Merge pull request #239 from microsoft/master

merge master

examples/nas/textnas/retrain.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import sys
+import os
+import logging
+import pickle
+import shutil
+import random
+import math
+
+import time
+import datetime
+import argparse
+import distutils.util
+
+import numpy as np
+import torch
+from torch import nn
+from torch import optim
+from torch.utils.data import DataLoader
+import torch.nn.functional as Func
+
+from model import Model
+from nni.nas.pytorch.fixed import apply_fixed_architecture
+from dataloader import read_data_sst
+
+
+logger = logging.getLogger("nni.textnas")
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--reset_output_dir",
+        type=distutils.util.strtobool,
+        default=True,
+        help="Whether to clean the output dir if existed. (default: %(default)s)")
+    parser.add_argument(
+        "--child_fixed_arc",
+        type=str,
+        required=True,
+        help="Architecture json file. (default: %(default)s)")
+    parser.add_argument(
+        "--data_path",
+        type=str,
+        default="data",
+        help="Directory containing the dataset and embedding file. (default: %(default)s)")
+    parser.add_argument(
+        "--output_dir",
+        type=str,
+        default="output",
+        help="The output directory. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr_decay_scheme",
+        type=str,
+        default="cosine",
+        help="Learning rate annealing strategy, only 'cosine' supported. (default: %(default)s)")
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=128,
+        help="Number of samples each batch for training. (default: %(default)s)")
+    parser.add_argument(
+        "--eval_batch_size",
+        type=int,
+        default=128,
+        help="Number of samples each batch for evaluation. (default: %(default)s)")
+    parser.add_argument(
+        "--class_num",
+        type=int,
+        default=5,
+        help="The number of categories. (default: %(default)s)")
+    parser.add_argument(
+        "--global_seed",
+        type=int,
+        default=1234,
+        help="Seed for reproduction. (default: %(default)s)")
+    parser.add_argument(
+        "--max_input_length",
+        type=int,
+        default=64,
+        help="The maximum length of the sentence. (default: %(default)s)")
+    parser.add_argument(
+        "--num_epochs",
+        type=int,
+        default=10,
+        help="The number of training epochs. (default: %(default)s)")
+    parser.add_argument(
+        "--child_num_layers",
+        type=int,
+        default=24,
+        help="The layer number of the architecture. (default: %(default)s)")
+    parser.add_argument(
+        "--child_out_filters",
+        type=int,
+        default=256,
+        help="The dimension of hidden states. (default: %(default)s)")
+    parser.add_argument(
+        "--child_out_filters_scale",
+        type=int,
+        default=1,
+        help="The scale of hidden state dimension. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr_T_0",
+        type=int,
+        default=10,
+        help="The length of one cycle. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr_T_mul",
+        type=int,
+        default=2,
+        help="The multiplication factor per cycle. (default: %(default)s)")
+    parser.add_argument(
+        "--min_count",
+        type=int,
+        default=1,
+        help="The threshold to cut off low frequent words. (default: %(default)s)")
+    parser.add_argument(
+        "--train_ratio",
+        type=float,
+        default=1.0,
+        help="The sample ratio for the training set. (default: %(default)s)")
+    parser.add_argument(
+        "--valid_ratio",
+        type=float,
+        default=1.0,
+        help="The sample ratio for the dev set. (default: %(default)s)")
+    parser.add_argument(
+        "--child_grad_bound",
+        type=float,
+        default=5.0,
+        help="The threshold for gradient clipping. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr",
+        type=float,
+        default=0.02,
+        help="The initial learning rate. (default: %(default)s)")
+    parser.add_argument(
+        "--cnn_keep_prob",
+        type=float,
+        default=0.8,
+        help="Keep prob for cnn layer. (default: %(default)s)")
+    parser.add_argument(
+        "--final_output_keep_prob",
+        type=float,
+        default=1.0,
+        help="Keep prob for the last output layer. (default: %(default)s)")
+    parser.add_argument(
+        "--lstm_out_keep_prob",
+        type=float,
+        default=0.8,
+        help="Keep prob for the RNN layer. (default: %(default)s)")
+    parser.add_argument(
+        "--embed_keep_prob",
+        type=float,
+        default=0.8,
+        help="Keep prob for the embedding layer. (default: %(default)s)")
+    parser.add_argument(
+        "--attention_keep_prob",
+        type=float,
+        default=0.8,
+        help="Keep prob for the self-attention layer. (default: %(default)s)")
+    parser.add_argument(
+        "--child_l2_reg",
+        type=float,
+        default=3e-6,
+        help="Weight decay factor. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr_max",
+        type=float,
+        default=0.002,
+        help="The max learning rate. (default: %(default)s)")
+    parser.add_argument(
+        "--child_lr_min",
+        type=float,
+        default=0.001,
+        help="The min learning rate. (default: %(default)s)")
+    parser.add_argument(
+        "--child_optim_algo",
+        type=str,
+        default="adam",
+        help="Optimization algorithm. (default: %(default)s)")
+    parser.add_argument(
+        "--checkpoint_dir",
+        type=str,
+        default="best_checkpoint",
+        help="Path for saved checkpoints. (default: %(default)s)")
+    parser.add_argument(
+        "--output_type",
+        type=str,
+        default="avg",
+        help="Opertor type for the time steps reduction. (default: %(default)s)")
+    parser.add_argument(
+        "--multi_path",
+        type=distutils.util.strtobool,
+        default=False,
+        help="Search for multiple path in the architecture. (default: %(default)s)")
+    parser.add_argument(
+        "--is_binary",
+        type=distutils.util.strtobool,
+        default=False,
+        help="Binary label for sst dataset. (default: %(default)s)")
+    parser.add_argument(
+        "--is_cuda",
+        type=distutils.util.strtobool,
+        default=True,
+        help="Specify the device type. (default: %(default)s)")
+    parser.add_argument(
+        "--is_mask",
+        type=distutils.util.strtobool,
+        default=True,
+        help="Apply mask. (default: %(default)s)")
+    parser.add_argument(
+        "--fixed_seed",
+        type=distutils.util.strtobool,
+        default=True,
+        help="Fix the seed. (default: %(default)s)")
+    parser.add_argument(
+        "--load_checkpoint",
+        type=distutils.util.strtobool,
+        default=False,
+        help="Wether to load checkpoint. (default: %(default)s)")
+    parser.add_argument(
+        "--log_every",
+        type=int,
+        default=50,
+        help="How many steps to log. (default: %(default)s)")
+    parser.add_argument(
+        "--eval_every_epochs",
+        type=int,
+        default=1,
+        help="How many epochs to eval. (default: %(default)s)")
+
+    global FLAGS
+
+    FLAGS = parser.parse_args()
+
+
+def set_random_seed(seed):
+    logger.info("set random seed for data reading: {}".format(seed))
+    random.seed(seed)
+    os.environ['PYTHONHASHSEED'] = str(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    torch.manual_seed(seed)
+    if FLAGS.is_cuda:
+        torch.cuda.manual_seed(seed)
+        torch.backends.cudnn.deterministic = True
+
+
+def get_model(embedding, num_layers):
+    logger.info("num layers: {0}".format(num_layers))
+    assert FLAGS.child_fixed_arc is not None, "Architecture should be provided."
+
+    child_model = Model(
+        embedding=embedding,
+        hidden_units=FLAGS.child_out_filters_scale * FLAGS.child_out_filters,
+        num_layers=num_layers,
+        num_classes=FLAGS.class_num,
+        choose_from_k=5 if FLAGS.multi_path else 1,
+        lstm_keep_prob=FLAGS.lstm_out_keep_prob,
+        cnn_keep_prob=FLAGS.cnn_keep_prob,
+        att_keep_prob=FLAGS.attention_keep_prob,
+        att_mask=FLAGS.is_mask,
+        embed_keep_prob=FLAGS.embed_keep_prob,
+        final_output_keep_prob=FLAGS.final_output_keep_prob,
+        global_pool=FLAGS.output_type)
+
+    apply_fixed_architecture(child_model, FLAGS.child_fixed_arc)
+    return child_model
+
+
+def eval_once(child_model, device, eval_set, criterion, valid_dataloader=None, test_dataloader=None):
+    if eval_set == "test":
+        assert test_dataloader is not None
+        dataloader = test_dataloader
+    elif eval_set == "valid":
+        assert valid_dataloader is not None
+        dataloader = valid_dataloader
+    else:
+        raise NotImplementedError("Unknown eval_set '{}'".format(eval_set))
+
+    tot_acc = 0
+    tot = 0
+    losses = []
+
+    with torch.no_grad():  # save memory
+        for batch in dataloader:
+            (sent_ids, mask), labels = batch
+
+            sent_ids = sent_ids.to(device, non_blocking=True)
+            mask = mask.to(device, non_blocking=True)
+            labels = labels.to(device, non_blocking=True)
+
+            logits = child_model((sent_ids, mask))  # run
+
+            loss = criterion(logits, labels.long())
+            loss = loss.mean()
+            preds = logits.argmax(dim=1).long()
+            acc = torch.eq(preds, labels.long()).long().sum().item()
+
+            losses.append(loss)
+            tot_acc += acc
+            tot += len(labels)
+
+    losses = torch.tensor(losses)
+    loss = losses.mean()
+    if tot > 0:
+        final_acc = float(tot_acc) / tot
+    else:
+        final_acc = 0
+        logger.info("Error in calculating final_acc")
+    return final_acc, loss
+
+
+def print_user_flags(FLAGS, line_limit=80):
+    log_strings = "\n" + "-" * line_limit + "\n"
+    for flag_name in sorted(vars(FLAGS)):
+        value = "{}".format(getattr(FLAGS, flag_name))
+        log_string = flag_name
+        log_string += "." * (line_limit - len(flag_name) - len(value))
+        log_string += value
+        log_strings = log_strings + log_string
+        log_strings = log_strings + "\n"
+    log_strings += "-" * line_limit
+    logger.info(log_strings)
+
+
+def count_model_params(trainable_params):
+    num_vars = 0
+    for var in trainable_params:
+        num_vars += np.prod([dim for dim in var.size()])
+    return num_vars
+
+
+def update_lr(
+        optimizer,
+        epoch,
+        l2_reg=1e-4,
+        lr_warmup_val=None,
+        lr_init=0.1,
+        lr_decay_scheme="cosine",
+        lr_max=0.002,
+        lr_min=0.000000001,
+        lr_T_0=4,
+        lr_T_mul=1,
+        sync_replicas=False,
+        num_aggregate=None,
+        num_replicas=None):
+    if lr_decay_scheme == "cosine":
+        assert lr_max is not None, "Need lr_max to use lr_cosine"
+        assert lr_min is not None, "Need lr_min to use lr_cosine"
+        assert lr_T_0 is not None, "Need lr_T_0 to use lr_cosine"
+        assert lr_T_mul is not None, "Need lr_T_mul to use lr_cosine"
+
+        T_i = lr_T_0
+        t_epoch = epoch
+        last_reset = 0
+        while True:
+            t_epoch -= T_i
+            if t_epoch < 0:
+              break
+            last_reset += T_i
+            T_i *= lr_T_mul
+
+        T_curr = epoch - last_reset
+
+        def _update():
+            rate = T_curr / T_i * 3.1415926
+            lr = lr_min + 0.5 * (lr_max - lr_min) * (1.0 + math.cos(rate))
+            return lr
+
+        learning_rate = _update()
+    else:
+        raise ValueError("Unknown learning rate decay scheme {}".format(lr_decay_scheme))
+
+    #update lr in optimizer
+    for params_group in optimizer.param_groups:
+        params_group['lr'] = learning_rate
+    return learning_rate
+
+
+def train(device, data_path, output_dir, num_layers):
+    logger.info("Build dataloader")
+    train_dataset, valid_dataset, test_dataset, embedding = \
+        read_data_sst(data_path,
+                      FLAGS.max_input_length,
+                      FLAGS.min_count,
+                      train_ratio=FLAGS.train_ratio,
+                      valid_ratio=FLAGS.valid_ratio,
+                      is_binary=FLAGS.is_binary)
+    train_dataloader = DataLoader(train_dataset, batch_size=FLAGS.batch_size, shuffle=True, pin_memory=True)
+    test_dataloader = DataLoader(test_dataset, batch_size=FLAGS.eval_batch_size, pin_memory=True)
+    valid_dataloader = DataLoader(valid_dataset, batch_size=FLAGS.eval_batch_size, pin_memory=True)
+
+    logger.info("Build model")
+    child_model = get_model(embedding, num_layers)
+    logger.info("Finish build model")
+
+    #for name, var in child_model.named_parameters():
+    #    logger.info(name, var.size(), var.requires_grad)  # output all params
+
+    num_vars = count_model_params(child_model.parameters())
+    logger.info("Model has {} params".format(num_vars))
+
+    for m in child_model.modules():  # initializer
+        if isinstance(m, (nn.Conv1d, nn.Linear)):
+            nn.init.xavier_uniform_(m.weight)
+
+    criterion = nn.CrossEntropyLoss()
+
+    # get optimizer
+    if FLAGS.child_optim_algo == "adam":
+        optimizer = optim.Adam(child_model.parameters(), eps=1e-3, weight_decay=FLAGS.child_l2_reg)  # with L2
+    else:
+        raise ValueError("Unknown optim_algo {}".format(FLAGS.child_optim_algo))
+
+    child_model.to(device)
+    criterion.to(device)
+
+    logger.info("Start training")
+    start_time = time.time()
+    step = 0
+
+    # save path
+    model_save_path = os.path.join(FLAGS.output_dir, "model.pth")
+    best_model_save_path = os.path.join(FLAGS.output_dir, "best_model.pth")
+    best_acc = 0
+    start_epoch = 0
+    if FLAGS.load_checkpoint:
+        if os.path.isfile(model_save_path):
+            checkpoint = torch.load(model_save_path, map_location = torch.device('cpu'))
+            step = checkpoint['step']
+            start_epoch = checkpoint['epoch']
+            child_model.load_state_dict(checkpoint['child_model_state_dict'])
+            optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+
+    for epoch in range(start_epoch, FLAGS.num_epochs):
+        lr = update_lr(optimizer,
+                       epoch,
+                       l2_reg=FLAGS.child_l2_reg,
+                       lr_warmup_val=None,
+                       lr_init=FLAGS.child_lr,
+                       lr_decay_scheme=FLAGS.child_lr_decay_scheme,
+                       lr_max=FLAGS.child_lr_max,
+                       lr_min=FLAGS.child_lr_min,
+                       lr_T_0=FLAGS.child_lr_T_0,
+                       lr_T_mul=FLAGS.child_lr_T_mul)
+        child_model.train()
+        for batch in train_dataloader:
+            (sent_ids, mask), labels = batch
+
+            sent_ids = sent_ids.to(device, non_blocking=True)
+            mask = mask.to(device, non_blocking=True)
+            labels = labels.to(device, non_blocking=True)
+
+            step += 1
+
+            logits = child_model((sent_ids, mask))  # run
+
+            loss = criterion(logits, labels.long())
+            loss = loss.mean()
+            preds = logits.argmax(dim=1).long()
+            acc = torch.eq(preds, labels.long()).long().sum().item()
+
+            optimizer.zero_grad()
+            loss.backward()
+            grad_norm = 0
+            trainable_params = child_model.parameters()
+
+            assert FLAGS.child_grad_bound is not None, "Need grad_bound to clip gradients."
+            # compute the gradient norm value
+            grad_norm = nn.utils.clip_grad_norm_(trainable_params, 99999999)
+            for param in trainable_params:
+                nn.utils.clip_grad_norm_(param, FLAGS.child_grad_bound)  # clip grad
+
+            optimizer.step()
+
+            if step % FLAGS.log_every == 0:
+                curr_time = time.time()
+                log_string = ""
+                log_string += "epoch={:<6d}".format(epoch)
+                log_string += "ch_step={:<6d}".format(step)
+                log_string += " loss={:<8.6f}".format(loss)
+                log_string += " lr={:<8.4f}".format(lr)
+                log_string += " |g|={:<8.4f}".format(grad_norm)
+                log_string += " tr_acc={:<3d}/{:>3d}".format(acc, logits.size()[0])
+                log_string += " mins={:<10.2f}".format(float(curr_time - start_time) / 60)
+                logger.info(log_string)
+
+        epoch += 1
+        save_state = {
+            'step' : step,
+            'epoch' : epoch,
+            'child_model_state_dict' : child_model.state_dict(),
+            'optimizer_state_dict' : optimizer.state_dict()}
+        torch.save(save_state, model_save_path)
+        child_model.eval()
+        logger.info("Epoch {}: Eval".format(epoch))
+        eval_acc, eval_loss = eval_once(child_model, device, "test", criterion, test_dataloader=test_dataloader)
+        logger.info("ch_step={} {}_accuracy={:<6.4f} {}_loss={:<6.4f}".format(step, "test", eval_acc, "test", eval_loss))
+        if eval_acc > best_acc:
+            best_acc = eval_acc
+            logger.info("Save best model")
+            save_state = {
+                'step' : step,
+                'epoch' : epoch,
+                'child_model_state_dict' : child_model.state_dict(),
+                'optimizer_state_dict' : optimizer.state_dict()}
+            torch.save(save_state, best_model_save_path)
+
+    return eval_acc
+
+
+def main():
+    parse_args()
+    if not os.path.isdir(FLAGS.output_dir):
+        logger.info("Path {} does not exist. Creating.".format(FLAGS.output_dir))
+        os.makedirs(FLAGS.output_dir)
+    elif FLAGS.reset_output_dir:
+        logger.info("Path {} exists. Remove and remake.".format(FLAGS.output_dir))
+        shutil.rmtree(FLAGS.output_dir, ignore_errors=True)
+        os.makedirs(FLAGS.output_dir)
+
+    print_user_flags(FLAGS)
+
+    if FLAGS.fixed_seed:
+        set_random_seed(FLAGS.global_seed)
+
+    device = torch.device("cuda" if FLAGS.is_cuda else "cpu")
+    train(device, FLAGS.data_path, FLAGS.output_dir, FLAGS.child_num_layers)
+
+
+if __name__ == "__main__":
+  main()

examples/nas/textnas/run_retrain.sh

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+export PYTHONPATH="$(pwd)"
+export CUDA_VISIBLE_DEVICES=0
+
+python -u retrain.py \
+  --train_ratio=1.0 \
+  --valid_ratio=1.0 \
+  --min_count=1 \
+  --is_mask=True \
+  --is_binary=True \
+  --child_lr_decay_scheme="cosine" \
+  --data_path="data" \
+  --class_num=2 \
+  --child_optim_algo="adam" \
+  --output_dir="output_sst2" \
+  --global_seed=1234 \
+  --max_input_length=64 \
+  --batch_size=128 \
+  --eval_batch_size=128 \
+  --num_epochs=10 \
+  --log_every=50 \
+  --eval_every_epochs=1 \
+  --child_num_layers=24 \
+  --child_out_filters=256 \
+  --child_l2_reg=1e-6 \
+  --cnn_keep_prob=0.8 \
+  --final_output_keep_prob=1.0 \
+  --embed_keep_prob=0.8 \
+  --lstm_out_keep_prob=0.8 \
+  --attention_keep_prob=0.8 \
+  --child_lr=0.02 \
+  --child_lr_max=0.002 \
+  --child_lr_min=5e-6 \
+  --child_lr_T_0=10 \
+  --child_lr_T_mul=2 \
+  --multi_path=True \
+  --child_fixed_arc="./checkpoints/architecture_00.json" \
+  --fixed_seed=True \
+  "$@"

examples/trials/mnist-pbt-tuner-pytorch/.gitignore

+tmp

examples/trials/mnist-pbt-tuner-pytorch/config.yml

+authorName: default
+experimentName: example_mnist_pbt_tuner_pytorch
+trialConcurrency: 3
+maxExecDuration: 2h
+maxTrialNum: 100
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+#  codeDir: ~/nni/src/sdk/pynni/nni/pbt_tuner
+#  classFileName: pbt_tuner.py
+#  className: PBTTuner
+  builtinTunerName: PBTTuner
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 mnist.py
+  codeDir: .
+  gpuNum: 1

examples/trials/mnist-pbt-tuner-pytorch/mnist.py

+import argparse
+import logging
+
+import os
+import nni
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+
+
+logger = logging.getLogger('mnist_pbt_tuner_pytorch_AutoML')
+
+class Net(nn.Module):
+    def __init__(self, hidden_size):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 20, 5, 1)
+        self.conv2 = nn.Conv2d(20, 50, 5, 1)
+        self.fc1 = nn.Linear(4*4*50, hidden_size)
+        self.fc2 = nn.Linear(hidden_size, 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 = F.relu(self.fc1(x))
+        x = self.fc2(x)
+        return F.log_softmax(x, dim=1)
+
+
+def train(args, model, device, train_loader, optimizer, epoch):
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = data.to(device), target.to(device)
+        optimizer.zero_grad()
+        output = model(data)
+        loss = F.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % args['log_interval'] == 0:
+            logger.info('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item()))
+
+
+def test(args, model, device, test_loader):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            # sum up batch loss
+            test_loss += F.nll_loss(output, target, reduction='sum').item()
+            # get the index of the max log-probability
+            pred = output.argmax(dim=1, keepdim=True)
+            correct += pred.eq(target.view_as(pred)).sum().item()
+
+    test_loss /= len(test_loader.dataset)
+
+    accuracy = 100. * correct / len(test_loader.dataset)
+
+    logger.info('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+        test_loss, correct, len(test_loader.dataset), accuracy))
+
+    return accuracy
+
+
+def save_checkpoint(model, checkpoint_path):
+    torch.save(model.state_dict(), checkpoint_path)
+
+
+def load_checkpoint(checkpoint_path):
+    model_state_dict = torch.load(checkpoint_path)
+    return model_state_dict
+
+
+def main(args):
+    use_cuda = not args['no_cuda'] and torch.cuda.is_available()
+
+    torch.manual_seed(args['seed'])
+
+    device = torch.device("cuda" if use_cuda else "cpu")
+
+    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
+
+    data_dir = os.path.join(args['data_dir'], nni.get_trial_id())
+
+    train_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(data_dir, train=True, download=True,
+                       transform=transforms.Compose([
+                           transforms.ToTensor(),
+                           transforms.Normalize((0.1307,), (0.3081,))
+                       ])),
+        batch_size=args['batch_size'], shuffle=True, **kwargs)
+    test_loader = torch.utils.data.DataLoader(
+        datasets.MNIST(data_dir, train=False, transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))
+        ])),
+        batch_size=1000, shuffle=True, **kwargs)
+
+    hidden_size = args['hidden_size']
+
+    model = Net(hidden_size=hidden_size).to(device)
+
+    save_checkpoint_dir = args['save_checkpoint_dir']
+    save_checkpoint_path = os.path.join(save_checkpoint_dir, 'model.pth')
+    load_checkpoint_path = os.path.join(args['load_checkpoint_dir'], 'model.pth')
+
+    if os.path.isfile(load_checkpoint_path):
+        model_state_dict = load_checkpoint(load_checkpoint_path)
+        logger.info("test : " + load_checkpoint_path)
+        logger.info(type(model_state_dict))
+        model.load_state_dict(model_state_dict)
+
+    optimizer = optim.SGD(model.parameters(), lr=args['lr'],
+                          momentum=args['momentum'])
+
+    #epoch is perturbation interval
+    for epoch in range(1, args['epochs'] + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test_acc = test(args, model, device, test_loader)
+
+        if epoch < args['epochs']:
+            # report intermediate result
+            nni.report_intermediate_result(test_acc)
+            logger.debug('test accuracy %g', test_acc)
+            logger.debug('Pipe send intermediate result done.')
+        else:
+            # report final result
+            nni.report_final_result(test_acc)
+            logger.debug('Final result is %g', test_acc)
+            logger.debug('Send final result done.')
+
+    if not os.path.exists(save_checkpoint_dir):
+        os.makedirs(save_checkpoint_dir)
+    save_checkpoint(model, save_checkpoint_path)
+
+
+def get_params():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument("--data_dir", type=str,
+                        default='./tmp/pytorch/mnist/input_data', help="data directory")
+    parser.add_argument('--batch_size', type=int, default=64, metavar='N',
+                        help='input batch size for training (default: 64)')
+    parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
+                        help='hidden layer size (default: 512)')
+    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
+                        help='learning rate (default: 0.01)')
+    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
+                        help='SGD momentum (default: 0.5)')
+    parser.add_argument('--epochs', type=int, default=10, metavar='N',
+                        help='number of epochs to train (default: 10)')
+    parser.add_argument('--seed', type=int, default=1, metavar='S',
+                        help='random seed (default: 1)')
+    parser.add_argument('--no_cuda', action='store_true', default=False,
+                        help='disables CUDA training')
+    parser.add_argument('--log_interval', type=int, default=1000, metavar='N',
+                        help='how many batches to wait before logging training status')
+
+    parser.add_argument('--save_checkpoint_dir', type=str,
+                        help='where to save checkpoint of this trial')
+    parser.add_argument('--load_checkpoint_dir', type=str,
+                        help='where to load the model')
+
+
+    args, _ = parser.parse_known_args()
+    return args
+
+
+if __name__ == '__main__':
+    try:
+        # get parameters form tuner
+        tuner_params = nni.get_next_parameter()
+        logger.debug(tuner_params)
+        params = vars(get_params())
+        params.update(tuner_params)
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise
\ No newline at end of file

examples/trials/mnist-pbt-tuner-pytorch/search_space.json

+{
+    "batch_size": {"_type":"choice", "_value": [16, 32, 64, 128]},
+    "hidden_size":{"_type":"choice","_value":[128, 256, 512, 1024]},
+    "lr":{"_type":"choice","_value":[0.0001, 0.001, 0.01, 0.1]},
+    "momentum":{"_type":"uniform","_value":[0, 1]}
+}

examples/trials/mnist-pytorch/mnist.py

@@ -15,14 +15,6 @@ import torch.nn.functional as F
 import torch.optim as optim
 from torchvision import datasets, transforms
 
-# Temporary patch this example until the MNIST dataset download issue get resolved
-# https://github.com/pytorch/vision/issues/1938
-import urllib
-
-opener = urllib.request.build_opener()
-opener.addheaders = [('User-agent', 'Mozilla/5.0')]
-urllib.request.install_opener(opener)
-
 logger = logging.getLogger('mnist_AutoML')
 
 
@@ -48,6 +40,8 @@ class Net(nn.Module):
 def train(args, model, device, train_loader, optimizer, epoch):
     model.train()
     for batch_idx, (data, target) in enumerate(train_loader):
+        if (args['batch_num'] is not None) and batch_idx >= args['batch_num']:
+            break
         data, target = data.to(device), target.to(device)
         optimizer.zero_grad()
         output = model(data)
@@ -119,16 +113,15 @@ def main(args):
         train(args, model, device, train_loader, optimizer, epoch)
         test_acc = test(args, model, device, test_loader)
 
-        if epoch < args['epochs']:
-            # report intermediate result
-            nni.report_intermediate_result(test_acc)
-            logger.debug('test accuracy %g', test_acc)
-            logger.debug('Pipe send intermediate result done.')
-        else:
-            # report final result
-            nni.report_final_result(test_acc)
-            logger.debug('Final result is %g', test_acc)
-            logger.debug('Send final result done.')
+        # report intermediate result
+        nni.report_intermediate_result(test_acc)
+        logger.debug('test accuracy %g', test_acc)
+        logger.debug('Pipe send intermediate result done.')
+
+    # report final result
+    nni.report_final_result(test_acc)
+    logger.debug('Final result is %g', test_acc)
+    logger.debug('Send final result done.')
 
 
 def get_params():
@@ -138,6 +131,7 @@ def get_params():
                         default='/tmp/pytorch/mnist/input_data', help="data directory")
     parser.add_argument('--batch_size', type=int, default=64, metavar='N',
                         help='input batch size for training (default: 64)')
+    parser.add_argument("--batch_num", type=int, default=None)
     parser.add_argument("--hidden_size", type=int, default=512, metavar='N',
                         help='hidden layer size (default: 512)')
     parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
@@ -165,6 +159,7 @@ if __name__ == '__main__':
         logger.debug(tuner_params)
         params = vars(get_params())
         params.update(tuner_params)
+        print(params)
         main(params)
     except Exception as exception:
         logger.exception(exception)

src/nasui/yarn.lock

@@ -1784,11 +1784,6 @@ abab@^2.0.0:
   resolved "https://registry.yarnpkg.com/abab/-/abab-2.0.3.tgz#623e2075e02eb2d3f2475e49f99c91846467907a"
   integrity sha512-tsFzPpcttalNjFBCFMqsKYQcWxxen1pgJR56by//QwvJc4/OUS3kPOOttx2tSIfjsylB0pYu7f5D3K1RCxUnUg==
 
-abbrev@1:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/abbrev/-/abbrev-1.1.1.tgz#f8f2c887ad10bf67f634f005b6987fed3179aac8"
-  integrity sha512-nne9/IiQ/hzIhY6pdDnbBtz7DjPTKrY00P/zvPSm5pOFkl6xuGrGnXn/VtTNNfNtAfZ9/1RtehkszU9qcTii0Q==
-
 accepts@~1.3.4, accepts@~1.3.5, accepts@~1.3.7:
   version "1.3.7"
   resolved "https://registry.yarnpkg.com/accepts/-/accepts-1.3.7.tgz#531bc726517a3b2b41f850021c6cc15eaab507cd"
@@ -1949,19 +1944,11 @@ anymatch@~3.1.1:
     normalize-path "^3.0.0"
     picomatch "^2.0.4"
 
-aproba@^1.0.3, aproba@^1.1.1:
+aproba@^1.1.1:
   version "1.2.0"
   resolved "https://registry.yarnpkg.com/aproba/-/aproba-1.2.0.tgz#6802e6264efd18c790a1b0d517f0f2627bf2c94a"
   integrity sha512-Y9J6ZjXtoYh8RnXVCMOU/ttDmk1aBjunq9vO0ta5x85WDQiQfUF9sIPBITdbiiIVcBo03Hi3jMxigBtsddlXRw==
 
-are-we-there-yet@~1.1.2:
-  version "1.1.5"
-  resolved "https://registry.yarnpkg.com/are-we-there-yet/-/are-we-there-yet-1.1.5.tgz#4b35c2944f062a8bfcda66410760350fe9ddfc21"
-  integrity sha512-5hYdAkZlcG8tOLujVDTgCT+uPX0VnpAH28gWsLfzpXYm7wP6mp5Q/gYyR7YQ0cKVJcXJnl3j2kpBan13PtQf6w==
-  dependencies:
-    delegates "^1.0.0"
-    readable-stream "^2.0.6"
-
 argparse@^1.0.7:
   version "1.0.10"
   resolved "https://registry.yarnpkg.com/argparse/-/argparse-1.0.10.tgz#bcd6791ea5ae09725e17e5ad988134cd40b3d911"
@@ -3029,11 +3016,6 @@ console-browserify@^1.1.0:
   resolved "https://registry.yarnpkg.com/console-browserify/-/console-browserify-1.2.0.tgz#67063cef57ceb6cf4993a2ab3a55840ae8c49336"
   integrity sha512-ZMkYO/LkF17QvCPqM0gxw8yUzigAOZOSWSHg91FH6orS7vcEj5dVZTidN2fQ14yBSdg97RqhSNwLUXInd52OTA==
 
-console-control-strings@^1.0.0, console-control-strings@~1.1.0:
-  version "1.1.0"
-  resolved "https://registry.yarnpkg.com/console-control-strings/-/console-control-strings-1.1.0.tgz#3d7cf4464db6446ea644bf4b39507f9851008e8e"
-  integrity sha1-PXz0Rk22RG6mRL9LOVB/mFEAjo4=
-
 constants-browserify@^1.0.0:
   version "1.0.0"
   resolved "https://registry.yarnpkg.com/constants-browserify/-/constants-browserify-1.0.0.tgz#c20b96d8c617748aaf1c16021760cd27fcb8cb75"
@@ -3504,7 +3486,7 @@ debug@2.6.9, debug@^2.2.0, debug@^2.3.3, debug@^2.6.0, debug@^2.6.9:
   dependencies:
     ms "2.0.0"
 
-debug@^3.0.0, debug@^3.1.1, debug@^3.2.5, debug@^3.2.6:
+debug@^3.0.0, debug@^3.1.1, debug@^3.2.5:
   version "3.2.6"
   resolved "https://registry.yarnpkg.com/debug/-/debug-3.2.6.tgz#e83d17de16d8a7efb7717edbe5fb10135eee629b"
   integrity sha512-mel+jf7nrtEl5Pn1Qx46zARXKDpBbvzezse7p7LqINmdoIk8PYP5SySaxEmYv6TZ0JyEKA1hsCId6DIhgITtWQ==
@@ -3540,11 +3522,6 @@ deep-equal@^1.0.1:
     object-keys "^1.1.1"
     regexp.prototype.flags "^1.2.0"
 
-deep-extend@^0.6.0:
-  version "0.6.0"
-  resolved "https://registry.yarnpkg.com/deep-extend/-/deep-extend-0.6.0.tgz#c4fa7c95404a17a9c3e8ca7e1537312b736330ac"
-  integrity sha512-LOHxIOaPYdHlJRtCQfDIVZtfw/ufM8+rVj649RIHzcm/vGwQRXFt6OPqIFWsm2XEMrNIEtWR64sY1LEKD2vAOA==
-
 deep-is@~0.1.3:
   version "0.1.3"
   resolved "https://registry.yarnpkg.com/deep-is/-/deep-is-0.1.3.tgz#b369d6fb5dbc13eecf524f91b070feedc357cf34"
@@ -3605,11 +3582,6 @@ delayed-stream@~1.0.0:
   resolved "https://registry.yarnpkg.com/delayed-stream/-/delayed-stream-1.0.0.tgz#df3ae199acadfb7d440aaae0b29e2272b24ec619"
   integrity sha1-3zrhmayt+31ECqrgsp4icrJOxhk=
 
-delegates@^1.0.0:
-  version "1.0.0"
-  resolved "https://registry.yarnpkg.com/delegates/-/delegates-1.0.0.tgz#84c6e159b81904fdca59a0ef44cd870d31250f9a"
-  integrity sha1-hMbhWbgZBP3KWaDvRM2HDTElD5o=
-
 depd@~1.1.2:
   version "1.1.2"
   resolved "https://registry.yarnpkg.com/depd/-/depd-1.1.2.tgz#9bcd52e14c097763e749b274c4346ed2e560b5a9"
@@ -3628,11 +3600,6 @@ destroy@~1.0.4:
   resolved "https://registry.yarnpkg.com/destroy/-/destroy-1.0.4.tgz#978857442c44749e4206613e37946205826abd80"
   integrity sha1-l4hXRCxEdJ5CBmE+N5RiBYJqvYA=
 
-detect-libc@^1.0.2:
-  version "1.0.3"
-  resolved "https://registry.yarnpkg.com/detect-libc/-/detect-libc-1.0.3.tgz#fa137c4bd698edf55cd5cd02ac559f91a4c4ba9b"
-  integrity sha1-+hN8S9aY7fVc1c0CrFWfkaTEups=
-
 detect-newline@^2.1.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/detect-newline/-/detect-newline-2.1.0.tgz#f41f1c10be4b00e87b5f13da680759f2c5bfd3e2"
@@ -4678,13 +4645,6 @@ fs-extra@^8.1.0:
     jsonfile "^4.0.0"
     universalify "^0.1.0"
 
-fs-minipass@^1.2.5:
-  version "1.2.7"
-  resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-1.2.7.tgz#ccff8570841e7fe4265693da88936c55aed7f7c7"
-  integrity sha512-GWSSJGFy4e9GUeCcbIkED+bgAoFyj7XF1mV8rma3QW4NIqX9Kyx79N/PF61H5udOV3aY1IaMLs6pGbH71nlCTA==
-  dependencies:
-    minipass "^2.6.0"
-
 fs-minipass@^2.0.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/fs-minipass/-/fs-minipass-2.1.0.tgz#7f5036fdbf12c63c169190cbe4199c852271f9fb"
@@ -4730,20 +4690,6 @@ functional-red-black-tree@^1.0.1:
   resolved "https://registry.yarnpkg.com/functional-red-black-tree/-/functional-red-black-tree-1.0.1.tgz#1b0ab3bd553b2a0d6399d29c0e3ea0b252078327"
   integrity sha1-GwqzvVU7Kg1jmdKcDj6gslIHgyc=
 
-gauge@~2.7.3:
-  version "2.7.4"
-  resolved "https://registry.yarnpkg.com/gauge/-/gauge-2.7.4.tgz#2c03405c7538c39d7eb37b317022e325fb018bf7"
-  integrity sha1-LANAXHU4w51+s3sxcCLjJfsBi/c=
-  dependencies:
-    aproba "^1.0.3"
-    console-control-strings "^1.0.0"
-    has-unicode "^2.0.0"
-    object-assign "^4.1.0"
-    signal-exit "^3.0.0"
-    string-width "^1.0.1"
-    strip-ansi "^3.0.1"
-    wide-align "^1.1.0"
-
 gensync@^1.0.0-beta.1:
   version "1.0.0-beta.1"
   resolved "https://registry.yarnpkg.com/gensync/-/gensync-1.0.0-beta.1.tgz#58f4361ff987e5ff6e1e7a210827aa371eaac269"
@@ -4937,11 +4883,6 @@ has-symbols@^1.0.0, has-symbols@^1.0.1:
   resolved "https://registry.yarnpkg.com/has-symbols/-/has-symbols-1.0.1.tgz#9f5214758a44196c406d9bd76cebf81ec2dd31e8"
   integrity sha512-PLcsoqu++dmEIZB+6totNFKq/7Do+Z0u4oT0zKOJNl3lYK6vGwwu2hjHs+68OEZbTjiUE9bgOABXbP/GvrS0Kg==
 
-has-unicode@^2.0.0:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/has-unicode/-/has-unicode-2.0.1.tgz#e0e6fe6a28cf51138855e086d1691e771de2a8b9"
-  integrity sha1-4Ob+aijPUROIVeCG0Wkedx3iqLk=
-
 has-value@^0.3.1:
   version "0.3.1"
   resolved "https://registry.yarnpkg.com/has-value/-/has-value-0.3.1.tgz#7b1f58bada62ca827ec0a2078025654845995e1f"
@@ -5191,7 +5132,7 @@ hyphenate-style-name@^1.0.3:
   resolved "https://registry.yarnpkg.com/hyphenate-style-name/-/hyphenate-style-name-1.0.3.tgz#097bb7fa0b8f1a9cf0bd5c734cf95899981a9b48"
   integrity sha512-EcuixamT82oplpoJ2XU4pDtKGWQ7b00CD9f1ug9IaQ3p1bkHMiKCZ9ut9QDI6qsa6cpUuB+A/I+zLtdNK4n2DQ==
 
-iconv-lite@0.4.24, iconv-lite@^0.4.24, iconv-lite@^0.4.4:
+iconv-lite@0.4.24, iconv-lite@^0.4.24:
   version "0.4.24"
   resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.4.24.tgz#2022b4b25fbddc21d2f524974a474aafe733908b"
   integrity sha512-v3MXnZAcvnywkTUEZomIActle7RXXeedOR31wwl7VlyoXO4Qi9arvSenNQWne1TcRwhCL1HwLI21bEqdpj8/rA==
@@ -5222,13 +5163,6 @@ iferr@^0.1.5:
   resolved "https://registry.yarnpkg.com/iferr/-/iferr-0.1.5.tgz#c60eed69e6d8fdb6b3104a1fcbca1c192dc5b501"
   integrity sha1-xg7taebY/bazEEofy8ocGS3FtQE=
 
-ignore-walk@^3.0.1:
-  version "3.0.3"
-  resolved "https://registry.yarnpkg.com/ignore-walk/-/ignore-walk-3.0.3.tgz#017e2447184bfeade7c238e4aefdd1e8f95b1e37"
-  integrity sha512-m7o6xuOaT1aqheYHKf8W6J5pYH85ZI9w077erOzLje3JsB1gkafkAhHHY19dqjulgIZHFm32Cp5uNZgcQqdJKw==
-  dependencies:
-    minimatch "^3.0.4"
-
 ignore@^3.3.5:
   version "3.3.10"
   resolved "https://registry.yarnpkg.com/ignore/-/ignore-3.3.10.tgz#0a97fb876986e8081c631160f8f9f389157f0043"
@@ -5325,7 +5259,7 @@ inherits@2.0.3:
   resolved "https://registry.yarnpkg.com/inherits/-/inherits-2.0.3.tgz#633c2c83e3da42a502f52466022480f4208261de"
   integrity sha1-Yzwsg+PaQqUC9SRmAiSA9CCCYd4=
 
-ini@^1.3.5, ini@~1.3.0:
+ini@^1.3.5:
   version "1.3.5"
   resolved "https://registry.yarnpkg.com/ini/-/ini-1.3.5.tgz#eee25f56db1c9ec6085e0c22778083f596abf927"
   integrity sha512-RZY5huIKCMRWDUqZlEi72f/lmXKMvuszcMBduliQ3nnWbx9X/ZBQO7DijMEYS9EhHBb2qacRUMtC7svLwe0lcw==
@@ -6888,14 +6822,6 @@ minipass-pipeline@^1.2.2:
   dependencies:
     minipass "^3.0.0"
 
-minipass@^2.6.0, minipass@^2.8.6, minipass@^2.9.0:
-  version "2.9.0"
-  resolved "https://registry.yarnpkg.com/minipass/-/minipass-2.9.0.tgz#e713762e7d3e32fed803115cf93e04bca9fcc9a6"
-  integrity sha512-wxfUjg9WebH+CUDX/CdbRlh5SmfZiy/hpkxaRI16Y9W56Pa75sWgd/rvFilSgrauD9NyFymP/+JFV3KwzIsJeg==
-  dependencies:
-    safe-buffer "^5.1.2"
-    yallist "^3.0.0"
-
 minipass@^3.0.0, minipass@^3.1.1:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/minipass/-/minipass-3.1.1.tgz#7607ce778472a185ad6d89082aa2070f79cedcd5"
@@ -6903,13 +6829,6 @@ minipass@^3.0.0, minipass@^3.1.1:
   dependencies:
     yallist "^4.0.0"
 
-minizlib@^1.2.1:
-  version "1.3.3"
-  resolved "https://registry.yarnpkg.com/minizlib/-/minizlib-1.3.3.tgz#2290de96818a34c29551c8a8d301216bd65a861d"
-  integrity sha512-6ZYMOEnmVsdCeTJVE0W9ZD+pVnE8h9Hma/iOwwRDsdQoePpoX56/8B6z3P9VNwppJuBKNRuFDRNRqRWexT9G9Q==
-  dependencies:
-    minipass "^2.9.0"
-
 mississippi@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/mississippi/-/mississippi-3.0.0.tgz#ea0a3291f97e0b5e8776b363d5f0a12d94c67022"
@@ -6942,7 +6861,7 @@ mixin-object@^2.0.1:
     for-in "^0.1.3"
     is-extendable "^0.1.1"
 
-mkdirp@0.5.1, mkdirp@^0.5.0, mkdirp@^0.5.1, mkdirp@~0.5.1:
+mkdirp@0.5.1, mkdirp@^0.5.1, mkdirp@~0.5.1:
   version "0.5.1"
   resolved "https://registry.yarnpkg.com/mkdirp/-/mkdirp-0.5.1.tgz#30057438eac6cf7f8c4767f38648d6697d75c903"
   integrity sha1-MAV0OOrGz3+MR2fzhkjWaX11yQM=
@@ -7021,15 +6940,6 @@ natural-compare@^1.4.0:
   resolved "https://registry.yarnpkg.com/natural-compare/-/natural-compare-1.4.0.tgz#4abebfeed7541f2c27acfb29bdbbd15c8d5ba4f7"
   integrity sha1-Sr6/7tdUHywnrPspvbvRXI1bpPc=
 
-needle@^2.2.1:
-  version "2.3.2"
-  resolved "https://registry.yarnpkg.com/needle/-/needle-2.3.2.tgz#3342dea100b7160960a450dc8c22160ac712a528"
-  integrity sha512-DUzITvPVDUy6vczKKYTnWc/pBZ0EnjMJnQ3y+Jo5zfKFimJs7S3HFCxCRZYB9FUZcrzUQr3WsmvZgddMEIZv6w==
-  dependencies:
-    debug "^3.2.6"
-    iconv-lite "^0.4.4"
-    sax "^1.2.4"
-
 negotiator@0.6.2:
   version "0.6.2"
   resolved "https://registry.yarnpkg.com/negotiator/-/negotiator-0.6.2.tgz#feacf7ccf525a77ae9634436a64883ffeca346fb"
@@ -7113,22 +7023,6 @@ node-notifier@^5.4.2:
     shellwords "^0.1.1"
     which "^1.3.0"
 
-node-pre-gyp@*:
-  version "0.14.0"
-  resolved "https://registry.yarnpkg.com/node-pre-gyp/-/node-pre-gyp-0.14.0.tgz#9a0596533b877289bcad4e143982ca3d904ddc83"
-  integrity sha512-+CvDC7ZttU/sSt9rFjix/P05iS43qHCOOGzcr3Ry99bXG7VX953+vFyEuph/tfqoYu8dttBkE86JSKBO2OzcxA==
-  dependencies:
-    detect-libc "^1.0.2"
-    mkdirp "^0.5.1"
-    needle "^2.2.1"
-    nopt "^4.0.1"
-    npm-packlist "^1.1.6"
-    npmlog "^4.0.2"
-    rc "^1.2.7"
-    rimraf "^2.6.1"
-    semver "^5.3.0"
-    tar "^4.4.2"
-
 node-releases@^1.1.47, node-releases@^1.1.50:
   version "1.1.50"
   resolved "https://registry.yarnpkg.com/node-releases/-/node-releases-1.1.50.tgz#803c40d2c45db172d0410e4efec83aa8c6ad0592"
@@ -7136,14 +7030,6 @@ node-releases@^1.1.47, node-releases@^1.1.50:
   dependencies:
     semver "^6.3.0"
 
-nopt@^4.0.1:
-  version "4.0.1"
-  resolved "https://registry.yarnpkg.com/nopt/-/nopt-4.0.1.tgz#d0d4685afd5415193c8c7505602d0d17cd64474d"
-  integrity sha1-0NRoWv1UFRk8jHUFYC0NF81kR00=
-  dependencies:
-    abbrev "1"
-    osenv "^0.1.4"
-
 normalize-package-data@^2.3.2:
   version "2.5.0"
   resolved "https://registry.yarnpkg.com/normalize-package-data/-/normalize-package-data-2.5.0.tgz#e66db1838b200c1dfc233225d12cb36520e234a8"
@@ -7186,27 +7072,6 @@ normalize-url@^3.0.0:
   resolved "https://registry.yarnpkg.com/normalize-url/-/normalize-url-3.3.0.tgz#b2e1c4dc4f7c6d57743df733a4f5978d18650559"
   integrity sha512-U+JJi7duF1o+u2pynbp2zXDW2/PADgC30f0GsHZtRh+HOcXHnw137TrNlyxxRvWW5fjKd3bcLHPxofWuCjaeZg==
 
-npm-bundled@^1.0.1:
-  version "1.1.1"
-  resolved "https://registry.yarnpkg.com/npm-bundled/-/npm-bundled-1.1.1.tgz#1edd570865a94cdb1bc8220775e29466c9fb234b"
-  integrity sha512-gqkfgGePhTpAEgUsGEgcq1rqPXA+tv/aVBlgEzfXwA1yiUJF7xtEt3CtVwOjNYQOVknDk0F20w58Fnm3EtG0fA==
-  dependencies:
-    npm-normalize-package-bin "^1.0.1"
-
-npm-normalize-package-bin@^1.0.1:
-  version "1.0.1"
-  resolved "https://registry.yarnpkg.com/npm-normalize-package-bin/-/npm-normalize-package-bin-1.0.1.tgz#6e79a41f23fd235c0623218228da7d9c23b8f6e2"
-  integrity sha512-EPfafl6JL5/rU+ot6P3gRSCpPDW5VmIzX959Ob1+ySFUuuYHWHekXpwdUZcKP5C+DS4GEtdJluwBjnsNDl+fSA==
-
-npm-packlist@^1.1.6:
-  version "1.4.8"
-  resolved "https://registry.yarnpkg.com/npm-packlist/-/npm-packlist-1.4.8.tgz#56ee6cc135b9f98ad3d51c1c95da22bbb9b2ef3e"
-  integrity sha512-5+AZgwru5IevF5ZdnFglB5wNlHG1AOOuw28WhUq8/8emhBmLv6jX5by4WJCh7lW0uSYZYS6DXqIsyZVIXRZU9A==
-  dependencies:
-    ignore-walk "^3.0.1"
-    npm-bundled "^1.0.1"
-    npm-normalize-package-bin "^1.0.1"
-
 npm-run-path@^2.0.0:
   version "2.0.2"
   resolved "https://registry.yarnpkg.com/npm-run-path/-/npm-run-path-2.0.2.tgz#35a9232dfa35d7067b4cb2ddf2357b1871536c5f"
@@ -7214,16 +7079,6 @@ npm-run-path@^2.0.0:
   dependencies:
     path-key "^2.0.0"
 
-npmlog@^4.0.2:
-  version "4.1.2"
-  resolved "https://registry.yarnpkg.com/npmlog/-/npmlog-4.1.2.tgz#08a7f2a8bf734604779a9efa4ad5cc717abb954b"
-  integrity sha512-2uUqazuKlTaSI/dC8AzicUck7+IrEaOnN/e0jd3Xtt1KcGpwx30v50mL7oPyr/h9bL3E4aZccVwpwP+5W9Vjkg==
-  dependencies:
-    are-we-there-yet "~1.1.2"
-    console-control-strings "~1.1.0"
-    gauge "~2.7.3"
-    set-blocking "~2.0.0"
-
 nth-check@^1.0.2, nth-check@~1.0.1:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/nth-check/-/nth-check-1.0.2.tgz#b2bd295c37e3dd58a3bf0700376663ba4d9cf05c"
@@ -7430,11 +7285,6 @@ os-browserify@^0.3.0:
   resolved "https://registry.yarnpkg.com/os-browserify/-/os-browserify-0.3.0.tgz#854373c7f5c2315914fc9bfc6bd8238fdda1ec27"
   integrity sha1-hUNzx/XCMVkU/Jv8a9gjj92h7Cc=
 
-os-homedir@^1.0.0:
-  version "1.0.2"
-  resolved "https://registry.yarnpkg.com/os-homedir/-/os-homedir-1.0.2.tgz#ffbc4988336e0e833de0c168c7ef152121aa7fb3"
-  integrity sha1-/7xJiDNuDoM94MFox+8VISGqf7M=
-
 os-locale@^3.0.0:
   version "3.1.0"
   resolved "https://registry.yarnpkg.com/os-locale/-/os-locale-3.1.0.tgz#a802a6ee17f24c10483ab9935719cef4ed16bf1a"
@@ -7444,19 +7294,11 @@ os-locale@^3.0.0:
     lcid "^2.0.0"
     mem "^4.0.0"
 
-os-tmpdir@^1.0.0, os-tmpdir@~1.0.2:
+os-tmpdir@~1.0.2:
   version "1.0.2"
   resolved "https://registry.yarnpkg.com/os-tmpdir/-/os-tmpdir-1.0.2.tgz#bbe67406c79aa85c5cfec766fe5734555dfa1274"
   integrity sha1-u+Z0BseaqFxc/sdm/lc0VV36EnQ=
 
-osenv@^0.1.4:
-  version "0.1.5"
-  resolved "https://registry.yarnpkg.com/osenv/-/osenv-0.1.5.tgz#85cdfafaeb28e8677f416e287592b5f3f49ea410"
-  integrity sha512-0CWcCECdMVc2Rw3U5w9ZjqX6ga6ubk1xDVKxtBQPK7wis/0F2r9T6k4ydGYhecl7YUBxBVxhL5oisPsNxAPe2g==
-  dependencies:
-    os-homedir "^1.0.0"
-    os-tmpdir "^1.0.0"
-
 p-defer@^1.0.0:
   version "1.0.0"
   resolved "https://registry.yarnpkg.com/p-defer/-/p-defer-1.0.0.tgz#9f6eb182f6c9aa8cd743004a7d4f96b196b0fb0c"
@@ -8727,16 +8569,6 @@ raw-body@2.4.0:
     iconv-lite "0.4.24"
     unpipe "1.0.0"
 
-rc@^1.2.7:
-  version "1.2.8"
-  resolved "https://registry.yarnpkg.com/rc/-/rc-1.2.8.tgz#cd924bf5200a075b83c188cd6b9e211b7fc0d3ed"
-  integrity sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==
-  dependencies:
-    deep-extend "^0.6.0"
-    ini "~1.3.0"
-    minimist "^1.2.0"
-    strip-json-comments "~2.0.1"
-
 react-app-polyfill@^1.0.6:
   version "1.0.6"
   resolved "https://registry.yarnpkg.com/react-app-polyfill/-/react-app-polyfill-1.0.6.tgz#890f8d7f2842ce6073f030b117de9130a5f385f0"
@@ -8912,7 +8744,7 @@ read-pkg@^3.0.0:
     normalize-package-data "^2.3.2"
     path-type "^3.0.0"
 
-"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.0.6, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3, readable-stream@^2.3.6, readable-stream@~2.3.6:
+"readable-stream@1 || 2", readable-stream@^2.0.0, readable-stream@^2.0.1, readable-stream@^2.0.2, readable-stream@^2.1.5, readable-stream@^2.2.2, readable-stream@^2.3.3, readable-stream@^2.3.6, readable-stream@~2.3.6:
   version "2.3.7"
   resolved "https://registry.yarnpkg.com/readable-stream/-/readable-stream-2.3.7.tgz#1eca1cf711aef814c04f62252a36a62f6cb23b57"
   integrity sha512-Ebho8K4jIbHAxnuxi7o42OrZgF/ZTNcsZj6nRKyUmkhLFq8CHItp/fy6hQZuZmP/n3yZ9VBUbp4zz/mX8hmYPw==
@@ -9254,7 +9086,7 @@ rimraf@2.6.3:
   dependencies:
     glob "^7.1.3"
 
-rimraf@^2.5.4, rimraf@^2.6.1, rimraf@^2.6.3, rimraf@^2.7.1:
+rimraf@^2.5.4, rimraf@^2.6.3, rimraf@^2.7.1:
   version "2.7.1"
   resolved "https://registry.yarnpkg.com/rimraf/-/rimraf-2.7.1.tgz#35797f13a7fdadc566142c29d4f07ccad483e3ec"
   integrity sha512-uWjbaKIK3T1OSVptzX7Nl6PvQ3qAGtKEtVRjRuazjfL3Bx5eI409VZSqgND+4UNnmzLVdPj9FqFJNPqBZFve4w==
@@ -9397,7 +9229,7 @@ selfsigned@^1.10.7:
   dependencies:
     node-forge "0.9.0"
 
-"semver@2 || 3 || 4 || 5", semver@^5.3.0, semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0:
+"semver@2 || 3 || 4 || 5", semver@^5.4.1, semver@^5.5.0, semver@^5.5.1, semver@^5.6.0:
   version "5.7.1"
   resolved "https://registry.yarnpkg.com/semver/-/semver-5.7.1.tgz#a954f931aeba508d307bbf069eff0c01c96116f7"
   integrity sha512-sauaDf/PZdVgrLTNYHRtpXa1iRiKcaebiKQ1BJdpQlWH2lCvexQdX55snPFyK7QzpudqbCI0qXFfOasHdyNDGQ==
@@ -9459,7 +9291,7 @@ serve-static@1.14.1:
     parseurl "~1.3.3"
     send "0.17.1"
 
-set-blocking@^2.0.0, set-blocking@~2.0.0:
+set-blocking@^2.0.0:
   version "2.0.0"
   resolved "https://registry.yarnpkg.com/set-blocking/-/set-blocking-2.0.0.tgz#045f9782d011ae9a6803ddd382b24392b3d890f7"
   integrity sha1-BF+XgtARrppoA93TgrJDkrPYkPc=
@@ -9866,7 +9698,7 @@ string-width@^1.0.1:
     is-fullwidth-code-point "^1.0.0"
     strip-ansi "^3.0.0"
 
-"string-width@^1.0.2 || 2", string-width@^2.0.0, string-width@^2.1.1:
+string-width@^2.0.0, string-width@^2.1.1:
   version "2.1.1"
   resolved "https://registry.yarnpkg.com/string-width/-/string-width-2.1.1.tgz#ab93f27a8dc13d28cac815c462143a6d9012ae9e"
   integrity sha512-nOqH59deCq9SRHlxq1Aw85Jnt4w6KvLKqWVik6oA9ZklXLNIOlqg4F2yrT1MVaTjAqvVwdfeZ7w7aCvJD7ugkw==
@@ -9989,11 +9821,6 @@ strip-json-comments@^3.0.1:
   resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-3.0.1.tgz#85713975a91fb87bf1b305cca77395e40d2a64a7"
   integrity sha512-VTyMAUfdm047mwKl+u79WIdrZxtFtn+nBxHeb844XBQ9uMNTuTHdx2hc5RiAJYqwTj3wc/xe5HLSdJSkJ+WfZw==
 
-strip-json-comments@~2.0.1:
-  version "2.0.1"
-  resolved "https://registry.yarnpkg.com/strip-json-comments/-/strip-json-comments-2.0.1.tgz#3c531942e908c2697c0ec344858c286c7ca0a60a"
-  integrity sha1-PFMZQukIwml8DsNEhYwobHygpgo=
-
 style-loader@0.23.1:
   version "0.23.1"
   resolved "https://registry.yarnpkg.com/style-loader/-/style-loader-0.23.1.tgz#cb9154606f3e771ab6c4ab637026a1049174d925"
@@ -10081,19 +9908,6 @@ tapable@^1.0.0, tapable@^1.1.3:
   resolved "https://registry.yarnpkg.com/tapable/-/tapable-1.1.3.tgz#a1fccc06b58db61fd7a45da2da44f5f3a3e67ba2"
   integrity sha512-4WK/bYZmj8xLr+HUCODHGF1ZFzsYffasLUgEiMBY4fgtltdO6B4WJtlSbPaDTLpYTcGVwM2qLnFTICEcNxs3kA==
 
-tar@^4.4.2:
-  version "4.4.13"
-  resolved "https://registry.yarnpkg.com/tar/-/tar-4.4.13.tgz#43b364bc52888d555298637b10d60790254ab525"
-  integrity sha512-w2VwSrBoHa5BsSyH+KxEqeQBAllHhccyMFVHtGtdMpF4W7IRWfZjFiQceJPChOeTsSDVUpER2T8FA93pr0L+QA==
-  dependencies:
-    chownr "^1.1.1"
-    fs-minipass "^1.2.5"
-    minipass "^2.8.6"
-    minizlib "^1.2.1"
-    mkdirp "^0.5.0"
-    safe-buffer "^5.1.2"
-    yallist "^3.0.3"
-
 terser-webpack-plugin@2.3.4:
   version "2.3.4"
   resolved "https://registry.yarnpkg.com/terser-webpack-plugin/-/terser-webpack-plugin-2.3.4.tgz#ac045703bd8da0936ce910d8fb6350d0e1dee5fe"
@@ -10784,13 +10598,6 @@ which@^2.0.1:
   dependencies:
     isexe "^2.0.0"
 
-wide-align@^1.1.0:
-  version "1.1.3"
-  resolved "https://registry.yarnpkg.com/wide-align/-/wide-align-1.1.3.tgz#ae074e6bdc0c14a431e804e624549c633b000457"
-  integrity sha512-QGkOQc8XL6Bt5PwnsExKBPuMKBxnGxWWW3fU55Xt4feHozMUhdUMaBCk290qpm/wG5u/RSKzwdAC4i51YigihA==
-  dependencies:
-    string-width "^1.0.2 || 2"
-
 word-wrap@~1.2.3:
   version "1.2.3"
   resolved "https://registry.yarnpkg.com/word-wrap/-/word-wrap-1.2.3.tgz#610636f6b1f703891bd34771ccb17fb93b47079c"
@@ -11017,7 +10824,7 @@ xtend@^4.0.0, xtend@~4.0.1:
   resolved "https://registry.yarnpkg.com/y18n/-/y18n-4.0.0.tgz#95ef94f85ecc81d007c264e190a120f0a3c8566b"
   integrity sha512-r9S/ZyXu/Xu9q1tYlpsLIsa3EeLXXk0VwlxqTcFRfg9EhMW+17kbt9G0NrgCmhGb5vT2hyhJZLfDGx+7+5Uj/w==
 
-yallist@^3.0.0, yallist@^3.0.2, yallist@^3.0.3:
+yallist@^3.0.2:
   version "3.1.1"
   resolved "https://registry.yarnpkg.com/yallist/-/yallist-3.1.1.tgz#dbb7daf9bfd8bac9ab45ebf602b8cbad0d5d08fd"
   integrity sha512-a4UGQaWPH59mOXUYnAG2ewncQS4i4F43Tv3JoAM+s2VDAmS9NsK8GpDMLrCHPksFT7h3K6TOoUNn2pb7RoXx4g==

src/nni_manager/rest_server/restValidationSchemas.ts

@@ -178,7 +178,7 @@ export namespace ValidationSchemas {
                 gpuIndices: joi.string()
             }),
             tuner: joi.object({
-                builtinTunerName: joi.string().valid('TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'BatchTuner', 'GridSearch', 'NetworkMorphism', 'MetisTuner', 'GPTuner', 'PPOTuner'),
+                builtinTunerName: joi.string().valid('TPE', 'Random', 'Anneal', 'Evolution', 'SMAC', 'BatchTuner', 'GridSearch', 'NetworkMorphism', 'MetisTuner', 'GPTuner', 'PPOTuner', 'PBTTuner'),
                 codeDir: joi.string(),
                 classFileName: joi.string(),
                 className: joi.string(),

src/sdk/pynni/nni/compression/speedup/torch/compressor.py

@@ -229,42 +229,39 @@ class ModelSpeedup:
         list
             a list of scope name of all the leaf modules
         """
-        pieces = [] # each element is a dict
+        class SNode:
+            def __init__(self, name):
+                self.sname = name
+                self.childs = {}
+
+        root = None
         for node in graph.nodes():
             scope_name = node.scopeName()
             if scope_name == '':
                 continue
             segs = scope_name.split('/')
-            segs_len = len(segs)
-            # increase the length of `pieces` if not enough
-            for _ in range(segs_len - len(pieces)):
-                pieces.append({})
-            # process internal segments of the scope name
-            # 'L' means leaf segment
-            # 'I' means internal segment
-            # internal segment can replace leaf segment at the same position of `pieces`
-            for i, seg in enumerate(segs[:-1]):
-                seg_name_dict = pieces[i]
-                if seg in seg_name_dict:
-                    if seg_name_dict[seg][0] == 'L':
-                        seg_name_dict[seg] = ('I', node)
-                else:
-                    seg_name_dict[seg] = ('I', node)
-            # process the leaf segment of the scope name
-            last_segs_dict = pieces[len(segs) - 1]
-            if not segs[-1] in last_segs_dict:
-                last_segs_dict[segs[-1]] = ('L', node)
-        # traverse `pieces` to obtain all the leaf modules which are labeled with 'L'
-        leaf_modules = []
-        for piece in pieces:
-            for _, value in piece.items():
-                if value[0] == 'L':
-                    assert value[1].scopeName() not in leaf_modules
-                    # if this is a leaf module, the last segment of its scope name
-                    # must be in pattern `xxx[xxx]`
-                    if value[1].scopeName()[-1] == ']':
-                        leaf_modules.append(value[1].scopeName())
-        return leaf_modules
+            if root is None:
+                root = SNode(segs[0])
+            curr = root
+            for seg in segs[1:]:
+                if not seg in curr.childs:
+                    curr.childs[seg] = SNode(seg)
+                curr = curr.childs[seg]
+
+        leaf_nodes = []
+        def traverse_tree(node, scope_name):
+            if scope_name == '':
+                sn = node.sname
+            else:
+                sn = scope_name + '/' + node.sname
+            if not node.childs:
+                if node.sname[-1] == ']':
+                    leaf_nodes.append(sn)
+            else:
+                for key in node.childs:
+                    traverse_tree(node.childs[key], sn)
+        traverse_tree(root, '')
+        return leaf_nodes
 
     def _build_graph(self):
         """

src/sdk/pynni/nni/constants.py

@@ -15,7 +15,8 @@ ModuleName = {
     'Curvefitting': 'nni.curvefitting_assessor.curvefitting_assessor',
     'MetisTuner': 'nni.metis_tuner.metis_tuner',
     'GPTuner': 'nni.gp_tuner.gp_tuner',
-    'PPOTuner': 'nni.ppo_tuner.ppo_tuner'
+    'PPOTuner': 'nni.ppo_tuner.ppo_tuner',
+    'PBTTuner': 'nni.pbt_tuner.pbt_tuner'
 }
 
 ClassName = {
@@ -30,6 +31,7 @@ ClassName = {
     'MetisTuner':'MetisTuner',
     'GPTuner':'GPTuner',
     'PPOTuner': 'PPOTuner',
+    'PBTTuner': 'PBTTuner',
 
     'Medianstop': 'MedianstopAssessor',
     'Curvefitting': 'CurvefittingAssessor'

src/sdk/pynni/nni/evolution_tuner/evolution_tuner.py

@@ -10,97 +10,8 @@ import random
 
 import numpy as np
 from nni.tuner import Tuner
-from nni.utils import NodeType, OptimizeMode, extract_scalar_reward, split_index
+from nni.utils import OptimizeMode, extract_scalar_reward, split_index, json2parameter, json2space
 
-import nni.parameter_expressions as parameter_expressions
-
-
-def json2space(x, oldy=None, name=NodeType.ROOT):
-    """
-    Change search space from json format to hyperopt format
-
-    """
-    y = list()
-    if isinstance(x, dict):
-        if NodeType.TYPE in x.keys():
-            _type = x[NodeType.TYPE]
-            name = name + '-' + _type
-            if _type == 'choice':
-                if oldy is not None:
-                    _index = oldy[NodeType.INDEX]
-                    y += json2space(x[NodeType.VALUE][_index],
-                                    oldy[NodeType.VALUE], name=name+'[%d]' % _index)
-                else:
-                    y += json2space(x[NodeType.VALUE], None, name=name)
-            y.append(name)
-        else:
-            for key in x.keys():
-                y += json2space(x[key], oldy[key] if oldy else None, name+"[%s]" % str(key))
-    elif isinstance(x, list):
-        for i, x_i in enumerate(x):
-            if isinstance(x_i, dict):
-                if NodeType.NAME not in x_i.keys():
-                    raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y += json2space(x_i, oldy[i] if oldy else None, name + "[%d]" % i)
-    return y
-
-def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
-    """
-    Json to pramaters.
-
-    """
-    if isinstance(x, dict):
-        if NodeType.TYPE in x.keys():
-            _type = x[NodeType.TYPE]
-            _value = x[NodeType.VALUE]
-            name = name + '-' + _type
-            Rand |= is_rand[name]
-            if Rand is True:
-                if _type == 'choice':
-                    _index = random_state.randint(len(_value))
-                    y = {
-                        NodeType.INDEX: _index,
-                        NodeType.VALUE: json2parameter(
-                            x[NodeType.VALUE][_index],
-                            is_rand,
-                            random_state,
-                            None,
-                            Rand,
-                            name=name+"[%d]" % _index
-                        )
-                    }
-                else:
-                    y = getattr(parameter_expressions, _type)(*(_value + [random_state]))
-            else:
-                y = copy.deepcopy(oldy)
-        else:
-            y = dict()
-            for key in x.keys():
-                y[key] = json2parameter(
-                    x[key],
-                    is_rand,
-                    random_state,
-                    oldy[key] if oldy else None,
-                    Rand,
-                    name + "[%s]" % str(key)
-                )
-    elif isinstance(x, list):
-        y = list()
-        for i, x_i in enumerate(x):
-            if isinstance(x_i, dict):
-                if NodeType.NAME not in x_i.keys():
-                    raise RuntimeError('\'_name\' key is not found in this nested search space.')
-            y.append(json2parameter(
-                x_i,
-                is_rand,
-                random_state,
-                oldy[i] if oldy else None,
-                Rand,
-                name + "[%d]" % i
-            ))
-    else:
-        y = copy.deepcopy(x)
-    return y
 
 class Individual:
     """

src/sdk/pynni/nni/evolution_tuner/test_evolution_tuner.py

@@ -9,7 +9,7 @@ import numpy as np
 
 from unittest import TestCase, main
 
-from nni.evolution_tuner.evolution_tuner import json2space, json2parameter
+from nni.utils import json2space, json2parameter
 
 
 class EvolutionTunerTestCase(TestCase):

src/sdk/pynni/nni/pbt_tuner/pbt_tuner.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT license.
+
+import copy
+import logging
+import os
+import numpy as np
+
+import nni
+from nni.tuner import Tuner
+from nni.utils import OptimizeMode, extract_scalar_reward, split_index, json2parameter, json2space
+
+
+logger = logging.getLogger('pbt_tuner_AutoML')
+
+
+def exploit_and_explore(bot_trial_info, top_trial_info, factors, epoch, search_space):
+    """
+    Replace checkpoint of bot_trial with top, and perturb hyperparameters
+
+    Parameters
+    ----------
+    bot_trial_info : TrialInfo
+        bottom model whose parameters should be replaced
+    top_trial_info : TrialInfo
+        better model
+    factors : float
+        factors for perturbation
+    epoch : int
+        step of PBTTuner
+    search_space : dict
+        search_space to keep perturbed hyperparameters in range
+    """
+    bot_checkpoint_dir = bot_trial_info.checkpoint_dir
+    top_hyper_parameters = top_trial_info.hyper_parameters
+    hyper_parameters = copy.deepcopy(top_hyper_parameters)
+    # TODO think about different type of hyperparameters for 1.perturbation 2.within search space
+    for key in hyper_parameters.keys():
+        if key == 'load_checkpoint_dir':
+            hyper_parameters[key] = hyper_parameters['save_checkpoint_dir']
+        elif key == 'save_checkpoint_dir':
+            hyper_parameters[key] = os.path.join(bot_checkpoint_dir, str(epoch))
+        elif isinstance(hyper_parameters[key], float):
+            perturb = np.random.choice(factors)
+            val = hyper_parameters[key] * perturb
+            lb, ub = search_space[key]["_value"][:2]
+            if search_space[key]["_type"] in ("uniform", "normal"):
+                val = np.clip(val, lb, ub).item()
+                hyper_parameters[key] = val
+        else:
+            continue
+    bot_trial_info.hyper_parameters = hyper_parameters
+    bot_trial_info.clean_id()
+
+
+class TrialInfo:
+    """
+    Information of each trial, refresh for each epoch
+
+    """
+
+    def __init__(self, checkpoint_dir=None, hyper_parameters=None, parameter_id=None, score=None):
+        self.checkpoint_dir = checkpoint_dir
+        self.hyper_parameters = hyper_parameters
+        self.parameter_id = parameter_id
+        self.score = score
+
+    def clean_id(self):
+        self.parameter_id = None
+
+
+class PBTTuner(Tuner):
+    def __init__(self, optimize_mode="maximize", all_checkpoint_dir=None, population_size=10, factors=(1.2, 0.8), fraction=0.2):
+        """
+        Initialization
+
+        Parameters
+        ----------
+        optimize_mode : str
+            maximize or minimize
+        all_checkpoint_dir : str
+            directory to store training model checkpoint
+        population_size : int
+            number of trials for each epoch
+        factors : tuple
+            factors for perturbation
+        fraction : float
+            fraction for selecting bottom and top trials
+        """
+        self.optimize_mode = OptimizeMode(optimize_mode)
+        if all_checkpoint_dir is None:
+            all_checkpoint_dir = os.getenv('NNI_CHECKPOINT_DIRECTORY')
+            logger.info("Checkpoint dir is set to %s by default.", all_checkpoint_dir)
+        self.all_checkpoint_dir = all_checkpoint_dir
+        self.population_size = population_size
+        self.factors = factors
+        self.fraction = fraction
+        # defined in trial code
+        #self.perturbation_interval = perturbation_interval
+
+        self.population = None
+        self.pos = -1
+        self.param_ids = []
+        self.running = {}
+        self.finished = []
+        self.credit = 0
+        self.finished_trials = 0
+        self.epoch = 0
+
+        self.searchspace_json = None
+        self.space = None
+
+        self.send_trial_callback = None
+
+        logger.info('PBT tuner initialization')
+
+    def update_search_space(self, search_space):
+        """
+        Get search space
+
+        Parameters
+        ----------
+        search_space : dict
+            Search space
+        """
+        logger.info('Update search space %s', search_space)
+        self.searchspace_json = search_space
+        self.space = json2space(self.searchspace_json)
+
+        self.random_state = np.random.RandomState()
+        self.population = []
+        is_rand = dict()
+
+        for item in self.space:
+            is_rand[item] = True
+
+        for i in range(self.population_size):
+            hyper_parameters = json2parameter(
+                self.searchspace_json, is_rand, self.random_state)
+            checkpoint_dir = os.path.join(self.all_checkpoint_dir, str(i))
+            hyper_parameters['load_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
+            hyper_parameters['save_checkpoint_dir'] = os.path.join(checkpoint_dir, str(self.epoch))
+            self.population.append(TrialInfo(checkpoint_dir=checkpoint_dir, hyper_parameters=hyper_parameters))
+
+    def generate_multiple_parameters(self, parameter_id_list, **kwargs):
+        """
+        Returns multiple sets of trial (hyper-)parameters, as iterable of serializable objects.
+
+        Parameters
+        ----------
+        parameter_id_list : list of int
+            Unique identifiers for each set of requested hyper-parameters.
+            These will later be used in :meth:`receive_trial_result`.
+        **kwargs
+            Used for send_trial_callback.
+
+        Returns
+        -------
+        list
+            A list of newly generated configurations
+        """
+        result = []
+        self.send_trial_callback = kwargs['st_callback']
+        for parameter_id in parameter_id_list:
+            had_exception = False
+            try:
+                logger.debug("generating param for %s", parameter_id)
+                res = self.generate_parameters(parameter_id, **kwargs)
+            except nni.NoMoreTrialError:
+                had_exception = True
+            if not had_exception:
+                result.append(res)
+        return result
+
+    def generate_parameters(self, parameter_id, **kwargs):
+        """
+        Generate parameters, if no trial configration for now, self.credit plus 1 to send the config later
+
+        Parameters
+        ----------
+        parameter_id : int
+            Unique identifier for requested hyper-parameters.
+            This will later be used in :meth:`receive_trial_result`.
+        **kwargs
+            Not used
+
+        Returns
+        -------
+        dict
+            One newly generated configuration
+
+        """
+        if self.pos == self.population_size - 1:
+            logger.debug('Credit added by one in parameters request')
+            self.credit += 1
+            self.param_ids.append(parameter_id)
+            raise nni.NoMoreTrialError('No more parameters now.')
+        self.pos += 1
+        trial_info = self.population[self.pos]
+        trial_info.parameter_id = parameter_id
+        self.running[parameter_id] = trial_info
+        logger.info('Generate parameter : %s', trial_info.hyper_parameters)
+        return split_index(trial_info.hyper_parameters)
+
+    def receive_trial_result(self, parameter_id, parameters, value, **kwargs):
+        """
+        Receive trial's result. if the number of finished trials equals ``self.population_size``, start the next epoch to
+        train the model.
+
+        Parameters
+        ----------
+        parameter_id : int
+            Unique identifier of used hyper-parameters, same with :meth:`generate_parameters`.
+        parameters : dict
+            Hyper-parameters generated by :meth:`generate_parameters`.
+        value : dict
+            Result from trial (the return value of :func:`nni.report_final_result`).
+        """
+        logger.info('Get one trial result, id = %d, value = %s', parameter_id, value)
+        value = extract_scalar_reward(value)
+        if self.optimize_mode == OptimizeMode.Minimize:
+            value = -value
+        trial_info = self.running.pop(parameter_id, None)
+        trial_info.score = value
+        self.finished.append(trial_info)
+        self.finished_trials += 1
+        if self.finished_trials == self.population_size:
+            logger.info('Proceeding to next epoch')
+            self.epoch += 1
+            self.population = []
+            self.pos = -1
+            self.running = {}
+            #exploit and explore
+            self.finished = sorted(self.finished, key=lambda x: x.score, reverse=True)
+            cutoff = int(np.ceil(self.fraction * len(self.finished)))
+            tops = self.finished[:cutoff]
+            bottoms = self.finished[self.finished_trials - cutoff:]
+            for bottom in bottoms:
+                top = np.random.choice(tops)
+                exploit_and_explore(bottom, top, self.factors, self.epoch, self.searchspace_json)
+            for trial in self.finished:
+                if trial not in bottoms:
+                    trial.clean_id()
+                    trial.hyper_parameters['load_checkpoint_dir'] = trial.hyper_parameters['save_checkpoint_dir']
+                    trial.hyper_parameters['save_checkpoint_dir'] = os.path.join(trial.checkpoint_dir, str(self.epoch))
+            self.finished_trials = 0
+            for _ in range(self.population_size):
+                trial_info = self.finished.pop()
+                self.population.append(trial_info)
+            while self.credit > 0 and self.pos + 1 < len(self.population):
+                self.credit -= 1
+                self.pos += 1
+                parameter_id = self.param_ids.pop()
+                trial_info = self.population[self.pos]
+                trial_info.parameter_id = parameter_id
+                self.running[parameter_id] = trial_info
+                self.send_trial_callback(parameter_id, split_index(trial_info.hyper_parameters))
+
+    def import_data(self, data):
+        pass

src/sdk/pynni/nni/utils.py

@@ -2,13 +2,16 @@
 # Licensed under the MIT license.
 
 import os
+import copy
 import functools
 from enum import Enum, unique
 import json_tricks
 
+from . import parameter_expressions
 from .common import init_logger
 from .env_vars import dispatcher_env_vars
 
+
 to_json = functools.partial(json_tricks.dumps, allow_nan=True)
 
 @unique
@@ -124,3 +127,92 @@ def init_dispatcher_logger():
     if dispatcher_env_vars.NNI_LOG_DIRECTORY is not None:
         logger_file_path = os.path.join(dispatcher_env_vars.NNI_LOG_DIRECTORY, logger_file_path)
     init_logger(logger_file_path, dispatcher_env_vars.NNI_LOG_LEVEL)
+
+
+def json2space(x, oldy=None, name=NodeType.ROOT):
+    """
+    Change search space from json format to hyperopt format
+
+    """
+    y = list()
+    if isinstance(x, dict):
+        if NodeType.TYPE in x.keys():
+            _type = x[NodeType.TYPE]
+            name = name + '-' + _type
+            if _type == 'choice':
+                if oldy is not None:
+                    _index = oldy[NodeType.INDEX]
+                    y += json2space(x[NodeType.VALUE][_index],
+                                    oldy[NodeType.VALUE], name=name+'[%d]' % _index)
+                else:
+                    y += json2space(x[NodeType.VALUE], None, name=name)
+            y.append(name)
+        else:
+            for key in x.keys():
+                y += json2space(x[key], oldy[key] if oldy else None, name+"[%s]" % str(key))
+    elif isinstance(x, list):
+        for i, x_i in enumerate(x):
+            if isinstance(x_i, dict):
+                if NodeType.NAME not in x_i.keys():
+                    raise RuntimeError('\'_name\' key is not found in this nested search space.')
+            y += json2space(x_i, oldy[i] if oldy else None, name + "[%d]" % i)
+    return y
+
+
+def json2parameter(x, is_rand, random_state, oldy=None, Rand=False, name=NodeType.ROOT):
+    """
+    Json to pramaters.
+
+    """
+    if isinstance(x, dict):
+        if NodeType.TYPE in x.keys():
+            _type = x[NodeType.TYPE]
+            _value = x[NodeType.VALUE]
+            name = name + '-' + _type
+            Rand |= is_rand[name]
+            if Rand is True:
+                if _type == 'choice':
+                    _index = random_state.randint(len(_value))
+                    y = {
+                        NodeType.INDEX: _index,
+                        NodeType.VALUE: json2parameter(
+                            x[NodeType.VALUE][_index],
+                            is_rand,
+                            random_state,
+                            None,
+                            Rand,
+                            name=name+"[%d]" % _index
+                        )
+                    }
+                else:
+                    y = getattr(parameter_expressions, _type)(*(_value + [random_state]))
+            else:
+                y = copy.deepcopy(oldy)
+        else:
+            y = dict()
+            for key in x.keys():
+                y[key] = json2parameter(
+                    x[key],
+                    is_rand,
+                    random_state,
+                    oldy[key] if oldy else None,
+                    Rand,
+                    name + "[%s]" % str(key)
+                )
+    elif isinstance(x, list):
+        y = list()
+        for i, x_i in enumerate(x):
+            if isinstance(x_i, dict):
+                if NodeType.NAME not in x_i.keys():
+                    raise RuntimeError('\'_name\' key is not found in this nested search space.')
+            y.append(json2parameter(
+                x_i,
+                is_rand,
+                random_state,
+                oldy[i] if oldy else None,
+                Rand,
+                name + "[%d]" % i
+            ))
+    else:
+        y = copy.deepcopy(x)
+    return y

src/sdk/pynni/tests/test_builtin_tuners.py

@@ -8,6 +8,7 @@ import os
 import random
 import shutil
 import sys
+from collections import deque
 from unittest import TestCase, main
 
 from nni.batch_tuner.batch_tuner import BatchTuner
@@ -16,6 +17,8 @@ from nni.gp_tuner.gp_tuner import GPTuner
 from nni.gridsearch_tuner.gridsearch_tuner import GridSearchTuner
 from nni.hyperopt_tuner.hyperopt_tuner import HyperoptTuner
 from nni.metis_tuner.metis_tuner import MetisTuner
+from nni.msg_dispatcher import _pack_parameter, MsgDispatcher
+from nni.pbt_tuner.pbt_tuner import PBTTuner
 
 try:
     from nni.smac_tuner.smac_tuner import SMACTuner
@@ -23,6 +26,7 @@ except ImportError:
     assert sys.platform == "win32"
 from nni.tuner import Tuner
 
+
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger('test_tuner')
 
@@ -44,18 +48,29 @@ class BuiltinTunersTestCase(TestCase):
         self.params_each_round = 50
         self.exhaustive = False
 
+    def send_trial_callback(self, param_queue):
+        def receive(*args):
+            param_queue.append(tuple(args))
+        return receive
+
     def search_space_test_one(self, tuner_factory, search_space):
         tuner = tuner_factory()
         self.assertIsInstance(tuner, Tuner)
         tuner.update_search_space(search_space)
 
         for i in range(self.test_round):
+            queue = deque()
             parameters = tuner.generate_multiple_parameters(list(range(i * self.params_each_round,
-                                                                       (i + 1) * self.params_each_round)))
+                                                                       (i + 1) * self.params_each_round)),
+                                                            st_callback=self.send_trial_callback(queue))
             logger.debug(parameters)
             self.check_range(parameters, search_space)
             for k in range(min(len(parameters), self.params_each_round)):
                 tuner.receive_trial_result(self.params_each_round * i + k, parameters[k], random.uniform(-100, 100))
+            while queue:
+                id_, params = queue.popleft()
+                self.check_range([params], search_space)
+                tuner.receive_trial_result(id_, params, random.uniform(-100, 100))
             if not parameters and not self.exhaustive:
                 raise ValueError("No parameters generated")
 
@@ -65,6 +80,9 @@ class BuiltinTunersTestCase(TestCase):
             if self._testMethodName == "test_batch":
                 param = {list(search_space.keys())[0]: param}
             for k, v in param.items():
+                if k == "load_checkpoint_dir" or k == "save_checkpoint_dir":
+                    self.assertIsInstance(v, str)
+                    continue
                 if k.startswith("_mutable_layer"):
                     _, block, layer, choice = k.split("/")
                     cand = search_space[block]["_value"][layer].get(choice)
@@ -124,8 +142,8 @@ class BuiltinTunersTestCase(TestCase):
             if any(single.startswith(t) for t in ignore_types):
                 continue
             expected_fail = not any(single.startswith(t) for t in supported_types) or \
-                            any(single.startswith(t) for t in fail_types) or \
-                            "fail" in single  # name contains fail (fail on all)
+                any(single.startswith(t) for t in fail_types) or \
+                "fail" in single  # name contains fail (fail on all)
             single_search_space = {single: space}
             if not expected_fail:
                 # supports this key
@@ -270,6 +288,16 @@ class BuiltinTunersTestCase(TestCase):
     def test_ppo(self):
         pass
 
+    def test_pbt(self):
+        self.search_space_test_all(lambda: PBTTuner(
+            all_checkpoint_dir=os.path.expanduser("~/nni/checkpoint/test/"),
+            population_size=12
+        ))
+        self.search_space_test_all(lambda: PBTTuner(
+            all_checkpoint_dir=os.path.expanduser("~/nni/checkpoint/test/"),
+            population_size=100
+        ))
+
     def tearDown(self):
         file_list = glob.glob("smac3*") + ["param_config_space.pcs", "scenario.txt", "model_path"]
         for file in file_list:

src/webui/yarn.lock

@@ -1518,8 +1518,8 @@ acorn-jsx@^5.0.0:
   resolved "https://registry.yarnpkg.com/acorn-jsx/-/acorn-jsx-5.1.0.tgz#294adb71b57398b0680015f0a38c563ee1db5384"
 
 acorn@^6.0.5, acorn@^6.0.7:
-  version "6.4.0"
-  resolved "https://registry.yarnpkg.com/acorn/-/acorn-6.4.0.tgz#b659d2ffbafa24baf5db1cdbb2c94a983ecd2784"
+  version "6.4.1"
+  resolved "https://registry.yarnpkg.com/acorn/-/acorn-6.4.1.tgz#531e58ba3f51b9dacb9a6646ca4debf5b14ca474"
 
 address@1.1.2, address@^1.0.1:
   version "1.1.2"

test/config/examples/mnist-pytorch.yml

 authorName: nni
 experimentName: default_test
 maxExecDuration: 15m
-maxTrialNum: 4
+maxTrialNum: 2
 trialConcurrency: 2
 searchSpacePath: ./mnist_pytorch_search_space.json
 
@@ -13,7 +13,7 @@ assessor:
     optimize_mode: maximize
 trial:
   codeDir: ../../../examples/trials/mnist-pytorch
-  command: python3 mnist.py  --epochs 1
+  command: python3 mnist.py  --epochs 1 --batch_num 10
   gpuNum: 0
 
 useAnnotation: false