TorchZQ: A PyTorch experiment runner.
Project description
TorchZQ: a PyTorch experiment runner
Installation
Install from PyPI (latest):
pip install torchzq --pre --upgrade
A customized runner for MNIST classification
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import torchzq
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
x = F.relu(x)
x = self.dropout2(x)
x = self.fc2(x)
output = F.log_softmax(x, dim=1)
return output
class Runner(torchzq.Runner):
class HParams(torchzq.Runner.HParams):
lr: float = 1e-3
hp: HParams
def create_model(self):
return Net()
def create_dataloader(self, mode):
hp = self.hp
dataset = datasets.MNIST(
"../data",
train=mode == "training",
download=True,
transform=transforms.Compose(
[
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,)),
]
),
)
return DataLoader(
dataset,
batch_size=hp.batch_size,
num_workers=hp.nj,
shuffle=mode == mode.TRAIN,
drop_last=mode == mode.TRAIN,
)
def create_metrics(self):
metrics = super().create_metrics()
def early_stop(count):
if count >= 2:
# the metric does not go down for the latest two validations
self.hp.max_epochs = -1 # this terminates the training
metrics.add_metric("val/nll_loss", [early_stop])
return metrics
def prepare_batch(self, batch, _):
x, y = batch
x = x.to(self.hp.device)
y = y.to(self.hp.device)
return x, y
def training_step(self, batch, optimizer_index):
x, y = batch
loss = F.nll_loss(self.model(x), y)
return loss, {"nll_loss": loss.item()}
@torch.no_grad()
def testing_step(self, batch, batch_index):
x, y = batch
y_ = self.model(x).argmax(dim=-1)
return {"accuracy": (y_ == y).float().mean().item()}
if __name__ == "__main__":
Runner().start()
Execute the runner
Training
tzq example/config/mnist.yml train
Testing
tzq example/config/mnist.yml test
Weights & Biases
Before you run, login Weights & Biases first.
pip install wandb # install weight & bias client
wandb login # login
Supported features
- Model checkpoints
- Logging (Weights & Biases)
- Gradient accumulation
- Configuration file
- FP16
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
Close
Hashes for torchzq-1.1.0.dev20220101011716.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | f1275a04a10ed4cb1aa7e04daf57f6a0cd17e167f25e80df8561e45f197c0523 |
|
MD5 | 244d9528031a6b8469e55b21c6ace9b9 |
|
BLAKE2b-256 | 6f71a2e3a59e72187b8f0c1d449c070cc6bb17f2311f44629860a2ea674e3718 |
Close
Hashes for torchzq-1.1.0.dev20220101011716-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 72079de6fb2e839982542d580f5654b339105adfa939300e75bd745302b4706d |
|
MD5 | 498c28b43c7a0fa3063c490abfc14632 |
|
BLAKE2b-256 | 74c6752816168a221a8f6b70f7b55a5afbe3ac194aa145e3810eacbbf747bf26 |