semvec/pypi-packages huggingface dataset:
wah 1.18.2
a library so simple you will learn Within An Hour
License: MIT

### Think training a ResNet-18 on CIFAR-10 is a breeze? 🌬️💨
It might seem simple at first — until you find yourself drowning in boilerplate code:

- Setting up data loaders
- Defining model architectures
- Configuring loss functions
- Choosing and tuning optimizers
- ...and so much more! 🤯

What if you could skip all that hassle?

With this approach, ***you won't have to write a single line of code*** — just define a YAML configuration file:

```yaml
# config.yaml
batch_size: 256
num_workers: 8
epochs: 90
init_lr: 1.e-1
optimizer: SGD
optimizer_cfg:
  momentum: 0.9
  weight_decay: 1.e-4
lr_scheduler: StepLR
lr_scheduler_cfg:
  step_size: 30
  gamma: 0.1
criterion: CrossEntropyLoss
```

and simply run:

```bash
wah train --dataset cifar10 --dataset-root ./dataset --model resnet18 \
    --cfg-path ./config.yaml --log-root ./logs --device auto
```

### What Happens Next?
This single command will:

✅ Automatically download CIFAR-10 to `./dataset`\
✅ Train a ResNet-18 model on it\
✅ Save checkpoints and TensorBoard logs to `./logs`\
✅ Detect available hardware (CPU/GPU) with multi-GPU support (DDP)

No tedious setup, no redundant scripting — just efficient, streamlined model training. 🚀

### And that’s just the beginning!

You’ve found more than just a training tool — a powerful, flexible framework designed to accelerate deep learning research.

### Produly presents:

# WAH

![logo](https://github.com/yupeeee/WAH/blob/main/WAH.png?raw=true)

## Install

```commandline
pip install wah
```

### Requirements

You might want to manually install [**PyTorch**](https://pytorch.org/get-started/locally/)
for GPU computation.

```text
lightning
matplotlib
numpy
pandas
pillow
requests
timm
torch
torchmetrics
torchvision
tqdm
yaml
```

## Structure

### `wah`
- `classification`
	- `datasets`
		- `CIFAR10`
		- `CIFAR100`
		- `compute_mean_and_std`
		- `ImageNet`
		- `load_dataloader`
		- `portion_dataset`
		- `SDM1K`
		- `STL10`
	- `models`
		- `FeatureExtractor`
		- `load`
		- `load_state_dict`
		- `replace`
			- `gelu_with_relu`
			- `relu_with_gelu`
			- `bn_with_ln`
			- `ln_with_bn`
		- `summary`
	- `test`
		- `brier_score`
		- `ece`
	- `Trainer`
- `clip`
	- `CLIP`
- `cuda`
- `decor`
	- `config`
- `dicts`
- `diffusion`
	- `StableDiffusion`
	- `login`
- `fun`
	- `RecursionWrapper`
- `hessian`
	- `Hessian`
- `lists`
- `mods`
- `path`
- `random`
- `tensor`
- `time`
- `utils`
	- `ArgumentParser`
	- `download`
	- `zips`