Utilities for training models in pytorch
Project description
xt-training
Description
This repo contains utilities for training deep learning models in pytorch, developed by Xtract AI.
Installation
From PyPI:
pip install xt-training
From source:
git clone https://github.com/XtractTech/xt-training.git
pip install ./xt-training
Usage
See specific help on a class or function using help
. E.g., help(Runner)
.
Training a model
Using xt-training (High Level)
First, you must define a config file with the necessary items. To generate a template config file, run:
python -m xt_training template path/to/save/dir
To generate template files for nni, add the --nni
flag
Instructions for defining a valid config file can be seen at the top of the config file.
After defining a valid config file, you can train your model by running:
python -m xt_training train path/to/config.py /path/to/save_dir
You can test the model by running
python -m xt_training test path/to/config.py /path/to/save_dir
Using Runner (Low Level)
If you want a little more control and want to define the trianing code yourself, you can utilize the Runner like so:
from xt_training import Runner, metrics
from torch.utils.tensorboard import SummaryWriter
# Here, define class instances for the required objects
# model =
# optimizer =
# scheduler =
# loss_fn =
# Define metrics - each of these will be printed for each iteration
# Either per-batch or running-average values can be printed
batch_metrics = {
'eps': metrics.EPS(),
'acc': metrics.Accuracy(),
'kappa': metrics.Kappa(),
'cm': metrics.ConfusionMatrix()
}
# Define tensorboard writer
writer = SummaryWriter()
# Create runner
runner = Runner(
model=model,
loss_fn=loss_fn,
optimizer=optimizer,
scheduler=scheduler,
batch_metrics=batch_metrics,
device='cuda:0',
writer=writer
)
# Define dataset and loaders
# dataset =
# train_loader =
# val_loader =
# Train
model.train()
runner(train_loader)
batch_metrics['cm'].print()
# Evaluate
model.eval()
runner(val_loader)
batch_metrics['cm'].print()
# Print training and evaluation history
print(runner)
Scoring a model
import torch
from xt_training import Runner
# Here, define the model
# model =
# model.load_state_dict(torch.load(<checkpoint file>))
# Create runner
# (alternatively, can use a fully-specified training runner as in the example above)
runner = Runner(model=model, device='cuda:0')
# Define dataset and loaders
# dataset =
# test_loader =
# Score
model.eval()
y_pred, y = runner(test_loader, return_preds=True)
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for xt_training-1.11.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d25d940593eb6d9bbd466218550e6592203daa23107c534f4f699c984585e1e6 |
|
MD5 | dec7ea40ed01aa0ceda2ce67b6e86650 |
|
BLAKE2b-256 | 0d103e708b7dddd73d7086e793450f87f58ee50a104348135430c6aceffca16d |