semvec/pypi-packages huggingface dataset:
datamole 0.1.0
Dataset versioning and management for ML projects.
License: MIT
Requires Python: >=3.8
Keywords: data, versioning, ml, datasets, dvc-alternative

# datamole

Simple data versioning for ML projects. Track, version, and share your datasets with minimal overhead.

## Features
- 🚀 Simple CLI interface (`dtm` command)
- 📦 Version datasets with automatic hashing
- 🏷️ Tag versions for easy reference
- 🔍 Smart lookup: pull by hash, prefix, or tag
- 💾 Multiple storage backends (local, GCS, S3, Azure)
- 🔒 Transaction-safe uploads
- 🤝 Collaboration-friendly with shared storage

## Installation

```bash
pip install datamole
```

After installation, the `dtm` command will be available globally.

## Quick Start

```bash
# Configure storage backend (one-time setup)
dtm config --backend local --remote-uri /path/to/shared/storage

# Initialize in your project
cd my-ml-project
dtm init

# Add your data and create a version
dtm add-version -m "Initial dataset" -t v1.0

# List versions
dtm list-versions

# Pull a specific version (by tag, hash, or prefix)
dtm pull v1.0
dtm pull abc123  # by hash prefix
dtm pull latest  # pull current version
```

## CLI Commands

### Setup & Configuration
```bash
# Configure storage backend
dtm config --backend local --remote-uri /path/to/storage

# Initialize project
dtm init [--data-dir data] [--backend local] [--no-pull]
```

### Version Management
```bash
# Create a new version
dtm add-version [-m "message"] [-t tag-name]

# Pull a version
dtm pull [version] [-f]

# List all versions
dtm list-versions

# Show current version
dtm current-version
```

## Python API

```python
from datamole.core import DataMole

# Initialize
dtm = DataMole()
dtm.init(data_dir="data", backend="local")

# Create versions
dtm.add_version(message="Initial dataset", tag="v1.0")

# Pull versions
dtm.pull("v1.0")
dtm.pull("abc123")  # by hash prefix
dtm.pull()  # pull current version
```

## Storage Backends

- **local**: Local filesystem storage
- **gcs**: Google Cloud Storage (coming soon)
- **s3**: AWS S3 (coming soon)
- **azure**: Azure Blob Storage (coming soon)

## Development

```bash
# Clone repository
git clone https://github.com/yourusername/datamole.git
cd datamole

# Install in development mode
uv pip install -e ".[dev]"

# Run tests
pytest
```

## License

MIT
datamole-anshu-test 0.1.0
Dataset versioning and management for ML projects.
License: MIT
Requires Python: >=3.8
Keywords: data, versioning, ml, datasets, dvc-alternative

# datamole

Simple data versioning for ML projects. Track, version, and share your datasets with minimal overhead.

## Features
- 🚀 Simple CLI interface (`dtm` command)
- 📦 Version datasets with automatic hashing
- 🏷️ Tag versions for easy reference
- 🔍 Smart lookup: pull by hash, prefix, or tag
- 💾 Multiple storage backends (local, GCS, S3, Azure)
- 🔒 Transaction-safe uploads
- 🤝 Collaboration-friendly with shared storage

## Installation

```bash
pip install datamole
```

After installation, the `dtm` command will be available globally.

## Quick Start

```bash
# Configure storage backend (one-time setup)
dtm config --backend local --remote-uri /path/to/shared/storage

# Initialize in your project
cd my-ml-project
dtm init

# Add your data and create a version
dtm add-version -m "Initial dataset" -t v1.0

# List versions
dtm list-versions

# Pull a specific version (by tag, hash, or prefix)
dtm pull v1.0
dtm pull abc123  # by hash prefix
dtm pull latest  # pull current version
```

## CLI Commands

### Setup & Configuration
```bash
# Configure storage backend
dtm config --backend local --remote-uri /path/to/storage

# Initialize project
dtm init [--data-dir data] [--backend local] [--no-pull]
```

### Version Management
```bash
# Create a new version
dtm add-version [-m "message"] [-t tag-name]

# Pull a version
dtm pull [version] [-f]

# List all versions
dtm list-versions

# Show current version
dtm current-version
```

## Python API

```python
from datamole.core import DataMole

# Initialize
dtm = DataMole()
dtm.init(data_dir="data", backend="local")

# Create versions
dtm.add_version(message="Initial dataset", tag="v1.0")

# Pull versions
dtm.pull("v1.0")
dtm.pull("abc123")  # by hash prefix
dtm.pull()  # pull current version
```

## Storage Backends

- **local**: Local filesystem storage
- **gcs**: Google Cloud Storage (coming soon)
- **s3**: AWS S3 (coming soon)
- **azure**: Azure Blob Storage (coming soon)

## Development

```bash
# Clone repository
git clone https://github.com/yourusername/datamole.git
cd datamole

# Install in development mode
uv pip install -e ".[dev]"

# Run tests
pytest
```

## License

MIT
mesh-toolkit 0.1.0
Python SDK for Meshy AI 3D asset generation API
License: MIT
Requires Python: >=3.9
Keywords: 3d, ai, game-assets, glb, meshy, procedural-generation

# mesh-toolkit

Python SDK for [Meshy AI](https://www.meshy.ai/) 3D asset generation API.

## Installation

```bash
pip install mesh-toolkit
```

## Usage

```python
from mesh_toolkit import text3d, rigging, animate, retexture

# Generate a 3D model
model = text3d.generate("a medieval sword with ornate handle")

# Rig it for animation
rigged = rigging.rig(model.id)

# Apply an animation (678 available)
animated = animate.apply(rigged.id, animation_id=0)  # Idle

# Or retexture it
gold = retexture.apply(model.id, "golden with embedded gems")
```

## Modules

### text3d

```python
from mesh_toolkit import text3d

# Generate model
result = text3d.generate("a wooden chest", art_style="realistic")
print(result.model_urls.glb)

# Or manually control the workflow
task_id = text3d.create(request)  # Returns immediately
result = text3d.get(task_id)      # Check status
result = text3d.poll(task_id)     # Wait for completion
```

### rigging

```python
from mesh_toolkit import rigging

result = rigging.rig(model_id)
# Or from URL
result = rigging.rig_from_url("https://example.com/model.glb")
```

### animate

```python
from mesh_toolkit import animate
from mesh_toolkit.animations import ANIMATIONS

# Apply animation
result = animate.apply(rigged_id, animation_id=0)

# Browse 678 animations
for anim in ANIMATIONS.values():
    print(f"{anim.id}: {anim.name} ({anim.category})")
```

### retexture

```python
from mesh_toolkit import retexture

result = retexture.apply(model_id, "rusty metal with scratches")
# Or from reference image
result = retexture.apply_from_image(model_id, "https://example.com/style.png")
```

## Architecture

```
mesh_toolkit/
├── base.py         # HTTP infrastructure (auth, retries, rate limiting)
├── text3d.py       # Text-to-3D API
├── rigging.py      # Rigging API  
├── animate.py      # Animation API
├── retexture.py    # Retexture API
├── animations.py   # 678 animation catalog
├── models.py       # Pydantic models
└── jobs.py         # Batch workflow orchestration
```

Each API module imports `base` and implements its endpoints directly. No monolithic client class.

## Environment Variables

| Variable | Description |
|----------|-------------|
| `MESHY_API_KEY` | Your Meshy API key (required) |

## License

MIT