qqtools 1.2.27

A small tool package for qq

✨qqtools✨

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.

What it includes

At its core, qqtools is a collection of small utilities I use around PyTorch projects:

• data containers such as qDict and qData
• dataset and dataloader helpers such as qDictDataset and qDictDataloader
• small neural network helpers such as qMLP
• a lightweight training framework, qpipeline
• a command-line experiment queue for Linux, qexp
• config and serialization helpers for YAML, JSON, pickle, and LMDB

At the core, it is still a practical toolbox for the repetitive parts around experiments.

Install

# Core install
pip install qqtools

# Full install
pip install qqtools[full]

# If you only want the experiment queue extras:
pip install qqtools[exp]

While some parts still work with torch==1.x, torch>=2.4 is recommended

qDict

qDict is mainly there for cleaner attribute access in batch-like code:

# Instead of dirty dict brackets:
# batch["input_ids"], batch["attention_mask"]

# Use clean attribute access:
batch = qt.qDict({"input_ids": input_ids, "attention_mask": attention_mask})
out = model(batch.input_ids)

Context scope and qt.use_ctx

qt.ctx provides a lightweight scoped context. Values set inside with qt.ctx(...) are visible only in that scope and its nested calls, and the outer state is restored automatically when the block exits.

Scope exit restores the previous key bindings. If you intentionally mutate a shared mutable object in place through the live context, that mutation is considered caller-managed behavior and may remain visible outside the block.

import qqtools as qt

with qt.ctx(dim=512):
    print(qt.ctx.dim)  # 512

print(qt.ctx.get("dim"))  # None

@qt.use_ctx is the simplest way to inject context values into a class constructor:

import qqtools as qt


@qt.use_ctx
class AttentionLayer:
    def __init__(self, dim=64, heads=8):
        self.dim = dim
        self.heads = heads


with qt.ctx(dim=512, heads=16):
    layer = AttentionLayer()
    print(layer.dim, layer.heads)  # 512 16

Manual constructor arguments still take precedence over injected context values.

qexp

qexp is a lightweight experiment queue for Linux hosts. It is built around a shared project root, can work on multi-machines with multi-GPUs.

Quick start:

qexp init --shared-root /mnt/share/myproject/.qexp --machine gpu-a
qexp submit --name demo1 -- python train.py -c config1.yaml
qexp submit --name demo2 -- python train.py -c config2.yaml
qexp submit --name demo3 -- python train.py -c config3.yaml
# 3 tasks will be queued and run sequentially

After init, qexp saves the current shared_root and machine as CLI context, so you usually do not need to repeat them on every command.

Python API:

from qqtools.plugins import qexp

task = qexp.submit(
    qexp.load_root_config("/mnt/share/myproject/.qexp", "gpu-a"),
    command=["python", "train.py", "--epochs", "10"],
    name="demo",
)
print(task.task_id)

Note: Run pip install qqtools[exp] before use qexp command.

qpipeline

qpipeline is a minimal training loop scaffold. It doesn't try to be a heavy framework. You write the project-specific model and task logic, and qpipeline handles the repetitive boilerplate: config-driven startup, train/val loops, metric aggregation, and checkpointing.

A tight training entry:

import torch
from qqtools.plugins.qpipeline import prepare_cmd_args, qPipeline
from qqtools.nn import qMLP

class MyTask:
    def __init__(self, args):
        # Your custom data logic goes here
        self.train_loader, self.val_loader = build_loaders(args)

    def batch_forward(self, model, batch):
        return {"pred": model(batch.x)}

    def batch_loss(self, out, batch):
        loss = torch.nn.functional.mse_loss(out["pred"], batch.y)
        return {"loss": (loss, len(batch.y))}

    def batch_metric(self, out, batch):
        mae = (out["pred"] - batch.y).abs().mean()
        return {"mae": (mae, len(batch.y))}

    def post_metric_to_err(self, result):
        return result["mae"]

class MyPipeline(qPipeline):
    @staticmethod
    def prepare_model(args):
        return qMLP([16, 8, 1])

    @staticmethod
    def prepare_task(args):
        return MyTask(args)

if __name__ == "__main__":
    args = prepare_cmd_args()
    pipe = MyPipeline(args, train=True)
    pipe.fit()

Because qpipeline enforces a stable entry contract, it pairs perfectly with qexp for queued execution:

qexp submit -- python entry.py --config configs/train.yaml

For one-off runtime config edits, qpipeline also supports dotted CLI overrides after normal parser handling:

python entry.py \
  --config configs/train.yaml \
  --task.dataloader.eval_batch_size 32 \
  --task.val_split val_ood \
  --runner.fast_dev_run

Configuration follows a standard YAML structure. See qConfig.md for details.

Plugin modules

Under src/qqtools/plugins/, there are also:

• qchem - tools for reading and processing quantum chemistry outputs
• qpipeline - a training pipeline framework built on top of the core torch utilities
• qhyperconnect - an implementation of Hyper-Connection for PyTorch

Test

tox