torchtracer 0.1.4

A python package for visualization and storage management in a pytorch AI task.

torchtracer

torchtracer is a tool package for visualization and storage management in pytorch AI task.

Getting Started

PyTorch Required

This tool is developed for PyTorch AI task. Thus, PyTorch is needed of course.

Installing

You can use pip to install torchtracer.

pip install torchtracer

How to use?

Import torchtracer

from torchtracer import Tracer

Create an instance of Tracer

Assume that the root is ./checkpoints and current task id is lmmnb.

Avoiding messing working directory, you should make root directory manually.

tracer = Tracer('checkpoints').attach('lmmnb')

This step will create a directory checkpoints inside which is a directory lmmnb for current AI task.

Also, you could call .attach() without task id. Datetime will be used as task id.

tracer = Tracer('checkpoints').attach()

Saving config

Raw config should be a dict like this:

# `net` is a defined nn.Module
args = {'epoch_n': 120,
        'batch_size': 10,
        'criterion': nn.MSELoss(),
        'optimizer': torch.optim.RMSprop(net.parameters(), lr=1e-3)}

The config dict should be wrapped with torchtracer.data.Config

cfg = Config(args)
tracer.store(cfg)

This step will create config.json in ./checkpoints/lmmnb/, which contains JSON information like this:

{
  "epoch_n": 120,
  "batch_size": 10,
  "criterion": "MSELoss",
  "optimizer": {
    "lr": 0.001,
    "momentum": 0,
    "alpha": 0.99,
    "eps": 1e-08,
    "centered": false,
    "weight_decay": 0,
    "name": "RMSprop"
  }
}

Logging

During the training iteration, you could print any information you want by using Tracer.log(msg, file).

If file not specified, it will output msg to ./checkpoints/lmmnb/log. Otherwise, it will be ./checkpoints/lmmnb/something.log.

tracer.log(msg='Epoch #{:03d}\ttrain_loss: {:.4f}\tvalid_loss: {:.4f}'.format(epoch, train_loss, valid_loss),
           file='losses')

This step will create a log file losses.log in ./checkpoints/lmmnb/, which contains logs like:

Epoch #001	train_loss: 18.6356	valid_loss: 21.3882
Epoch #002	train_loss: 19.1731	valid_loss: 17.8482
Epoch #003	train_loss: 19.6756	valid_loss: 19.1418
Epoch #004	train_loss: 20.0638	valid_loss: 18.3875
Epoch #005	train_loss: 18.4679	valid_loss: 19.6304
...

Saving model

The model object should be wrapped with torchtracer.data.Model

If file not specified, it will generates model files model.txt. Otherwise, it will be somename.txt

tracer.store(Model(model), file='somename')

This step will create 2 files:

• description: somename.txt

Sequential
Sequential(
  (0): Linear(in_features=1, out_features=6, bias=True)
  (1): ReLU()
  (2): Linear(in_features=6, out_features=12, bias=True)
  (3): ReLU()
  (4): Linear(in_features=12, out_features=12, bias=True)
  (5): ReLU()
  (6): Linear(in_features=12, out_features=1, bias=True)
)

• parameters: somename.pth

Saving matplotlib images

Use tracer.store(figure, file) to save matplotlib figure in images/

# assume that `train_losses` and `valid_losses` are lists of losses. 
# create figure manually.
plt.plot(train_losses, label='train loss', c='b')
plt.plot(valid_losses, label='valid loss', c='r')
plt.title('Demo Learning on SQRT')
plt.legend()
# save figure. remember to call `plt.gcf()`
tracer.store(plt.gcf(), 'losses.png')

This step will save a png file losses.png representing losses curves.

Contribute

If you like this project, welcome to pull request & create issues.