(x_bounds_minmax)
+
+    for starting_point in numpy.array(minimize_starting_points):
+        res = minimize(fun=_expected_improvement,
+                       x0=starting_point.reshape(1, -1),
+                       bounds=x_bounds_minmax,
+                       method="L-BFGS-B",
+                       args=(fun_prediction,
+                             fun_prediction_args,
+                             x_bounds,
+                             x_types,
+                             samples_y_aggregation,
+                             minimize_constraints_fun))
+
+        if (best_acquisition_value is None) or \
+                (res.fun < best_acquisition_value):
+            res.x = numpy.ndarray.tolist(res.x)
+            res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
+            if (minimize_constraints_fun is None) or \
+                    (minimize_constraints_fun(res.x) is True):
+                best_acquisition_value = res.fun
+                best_x = res.x
+
+    outputs = None
+    if best_x is not None:
+        mu, sigma = fun_prediction(best_x, *fun_prediction_args)
+        outputs = {'hyperparameter': best_x, 'expected_mu': mu,
+                   'expected_sigma': sigma, 'acquisition_func': "ei"}
+
+    return outputs
+
+def _expected_improvement(x, fun_prediction, fun_prediction_args,
+                          x_bounds, x_types, samples_y_aggregation,
+                          minimize_constraints_fun):
+    # This is only for step-wise optimization
+    x = lib_data.match_val_type(x, x_bounds, x_types)
+
+    expected_improvement = sys.maxsize
+    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+        mu, sigma = fun_prediction(x, *fun_prediction_args)
+
+        loss_optimum = min(samples_y_aggregation)
+        scaling_factor = -1
+
+        # In case sigma equals zero
+        with numpy.errstate(divide="ignore"):
+            Z = scaling_factor * (mu - loss_optimum) / sigma
+            expected_improvement = scaling_factor * (mu - loss_optimum) * \
+                                        norm.cdf(Z) + sigma * norm.pdf(Z)
+            expected_improvement = 0.0 if sigma == 0.0 else expected_improvement
+
+        # We want expected_improvement to be as large as possible
+        # (i.e., as small as possible for minimize(...))
+        expected_improvement = -1 * expected_improvement
+    return expected_improvement
+
+
+def next_hyperparameter_lowest_confidence(fun_prediction,
+                                          fun_prediction_args,
+                                          x_bounds, x_types,
+                                          minimize_starting_points,
+                                          minimize_constraints_fun=None):
+    '''
+    "Lowest Confidence" acquisition function
+    '''
+    best_x = None
+    best_acquisition_value = None
+    x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
+    x_bounds_minmax = numpy.array(x_bounds_minmax)
+
+    for starting_point in numpy.array(minimize_starting_points):
+        res = minimize(fun=_lowest_confidence,
+                       x0=starting_point.reshape(1, -1),
+                       bounds=x_bounds_minmax,
+                       method="L-BFGS-B",
+                       args=(fun_prediction,
+                             fun_prediction_args,
+                             x_bounds,
+                             x_types,
+                             minimize_constraints_fun))
+
+        if (best_acquisition_value) is None or (res.fun < best_acquisition_value):
+            res.x = numpy.ndarray.tolist(res.x)
+            res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
+            if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
+                best_acquisition_value = res.fun
+                best_x = res.x
+
+    outputs = None
+    if best_x is not None:
+        mu, sigma = fun_prediction(best_x, *fun_prediction_args)
+        outputs = {'hyperparameter': best_x, 'expected_mu': mu,
+                   'expected_sigma': sigma, 'acquisition_func': "lc"}
+    return outputs
+
+def _lowest_confidence(x, fun_prediction, fun_prediction_args,
+                       x_bounds, x_types, minimize_constraints_fun):
+    # This is only for step-wise optimization
+    x = lib_data.match_val_type(x, x_bounds, x_types)
+
+    ci = sys.maxsize
+    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+        mu, sigma = fun_prediction(x, *fun_prediction_args)
+        ci = (sigma * 1.96 * 2) / mu
+        # We want ci to be as large as possible
+        # (i.e., as small as possible for minimize(...),
+        # because this would mean lowest confidence
+        ci = -1 * ci
+
+    return ci
+
+
+def next_hyperparameter_lowest_mu(fun_prediction,
+                                  fun_prediction_args,
+                                  x_bounds, x_types,
+                                  minimize_starting_points,
+                                  minimize_constraints_fun=None):
+    '''
+    "Lowest Mu" acquisition function
+    '''
+    best_x = None
+    best_acquisition_value = None
+    x_bounds_minmax = [[i[0], i[-1]] for i in x_bounds]
+    x_bounds_minmax = numpy.array(x_bounds_minmax)
+
+    for starting_point in numpy.array(minimize_starting_points):
+        res = minimize(fun=_lowest_mu,
+                       x0=starting_point.reshape(1, -1),
+                       bounds=x_bounds_minmax,
+                       method="L-BFGS-B",
+                       args=(fun_prediction, fun_prediction_args, \
+                             x_bounds, x_types, minimize_constraints_fun))
+
+        if (best_acquisition_value is None) or (res.fun < best_acquisition_value):
+            res.x = numpy.ndarray.tolist(res.x)
+            res.x = lib_data.match_val_type(res.x, x_bounds, x_types)
+            if (minimize_constraints_fun is None) or (minimize_constraints_fun(res.x) is True):
+                best_acquisition_value = res.fun
+                best_x = res.x
+
+    outputs = None
+    if best_x is not None:
+        mu, sigma = fun_prediction(best_x, *fun_prediction_args)
+        outputs = {'hyperparameter': best_x, 'expected_mu': mu,
+                   'expected_sigma': sigma, 'acquisition_func': "lm"}
+    return outputs
+
+
+def _lowest_mu(x, fun_prediction, fun_prediction_args,
+               x_bounds, x_types, minimize_constraints_fun):
+    '''
+    Calculate the lowest mu
+    '''
+    # This is only for step-wise optimization
+    x = lib_data.match_val_type(x, x_bounds, x_types)
+
+    mu = sys.maxsize
+    if (minimize_constraints_fun is None) or (minimize_constraints_fun(x) is True):
+        mu, _ = fun_prediction(x, *fun_prediction_args)
+    return mu
+    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/lib_constraint_summation.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge,
+# to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import math
+import random
+
+from operator import itemgetter
+
+
+def check_feasibility(x_bounds, lowerbound, upperbound):
+    '''
+    This can have false positives.
+    For examples, parameters can only be 0 or 5, and the summation constraint is between 6 and 7.
+    '''
+    # x_bounds should be sorted, so even for "discrete_int" type,
+    # the smallest and the largest number should the first and the last element
+    x_bounds_lowerbound = sum([x_bound[0] for x_bound in x_bounds])
+    x_bounds_upperbound = sum([x_bound[-1] for x_bound in x_bounds])
+
+    # return ((x_bounds_lowerbound <= lowerbound) and (x_bounds_upperbound >= lowerbound)) or \
+    #        ((x_bounds_lowerbound <= upperbound) and (x_bounds_upperbound >= upperbound))
+    return (x_bounds_lowerbound <= lowerbound <= x_bounds_upperbound) or \
+           (x_bounds_lowerbound <= upperbound <= x_bounds_upperbound)
+
+def rand(x_bounds, x_types, lowerbound, upperbound, max_retries=100):
+    '''
+    Key idea is that we try to move towards upperbound, by randomly choose one
+    value for each parameter. However, for the last parameter,
+    we need to make sure that its value can help us get above lowerbound
+    '''
+    outputs = None
+
+    if check_feasibility(x_bounds, lowerbound, upperbound) is True:
+        # Order parameters by their range size. We want the smallest range first,
+        # because the corresponding parameter has less numbers to choose from
+        x_idx_sorted = []
+        for i, _ in enumerate(x_bounds):
+            if x_types[i] == "discrete_int":
+                x_idx_sorted.append([i, len(x_bounds[i])])
+            elif (x_types[i] == "range_int") or (x_types[i] == "range_continuous"):
+                x_idx_sorted.append([i, math.floor(x_bounds[i][1] - x_bounds[i][0])])
+        x_idx_sorted = sorted(x_idx_sorted, key=itemgetter(1))
+
+        for _ in range(max_retries):
+            budget_allocated = 0
+            outputs = [None] * len(x_bounds)
+
+            for i, _ in enumerate(x_idx_sorted):
+                x_idx = x_idx_sorted[i][0]
+                # The amount of unallocated space that we have
+                budget_max = upperbound - budget_allocated
+                # NOT the Last x that we need to assign a random number
+                if i < (len(x_idx_sorted) - 1):
+                    if x_bounds[x_idx][0] <= budget_max:
+                        if x_types[x_idx] == "discrete_int":
+                            # Note the valid integer
+                            temp = []
+                            for j in x_bounds[x_idx]:
+                                if j <= budget_max:
+                                    temp.append(j)
+                            # Randomly pick a number from the integer array
+                            if temp:
+                                outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
+
+                        elif (x_types[x_idx] == "range_int") or \
+                                    (x_types[x_idx] == "range_continuous"):
+                            outputs[x_idx] = random.randint(x_bounds[x_idx][0],
+                                                            min(x_bounds[x_idx][-1], budget_max))
+
+                else:
+                    # The last x that we need to assign a random number
+                    randint_lowerbound = lowerbound - budget_allocated
+                    randint_lowerbound = 0 if randint_lowerbound < 0 else randint_lowerbound
+
+                    # This check:
+                    # is our smallest possible value going to overflow the available budget space,
+                    # and is our largest possible value going to underflow the lower bound
+                    if (x_bounds[x_idx][0] <= budget_max) and \
+                            (x_bounds[x_idx][-1] >= randint_lowerbound):
+                        if x_types[x_idx] == "discrete_int":
+                            temp = []
+                            for j in x_bounds[x_idx]:
+                                # if (j <= budget_max) and (j >= randint_lowerbound):
+                                if randint_lowerbound <= j <= budget_max:
+                                    temp.append(j)
+                            if temp:
+                                outputs[x_idx] = temp[random.randint(0, len(temp) - 1)]
+                        elif (x_types[x_idx] == "range_int") or \
+                                (x_types[x_idx] == "range_continuous"):
+                            outputs[x_idx] = random.randint(randint_lowerbound,
+                                                            min(x_bounds[x_idx][1], budget_max))
+                if outputs[x_idx] is None:
+                    break
+                else:
+                    budget_allocated += outputs[x_idx]
+            if None not in outputs:
+                break
+    return outputs
+    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/lib_data.py

+# Copyright (c) Microsoft Corporation
+# All rights reserved.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge,
+# to any person obtaining a copy of this software and associated
+# documentation files (the "Software"), to deal in the Software without restriction,
+# including without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and
+# to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED *AS IS*, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING
+# BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
+# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+import math
+import random
+
+
+def match_val_type(vals, vals_bounds, vals_types):
+    '''
+    Update values in the array, to match their corresponding type
+    '''
+    vals_new = []
+
+    for i, _ in enumerate(vals_types):
+        if vals_types[i] == "discrete_int":
+            # Find the closest integer in the array, vals_bounds
+            vals_new.append(min(vals_bounds[i], key=lambda x: abs(x - vals[i])))
+        elif vals_types[i] == "range_int":
+            # Round down to the nearest integer
+            vals_new.append(math.floor(vals[i]))
+        elif vals_types[i] == "range_continuous":
+            # Don't do any processing for continous numbers
+            vals_new.append(vals[i])
+        else:
+            return None
+
+    return vals_new
+
+
+def rand(x_bounds, x_types):
+    '''
+    Random generate variable value within their bounds
+    '''
+    outputs = []
+
+    for i, _ in enumerate(x_bounds):
+        if x_types[i] == "discrete_int":
+            temp = x_bounds[i][random.randint(0, len(x_bounds[i]) - 1)]
+            outputs.append(temp)
+        elif x_types[i] == "range_int":
+            temp = random.randint(x_bounds[i][0], x_bounds[i][1])
+            outputs.append(temp)
+        elif x_types[i] == "range_continuous":
+            temp = random.uniform(x_bounds[i][0], x_bounds[i][1])
+            outputs.append(temp)
+        else:
+            return None
+
+    return outputs
+    
\ No newline at end of file

src/sdk/pynni/nni/metis_tuner/requirments.txt

+sklearn
\ No newline at end of file

src/sdk/pynni/requirements.txt

@@ -4,4 +4,7 @@ json_tricks
 # hyperopt tuner
 numpy
 scipy
-hyperopt
\ No newline at end of file
+hyperopt
+
+# metis tuner
+sklearn

src/webui/src/components/Overview.tsx

@@ -209,6 +209,10 @@ class Overview extends React.Component<{}, OverviewState> {
                                 profile.failTrial += 1;
                                 break;
 
+                            case 'RUNNING':
+                                profile.runTrial += 1;
+                                break;
+
                             case 'USER_CANCELED':
                             case 'SYS_CANCELED':
                                 profile.stopTrial += 1;

src/webui/src/components/TrialsDetail.tsx

 import * as React from 'react';
 import axios from 'axios';
 import { MANAGER_IP } from '../static/const';
-import { Row, Col, Tabs, Input, Select } from 'antd';
+import { Row, Col, Tabs, Input, Select, Button } from 'antd';
 const Option = Select.Option;
-import { TableObj, Parameters, DetailAccurPoint, TooltipForAccuracy } from '../static/interface';
-import { getFinalResult } from '../static/function';
+import { TableObjFianl, Parameters, DetailAccurPoint, TooltipForAccuracy } from '../static/interface';
+import { getFinalResult, getFinal } from '../static/function';
 import Accuracy from './overview/Accuracy';
 import Duration from './trial-detail/Duration';
 import Title1 from './overview/Title1';
@@ -16,8 +16,8 @@ import '../static/style/trialsDetail.scss';
 interface TrialDetailState {
     accSource: object;
     accNodata: string;
-    tableListSource: Array<TableObj>;
-    searchResultSource: Array<TableObj>;
+    tableListSource: Array<TableObjFianl>;
+    searchResultSource: Array<TableObjFianl>;
     isHasSearch: boolean;
     experimentStatus: string;
     entriesTable: number;
@@ -30,6 +30,8 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
     public interTableList = 1;
     public interAllTableList = 2;
 
+    public tableList: TableList | null;
+
     constructor(props: {}) {
         super(props);
 
@@ -40,7 +42,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
             searchResultSource: [],
             experimentStatus: '',
             entriesTable: 20,
-            isHasSearch: false
+            isHasSearch: false,
         };
     }
     // trial accuracy graph
@@ -132,7 +134,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
             .then(res => {
                 if (res.status === 200) {
                     const trialJobs = res.data;
-                    const trialTable: Array<TableObj> = [];
+                    const trialTable: Array<TableObjFianl> = [];
                     Object.keys(trialJobs).map(item => {
                         // only succeeded trials have finalMetricData
                         let desc: Parameters = {
@@ -167,7 +169,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
                         if (trialJobs[item].logPath !== undefined) {
                             desc.logPath = trialJobs[item].logPath;
                         }
-                        const acc = getFinalResult(trialJobs[item].finalMetricData);
+                        const acc = getFinal(trialJobs[item].finalMetricData);
                         trialTable.push({
                             key: trialTable.length,
                             sequenceId: trialJobs[item].sequenceId,
@@ -185,7 +187,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
                         Object.keys(searchResultSource).map(index => {
                             temp.push(searchResultSource[index].id);
                         });
-                        const searchResultList: Array<TableObj> = [];
+                        const searchResultList: Array<TableObjFianl> = [];
                         for (let i = 0; i < temp.length; i++) {
                             Object.keys(trialTable).map(key => {
                                 const item = trialTable[key];
@@ -217,7 +219,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
             .then(res => {
                 if (res.status === 200) {
                     const trialJobs = res.data;
-                    const trialTable: Array<TableObj> = [];
+                    const trialTable: Array<TableObjFianl> = [];
                     Object.keys(trialJobs).map(item => {
                         // only succeeded trials have finalMetricData
                         let desc: Parameters = {
@@ -252,7 +254,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
                         if (trialJobs[item].logPath !== undefined) {
                             desc.logPath = trialJobs[item].logPath;
                         }
-                        const acc = getFinalResult(trialJobs[item].finalMetricData);
+                        const acc = getFinal(trialJobs[item].finalMetricData);
                         trialTable.push({
                             key: trialTable.length,
                             sequenceId: trialJobs[item].sequenceId,
@@ -308,7 +310,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
         } else {
             window.clearInterval(this.interAllTableList);
             const { tableListSource } = this.state;
-            const searchResultList: Array<TableObj> = [];
+            const searchResultList: Array<TableObjFianl> = [];
             Object.keys(tableListSource).map(key => {
                 const item = tableListSource[key];
                 if (item.sequenceId.toString() === targetValue || item.id.includes(targetValue)) {
@@ -364,6 +366,10 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
         }
     }
 
+    test = () => {
+        alert('TableList component was not properly initialized.');
+    }
+
     componentDidMount() {
 
         this._isMounted = true;
@@ -429,13 +435,26 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
                         <span>entries</span>
                     </Col>
                     <Col span={12} className="right">
-                        {/* <span>Search:</span> */}
-                        <Input
-                            type="text"
-                            placeholder="search by Trial No. and id"
-                            onChange={this.searchTrial}
-                            style={{ width: 200, marginLeft: 6 }}
-                        />
+                        <Row>
+                            <Col span={12}>
+                                <Button
+                                    type="primary"
+                                    className="tableButton editStyle"
+                                    onClick={this.tableList ? this.tableList.addColumn : this.test}
+                                >
+                                    AddColumn
+                                </Button>
+                            </Col>
+                            <Col span={12}>
+                                {/* <span>Search:</span> */}
+                                <Input
+                                    type="text"
+                                    placeholder="search by Trial No. and id"
+                                    onChange={this.searchTrial}
+                                    style={{ width: 200, marginLeft: 6 }}
+                                />
+                            </Col>
+                        </Row>
                     </Col>
                 </Row>
                 <TableList
@@ -444,6 +463,7 @@ class TrialsDetail extends React.Component<{}, TrialDetailState> {
                     updateList={this.drawTableList}
                     searchResult={searchResultSource}
                     isHasSearch={isHasSearch}
+                    ref={(tabList) => this.tableList = tabList}
                 />
             </div>
         );

src/webui/src/components/overview/Progress.tsx

@@ -242,45 +242,45 @@ class Progressed extends React.Component<ProgressProps, ProgressState> {
                     maxString={`MaxTrialNumber: ${trialProfile.MaxTrialNum}`}
                 />
                 <Row className="basic colorOfbasic mess">
-                    <p>Best Default Metric</p>
+                    <p>best metric</p>
                     <div>{bestAccuracy}</div>
                 </Row>
                 <Row className="mess">
                     <Col span={8}>
                         <Row className="basic colorOfbasic">
-                            <p>Time Spent</p>
+                            <p>spent</p>
                             <div>{convertTime(trialProfile.execDuration)}</div>
                         </Row>
                     </Col>
                     <Col span={9}>
                         <Row className="basic colorOfbasic">
-                            <p>Remaining Time</p>
+                            <p>remaining</p>
                             <div>{remaining}</div>
                         </Row>
                     </Col>
                     <Col span={7}>
                         <Row className="basic colorOfbasic">
-                            <p>MaxDuration</p>
-                            <div>{convertTime(trialProfile.maxDuration)}</div>
+                            <p>running</p>
+                            <div>{trialNumber.runTrial}</div>
                         </Row>
                     </Col>
                 </Row>
                 <Row className="mess">
                     <Col span={8}>
                         <Row className="basic colorOfbasic">
-                            <p>Succeed Trial</p>
+                            <p>succeed</p>
                             <div>{trialNumber.succTrial}</div>
                         </Row>
                     </Col>
                     <Col span={9}>
                         <Row className="basic">
-                            <p>Stopped Trial</p>
+                            <p>stopped</p>
                             <div>{trialNumber.stopTrial}</div>
                         </Row>
                     </Col>
                     <Col span={7}>
                         <Row className="basic">
-                            <p>Failed Trial</p>
+                            <p>failed</p>
                             <div>{trialNumber.failTrial}</div>
                         </Row>
                     </Col>

src/webui/src/static/const.ts

-export const MANAGER_IP = `/api/v1/nni`;
-export const DOWNLOAD_IP = `/logs`;
-export const trialJobStatus = [
+const MANAGER_IP = `/api/v1/nni`;
+const DOWNLOAD_IP = `/logs`;
+const trialJobStatus = [
     'UNKNOWN',
     'WAITING',
     'RUNNING',
@@ -10,12 +10,47 @@ export const trialJobStatus = [
     'SYS_CANCELED',
     'EARLY_STOPPED'
 ];
-export const CONTROLTYPE = [
+const CONTROLTYPE = [
     'SEARCH_SPACE',
     'TRIAL_CONCURRENCY',
     'MAX_EXEC_DURATION'
 ];
-export const MONACO = {
+const MONACO = {
     readOnly: true,
     automaticLayout: true
 };
+const COLUMN_INDEX = [
+    {
+        name: 'Trial No',
+        index: 1
+    },
+    {
+        name: 'id',
+        index: 2
+    },
+    {
+        name: 'duration',
+        index: 3
+    },
+    {
+        name: 'status',
+        index: 4
+    },
+    {
+        name: 'Default',
+        index: 5
+    },
+    {
+        name: 'Operation',
+        index: 10000
+    },
+    {
+        name: 'Intermediate Result',
+        index: 10001
+    }
+];
+const COLUMN = ['Trial No', 'id', 'duration', 'status', 'Default', 'Operation', 'Intermediate Result'];
+export {
+    MANAGER_IP, DOWNLOAD_IP, trialJobStatus,
+    CONTROLTYPE, MONACO, COLUMN, COLUMN_INDEX
+};

src/webui/src/static/function.ts

-import { FinalResult } from './interface';
+import { FinalResult, FinalType } from './interface';
 
 const convertTime = (num: number) => {
     if (num % 3600 === 0) {
@@ -28,6 +28,7 @@ const convertDuration = (num: number) => {
 };
 
 // get final result value
+// draw Accuracy point graph
 const getFinalResult = (final: FinalResult) => {
     let acc;
     let showDefault = 0;
@@ -46,6 +47,21 @@ const getFinalResult = (final: FinalResult) => {
     }
 };
 
+// get final result value // acc obj 
+const getFinal = (final: FinalResult) => {
+    let showDefault: FinalType;
+    if (final) {
+        showDefault = JSON.parse(final[0].data);
+        if (typeof showDefault === 'number') {
+            showDefault = { default: showDefault };
+        }
+        return showDefault;
+    } else {
+        return undefined;
+    }
+};
+
 export {
-    convertTime, convertDuration, getFinalResult
+    convertTime, convertDuration, getFinalResult,
+    getFinal
 };

src/webui/src/static/interface.ts

+// draw accuracy graph data interface
 interface TableObj {
     key: number;
     sequenceId: number;
     id: string;
     duration: number;
     status: string;
-    acc?: number;
+    acc?: number; // draw accuracy graph
     description: Parameters;
     color?: string;
 }
+
+interface TableObjFianl {
+    key: number;
+    sequenceId: number;
+    id: string;
+    duration: number;
+    status: string;
+    acc?: FinalType;
+    description: Parameters;
+    color?: string;
+}
+
+interface FinalType {
+    default: string;
+}
+
 interface ErrorParameter {
     error?: string;
 }
+
 interface Parameters {
     parameters: ErrorParameter;
     logPath?: string;
@@ -93,5 +111,6 @@ export {
     TableObj, Parameters, Experiment, 
     AccurPoint, TrialNumber, TrialJob,
     DetailAccurPoint, TooltipForAccuracy,
-    ParaObj, VisualMapValue, Dimobj, FinalResult
+    ParaObj, VisualMapValue, Dimobj, FinalResult,
+    TableObjFianl, FinalType
 };

src/webui/src/static/style/search.scss

+/* some buttons about trial-detail table */
 .allList{
     width: 96%;
     margin: 0 auto;
@@ -17,4 +18,17 @@
     }
 }
 
+.titleColumn{
+    .ant-checkbox-group-item{
+        display: block;
+    }
+}
+
+.applyfooter{
+    /* apply button style */
+    .apply{
+        text-align: right;
+    }
+}
+
 

tools/nni_cmd/config_schema.py

@@ -68,6 +68,16 @@ Optional('tuner'): Or({
         Optional('n_output_node'):  int,
         },
     Optional('gpuNum'): And(int, lambda x: 0 <= x <= 99999),
+},{
+    'builtinTunerName': 'MetisTuner',
+    'classArgs': {
+        Optional('optimize_mode'): Or('maximize', 'minimize'),
+        Optional('no_resampling'):  bool,
+        Optional('no_candidates'):  bool,
+        Optional('selection_num_starting_points'):  int,
+        Optional('cold_start_num'):  int,
+        },
+    Optional('gpuNum'): And(int, lambda x: 0 <= x <= 99999),
 },{
     'codeDir': os.path.exists,
     'classFileName': str,

tools/nni_cmd/launcher_utils.py

@@ -21,7 +21,7 @@
 import os
 import json
 from .config_schema import LOCAL_CONFIG_SCHEMA, REMOTE_CONFIG_SCHEMA, PAI_CONFIG_SCHEMA, KUBEFLOW_CONFIG_SCHEMA, FRAMEWORKCONTROLLER_CONFIG_SCHEMA
-from .common_utils import get_json_content, print_error, print_warning
+from .common_utils import get_json_content, print_error, print_warning, print_normal
 
 def expand_path(experiment_config, key):
     '''Change '~' to user home directory'''
@@ -32,7 +32,7 @@ def parse_relative_path(root_path, experiment_config, key):
     '''Change relative path to absolute path'''
     if experiment_config.get(key) and not os.path.isabs(experiment_config.get(key)):
         absolute_path = os.path.join(root_path, experiment_config.get(key))
-        print_warning('expand %s: %s to %s ' % (key, experiment_config[key], absolute_path))
+        print_normal('expand %s: %s to %s ' % (key, experiment_config[key], absolute_path))
         experiment_config[key] = absolute_path
 
 def parse_time(experiment_config):

tools/nni_trial_tool/log_utils.py

@@ -129,13 +129,15 @@ class PipeLogReader(threading.Thread):
         self.pipeReader = os.fdopen(self.fdRead)
         self.orig_stdout = sys.__stdout__
         self._is_read_completed = False
+        self.process_exit = False
 
         def _populateQueue(stream, queue):
             '''
             Collect lines from 'stream' and put them in 'quque'.
             '''
             time.sleep(5)
-            while True:                
+            while True:
+                cur_process_exit = self.process_exit       
                 try:
                     line = self.queue.get(True, 5)
                     try:
@@ -144,9 +146,10 @@ class PipeLogReader(threading.Thread):
                         self.orig_stdout.flush()
                     except Exception as e:
                         pass
-                except Exception as e:                    
-                    self._is_read_completed = True
-                    break
+                except Exception as e:
+                    if cur_process_exit == True:     
+                        self._is_read_completed = True
+                        break
 
         self.pip_log_reader_thread = threading.Thread(target = _populateQueue,
                 args = (self.pipeReader, self.queue))
@@ -175,4 +178,8 @@ class PipeLogReader(threading.Thread):
     def is_read_completed(self):
         """Return if read is completed
         """
-        return self._is_read_completed
\ No newline at end of file
+        return self._is_read_completed
+    
+    def set_process_exit(self):
+        self.process_exit = True
+        return self.process_exit
\ No newline at end of file