semvec/pypi-packages huggingface dataset:
seedee 0.3.1
A (de)serialization library for multidimensional arrays
License: MIT
Requires Python: >=3.6


# seedee

A data format for n-dimensional arrays.

> This project is in an early stage of development and should be used with caution


## Installation

### Python

Pre-built Python wheels can be installed via `pip`:

```
python3 -m pip install seedee
```

To build a wheel yourself, e.g., if your platform is not supported by the
pre-built wheels, download the latest release and extract it. Inside the source
directory, run:

```
$ cmake -S . -B build -DBUILD_PYTHON=ON -DCMAKE_BUILD_TYPE=Release
$ cmake --build build --target python-wheel --verbose
```

Building requires `cmake` version  `3.13` or newer.

If everything works, the wheel is created in the `build/python/dist/`
subdirectory and can be installed via `pip`.

### C/C++

To build and install the C/C++ libraries, run:
```
$ cmake -S . -B build -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=path/to/install/dir
$ cmake --build build --target seedee --verbose
$ cmake --install build
```

Building requires `cmake` version  `3.13` or newer.


## Usage example

### Python

```python
import numpy as np
import seedee

# Create a numpy array of some dimension, shape, and dtype
array = np.arange(48).reshape((6, 8)).astype("uint16")

# Serialize the array using the bitshuffle+lz4 compression method
buffer, meta = seedee.serialize(array, seedee.COMPRESSION_BITSHUFFLE)
# buffer is a 1D numpy array of type uint8
# meta is a string

# Send buffer and meta to a remote process using any preferred method,
# e.g., via a ZMQ multi-message
...

# Deserialize the received buffer and meta into a numpy array
new_array = seedee.deserialize(buffer, meta)

# The resulting array is equal to the original array
assert np.all(array == new_array)
```

### C/C++

See `test` directory for some examples. Note that the C/C++ interface will
likely change in the future.

## Limitations

Only arrays up to 1GB are supported.

## Development

Use the following commands to compile `seedee` and run the tests:
```
cmake -S . -B build -DBUILD_PYTHON=ON -DCMAKE_BUILD_TYPE=RelWithDebInfo
cmake --build build --target all --verbose
ctest --test-dir build -T test --output-on-failure
```

## Contribution

Please feel free to open issues or pull requests.

## Acknowledgments

`seedee` depends on multiple open source libraries that are included in this
repository under the `thirdparty` directory for convenience. The licenses of
these dependencies can be found in the corresponding subdirectories and in
LICENSE_THIRDPARTY.txt.

The chunk serializer implementations are based on the corresponding HDF5 filter
plugins which are subject to their own licenses:
* LZ4 filter plugin: https://github.com/nexusformat/HDF5-External-Filter-Plugins/blob/master/LZ4/COPYING
* bitshuffle filter plugin: https://github.com/kiyo-masui/bitshuffle/blob/master/LICENSE
* zlib filter plugin: https://github.com/HDFGroup/hdf5/blob/develop/COPYING
entropy-labs 0.1.16
Entropy Labs' Sentinel is an agent control plane that enables efficient oversight of thousands of parallel-running agents.
License: MIT
Requires Python: >=3.7

# Entropy Labs Python Package


## Sentinel
Sentinel is an agent control plane developed by Entropy Labs that allows you to efficiently oversee thousands of agents running in parallel. This is the Python package for Sentinel, which implements various types of approvers that can be integrated into the AISI Inspect platform or used separately.

### Features

- Efficient oversight of parallel-running agents
- Various types of approvers including human, bash, python or LLM-powered approvers
- Integration with AISI's Inspect platform
- Standalone usage capability

### More Information

For more detailed information, documentation, and usage examples, please visit the main GitHub repository:

[https://github.com/EntropyLabsAI/sentinel](https://github.com/EntropyLabsAI/sentinel)