Merge branch 'master' of github.com:Microsoft/nni into dev-nas-refactor

src/sdk/pynni/nni/trial.py

@@ -126,9 +126,10 @@ def report_intermediate_result(metric):
         serializable object.
     """
     global _intermediate_seq
-    assert _params is not None, 'nni.get_next_parameter() needs to be called before report_intermediate_result'
+    assert _params or trial_env_vars.NNI_PLATFORM is None, \
+        'nni.get_next_parameter() needs to be called before report_intermediate_result'
     metric = json_tricks.dumps({
-        'parameter_id': _params['parameter_id'],
+        'parameter_id': _params['parameter_id'] if _params else None,
         'trial_job_id': trial_env_vars.NNI_TRIAL_JOB_ID,
         'type': 'PERIODICAL',
         'sequence': _intermediate_seq,
@@ -147,9 +148,10 @@ def report_final_result(metric):
     metric:
         serializable object.
     """
-    assert _params is not None, 'nni.get_next_parameter() needs to be called before report_final_result'
+    assert _params or trial_env_vars.NNI_PLATFORM is None, \
+        'nni.get_next_parameter() needs to be called before report_final_result'
     metric = json_tricks.dumps({
-        'parameter_id': _params['parameter_id'],
+        'parameter_id': _params['parameter_id'] if _params else None,
         'trial_job_id': trial_env_vars.NNI_TRIAL_JOB_ID,
         'type': 'FINAL',
         'sequence': 0,

src/sdk/pynni/tests/test_tuner.py → src/sdk/pynni/tests/test_builtin_tuners.py

@@ -41,7 +41,7 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger('test_tuner')
 
 
-class TunerTestCase(TestCase):
+class BuiltinTunersTestCase(TestCase):
     """
     Targeted at testing functions of built-in tuners, including
         - [ ] load_checkpoint

src/sdk/pynni/tests/test_compressor.py

 from unittest import TestCase, main
+import numpy as np
 import tensorflow as tf
 import torch
 import torch.nn.functional as F
@@ -7,11 +8,11 @@ import nni.compression.torch as torch_compressor
 if tf.__version__ >= '2.0':
     import nni.compression.tensorflow as tf_compressor
 
-def get_tf_mnist_model():
+def get_tf_model():
     model = tf.keras.models.Sequential([
-        tf.keras.layers.Conv2D(filters=32, kernel_size=7, input_shape=[28, 28, 1], activation='relu', padding="SAME"),
+        tf.keras.layers.Conv2D(filters=5, kernel_size=7, input_shape=[28, 28, 1], activation='relu', padding="SAME"),
         tf.keras.layers.MaxPooling2D(pool_size=2),
-        tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation='relu', padding="SAME"),
+        tf.keras.layers.Conv2D(filters=10, kernel_size=3, activation='relu', padding="SAME"),
         tf.keras.layers.MaxPooling2D(pool_size=2),
         tf.keras.layers.Flatten(),
         tf.keras.layers.Dense(units=128, activation='relu'),
@@ -23,43 +24,51 @@ def get_tf_mnist_model():
         metrics=["accuracy"])
     return model
 
-class TorchMnist(torch.nn.Module):
+class TorchModel(torch.nn.Module):
     def __init__(self):
         super().__init__()
-        self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
-        self.conv2 = torch.nn.Conv2d(20, 50, 5, 1)
-        self.fc1 = torch.nn.Linear(4 * 4 * 50, 500)
-        self.fc2 = torch.nn.Linear(500, 10)
+        self.conv1 = torch.nn.Conv2d(1, 5, 5, 1)
+        self.conv2 = torch.nn.Conv2d(5, 10, 5, 1)
+        self.fc1 = torch.nn.Linear(4 * 4 * 10, 100)
+        self.fc2 = torch.nn.Linear(100, 10)
 
     def forward(self, x):
         x = F.relu(self.conv1(x))
         x = F.max_pool2d(x, 2, 2)
         x = F.relu(self.conv2(x))
         x = F.max_pool2d(x, 2, 2)
-        x = x.view(-1, 4 * 4 * 50)
+        x = x.view(-1, 4 * 4 * 10)
         x = F.relu(self.fc1(x))
         x = self.fc2(x)
         return F.log_softmax(x, dim=1)
 
 def tf2(func):
-    def test_tf2_func(self):
+    def test_tf2_func(*args):
         if tf.__version__ >= '2.0':
-            func()
+            func(*args)
     return test_tf2_func
 
+k1 = [[1]*3]*3
+k2 = [[2]*3]*3
+k3 = [[3]*3]*3
+k4 = [[4]*3]*3
+k5 = [[5]*3]*3
+
+w = [[k1, k2, k3, k4, k5]] * 10
+
 class CompressorTestCase(TestCase):
-    def test_torch_pruner(self):
-        model = TorchMnist()
+    def test_torch_level_pruner(self):
+        model = TorchModel()
         configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
         torch_compressor.LevelPruner(model, configure_list).compress()
 
-    def test_torch_fpgm_pruner(self):
-        model = TorchMnist()
-        configure_list = [{'sparsity': 0.5, 'op_types': ['Conv2d']}]
-        torch_compressor.FPGMPruner(model, configure_list).compress()
+    @tf2
+    def test_tf_level_pruner(self):
+        configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
+        tf_compressor.LevelPruner(get_tf_model(), configure_list).compress()
 
-    def test_torch_quantizer(self):
-        model = TorchMnist()
+    def test_torch_naive_quantizer(self):
+        model = TorchModel()
         configure_list = [{
             'quant_types': ['weight'],
             'quant_bits': {
@@ -70,18 +79,59 @@ class CompressorTestCase(TestCase):
         torch_compressor.NaiveQuantizer(model, configure_list).compress()
 
     @tf2
-    def test_tf_pruner(self):
-        configure_list = [{'sparsity': 0.8, 'op_types': ['default']}]
-        tf_compressor.LevelPruner(get_tf_mnist_model(), configure_list).compress()
+    def test_tf_naive_quantizer(self):
+        tf_compressor.NaiveQuantizer(get_tf_model(), [{'op_types': ['default']}]).compress()
 
-    @tf2
-    def test_tf_quantizer(self):
-        tf_compressor.NaiveQuantizer(get_tf_mnist_model(), [{'op_types': ['default']}]).compress()
+    def test_torch_fpgm_pruner(self):
+        """
+        With filters(kernels) defined as above (k1 - k5), it is obvious that k3 is the Geometric Median
+        which minimize the total geometric distance by defination of Geometric Median in this paper:
+        Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration,
+        https://arxiv.org/pdf/1811.00250.pdf
+
+        So if sparsity is 0.2, the expected masks should mask out all k3, this can be verified through:
+        `all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 90., 0., 90., 90.]))`
+
+        If sparsity is 0.6, the expected masks should mask out all k2, k3, k4, this can be verified through:
+        `all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 0., 0., 0., 90.]))`
+        """
+
+        model = TorchModel()
+        config_list = [{'sparsity': 0.2, 'op_types': ['Conv2d']}, {'sparsity': 0.6, 'op_types': ['Conv2d']}]
+        pruner = torch_compressor.FPGMPruner(model, config_list)
+
+        model.conv2.weight.data = torch.tensor(w).float()
+        layer = torch_compressor.compressor.LayerInfo('conv2', model.conv2)
+        masks = pruner.calc_mask(layer, config_list[0])
+        assert all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 90., 0., 90., 90.]))
+
+        pruner.update_epoch(1)
+        model.conv2.weight.data = torch.tensor(w).float()
+        masks = pruner.calc_mask(layer, config_list[1])
+        assert all(torch.sum(masks, (0, 2, 3)).numpy() == np.array([90., 0., 0., 0., 90.]))
 
     @tf2
     def test_tf_fpgm_pruner(self):
-        configure_list = [{'sparsity': 0.5, 'op_types': ['Conv2D']}]
-        tf_compressor.FPGMPruner(get_tf_mnist_model(), configure_list).compress()
+        model = get_tf_model()
+        config_list = [{'sparsity': 0.2, 'op_types': ['Conv2D']}, {'sparsity': 0.6, 'op_types': ['Conv2D']}]
+
+        pruner = tf_compressor.FPGMPruner(model, config_list)
+        weights = model.layers[2].weights
+        weights[0] = np.array(w).astype(np.float32).transpose([2, 3, 0, 1]).transpose([0, 1, 3, 2])
+        model.layers[2].set_weights([weights[0], weights[1].numpy()])
+
+        layer = tf_compressor.compressor.LayerInfo(model.layers[2])
+        masks = pruner.calc_mask(layer, config_list[0]).numpy()
+        masks = masks.transpose([2, 3, 0, 1]).transpose([1, 0, 2, 3])
+
+        assert all(masks.sum((0, 2, 3)) == np.array([90., 90., 0., 90., 90.]))
+
+        pruner.update_epoch(1)
+        model.layers[2].set_weights([weights[0], weights[1].numpy()])
+        masks = pruner.calc_mask(layer, config_list[1]).numpy()
+        masks = masks.transpose([2, 3, 0, 1]).transpose([1, 0, 2, 3])
+
+        assert all(masks.sum((0, 2, 3)) == np.array([90., 0., 0., 0., 90.]))
 
 
 if __name__ == '__main__':

src/sdk/pynni/tests/test_msg_dispatcher.py

@@ -80,8 +80,6 @@ class MsgDispatcherTestCase(TestCase):
         send(CommandType.ReportMetricData, '{"parameter_id":0,"type":"PERIODICAL","value":10}')
         send(CommandType.ReportMetricData, '{"parameter_id":1,"type":"FINAL","value":11}')
         send(CommandType.UpdateSearchSpace, '{"name":"SS0"}')
-        send(CommandType.AddCustomizedTrialJob, '{"param":-1}')
-        send(CommandType.ReportMetricData, '{"parameter_id":2,"type":"FINAL","value":22}')
         send(CommandType.RequestTrialJobs, '1')
         send(CommandType.KillTrialJob, 'null')
         _restore_io()
@@ -99,14 +97,7 @@ class MsgDispatcherTestCase(TestCase):
         self._assert_params(0, 2, [], None)
         self._assert_params(1, 4, [], None)
 
-        command, data = receive()  # this one is customized
-        data = json.loads(data)
-        self.assertIs(command, CommandType.NewTrialJob)
-        self.assertEqual(data['parameter_id'], 2)
-        self.assertEqual(data['parameter_source'], 'customized')
-        self.assertEqual(data['parameters'], {'param': -1})
-
-        self._assert_params(3, 6, [[1, 4, 11, False], [2, -1, 22, True]], {'name': 'SS0'})
+        self._assert_params(2, 6, [[1, 4, 11, False]], {'name': 'SS0'})
 
         self.assertEqual(len(_out_buf.read()), 0)  # no more commands
 

src/webui/src/components/Modal/CustomizedTrial.tsx

+import * as React from 'react';
+import axios from 'axios';
+import { Row, Col, Input, Modal, Form, Button, Icon } from 'antd';
+import { MANAGER_IP } from '../../static/const';
+import { EXPERIMENT, TRIALS } from '../../static/datamodel';
+import { FormComponentProps } from 'antd/lib/form';
+const FormItem = Form.Item;
+import './customized.scss';
+
+interface CustomizeProps extends FormComponentProps {
+    visible: boolean;
+    copyTrialId: string;
+    closeCustomizeModal: () => void;
+}
+
+interface CustomizeState {
+    isShowSubmitSucceed: boolean;
+    isShowSubmitFailed: boolean;
+    isShowWarning: boolean;
+    searchSpace: object;
+    copyTrialParameter: object; // user click the trial's parameters
+    customParameters: object; // customized trial, maybe user change trial's parameters
+    customID: number; // submit customized trial succeed, return the new customized trial id
+}
+
+class Customize extends React.Component<CustomizeProps, CustomizeState> {
+
+    constructor(props: CustomizeProps) {
+        super(props);
+        this.state = {
+            isShowSubmitSucceed: false,
+            isShowSubmitFailed: false,
+            isShowWarning: false,
+            searchSpace: EXPERIMENT.searchSpace,
+            copyTrialParameter: {},
+            customParameters: {},
+            customID: NaN
+        };
+    }
+
+    // [submit click] user add a new trial [submit a trial]
+    addNewTrial = () => {
+        const { searchSpace, copyTrialParameter } = this.state;
+        // get user edited hyperParameter, ps: will change data type if you modify the input val
+        const customized = this.props.form.getFieldsValue();
+        // true: parameters are wrong
+        let flag = false;
+        Object.keys(customized).map(item => {
+            if (item !== 'tag') {
+                // unified data type
+                if (typeof copyTrialParameter[item] === 'number' && typeof customized[item] === 'string') {
+                    customized[item] = JSON.parse(customized[item]);
+                }
+                if (searchSpace[item]._type === 'choice') {
+                    if (searchSpace[item]._value.find((val: string | number) =>
+                        val === customized[item]) === undefined) {
+                        flag = true;
+                        return;
+                    }
+                } else {
+                    if (customized[item] < searchSpace[item]._value[0]
+                        || customized[item] > searchSpace[item]._value[1]) {
+                        flag = true;
+                        return;
+                    }
+                }
+            }
+        });
+        if (flag !== false) {
+            // open the warning modal
+            this.setState(() => ({ isShowWarning: true, customParameters: customized }));
+        } else {
+            // submit a customized job
+            this.submitCustomize(customized);
+        }
+
+    }
+
+    warningConfirm = () => {
+        this.setState(() => ({ isShowWarning: false }));
+        const { customParameters } = this.state;
+        this.submitCustomize(customParameters);
+    }
+
+    warningCancel = () => {
+        this.setState(() => ({ isShowWarning: false }));
+    }
+
+    submitCustomize = (customized: Object) => {
+        // delete `tag` key
+        for (let i in customized) {
+            if (i === 'tag') {
+                delete customized[i];
+            }
+        }
+        axios(`${MANAGER_IP}/trial-jobs`, {
+            method: 'POST',
+            headers: { 'Content-Type': 'application/json' },
+            data: customized
+        })
+            .then(res => {
+                if (res.status === 200) {
+                    this.setState(() => ({ isShowSubmitSucceed: true, customID: res.data.sequenceId }));
+                    this.props.closeCustomizeModal();
+                } else {
+                    this.setState(() => ({ isShowSubmitFailed: true }));
+                }
+            })
+            .catch(error => {
+                this.setState(() => ({ isShowSubmitFailed: true }));
+            });
+    }
+
+    closeSucceedHint = () => {
+        // also close customized trial modal
+        this.setState(() => ({ isShowSubmitSucceed: false }));
+        this.props.closeCustomizeModal();
+    }
+
+    closeFailedHint = () => {
+        // also close customized trial modal
+        this.setState(() => ({ isShowSubmitFailed: false }));
+        this.props.closeCustomizeModal();
+    }
+
+    componentDidMount() {
+        const { copyTrialId } = this.props;
+        if (copyTrialId !== undefined && TRIALS.getTrial(copyTrialId) !== undefined) {
+            const originCopyTrialPara = TRIALS.getTrial(copyTrialId).description.parameters;
+            this.setState(() => ({ copyTrialParameter: originCopyTrialPara }));
+        }
+    }
+
+    componentWillReceiveProps(nextProps: CustomizeProps) {
+        const { copyTrialId } = nextProps;
+        if (copyTrialId !== undefined && TRIALS.getTrial(copyTrialId) !== undefined) {
+            const originCopyTrialPara = TRIALS.getTrial(copyTrialId).description.parameters;
+            this.setState(() => ({ copyTrialParameter: originCopyTrialPara }));
+        }
+    }
+
+    render() {
+        const { closeCustomizeModal, visible } = this.props;
+        const { isShowSubmitSucceed, isShowSubmitFailed, isShowWarning, customID, copyTrialParameter } = this.state;
+        const {
+            form: { getFieldDecorator },
+            // form: { getFieldDecorator, getFieldValue },
+        } = this.props;
+        const warning = 'The parameters you set are not in our search space, this may cause the tuner to crash, Are'
+            + ' you sure you want to continue submitting?';
+        return (
+            <Row>
+                {/* form: search space */}
+                <Modal
+                    title="Customized trial setting"
+                    visible={visible}
+                    onCancel={closeCustomizeModal}
+                    footer={null}
+                    destroyOnClose={true}
+                    maskClosable={false}
+                    centered={true}
+                >
+                    {/* search space form */}
+                    <Row className="hyper-box">
+                        <Form>
+                            {
+                                Object.keys(copyTrialParameter).map(item => (
+                                    <Row key={item} className="hyper-form">
+                                        <Col span={9} className="title">{item}</Col>
+                                        <Col span={15} className="inputs">
+                                            <FormItem key={item} style={{ marginBottom: 0 }}>
+                                                {getFieldDecorator(item, {
+                                                    initialValue: copyTrialParameter[item],
+                                                })(
+                                                    <Input />
+                                                )}
+                                            </FormItem>
+                                        </Col>
+                                    </Row>
+                                )
+                                )
+                            }
+                            <Row key="tag" className="hyper-form tag-input">
+                                <Col span={9} className="title">Tag</Col>
+                                <Col span={15} className="inputs">
+                                    <FormItem key="tag" style={{ marginBottom: 0 }}>
+                                        {getFieldDecorator('tag', {
+                                            initialValue: 'Customized',
+                                        })(
+                                            <Input />
+                                        )}
+                                    </FormItem>
+                                </Col>
+                            </Row>
+                        </Form>
+                    </Row>
+                    <Row className="modal-button">
+                        <Button
+                            type="primary"
+                            className="tableButton distance"
+                            onClick={this.addNewTrial}
+                        >
+                            Submit
+                        </Button>
+                        <Button
+                            className="tableButton cancelSty"
+                            onClick={this.props.closeCustomizeModal}
+                        >
+                            Cancel
+                        </Button>
+                    </Row>
+                    {/* control button */}
+                </Modal>
+                {/* clone: prompt succeed or failed */}
+                <Modal
+                    visible={isShowSubmitSucceed}
+                    footer={null}
+                    destroyOnClose={true}
+                    maskClosable={false}
+                    closable={false}
+                    centered={true}
+                >
+                    <Row className="resubmit">
+                        <Row>
+                            <h2 className="title">
+                                <span>
+                                    <Icon type="check-circle" className="color-succ" />
+                                    <b>Submit successfully</b>
+                                </span>
+                            </h2>
+                            <div className="hint">
+                                <span>You can find your customized trial by Trial No.{customID}</span>
+                            </div>
+                        </Row>
+                        <Row className="modal-button">
+                            <Button
+                                className="tableButton cancelSty"
+                                onClick={this.closeSucceedHint}
+                            >
+                                OK
+                            </Button>
+                        </Row>
+                    </Row>
+                </Modal>
+                <Modal
+                    visible={isShowSubmitFailed}
+                    footer={null}
+                    destroyOnClose={true}
+                    maskClosable={false}
+                    closable={false}
+                    centered={true}
+                >
+                    <Row className="resubmit">
+                        <Row>
+                            <h2 className="title">
+                                <span>
+                                    <Icon type="check-circle" className="color-error" />Submit Failed
+                                </span>
+                            </h2>
+                            <div className="hint">
+                                <span>Unknown error.</span>
+                            </div>
+                        </Row>
+                        <Row className="modal-button">
+                            <Button
+                                className="tableButton cancelSty"
+                                onClick={this.closeFailedHint}
+                            >
+                                OK
+                            </Button>
+                        </Row>
+                    </Row>
+                </Modal>
+                {/* hyperParameter not match search space, warning modal */}
+                <Modal
+                    visible={isShowWarning}
+                    footer={null}
+                    destroyOnClose={true}
+                    maskClosable={false}
+                    closable={false}
+                    centered={true}
+                >
+                    <Row className="resubmit">
+                        <Row>
+                            <h2 className="title">
+                                <span>
+                                    <Icon className="color-warn" type="warning" />Warning
+                                </span>
+                            </h2>
+                            <div className="hint">
+                                <span>{warning}</span>
+                            </div>
+                        </Row>
+                        <Row className="modal-button center">
+                            <Button
+                                className="tableButton cancelSty distance"
+                                onClick={this.warningConfirm}
+                            >
+                                Confirm
+                            </Button>
+                            <Button
+                                className="tableButton cancelSty"
+                                onClick={this.warningCancel}
+                            >
+                                Cancel
+                            </Button>
+                        </Row>
+                    </Row>
+                </Modal>
+
+            </Row>
+
+        );
+    }
+}
+
+export default Form.create<FormComponentProps>()(Customize);
\ No newline at end of file

src/webui/src/components/Modal/customized.scss

+.ant-modal-body{
+    border-radius: none;
+}
+.ant-modal-title {
+    font-size: 18px;
+}
+/* resubmit confirm modal style */
+.resubmit{
+    .title{
+        font-size: 16px;
+        color: #000;
+        .color-warn, .color-error{
+            color: red;
+        }
+        i{
+            margin-right: 10px;
+        }
+    }
+    .hint{
+        padding: 15px 0;
+        color: #333;
+        margin-left: 30px;
+    }
+    .color-succ{
+        color: green;
+    }
+}
+.hyper-box{
+    padding: 16px 18px 16px 16px;
+}
+.hyper-form{
+    height: 32px;
+    margin-bottom: 8px;
+    .title{
+        font-size: 14px;
+        font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+        line-height: 32px;
+    }
+    .inputs{
+        height: 32px;
+    }
+    input{
+        height: 32px;
+    }
+}
+.tag-input{
+    margin-top: 25px;
+}
+
+/* submit & cancel buttons style*/
+.modal-button{
+    text-align: right;
+    height: 28px;
+    /* cancel button style*/
+    .cancelSty{
+        width: 80px;
+        background-color: #dadada;
+        border: none;
+        color: #333;
+    }
+    .cancelSty:hover, .cancelSty:active, .cancelSty:focus{
+        background-color: #dadada;
+    }
+    .distance{
+        margin-right: 8px;
+    }
+}
+
+.center{
+    text-align: center;
+}

src/webui/src/components/trial-detail/TableList.tsx

@@ -7,10 +7,11 @@ const Option = Select.Option;
 const CheckboxGroup = Checkbox.Group;
 import { MANAGER_IP, trialJobStatus, COLUMN_INDEX, COLUMNPro } from '../../static/const';
 import { convertDuration, formatTimestamp, intermediateGraphOption, killJob } from '../../static/function';
-import { TRIALS } from '../../static/datamodel';
+import { EXPERIMENT, TRIALS } from '../../static/datamodel';
 import { TableRecord } from '../../static/interface';
 import OpenRow from '../public-child/OpenRow';
 import Compare from '../Modal/Compare';
+import Customize from '../Modal/CustomizedTrial';
 import '../../static/style/search.scss';
 require('../../static/style/tableStatus.css');
 require('../../static/style/logPath.scss');
@@ -45,6 +46,8 @@ interface TableListState {
     intermediateData: Array<object>; // a trial's intermediate results (include dict)
     intermediateId: string;
     intermediateOtherKeys: Array<string>;
+    isShowCustomizedModal: boolean;
+    copyTrialId: string; // user copy trial to submit a new customized trial
 }
 
 interface ColumnIndex {
@@ -71,7 +74,9 @@ class TableList extends React.Component<TableListProps, TableListState> {
             selectedRowKeys: [], // close selected trial message after modal closed
             intermediateData: [],
             intermediateId: '',
-            intermediateOtherKeys: []
+            intermediateOtherKeys: [],
+            isShowCustomizedModal: false,
+            copyTrialId: ''
         };
     }
 
@@ -236,17 +241,36 @@ class TableList extends React.Component<TableListProps, TableListState> {
         this.setState({ isShowCompareModal: false, selectedRowKeys: [], selectRows: [] });
     }
 
+    // open customized trial modal
+    setCustomizedTrial = (trialId: string) => {
+        this.setState({
+            isShowCustomizedModal: true,
+            copyTrialId: trialId
+        });
+    }
+
+    closeCustomizedTrial = () => {
+        this.setState({
+            isShowCustomizedModal: false,
+            copyTrialId: ''
+        });
+    }
     render() {
         const { pageSize, columnList } = this.props;
         const tableSource: Array<TableRecord> = JSON.parse(JSON.stringify(this.props.tableSource));
         const { intermediateOption, modalVisible, isShowColumn,
-            selectRows, isShowCompareModal, selectedRowKeys, intermediateOtherKeys } = this.state;
+            selectRows, isShowCompareModal, selectedRowKeys, intermediateOtherKeys,
+            isShowCustomizedModal, copyTrialId
+        } = this.state;
         const rowSelection = {
             selectedRowKeys: selectedRowKeys,
             onChange: (selected: string[] | number[], selectedRows: Array<TableRecord>) => {
                 this.fillSelectedRowsTostate(selected, selectedRows);
             }
         };
+        // [supportCustomizedTrial: true]
+        const supportCustomizedTrial = (EXPERIMENT.multiPhase === true) ? false : true;
+        const disabledAddCustomizedTrial = ['DONE', 'ERROR', 'STOPPED'].includes(EXPERIMENT.status);
         let showTitle = COLUMNPro;
         const showColumn: Array<object> = [];
 
@@ -361,6 +385,22 @@ class TableList extends React.Component<TableListProps, TableListState> {
                                                 </Button>
                                             </Popconfirm>
                                     }
+                                    {/* Add a new trial-customized trial */}
+                                    {
+                                        supportCustomizedTrial
+                                            ?
+                                            <Button
+                                                type="primary"
+                                                className="common-style"
+                                                disabled={disabledAddCustomizedTrial}
+                                                onClick={this.setCustomizedTrial.bind(this, record.id)}
+                                                title="Customized trial"
+                                            >
+                                                <Icon type="copy" />
+                                            </Button>
+                                            :
+                                            null
+                                    }
                                 </Row>
                             );
                         },
@@ -398,7 +438,7 @@ class TableList extends React.Component<TableListProps, TableListState> {
                         expandedRowRender={this.openRow}
                         dataSource={tableSource}
                         className="commonTableStyle"
-                        scroll={{x: 'max-content'}}
+                        scroll={{ x: 'max-content' }}
                         pagination={pageSize > 0 ? { pageSize } : false}
                     />
                     {/* Intermediate Result Modal */}
@@ -458,7 +498,14 @@ class TableList extends React.Component<TableListProps, TableListState> {
                         className="titleColumn"
                     />
                 </Modal>
+                {/* compare trials based message */}
                 <Compare compareRows={selectRows} visible={isShowCompareModal} cancelFunc={this.hideCompareModal} />
+                {/* clone trial parameters and could submit a customized trial */}
+                <Customize
+                    visible={isShowCustomizedModal}
+                    copyTrialId={copyTrialId}
+                    closeCustomizeModal={this.closeCustomizedTrial}
+                />
             </Row>
         );
     }

tools/nni_gpu_tool/gpu_metrics_collector.py

@@ -35,7 +35,7 @@ def check_ready_to_run():
         pidList.remove(os.getpid())
         return not pidList
     else:
-        pgrep_output = subprocess.check_output('pgrep -fx \'python3 -m nni_gpu_tool.gpu_metrics_collector\'', shell=True)
+        pgrep_output = subprocess.check_output('pgrep -fxu "$(whoami)" \'python3 -m nni_gpu_tool.gpu_metrics_collector\'', shell=True)
         pidList = []
         for pid in pgrep_output.splitlines():
             pidList.append(int(pid))