semvec/pypi-packages huggingface dataset:
backend-ai-test 25.6.2
Backend.AI Integration Test Suite
License: MIT
Requires Python: <3.14,>=3.13

# Backend.AI Testing Toolkit

Automated test suites to validate an installation and integration of clients and servers


## How to run CLI-based integration test

If there is no configuration script named `env-tester.sh`, copy it from `sample-env-tester.sh` and check the configuration env.

```console
$ source env-tester.sh
$ backend.ai test run-cli
```

NOTE: The ENV script is parsed with `"^export (\w+)=(.*)$"` regex pattern line by line.
backend-ai-logging 25.6.2
Backend.AI Logging Subsystem
License: MIT
Requires Python: <3.14,>=3.13

Backend.AI Logging Subsystem
============================

Package Structure
-----------------

* `ai.backend.logging`
  - `abc`: Abstract base classes
  - `logger`: The core logging facility
    - `Logger`: The standard multiprocess-friendly logger using `RelayHandler` based on ZeroMQ
    - `LocalLogger`: A minimalized console/file logger that does not require serialization via networks at all
  - `handler`: Collection of vendor-specific handler implementations
  - `formatter`: Collection of formatters
  - `types`: Definition of enums/types like `LogLevel`
  - `utils`: Brace-style message formatting adapters and other extras
backend-ai-install 25.6.2
Backend.AI Installer
License: MIT
Requires Python: <3.14,>=3.13

Backend.AI Installer
====================

Package Structure
-----------------

* `ai.backend.install`: The installer package

Development
-----------

### Using the textual debug mode

First, install the textual-dev package in the `python-default` venv.
```shell
./py -m pip install textual-dev
```

Open two terminal sessions.
In the first one, run:
```shell
dist/export/python/virtualenvs/python-default/3.13.3/bin/textual console
```

> **Warning**
> You should use the `textual` executable created *inside the venv's `bin` directory*.
> `./py -m textual` only shows the demo instead of executing the devtool command.

In the second one, run:
```shell
TEXTUAL=devtools,debug ./backend.ai install
```
backend-ai-cli 25.6.2
Backend.AI Command Line Interface Helper
License: MIT
Requires Python: <3.14,>=3.13

# backend.ai-cli

Unified command-line interface for Backend.AI


## How to adopt in CLI-enabled Backend.AI packages

An example `setup.cfg` in Backend.AI Manager:
```
[options.entry_points]
backendai_cli_v10 =
    mgr = ai.backend.manager.cli.__main__:main
    mgr.start-server = ai.backend.gateway.server:main
```

Define your package entry points that returns a Click command group using a
prefix, and add additional entry points that returns a Click command using a
prefix followed by a dot and sub-command name for shortcut access, under the
`backendai_cli_v10` entry point group.

Then add `backend.ai-cli` to the `install_requires` list.

You can do the same in `setup.py` as well.
nn-calc 0.1.2
A calculator for encoder and decoder layers of any neural networks
License: MIT
Requires Python: >=3.3

# Neural Network calculator

Neural Network calculator reads your hyperparameter choices (kernel size, stride, channels, etc.) and outputs:

- Encoder block: detailed Conv2d specs + parameter counts

- Decoder block: mirrored ConvTranspose2d specs + parameter counts

- Recurrent stacks: per-layer (input_size, hidden_size, params) for RNN/GRU/LSTM

## Features:

- Multi-layer CNN mode

   - Global defaults or per-layer overrides for stride, kernel size, padding

   - Automatic channel-chaining in encoder → mirrored decoder block


- Recurrent mode

   - RNN: single-gate formula

   - GRU: 3× single-gate formula

   - LSTM: 4× single-gate formula

   - Stacked layers automatically chain hidden sizes

- User-friendly CLI with automatic help pages & type-safe options

- Subcommand support, lazy loading, file arguments, and more via Click


## Usage:

### Example


### bash command

```
nn_calc -h
```

### output

```
  Calculate parameter counts for CNN encoder/decoder OR RNN/GRU/LSTM layers.

Options:
  --model [cnn|rnn|gru|lstm]  Type of network to compute parameters for.
                              [default: cnn]
  -n, --num-layers INTEGER    Number of layers (CNN) or RNN layers
                              (recurrent).  [default: 3]
  --stride INTEGER            Default stride for each conv layer.  [default:
                              1]
  --kernel-size INTEGER       Default kernel size for each conv layer.
                              [default: 3]
  --padding INTEGER           Default padding for each conv layer.  [default:
                              1]
  --in-channels INTEGER       CNN: input channels for first layer; RNN: input
                              size.  [default: 3]
  --out-channels INTEGER      CNN: output channels for each layer; RNN: hidden
                              size.  [default: 64]
  -S, --strides INTEGER       CNN: per-layer strides (e.g. -S 1 2 2).
  -K, --kernel-sizes INTEGER  CNN: per-layer kernel sizes.
  -P, --paddings INTEGER      CNN: per-layer paddings.
  -h, --help                  Show this message and exit.

```

## Requirements:

- Python >= 3.7
- click>=8.0

## Installation:

### Pip

```shell
pip install nn_calc
```

### git repository

```
https://github.com/pssv7/nn-calc.git
```

## Usage

```shell
nn_calc (OPTIONAL)
```