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https://github.com/OIdiotLin/torchtracer
A python package for visualization and storage management in a pytorch AI task.
https://github.com/OIdiotLin/torchtracer
lab machine-learning pytorch tools
Last synced: about 23 hours ago
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A python package for visualization and storage management in a pytorch AI task.
- Host: GitHub
- URL: https://github.com/OIdiotLin/torchtracer
- Owner: OIdiotLin
- License: mit
- Created: 2018-09-18T06:08:07.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-05-03T19:41:18.000Z (7 months ago)
- Last Synced: 2024-11-14T15:48:52.810Z (3 days ago)
- Topics: lab, machine-learning, pytorch, tools
- Language: Python
- Size: 21.5 KB
- Stars: 54
- Watchers: 4
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-deep-learning-tools - TorchTracer
README
# torchtracer
[](https://travis-ci.com/OIdiotLin/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`.
```bash
pip install torchtracer
```
## How to use?
### Import `torchtracer`
```python
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.***
```python
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.**
```python
tracer = Tracer('checkpoints').attach()
```
### Saving config
Raw config should be a `dict` like this:
```python
# `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`
```python
cfg = Config(args)
tracer.store(cfg)
```
This step will create `config.json` in `./checkpoints/lmmnb/`, which contains JSON information like this:
```json
{
"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`.
```python
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:
```text
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`
```python
tracer.store(Model(model), file='somename')
```
This step will create 2 files:
- **description**: `somename.txt`
```text
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/`
```python
# 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.
### Progress bar for epochs
Use `tracer.epoch_bar_init(total)` to initialize a progress bar.
```python
tracer.epoch_bar_init(epoch_n)
```
Use `tracer.epoch_bar.update(n=1, **params)` to update postfix of the progress bar.
```python
tracer.epoch_bar.update(train_loss=train_loss, valid_loss=train_loss)
```
```plain
(THIS IS A DEMO)
Tracer start at /home/oidiotlin/projects/torchtracer/checkpoints
Tracer attached with task: rabbit
Epoch: 100%|█████████| 120/120 [00:02<00:00, 41.75it/s, train_loss=0.417, valid_loss=0.417]
```
**DO NOT FORGET TO CALL** `tracer.epoch_bar.close()` to finish the bar.
## Contribute
If you like this project, welcome to pull request & create issues.