semvec/pypi-packages huggingface dataset:
sqlite-shelve 2.2.1
A SQLite implementation of the Python Shelf interface
Requires Python: >=3.6

sqlite-shelve
=============

* Implements a shelve-like store in SQLite
* Applications incorporating this module can utilize the interfaces of SQLiteShelve class just like they do the Shelve class
* In most ordinary use cases, applications that use the builtin shelve module can switch to SQLiteShelve with the followng:

```
import sqliteshelve as shelve

d = shelve.open(filename) # opens existing SQLite3 database if it exists. Creates a new one if it does not 

d[key] = data   # store data at key (overwrites old data if
                # using an existing key)
data = d[key]   # retrieve a COPY of data at key (raise KeyError if no
                # such key)
del d[key]      # delete data stored at key (raises KeyError
                # if no such key)
flag = d.has_key(key)   # true if the key exists
klist = d.keys() # a list of all existing keys

d.close() #commits changes to Sqlite3 shelve store

```


* data, like in dbm, or BerkeleyDB-based storage libraries, is pickled with cpickle, but pickles with HIGHEST_PROTOCOL. 

contains a script called shelve-tool, which is both a useful utility and provides an example for how sqlite-shelve can be used like the shelve module
monotonic-attention 0.0.4
Monotonic attention implementation
Requires Python: >=3.10

# monotonic-attention

Monotonic attention as a probabilistic graphical model

[Write-up explaining how this works](https://ben.bolte.cc/monotonic-attention)

Check out the `examples/` directory for more information.

## Getting Started

Install from PyPi:

```bash
pip install monotonic-attention
```

Install from source:

```bash
pip install git+https://github.com/codekansas/monotonic-attention.git
```

You should also install Triton if you plan to use the GPU kernels (highly recommended):

```bash
pip install triton
```

## Usage

```python
from monotonic_attention import OneToManyMultiheadMonotonicAttention

# Many keys mapped to a single query.
attn = OneToManyMultiheadMonotonicAttention(
  mode="many_keys_one_query",
  embed_dim=1024,
  num_heads=16,
)

output = attn(query, key, value)

# Many queries mapped to a single key.
attn = OneToManyMultiheadMonotonicAttention(
  mode="many_queries_one_key",
  embed_dim=1024,
  num_heads=16,
)

output = attn(query, key, value)
```
ml-starter 0.1.91
ML project template repository
Requires Python: >=3.10

<div align="center">

# ml-starter

[![python](https://img.shields.io/badge/-Python_3.11-blue?logo=python&logoColor=white)](https://github.com/pre-commit/pre-commit)
[![pytorch](https://img.shields.io/badge/PyTorch_2.0+-ee4c2c?logo=pytorch&logoColor=white)](https://pytorch.org/get-started/locally/)
[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)
[![ruff](https://img.shields.io/badge/Linter-Ruff-red.svg?labelColor=gray)](https://github.com/charliermarsh/ruff)
[![license](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](https://github.com/codekansas/ml-starter/blob/master/LICENSE)

[<kbd>Click Here</kbd>](https://github.com/codekansas/ml-project-template/generate) to initialize a new repository from [this template](https://github.com/codekansas/ml-project-template).

</div>

<br />

## What is this?

This is a framework for quickly trying out ML ideas, similar to PyTorch Lightning or Fairseq. Check out the docs for more. 😊