semvec/pypi-packages huggingface dataset:
mps-watch 0.1.2
Apple Silicon MPS (Metal Performance Shaders) memory monitoring tool for PyTorch.
Requires Python: >=3.8

# mps-watch

The Apple Silicon equivalent of `nvidia-smi` or `gpustat` for PyTorch. Monitor your MPS (Metal Performance Shaders) memory usage in real-time.

## Features

- **Python API**: Context manager and decorator for tracking VRAM usage of specific code blocks.
- **CLI Tool**: Terminal-based dashboard for system unified memory monitoring.
- **Safe Fallbacks**: Graceful handling on non-Mac systems or without MPS.

## Installation

```bash
pip install mps-watch
```

## Usage

### Python API

Monitor memory usage of specific blocks or functions:

```python
from mps_watch import monitor
import torch

# As context manager
with monitor("Training Block"):
    # Your MPS code here
    model.to("mps")
    output = model(input)

# As decorator
@monitor("Inference")
def predict(x):
    return model(x)

# Get current usage directly
from mps_watch import get_current_memory_usage, get_system_memory

allocated, reserved = get_current_memory_usage()
print(f"Current Allocated: {allocated} bytes")

# Get system-wide unified memory
sys_mem = get_system_memory()
print(f"System Total: {sys_mem['total']} bytes")
print(f"System Used: {sys_mem['used']} bytes")
```

**Output:**
It will print a table showing Initial, Final, and Delta memory usage (Allocated vs Reserved).

### CLI Tool

Run the dashboard in your terminal:

```bash
mps-watch
```

This will show a live-updating dashboard of your System Unified Memory and MPS availability status.

## Requirements

- macOS 12.3+ (for MPS support)
- Python 3.8+
- PyTorch
- rich
- psutil

## Understanding the Stats

### CLI vs Python API
- **The CLI Tool (`mps-watch`)**: Shows **System-wide** memory usage. Since Apple Silicon uses Unified Memory, this reflects the total RAM used by the OS and *all* applications combined. It gives you an idea of the overall system pressure.
- **The Python API (`monitor` / `get_current_memory_usage`)**: Shows **Process-specific** MPS memory usage. This is the amount of memory specifically allocated by PyTorch on the Metal device for *your* script.

**Why are they different?**
The CLI shows the whole pie (System RAM). The Python API shows just your slice (PyTorch MPS Usage).

## License

MIT
qiskit-braket-provider 0.11.0
Qiskit-Braket provider to execute Qiskit programs on devices via Amazon Braket.
Requires Python: >=3.11
Keywords: qiskit braket sdk quantum

# Qiskit-Braket Provider

[![Latest Version](https://img.shields.io/pypi/v/qiskit-braket-provider.svg)](https://pypi.python.org/pypi/qiskit-braket-provider)
[![Supported Python Versions](https://img.shields.io/pypi/pyversions/qiskit-braket-provider.svg)](https://pypi.python.org/pypi/qiskit-braket-provider)
[![Qiskit compatibility](https://img.shields.io/badge/Qiskit%20compatibility-%3E%3D0.34.2-blueviolet?logo=Qiskit)](https://github.com/Qiskit/qiskit/releases)
[![Build status](https://github.com/qiskit-community/qiskit-braket-provider/actions/workflows/test_latest_versions.yml/badge.svg?branch=main)](https://github.com/qiskit-community/qiskit-braket-provider/actions/workflows/test_latest_versions.yml)

Qiskit-Braket provider to execute Qiskit programs on devices via Amazon Braket.

### Table of Contents

##### For Users

1.  [Installation](https://github.com/qiskit-community/qiskit-braket-provider/blob/main/docs/installation_guide.md)
2.  [Quickstart Guide](https://github.com/qiskit-community/qiskit-braket-provider/blob/main/docs/quickstart_guide.md)
3.  [Tutorials](https://github.com/qiskit-community/qiskit-braket-provider/blob/main/docs/tutorials/)
4.  [How-Tos](https://github.com/qiskit-community/qiskit-braket-provider/blob/main/docs/how_tos/)
5.  [How to Give Feedback](#how-to-give-feedback)
6.  [Contribution Guidelines](#contribution-guidelines)
7.  [References and Acknowledgements](#references-and-acknowledgements)
8.  [License](#license)

##### For Developers/Contributors

1. [Contribution Guide](https://github.com/qiskit-community/qiskit-braket-provider/blob/main/CONTRIBUTING.md)


----------------------------------------------------------------------------------------------------

### How to Give Feedback

We encourage your feedback! You can share your thoughts with us by:
- [Opening an issue](https://github.com/qiskit-community/qiskit-braket-plugin/issues) in the repository


----------------------------------------------------------------------------------------------------

### Contribution Guidelines

For information on how to contribute to this project, please take a look at our [contribution guidelines](CONTRIBUTING.md).


----------------------------------------------------------------------------------------------------

## References and Acknowledgements
[1] Qiskit https://www.ibm.com/quantum/qiskit \
[2] Amazon Braket Python SDK https://github.com/amazon-braket/amazon-braket-sdk-python


----------------------------------------------------------------------------------------------------

### License
[Apache License 2.0](LICENSE.txt)
rebelai 0.1.5
Text to physics simulation in one line. Generate MuJoCo models from text prompts.
Requires Python: >=3.10
Keywords: convex-decomposition, gltf, mesh, mujoco, physics, robotics, simulation, world-labs

# RebelAI

**Text → Physics Simulation in one line.**
```python
import rebelai

model = rebelai.generate("kitchen table with coffee mugs")
# That's it. You have a physics-ready MuJoCo model.
```

## The Problem

You want to simulate a robot in a realistic environment. Today that means:
- Manually modeling scenes in Blender (hours)
- Writing collision geometry by hand (tedious)
- Tuning physics properties (trial and error)

## The Solution

Describe what you want. Get a MuJoCo model.
```python
import rebelai
import mujoco

# Generate any scene from text
model = rebelai.generate("cluttered office desk with laptop and coffee cup")

# Full MuJoCo API - do whatever you want
data = mujoco.MjData(model)
mujoco.mj_step(model, data)
```

RebelAI handles:
- **World Labs API** → Generate 3D scenes from text
- **Collision geometry** → Automatic convex decomposition
- **Physics properties** → Mass, friction, contacts
- **MJCF generation** → Ready for MuJoCo

## Install
```bash
pip install rebelai
```

## Quick Start
```bash
# Set your World Labs API key
export WORLD_LABS_API_KEY="wl_xxx"
```
```python
import rebelai

# From text prompt
model = rebelai.generate("red sports car on a driveway")

# Or from existing mesh file
model = rebelai.load("scene.glb")
```

## Why RebelAI?

| Without RebelAI | With RebelAI |
|-----------------|--------------|
| Model scene in Blender | `generate("kitchen")` |
| Export, fix mesh issues | Automatic |
| Write collision geometry | Automatic |
| Tune mass/friction | Automatic |
| Debug MJCF XML | Just works |

---

## Configuration

```python
from rebelai import generate, ConversionConfig, CollisionMethod

config = ConversionConfig(
    collision_method=CollisionMethod.CONVEX_DECOMPOSITION,
    coacd_threshold=0.08,  # Coarser = fewer hulls, faster sim
    density=500.0,         # kg/m³
)

model = generate("wooden desk", config=config)
```

### Collision Methods

| Method | Description |
|--------|-------------|
| `CONVEX_DECOMPOSITION` | Multiple convex hulls via CoACD (default) |
| `CONVEX_HULL` | Single convex hull |
| `BOUNDING_BOX` | Axis-aligned box |
| `PRIMITIVES` | Fit box/sphere/cylinder |

### Error Handling

```python
from rebelai import generate, WorldLabsAuthError, WorldLabsAPIError

try:
    model = generate("office chair")
except WorldLabsAuthError:
    print("Check your API key")
except WorldLabsAPIError as e:
    print(f"Generation failed: {e}")
```

## API Reference

### `rebelai.generate(prompt, api_key=None, config=None, model="Marble 0.1-plus")`
Generate scene from text → MuJoCo model

**Important:** Use `model="Marble 0.1-plus"` (default) for collision meshes. `Marble 0.1-mini` does not generate physics-ready meshes.

### `rebelai.load(source, config=None)`
Load mesh file → MuJoCo model

### `rebelai.to_mjcf(source, config=None)`
Convert mesh → MJCF XML string

## License

MIT