semvec/pypi-packages huggingface dataset:
qqtools 1.1.33
A small tool package for qq
Requires Python: >=3.11


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# ✨qqtools✨
[![PyPI Downloads](https://static.pepy.tech/personalized-badge/qqtools?period=total&units=ABBREVIATION&left_color=GREY&right_color=BRIGHTGREEN&left_text=PyPI+Downloads)](https://pepy.tech/projects/qqtools)  ![Python version](https://img.shields.io/badge/python->=3.11-blue)  


A lightweight library, crafted and battle-tested daily by *qq*, to make PyTorch life a little easier. 

I’ve gathered the repetitive parts of my day-to-day work and refined them into this slim utility library.
It serves as my personal toolkit for handling data, training, and experiments, designed to keep projects moving fast with cleaner code and smoother workflows (and hopefully yours too!).

>Built for me, shared for you. 



# Requirements

- torch>=2.0 for full functionality 
  - Some components maintain backward compatibility with torch==1.x
  - Recommended: torch>=2.4
- pyyaml>=6.0
  - Recommended to use YAML format for all configuration files.

This provides a unified approach to drive and manage all workflow operations.

To get started quickly, install it via pip:
```bash
pip install qqtools
```

Install with full features:
```bash
pip install qqtools[full]
```


# Data Format Support

Non-torch formats:
```bash
qDict : Enhanced of basic Dict.
qScalaDict : Dict[str, num]. A dict that maps str to scala;
qListData : List[dict]. A list of dicts.
```

Torch-related data formats
```bash
qData
qBatchList
```



# Simple Training Loop

For jupyter users

```python
import qqtools as qt
qt.import_common(globals())

x = np.random.rand(100, 5)
y = np.random.rand(100)

# dataset wrap
xs = [ x[i] for i in range(len(x))]
ys = [ y[i] for i in range(len(y))]
data_list = [ qt.qData({'x': x[i], 'y':y[i]})  for i in range(len(x))] 
dataset = qt.qDictDataset(data_list=data_list)
dataloader = qt.qDictDataloader()

# model
model = qt.nn.qMLP([5,5,1], activation="relu")
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.AdamW(model.parameters(), lr=1.0e-4, weight_decay=0.01)

# device
device = torch.device("cuda")
model.to(device)

# loop
for epoch in range(100):
    for batch in dataloader:
        batch.to(device)
        out = model(batch.x)
        loss = loss_fn(out, batch.y)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    print(f"{epoch} {loss.item():4.6f}")
```


# Individual Modules

The following modules are consumers of the core functionality provided by this package. Each is designed to be independent, allowing for sole import.

under `plugins/`

- qchem
- qpipeline
- qhyperconnect


# Test

```bash
tox
```