semvec/pypi-packages huggingface dataset:
ipfs-accelerate-py 0.0.42
a python wrapper around nodejs orbitdb
Requires Python: >=3.8

# IPFS ACCELERATE

This is meant to be an extension of the Huggingface accelerate library, is to act as a model server, which can contain lists of other endpoints to call, or can call a local instance, and can respond to external calls for inference. I have some modular back ends, such as Libp2p, Akash, Lilypad, Huggingface Zero, Vast AI, which I use for autoscaling. If the model is already listed in the ipfs_model_manager there should be an associated hw_requirements key of the manifest. In the case of libp2p the request to do inference will go out to peers in the same trusted zone, if there are no peers in the network available to fulfill the task, and it has the resources to run the model localliy it will do so, otherwise a docker container will need to be launched with one of the providers here. 

# Instructions:

run.sh -- this will run the model server

init.sh -- this will initialize the endpoints and models that you want to use

queue.sh -- this will add an item to the model queue, and the endpoints will pull items from their model queue.

infer.sh -- this will select a random endpoint for the requested model to perform inference.

# Usage

```
from ipfs_accelerate_py import ipfs_accelerate_py
model_server = ipfs_accelerate_py()
init_model_server = model_server.init_endpoints(models, endpoints)
endpoints = model_server.resources["endpoint_handler"][model[0]]
for endpoint in list(endpoints.keys()):
    endpoint("hello world")
```

# BACKENDS
You can spin up additional model endpoints with the following:

# Method #1 Huggingface Hugs

# Method #2 Akash

# Method #3 Lilypad

# Method #4 Vast AI

# IPFS Huggingface Bridge:

for huggingface transformers python library visit:
https://github.com/endomorphosis/ipfs_transformers/

for huggingface datasets python library visit:
https://github.com/endomorphosis/ipfs_datasets/

for faiss KNN index python library visit:
https://github.com/endomorphosis/ipfs_faiss

for transformers.js visit:                          
https://github.com/endomorphosis/ipfs_transformers_js

for orbitdb_kit nodejs library visit:
https://github.com/endomorphosis/orbitdb_kit/

for fireproof_kit nodejs library visit:
https://github.com/endomorphosis/fireproof_kit

for ipfs_kit nodejs library visit:
https://github.com/endomorphosis/ipfs_kit/

for python model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager/

for nodejs model manager library visit: 
https://github.com/endomorphosis/ipfs_model_manager_js/

for nodejs ipfs huggingface scraper with pinning services visit:
https://github.com/endomorphosis/ipfs_huggingface_scraper/

for ipfs agents visit:
https://github.com/endomorphosis/ipfs_agents/

for ipfs accelerate visit:
https://github.com/endomorphosis/ipfs_accelerate/

Author - Benjamin Barber
QA - Kevin De Haan
wsp-matrix-butler 2.0.0
A SQLite-based mini-file system for organizing binary files for the Greater Golden Horseshoe Model
Requires Python: >=3.7

# Matrix Butler (wsp-matrix-butler)

[![Conda Latest Release](https://anaconda.org/wsp_sap/wsp-matrix-butler/badges/version.svg)](https://anaconda.org/wsp_sap/wsp-matrix-butler)
[![Conda Last Updated](https://anaconda.org/wsp_sap/wsp-matrix-butler/badges/latest_release_date.svg)](https://anaconda.org/wsp_sap/wsp-matrix-butler)
[![Platforms](https://anaconda.org/wsp_sap/wsp-matrix-butler/badges/platforms.svg)](https://anaconda.org/wsp_sap/wsp-matrix-butler)
[![License](https://anaconda.org/wsp_sap/wsp-matrix-butler/badges/license.svg)](https://github.com/wsp-sag/wsp-matrix-butler/blob/master/LICENSE)

A SQLite-based mini-file system for organizing matrix files for MTO's Greater Golden Horseshoe Model

## Installation

Matrix Butler can be installed with conda by running:

```batch
conda install -c wsp_sap wsp-matrix-butler
```

## Usage

> [!IMPORTANT]
> As of v2.0, this package is imported using `wsp_matrix_butler` instead of `matrix_butler`

```python
from wsp_matrix_butler import MatrixButler

# Connect to an existing GGHM cache folder
butler = MatrixButler.connect("path/to/cache")

# List the contents (matrices) available in the MatrixButler instance
butler.list_matrices()
```

## Development

Development of the MatrixButler uses [pixi](https://pixi.sh/) for Python package management and [rattler-build](https://rattler.build/) for package building.