semvec/pypi-packages huggingface dataset:
CKaPy 1.0.0
A collection of tools from my Ph.D. in physics.
Requires Python: >=3.9

# CKaPy

## Description
CKaPy is a Python3 package with personal tools commonly used in my day-to-day as a physics researcher. These tools are not project specific, but instead are used for common data science and visualization purposes. 

## Installation
This package is installable through PyPi with the command:

```Unix
pip install ckapy
```

## Usage
Currently this package contains methods for three primary purposes: returning the current date and time in a formatted string, printing a value with uncertainty, and printing the current progress of an interative loop.
Some of these methods are designed for output from commonly used methods.

## Support
Please submit any issues or short-falls you encounter to GitHub.

## Roadmap
I expect this project to remain fairly small in scope, with tools I used during my Ph.D. but not much more.
connect-n-3d 0.0.3
Connect-N game in a 3d board as a gymnasium environment
Requires Python: >=3.10

# Connect‑N‑3D

A generalised 3‑D Connect‑N game featuring:

- Configurable dimensions (height X width X depth) and target length N.
- Clean Board API (`ConnectNBoard3D`).
- A Gymnasium environment (ConnectN3DEnv) for RL research.

## Installation

```python
pip install connect-n-3d
```
port-mcp-gateway 0.0.1
A gateway for interacting with Port.io through a standardized MCP API
Requires Python: >=3.8
Keywords: port.io, mcp, api, gateway

# Port MCP Gateway

A comprehensive gateway for interacting with Port.io through the PyPort SDK using the Model Context Protocol (MCP).

## Installation

```bash
pip install port-mcp-gateway
```

## Quick Start

1. Set up your Port.io credentials:

```bash
# Create a .env file
echo "PORT_CLIENT_ID=your_client_id" > .env
echo "PORT_CLIENT_SECRET=your_client_secret" >> .env
```

2. Start the gateway:

```bash
port-mcp-gateway
```

3. Access the gateway at http://localhost:8000

## Command Line Options

```
usage: port-mcp-gateway [-h] [--host HOST] [--port PORT] [--reload]
                        [--log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}]
                        [--log-file LOG_FILE] [--env-file ENV_FILE]

Port MCP Gateway

options:
  -h, --help            show this help message and exit
  --host HOST           Host to bind the server to
  --port PORT           Port to bind the server to
  --reload              Enable auto-reload for development
  --log-level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        Logging level
  --log-file LOG_FILE   Log file path
  --env-file ENV_FILE   Environment file path
```

## Using with Cursor or Claude

### For Cursor

```python
import requests
import json

def get_blueprints():
    response = requests.post(
        "http://localhost:8000/mcp/tools/get_blueprints",
        headers={"Content-Type": "application/json"},
        data=json.dumps({"name": "get_blueprints", "input": {}})
    )
    return response.json()

# Use the function
blueprints = get_blueprints()
print(blueprints)
```

### For Claude (via Augment Code Agent)

1. Configure the MCP server in Augment Code Agent:
   - Go to Augment Code Agent settings
   - Navigate to "External Tools" or "Integrations" section
   - Add an MCP Server integration with these details:
     - **Integration Type**: MCP Server
     - **Name**: Port.io Gateway
     - **Command**: `npx -y @augment/mcp-client connect http://localhost:8000/mcp`

2. Use the gateway in conversations with Claude:
   - "Can you use the MCP server to get a list of all my blueprints in Port.io?"
   - "Please use the get_blueprints tool to show me my Port.io blueprints."

## Documentation

For full documentation, visit [GitHub](https://github.com/yourusername/port-mcp-gateway).
accmt-cli 1.4.7.1
CLI for AcceleratorModule library (accmt).
Requires Python: >=3.10

# AcceleratorModule CLI
CLI for AcceleratorModule library ([accmt](https://github.com/ghanvert/AcceleratorModule)).

This is a command-line tool wrapper around Accelerate's command-line utilities ('accelerate').

## Installation
**accmt-cli** is automatically installed when installing **accmt** library. You also install it via pip:
```bash
pip install accmt-cli
```

## Launch
You can launch any distributed training process with the following command:
```bash
accmt launch [-N][-n, --gpus][-O1][--strat] <your_python_script> [...]
```

Where:
- **-N** (*optional*): Corresponds to the number of processes, or a Python-like slice to take GPUs from a certain index (e.g. '-N=2:', to take GPUs from index 2).
- **-n** or **--gpus** (*optional*): Corresponds to a list of CUDA devices (e.g. '-n=1,3,5,6', to take GPUs indices 1, 3, 5 and 6).
- **-O1** (*optional*): Corresponds to the optimization of type 1, which calculates the efficient number for 'OMP_NUM_THREADS', depending on how many processes you will run you training script.
- **--strat** (*optional*): Corresponds to the specific strategy to implement, or a configuration file path from Accelerate ('accelerate config --config_file=your-config.yaml'). See 'accmt strats' for specific strategies.
**...** (*optional*): You can add here any additional arguments that your Python script might have.

## Get model from checkpoint
You can get a model from any checkpoint using the following command:
```bash
accmt get <checkpoint> --out=<output-model-directory> [--dtype]
```

Where:
- **--out** or **-O** (*REQUIRED*): Output model directory name where to save the model.
- **--dtype** (*Optional*): PyTorch data type of model parameters. Default is 'float32'.

## Strats
You can check the specific strats included with the following command:
```bash
accmt strats [--ddp][--fsdp][--deepspeed]
```

Where:
- **--ddp**: To only filter for DDP strategies.
- **--fsdp**: To only filter for FSDP strategies.
- **--deepspeed**: To only filter for DeepSpeed strategies.

## Example
Generate an example HPS file config with the following command:
```bash
accmt example
```

This will generate a file on your current directory called 'hps_example.yaml'.

## Debug
Enable debug mode with:
```bash
accmt debug [--level] ...
```

Where `--level` flag is an integer number, which indicates the level of debugging. Available levels are:
**LEVEL 1**:
- Disables logging (MLFlow, Tensorboard, etc).

**LEVEL 2**:
- Disables model and teacher compilation.

**LEVEL 3**:
- Disables model saving, checkpointing and resuming (no folders will be created).

**LEVEL 4** (default):
- Force `eval_when_start` (in Trainer) to False.

**LEVEL 5**:
- Disables any evaluation.