https://github.com/yusugomori/barbar

Progress bar for deep learning training iterations💈
https://github.com/yusugomori/barbar

deep-learning keras pytorch

Last synced: 3 months ago
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Progress bar for deep learning training iterations💈

• Host: GitHub
• URL: https://github.com/yusugomori/barbar
• Owner: yusugomori
• License: mit
• Created: 2019-04-11T04:06:44.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-04-12T02:23:16.000Z (over 5 years ago)
• Last Synced: 2024-09-30T10:02:47.577Z (3 months ago)
• Topics: deep-learning, keras, pytorch
• Language: Python
• Homepage:
• Size: 10.7 KB
• Stars: 34
• Watchers: 3
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Barbar💈

Progress bar for deep learning training iterations.

![screenshot](https://user-images.githubusercontent.com/770299/55931402-3bb76000-5c60-11e9-9686-f6ae23adcaf0.png)

## Quick glance

```python

from barbar import Bar

import torch

from torch.utils.data import DataLoader

from torchvision import datasets

mnist_train = datasets.MNIST(root=root,

                             download=True,

                             train=True)

train_dataloader = DataLoader(mnist_train,

                              batch_size=100,

                              shuffle=True)

model = MLP().to(device)

for epoch in range(epochs):

    print('Epoch: {}'.format(epoch+1))

    for idx, (x, t) in enumerate(Bar(train_dataloader)):

        x, t = x.to(device), t.to(device)

        loss, preds = train_step(x, t)

```

```

Epoch: 1

60000/60000: [===============================>] - ETA 0.0s

Epoch: 2

28100/60000: [==============>.................] - ETA 4.1s

```

Barbar works best with PyTorch DataLoader, but it also works with custom DataLoader. Minimal DataLoader example can be written as follows:

```python

class CustomDataLoader(object):

    def __init__(self, dataset,

                 batch_size=100,

                 shuffle=False,

                 random_state=None):

        self.dataset = list(zip(dataset[0], dataset[1]))

        self.batch_size = batch_size

        self.shuffle = shuffle

        if random_state is None:

            random_state = np.random.RandomState(1234)

        self.random_state = random_state

        self._idx = 0

        self._reset()

    def __len__(self):

        N = len(self.dataset)

        b = self.batch_size

        return N // b + bool(N % b)

    def __iter__(self):

        return self

    def __next__(self):

        if self._idx >= len(self.dataset):

            self._reset()

            raise StopIteration()

        x, y = \

            zip(*self.dataset[self._idx:(self._idx + self.batch_size)])

        # x = torch.Tensor(x)

        # y = torch.LongTensor(y)

        self._idx += self.batch_size

        return x, y

    def _reset(self):

        if self.shuffle:

            self.dataset = shuffle(self.dataset,

                                   random_state=self.random_state)

        self._idx = 0

mnist = datasets.fetch_openml('mnist_784', version=1,)

x, y = mnist.data.astype(np.float32), mnist.target.astype(np.int32)

x = x / 255.

x_train = x[:60000]

y_train = y[:60000]

train_dataloader = CustomDataLoader((x_train, y_train),

                                    batch_size=100,

                                    shuffle=True)

```

## Installation

- **Install Barbar from PyPI (recommended):**

```sh

pip install barbar

```

- **Alternatively: install Barbar from the GitHub source:**

First, clone Barbar using `git`:

```sh

git clone https://github.com/yusugomori/barbar.git

```

 Then, `cd` to the Barbar folder and run the install command:

```sh

cd barbar

sudo python setup.py install

```