Skip to main content

A small tool for PyTorch training

Project description

🔥 torchtrain 💪

A small tool for PyTorch training.

Features

  • Avoid boilerplate code for training.
  • Stepwise training.
  • Automatic TensorBoard logging, and tqdm bar.
  • Count model parameters and save hyperparameters.
  • DataParallel.
  • Early stop.
  • Save and load checkpoint. Continue training.
  • Catch out of memory exceptions to avoid breaking training.
  • Gradient accumulation.
  • Gradient clipping.
  • Only run few epochs, steps and batches for code test.

Install

pip install torchtrain

Example

Check doc string of Trainer class for detailed configurations.

An incomplete minimal example:

data_iter = get_data()
model = Bert()
optimizer = Adam(model.parameters(), lr=cfg["lr"])
criteria = {"loss": AverageAggregator(BCELoss())}
trainer = Trainer(model, data_iter, criteria, cfg, optimizer)
trainer.train(stepwise=True)

Or this version:

from argparse import ArgumentParser

from sklearn.model_selection import ParameterGrid
from torch.optim import Adam
from torch.optim.lr_scheduler import LambdaLR
from transformers import AutoModel, BertTokenizer

from data.load import get_batch_size, get_data
from metrics import BCELoss
from models import BertSumExt
from torchtrain import Trainer
from torchtrain.metrics import AverageAggregator
from torchtrain.utils import set_random_seeds


def get_args():
    parser = ArgumentParser()
    parser.add_argument("--seed", type=int, default=233666)
    parser.add_argument("--run_ckp", default="")
    parser.add_argument("--run_dataset", default="val")
    parser.add_argument("--batch_size", type=int, default=64)
    parser.add_argument("--warmup", type=int, default=10000)
    parser.add_argument("--stepwise", action="store_false")
    # torchtrain cfgs
    parser.add_argument("--max_n", type=int, default=50000)
    parser.add_argument("--val_step", type=int, default=1000)
    parser.add_argument("--save_path", default="/tmp/runs")
    parser.add_argument("--model_name", default="BertSumExt")
    parser.add_argument("--cuda_list", default="2,3")
    parser.add_argument("--grad_accum_batch", type=int, default=1)
    parser.add_argument("--train_few", action="store_true")
    return vars(parser.parse_args())


def get_param_grid():
    param_grid = [
        {"pretrained_model_name": ["voidful/albert_chinese_tiny"], "lr": [6e-5]},
    ]
    return ParameterGrid(param_grid)


def get_cfg(args={}, params={}):
    cfg = {**args, **params}
    # other cfgs
    return cfg


def run(cfg):
    set_random_seeds(cfg["seed"])
    tokenizer = BertTokenizer.from_pretrained(cfg["pretrained_model_name"])
    bert = AutoModel.from_pretrained(cfg["pretrained_model_name"])
    data_iter = get_data(
        cfg["batch_size"], tokenizer, bert.config.max_position_embeddings
    )
    model = BertSumExt(bert)
    optimizer = Adam(model.parameters(), lr=cfg["lr"])
    scheduler = LambdaLR(
        optimizer,
        lambda step: min(step ** (-0.5), step * (cfg["warmup"] ** (-1.5)))
        if step > 0
        else 0,
    )
    criteria = {"loss": AverageAggregator(BCELoss())}
    trainer = Trainer(
        model,
        data_iter,
        criteria,
        cfg,
        optimizer,
        scheduler,
        get_batch_size=get_batch_size,
    )
    if cfg["run_ckp"]:
        return trainer.test(cfg["run_ckp"], cfg["run_dataset"])
    return trainer.train(stepwise=cfg["stepwise"])


def main():
    param_grid = get_param_grid()
    for i, params in enumerate(param_grid):
        print("Config", str(i + 1), "/", str(len(param_grid)))
        cfg = get_cfg(get_args(), params)
        metrics = run(cfg)
        print("Best metrics:", metrics)


if __name__ == "__main__":
    main()

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

torchtrain-0.4.13.tar.gz (9.7 kB view hashes)

Uploaded source

Built Distribution

torchtrain-0.4.13-py3-none-any.whl (9.5 kB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page