semvec/pypi-packages huggingface dataset:
simple-file-transfer 0.2.2
A simple file transfer service for temporary file sharing
License: MIT
Requires Python: >=3.8

# Simple File Transfer (SFT)

A simple, temporary file transfer service for sharing files between machines.

## Installation

```bash
pip install simple-file-transfer
```

## Quick Start

### Server Setup

```bash
pip install simple-file-transfer
sft serve --port 12345
```

### Upload a File (Machine A)

```bash
sft upload ./my-file.tgz 1h
```

Output:
```
Uploaded my-file.tgz 5GB sha256:...
The file will be deleted in 1h (2025-01-01 00:01:00)
To download your file, enter: sft download 763298
```

### Download a File (Machine B)

```bash
sft download 763298
```

Output:
```
File downloaded my-file.tgz 5GB sha256:...
The file will be deleted in 55min (2025-01-01 00:01:00)
```

## Configuration

By default, SFT connects to `sft.pzjj.org:12345`. To use a different server, set the `SFT_SERVICE` environment variable:

```bash
export SFT_SERVICE=your-server.com:12345
```

Or use the `--service` flag:

```bash
sft upload --service your-server.com:12345 ./file.txt 1h
```

## Time Formats

Supported time formats for expiry:
- `1h` - 1 hour
- `30m` - 30 minutes
- `2d` - 2 days
- `1w` - 1 week

## Docker Deployment

```bash
docker build -t sft-server .
docker run -p 12345:12345 -v /data/sft:/data sft-server
```

## Features

- Simple CLI interface
- Automatic file expiry
- SHA256 checksums for integrity
- Minimal dependencies
- Easy deployment
For detailed deployment instructions, see [DEPLOYMENT.md](DEPLOYMENT.md).
datacanopy 0.0.2
Semantic versioning for ML datasets
License: MIT
Requires Python: >=3.10

# DataCanopy

**Semantic versioning for ML datasets - simpler than DVC**

⚠️ **Status: In Development** - Package name reserved, full release coming soon.

DataCanopy will provide:
- Content-addressed dataset catalogs
- One-line dataset loading for ML engineers
- Logical views over Delta/Parquet in S3
- Local-first development with optional S3 sync

Stay tuned for the full release.

## Installation
```bash
pip install datacanopy  # Coming soon
```

## Links

- GitHub: https://github.com/datacanopy/datacanopy
- Documentation: Coming soon
```

**`LICENSE`:**
```
MIT License

Copyright (c) 2025 Kevin Winkler

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
numscript 1.0.1
NumScript - A modern scripting language with advanced opcodes
License: MIT
Requires Python: <4.0,>=3.8
Keywords: language, interpreter, scripting, numscript, vm

# NumScript — Prototype

Prototype minimal de NumScript.

Fichiers clés:
- `specs/NUMSCRIPT.md` : spécification minimale.
- `src/numscript/interpreter.py` : interpréteur prototype.
- `examples/simple.ns` : exemple simple (2+3 et affichage).
- `run_numscript.py` : script d'exécution.

Exécuter l'exemple :

```bash
python run_numscript.py examples/simple.ns
```