semvec/pypi-packages huggingface dataset:
python-onix 0.0.3
Dataclasses for EDItEUR's ONIX standards
Requires Python: <4.0,>=3.10

# python-onix

A very simple module to provide dataclasses for EDItEUR's ONIX standards. Mostly generated using [xsdata](https://github.com/tefra/xsdata).

This module isn't even in the Alpha stage yet, and there may be breaking changes.

Currently, only ONIX for Books is covered; but I may add ONIX for Subscription Products in the near future.

## Installation
Install using pip:
~~~bash
pip install python-onix
~~~

## Getting Started
#### Parse an ONIX file
~~~python
from onix.book.v3_0.short.strict import Onixmessage
from xsdata.formats.dataclass.parsers import XmlParser

parser = XmlParser()
message = parser.parse("/some/path/file.onx", Onixmessage)
~~~
teserak-regorus 0.1.5
Regorus copy - for easy install
Requires Python: >=3.8
Keywords: interpreter, opa, policy-as-code, rego

# regorus

**Regorus** is

  - *Rego*-*Rus(t)*  - A fast, light-weight [Rego](https://www.openpolicyagent.org/docs/latest/policy-language/)
   interpreter written in Rust.
  - *Rigorous* - A rigorous enforcer of well-defined Rego semantics.

Regorus can be used in Python via `regorus` package. (It is not yet available in PyPI, but can be manually built.)

See [Repository](https://github.com/microsoft/regorus).

To build this binding, see [building](https://github.com/microsoft/regorus/blob/main/bindings/python/building.md)

## Usage
```Python
import regorus

# Create engine
engine = regorus.Engine()

# Load policies
engine.add_policy_from_file('../../tests/aci/framework.rego')
engine.add_policy_from_file('../../tests/aci/api.rego')
engine.add_policy_from_file('../../tests/aci/policy.rego')

# Add policy data
data = {
  "metadata": {
    "devices": {
      "/run/layers/p0-layer0": "1b80f120dbd88e4355d6241b519c3e25290215c469516b49dece9cf07175a766",
      "/run/layers/p0-layer1": "e769d7487cc314d3ee748a4440805317c19262c7acd2fdbdb0d47d2e4613a15c",
      "/run/layers/p0-layer2": "eb36921e1f82af46dfe248ef8f1b3afb6a5230a64181d960d10237a08cd73c79",
      "/run/layers/p0-layer3": "41d64cdeb347bf236b4c13b7403b633ff11f1cf94dbc7cf881a44d6da88c5156",
      "/run/layers/p0-layer4": "4dedae42847c704da891a28c25d32201a1ae440bce2aecccfa8e6f03b97a6a6c",
      "/run/layers/p0-layer5": "fe84c9d5bfddd07a2624d00333cf13c1a9c941f3a261f13ead44fc6a93bc0e7a"
    }
  }
}
engine.add_data(data)

# Set input
input = {
  "containerID": "container0",
  "layerPaths": [
    "/run/layers/p0-layer0",
    "/run/layers/p0-layer1",
    "/run/layers/p0-layer2",
    "/run/layers/p0-layer3",
    "/run/layers/p0-layer4",
    "/run/layers/p0-layer5"
  ],
  "target": "/run/gcs/c/container0/rootfs"
}
engine.set_input(input)

# Eval rule
value = engine.eval_rule('data.framework.mount_overlay')

# Print value
print(value)

```
checkptr 0.1.3
Simple standard for model checkpointing, framework-agnostic
Requires Python: >=3.10

# Checkptr

> Simple, framework-agnostic standard for model checkpointing

```bash
pip install checkptr
```

```python
from checkptr import Checkpointer

chkptr = Checkpointer.pytorch('path/to/checkpoints')
# or Checkpointer('path', save_fn=torch.save, load_fn=torch.load)

chkptr.checkpoint(model, f'model_{epoch}.pth')
model = checkptr.load(f'model_{epoch}.pth')


print('Checkpoints:', chkptr.checkpoints()) # ['model_0.pth', 'model_1.pth', ...]
```

Creates:

```
checkpoints/
  model_0.pth
  model_1.pth
  ...
```


And that's it! Simple.