add tensorboard doc

docs/en_US/TrialExample/Trials.rst

@@ -208,6 +208,7 @@ More Trial Examples
 -------------------
 
 
+* `Write logs to trial output directory for tensorboard <../Tutorial/Tensorboard.rst>`__
 * `MNIST examples <MnistExamples.rst>`__
 * `Finding out best optimizer for Cifar10 classification <Cifar10Examples.rst>`__
 * `How to tune Scikit-learn on NNI <SklearnExamples.rst>`__

docs/en_US/Tutorial/QuickStart.rst

@@ -260,6 +260,7 @@ Related Topic
 -------------
 
 
+* `Launch Tensorboard on WebUI <Tensorboard.rst>`__
 * `Try different Tuners <../Tuner/BuiltinTuner.rst>`__
 * `Try different Assessors <../Assessor/BuiltinAssessor.rst>`__
 * `How to use command line tool nnictl <Nnictl.rst>`__

docs/en_US/Tutorial/Tensorboard.rst

@@ -0,0 +1,51 @@
+How to Use Tensorboard within WebUI
+===================================
+
+You can launch a tensorboard process cross one or multi trials within webui since NNI v2.2. This feature supports local training service and reuse mode training service with shared storage for now, and will support more scenarios in later nni version.
+
+Preparation
+-----------
+
+Make sure tensorboard installed in your environment. If you never used tensorboard, here are getting start tutorials for your reference, `tensorboard with tensorflow <https://www.tensorflow.org/tensorboard/get_started>`__, `tensorboard with pytorch <https://pytorch.org/tutorials/recipes/recipes/tensorboard_with_pytorch.html>`__.
+
+Use WebUI Launch Tensorboard
+----------------------------
+
+1. Save Logs
+^^^^^^^^^^^^
+
+NNI will automatically fetch the ``tensorboard`` subfolder under trial's output folder as tensorboard logdir. So in trial's source code, you need to save the tensorboard logs under ``NNI_OUTPUT_DIR/tensorboard``. This log path can be joined as:
+
+.. code-block:: python
+
+    log_dir = os.path.join(os.environ["NNI_OUTPUT_DIR"], 'tensorboard')
+
+2. Launch Tensorboard
+^^^^^^^^^^^^^^^^^^^^^
+
+Like compare, select the trials you want to combine to launch the tensorboard at first, then click the ``Tensorboard`` button.
+
+.. image:: ../../img/Tensorboard_1.png
+   :target: ../../img/Tensorboard_1.png
+   :alt: 
+
+After click the ``OK`` button in the pop-up box, you will jump to the tensorboard portal.
+
+.. image:: ../../img/Tensorboard_2.png
+   :target: ../../img/Tensorboard_2.png
+   :alt: 
+
+You can see the ``SequenceID-TrialID`` on the tensorboard portal.
+
+.. image:: ../../img/Tensorboard_3.png
+   :target: ../../img/Tensorboard_3.png
+   :alt: 
+
+3. Stop All
+^^^^^^^^^^^^
+
+If you want to open the portal you have already launched, click the tensorboard id. If you don't need the tensorboard anymore, click ``Stop all tensorboard`` button.
+
+.. image:: ../../img/Tensorboard_4.png
+   :target: ../../img/Tensorboard_4.png
+   :alt: 

examples/trials/mnist-pytorch/config_tensorboard.yml

@@ -0,0 +1,21 @@
+authorName: default
+experimentName: example_mnist_pytorch
+trialConcurrency: 1
+maxExecDuration: 1h
+maxTrialNum: 10
+#choice: local, remote, pai
+trainingServicePlatform: local
+searchSpacePath: search_space.json
+#choice: true, false
+useAnnotation: false
+tuner:
+  #choice: TPE, Random, Anneal, Evolution, BatchTuner, MetisTuner, GPTuner
+  #SMAC (SMAC should be installed through nnictl)
+  builtinTunerName: TPE
+  classArgs:
+    #choice: maximize, minimize
+    optimize_mode: maximize
+trial:
+  command: python3 mnist_tensorboard.py
+  codeDir: .
+  gpuNum: 0

examples/trials/mnist-pytorch/mnist_tensorboard.py

@@ -0,0 +1,173 @@
+"""
+A deep MNIST classifier using convolutional layers.
+
+This file is a modification of the official pytorch mnist example:
+https://github.com/pytorch/examples/blob/master/mnist/main.py
+"""
+
+import os
+import argparse
+import logging
+import nni
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.utils.tensorboard import SummaryWriter
+from nni.utils import merge_parameter
+from torchvision import datasets, transforms
+
+
+logger = logging.getLogger('mnist_AutoML')
+
+writer = SummaryWriter(log_dir=os.path.join(os.environ['NNI_OUTPUT_DIR'], 'tensorboard'))
+
+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):
+        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)
+        loss = F.nll_loss(output, target)
+        writer.add_scalar('Loss/train', loss, epoch)
+        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 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 = args['data_dir']
+
+    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)
+    optimizer = optim.SGD(model.parameters(), lr=args['lr'],
+                          momentum=args['momentum'])
+
+    for epoch in range(1, args['epochs'] + 1):
+        train(args, model, device, train_loader, optimizer, epoch)
+        test_acc = test(args, model, device, test_loader)
+        writer.add_scalar('Accuracy/test', test_acc, epoch)
+
+        # report intermediate result
+        nni.report_intermediate_result(test_acc)
+        logger.debug('test accuracy %g', test_acc)
+        logger.debug('Pipe send intermediate result done.')
+
+    writer.close()
+
+    # 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():
+    # Training settings
+    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+    parser.add_argument("--data_dir", type=str,
+                        default='./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',
+                        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')
+
+
+    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(merge_parameter(get_params(), tuner_params))
+        print(params)
+        main(params)
+    except Exception as exception:
+        logger.exception(exception)
+        raise