torch-scope 0.5.1

A Toolkit for Training, Tracking and Saving PyTorch Models

# Torch-Scope

[](https://opensource.org/licenses/Apache-2.0)
[](http://tensorboard-wrapper.readthedocs.io/en/latest/?badge=latest)
[](https://pepy.tech/project/torch-scope)
[](https://badge.fury.io/py/torch-scope)

A Toolkit for training pytorch models, which has three features:

- Tracking environments, dependency, implementations and checkpoints;
- Providing a logger wrapper with two handlers (to ```std``` and ```file```);
- Supporting automatic device selection;
- Providing a tensorboard wrapper;
- Providing a spreadsheet writer to automatically summarizing notes and results;

We are in an early-release beta. Expect some adventures and rough edges.

## Quick Links

- [Installation](#installation)
- [Usage](#usage)

## Installation

To install via pypi:
```
pip install torch-scope
```

To build from source:
```
pip install git+https://github.com/LiyuanLucasLiu/Torch-Scope
```
or
```
git clone https://github.com/LiyuanLucasLiu/Torch-Scope.git
cd Torch-Scope
python setup.py install
```

## Usage

An example is provided as below, please read the doc for a detailed api explaination.

* set up the git in the server & add all source file to the git
* use tensorboard to track the model stats (tensorboard --logdir PATH/log/ --port ####)

```
from torch_scope import wrapper
...
logger = logging.getLogger(__name__)

if __name__ == '__main__':

parser = argparse.ArgumentParser()

parser.add_argument('--checkpoint_path', type=str, ...)
parser.add_argument('--name', type=str, ...)
parser.add_argument('--gpu', type=str, ...)
...
args = parser.parse_args()

pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, enable_git_track = False)
# Or if the current folder is binded with git, you can turn on the git tracking as below
# pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, enable_git_track = True)
# if you properly set the path to credential_path and want to use spreadsheet writer, turn on sheet tracking as below
# pw = wrapper(os.path.join(args.checkpoint_path, args.name), name = args.log_dir, \
# enable_git_track=args.git_tracking, sheet_track_name=args.spreadsheet_name, \
# credential_path="/data/work/jingbo/ll2/Torch-Scope/torch-scope-8acf12bee10f.json")


gpu_index = pw.auto_device() if 'auto' == args.gpu else int(args.gpu)
device = torch.device("cuda:" + str(gpu_index) if gpu_index >= 0 else "cpu")

pw.save_configue(args) # dump the config to config.json

# if the spreadsheet writer is enabled, you can add a description about the current model
# pw.add_description(args.description)

logger.info(str(args)) # would be plotted to std & file if level is 'info' or lower

...

batch_index = 0

for index in range(epoch):

...

for instance in ... :

loss = ...

tot_loss += loss.detach()
loss.backward()

if batch_index % ... = 0:
pw.add_loss_vs_batch({'loss': tot_loss / ..., ...}, batch_index, False)
pw.add_model_parameter_stats(model, batch_index, save=True)
optimizer.step()
pw.add_model_update_stats(model, batch_index)
tot_loss = 0
else:
optimizer.step()

batch_index += 1

dev_score = ...
pw.add_loss_vs_batch({'dev_score': dev_score, ...}, index, True)

if dev_score > best_score:
pw.save_checkpoint(model, optimizer, is_best = True)
best_score = dev_score
else:
pw.save_checkpoint(model, optimizer, is_best = False)
```

## Advanced Usage

### Auto Device

### Git Tracking

### Spreadsheet Logging

Share the spreadsheet with the following account ```[e-mail]```. And access the table with its name.