semvec/pypi-packages huggingface dataset:
torchminer 0.4.14
Run Torch With A Simple Miner
Requires Python: >=3.7.1,<4.0

Published on [pypi](https://pypi.org/project/torchminer/)

Packaged Using [Poetry](https://python-poetry.org/)

# Description

TorchMiner is designed to automatic process the training ,evaluating and testing process for PyTorch DeepLearning,with a
simple API.

You can access all Functions of MineTorch simply use `Miner`.

## Quick Start

```python
import TorchMiner
from TorchMiner import Miner
from TorchMiner.plugins.Logger.Jupyter import JupyterLogger, JupyterTqdm
from TorchMiner.plugins.Metrics import MultiClassesClassificationMetric
from TorchMiner.plugins.Recorder import TensorboardDrawer

miner = Miner(
    alchemy_directory='/the/route/to/log', 
    train_dataloader=train_dataloader, 
    val_dataloader=val_dataloader,  

    model=model, 
    loss_func=MSELoss,  
    optimizer=optimizer,  
    # or, by passing a function to optimizer, TorchMiner can auto cuda the params of optimizer
    # optimizer=lambda x: optim.SGD(x.parameters(), lr=0.01)，
    experiment="the-name-of-experiment",  # Subdistribution in the experimental directory
    resume=True,  # Whether to automatically load the previous model
    eval_epoch=1,  # How many rounds are evaluated
    persist_epoch=2,  # How many rounds are saved once a checkpoint
    accumulated_iter=1,  # How many times iterates the parameter update after accumulation
    in_notebook=True,
    amp=True,  # Whether to use amp
    plugins=[
        # Use the plugins to extend the function of miner
        JupyterLogger(),
        JupyterTqdm(),
        # or, you can use the below one to auto enable the above two
        # *JupyterEnvironmentAutoEnable(),
        # The two above plugins are designed to get better output in Jupyter Enviroment
        MultiClassesClassificationMetric(),
        # This Plugin can automaticly calculate Accuracy, kappa score and Confusion Matrix in Classification problems.
        TensorboardDrawer(input_to_model),
        # This Plugin can record the informations generate by training process or by other plugins in Tensorboard.
    ],
)

# And then, trigger the training process by
miner.train()
pymongo-paginate 1.5
Package to paginate pymongo queries
Requires Python: >=3.6

# Pymongo Paginate

> author: github.com/gabrielmrts/

## Example

```

from pymongo_paginate import PyMongoPaginate

collection = db['your_pymongo_collection']
query = collection.find()

page = 1 ## Current page
pageSize = 10 ## Items per page

pagination = PyMongoPaginate(query, page, pageSize)
paginate = pagination.paginate()

---------------------------------------------------
Output -> dict {
            "page": 1,
            "page_count": 1,
            "item_count": 10,
            "items": []
        }
        

```