semvec/pypi-packages huggingface dataset:
pyconsole-codes 0.0.2
终端彩色字解析
Requires Python: >=3.10

# pyconsole_codes 库

pyconsole_codes 库是一个处理 console_codes 的库, 它提供了一些函数和类，用于处理 console_codes。(终端彩色字解析)

## 安装

要安装pyconsole_codes库，请使用pip命令：(暂未上传pypi)

# ```
# pip install pyconsole_codes
# ```

## 使用
grobid-client 0.8.8
A client library for accessing Grobid
Requires Python: <4.0,>=3.7

# grobid-client
A client library for accessing [Grobid](https://github.com/kermitt2/grobid)

## Usage
First, create a client:

```python
from grobid_client import Client

client = Client(base_url="https://cloud.science-miner.com/grobid/api")
```

Now call your endpoint and use your models:

```python
from pathlib import Path
from grobid_client.api.pdf import process_fulltext_document
from grobid_client.models import Article, ProcessForm
from grobid_client.types import TEI, File
pdf_file = "MyPDFFile.pdf"
with pdf_file.open("rb") as fin:
    form = ProcessForm(
        segment_sentences="1",
        input_=File(file_name=pdf_file.name, payload=fin, mime_type="application/pdf),
    )
    r = process_fulltext_document.sync_detailed(client=client, multipart_data=form)
    if r.is_success:
        article: Article = TEI.parse(r.content, figures=False)
        assert article.title

```

Things to know:
1. Every path/method combo becomes a Python module with four functions:
    1. `sync`: Blocking request that returns parsed data (if successful) or `None`
    1. `sync_detailed`: Blocking request that always returns a `Request`, optionally with `parsed` set if the request was successful.
    1. `asyncio`: Like `sync` but the async instead of blocking
    1. `asyncio_detailed`: Like `sync_detailed` by async instead of blocking

1. All path/query params, and bodies become method arguments.
1. If your endpoint had any tags on it, the first tag will be used as a module name for the function (my_tag above)
1. Any endpoint which did not have a tag will be in `entifyfishing_client.api.default`

## Building / publishing this Client
This project uses [Poetry](https://python-poetry.org/) to manage dependencies  and packaging.  Here are the basics:
1. Update the metadata in pyproject.toml (e.g. authors, version)
1. If you're using a private repository, configure it with Poetry
    1. `poetry config repositories.<your-repository-name> <url-to-your-repository>`
    1. `poetry config http-basic.<your-repository-name> <username> <password>`
1. Publish the client with `poetry publish --build -r <your-repository-name>` or, if for public PyPI, just `poetry publish --build`

If you want to install this client into another project without publishing it (e.g. for development) then:
1. If that project **is using Poetry**, you can simply do `poetry add <path-to-this-client>` from that project
1. If that project is not using Poetry:
    1. Build a wheel with `poetry build -f wheel`
    1. Install that wheel from the other project `pip install <path-to-wheel>`
ObjectTrainer 0.1
Simplify YOLO model training and data splitting for object detection tasks.
Keywords: python, model training, YOLO, data splitting, deep learning


# ObjectTrainer



ObjectTrainer is a Python package designed to simplify the process of training YOLO (You Only Look Once) models for object detection tasks. With ObjectTrainer, users can easily train YOLO models using custom datasets and split their data into training, validation, and testing sets. The package provides classes for both model training and data splitting, allowing users to efficiently manage their training pipeline. Additionally, ObjectTrainer includes functionality for saving the best-performing model weights, making it easy to deploy trained models for inference tasks.



## Features



- Train YOLO models with custom datasets

- Split datasets into training, validation, and testing sets

- Save best-performing model weights for deployment



## Installation



You can install ObjectTrainer using pip:



```bash

pip install ObjectTrainer

````



## Usage



## Training a YOLO Model



```python

from ObjectTrainer import YOLO_trainer



# Initialize YOLO Trainer with absolute data.yaml folder path and absolute destination folder path for best weights

trainer = YOLO_trainer(Data_yaml_fold_path='path/to/data.yaml', Best_Weight_dest='path/to/destination', epochs=50)



# Run the full training process

trainer.run_full_training()

```



### Splitting Data



```python

from ObjectTrainer import data_splitter



# Initialize Data Splitter with absolute data folder path, destination folder path, and number of classes

splitter = data_splitter(data_folder='path/to/data', dest_fold='path/to/destination', no_classes=3)



# Run the full data splitting process

splitter.run_full_split()

```



## License



Model Trainer is licensed under the MIT License. See the [LICENSE](https://github.com/Rathoreatri03/Model_Trainer/blob/main/LICENSE) file for details.



## Support



For support, please open an issue on our [GitHub repository](https://github.com/Rathoreatri03/Model_Trainer/issues).

```



This Markdown-formatted README includes the updated usage instructions with the new single-method calls for training and data splitting, making it easy for users to follow and implement.
lit-ecology-classifier 2.0.3
Image Classifier optimised for ecology use-cases
Requires Python: >=3.6

# Lit Ecology Classifier
Documentation: https://lit-ecology-classifier.readthedocs.io/en/latest/
Lit Ecology Classifier is a machine learning project designed for image classification tasks. It leverages PyTorch Lightning for streamlined training and evaluation processes.

## Features

- Easy configuration and setup
- Utilizes PyTorch Lightning for robust training and evaluation
- Supports training on multiple GPUs
- Test Time Augmentation (TTA) for enhanced evaluation
- Integration with Weights and Biases for experiment tracking

## Installation

To install Lit Ecology Classifier, use pip:

```bash
pip install lit-ecology-classifier
```

## Usage

### Training

To train the model, use the following command:

```bash
python -m lit_ecology_classifier.main --max_epochs 20 --dataset phyto --priority config/priority.json
```

### Inference

To run inference on unlabelled data, use the following command:

```bash
python -m lit_ecology_classifier.predict --datapath /path/to/data.tar --model_path /path/to/model.ckpt --outpath ./predictions/
```

## Configuration

The project uses an argument parser for configuration. Here are some of the key arguments:

### Training Arguments

- `--datapath`: Path to the tar file containing the training data.
- `--train_outpath`: Output path for training artifacts.
- `--main_param_path`: Main directory where the training parameters are saved.
- `--dataset`: Name of the dataset.
- `--use_wandb`: Use Weights and Biases for logging.
- `--priority_classes`: Path to the JSON file with priority classes.
- `--balance_classes`: Balance the classes for training.
- `--batch_size`: Batch size for training.
- `--max_epochs`: Number of epochs to train.
- `--lr`: Learning rate for training.
- `--lr_factor`: Learning rate factor for training of full body.
- `--no_gpu`: Use no GPU for training.

### Inference Arguments

- `--outpath`: Directory where predictions are saved.
- `--model_path`: Path to the model file.
- `--datapath`: Path to the tar file containing the data to classify.
- `--no_gpu`: Use no GPU for inference.
- `--no_TTA`: Disable test-time augmentation.

## Documentation

Detailed documentation for this project is available at [Read the Docs](https://lit-ecology-classifier.readthedocs.io).

### Example SLURM Job Submission Script

Here is an example SLURM job submission script for training on multiple GPUs:

```bash
#!/bin/bash
#SBATCH --account="em09"
#SBATCH --constraint='gpu'
#SBATCH --nodes=2
#SBATCH --ntasks-per-core=1
#SBATCH --ntasks-per-node=1
#SBATCH --cpus-per-task=12
#SBATCH --partition=normal
#SBATCH --constraint=gpu
#SBATCH --hint=nomultithread
#SBATCH --output=slurm/slurm_%j.out
#SBATCH --error=slurm/slurm_%j.err
export OMP_NUM_THREADS=12 #$SLURM_CPUS_PER_TASK
cd ${SCRATCH}/lit_ecology_classifier
module purge
module load daint-gpu cray-python
source lit_ecology/bin/activate
python -m lit_ecology_classifier.main --max_epochs 2 --dataset phyto --priority config/priority.json
```
tensorflow-murmur 0.0.13
This is a small addons module for tensorflow
Requires Python: >=3.8
Keywords: tf addons

# tensorflow_murmur

<img src='https://github.com/jordmundgand/tensorflow_murmur/assets/109611636/80b10f1f-fdc2-4502-ab29-b195ee3cb57f' width='300'>

Small TF addons library

# Layers
  LSTMTransformerLayer, EncoderLayer, DecoderLayer, Weightened
  
  PositionalEmbedding, IdfEmbedding, SpatialEmbedding
  
  RandomIndexing, LanguageMasking, IndexedSlice, Splitter, MultiText, ColumnTransforner

# Losses
  masked_loss, masked_multi_loss, WeightenedBinaryCrossentropy

# Metrics
  masked_accuracy, masked_multi_cosine_similarity
  
  sparse_target_accuracy, batch_average
AuraAlerts 1
Beautiful Alerts For Desktop App Developers!!

# AuraAlerts

Simple Alerts For Desktop App Developers

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+
# DO NOT REMOVE /imgs FOLDER #
+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+

# Types Of Alerts:
+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=
Success Alert:


![SUCCESS](https://github.com/abdrzakk/AuraAlerts/assets/167257386/863f20a5-0818-4dc8-a845-0767a0249c7b)


Simple Usage:

```python
AuraAlerts().Aa().success(title='hello',content='content')
```
+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=
Error Alert:


![ERROR](https://github.com/abdrzakk/AuraAlerts/assets/167257386/e966117a-9cfb-4c63-9ced-a9881212f4a1)


Simple Usage:

```python
AuraAlerts().Aa().error(title='hello',content='content')
```

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=

Warn Alert:


![WARN](https://github.com/abdrzakk/AuraAlerts/assets/167257386/3d7ee7d9-1841-4e75-a2dc-ca98f6f2d88f)


Simple Usage:

```python
AuraAlerts().Aa().warn(title='hello',content='content')
```

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=

Info Alert:


![INFO](https://github.com/abdrzakk/AuraAlerts/assets/167257386/1284b363-9d22-4d7d-9139-c7f2f988274c)


Simple Usage:

```python
AuraAlerts().Aa().info(title='hello',content='content')
```

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=

Custom Alert:

![CUSTOM](https://github.com/abdrzakk/AuraAlerts/assets/167257386/c72fec5e-bd93-4450-b09e-7510e570e7df)

Simple Usage:

```python
AuraAlerts().Aa().custom(title='title', content='content', buttonText, buttonColor='#e08aff', buttonHoverColor='#d561ff', imagePath='default image', theme='light')
```

+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=+=


# customizable

# In Error, Success, Info, Warn you can Edit :
__________:title, content, buttonText, theme
# In Custom You Can Edit:
__________:title, content, buttonText, buttonColor, buttonHoverColor, imagePath , theme

# Simple Usage Code:

Simple Usage Code:
```python
from AuraAlerts import Aa

# Error Alert
Aa().error(title='Title', content='Content')


# Success Alert
Aa().success(title='Title', content='Content')


# Warn Alert
Aa().warn(title='Title', content='Content')


# Info Alert
Aa().info(title='Title', content='Content')

# Custom Alert [YOU CAN EDIT properties MORE THAN error,warn.. alerts]

Aa().custom(
    title='Title', 
    content='Content', 
    buttonText='Button Text', 
    buttonColor='#e08aff', 
    buttonHoverColor='#d561ff', 
    imagePath='default image',
    theme='light'
)
```





# DOWNLOAD #
* USING PIP:

``pip install AuraAlerts``

* from PyPi :

https://pypi.org/project/AuraAlerts/1.0.1/

or

* USING GIT:

``git clone https://github.com/abdrzakk/AuraAlerts.git``



# NOTES:
* IF YOU HAVE AN IDEA , ADD IT

* DO NOT REMOVE /imgs FROM SOURCE CODE 

# ENJOY :)