semvec/pypi-packages huggingface dataset:
nvpl-tensor 0.3.2.1
NVPL Tensor
Keywords: nvidia, nvpl, hpc, tensor

# NVPL Tensor
The NVPL Tensor library is a component of the NVIDIA Performance Libraries (NVPL), a collection of high performance mathematical libraries optimized for the NVIDIA Grace ARMv9.0-A architecture and future NVIDIA aarch64 CPUs.

These CPU-only libraries have no dependency on CUDA, and are drop in replacements for standard C and Fortran mathematical APIs allowing HPC applications to achieve maximum performance on NVIDIA CPU platforms.

- [NVPL Homepage](https://developer.nvidia.com/nvpl/)
- [NVPL Documentation](https://docs.nvidia.com/nvpl/)
- [NVPL Downloads](https://developer.nvidia.com/nvpl-downloads/)
nvpl-sparse 0.5.0
NVPL Sparse
Keywords: nvidia, nvpl, hpc, sparse, sparse matrix, linear algebra

# NVPL Sparse
The NVPL Sparse library is a component of the NVIDIA Performance Libraries (NVPL), a collection of high performance mathematical libraries optimized for the NVIDIA Grace ARMv9.0-A architecture and future NVIDIA aarch64 CPUs.

These CPU-only libraries have no dependency on CUDA, and are drop in replacements for standard C and Fortran mathematical APIs allowing HPC applications to achieve maximum performance on NVIDIA CPU platforms.

- [NVPL Homepage](https://developer.nvidia.com/nvpl/)
- [NVPL Documentation](https://docs.nvidia.com/nvpl/)
- [NVPL Downloads](https://developer.nvidia.com/nvpl-downloads/)
nvpl 25.11
NVPL Libraries
Keywords: nvidia, nvpl, hpc, math libraries

# NVPL

The NVIDIA Performance Libraries (NVPL) are a collection of high performance mathematical libraries optimized for the NVIDIA Grace ARMv9.0-A architecture and future NVIDIA aarch64 CPUs.

These CPU-only libraries have no dependency on CUDA, and are drop in replacements for standard C and Fortran mathematical APIs allowing HPC applications to achieve maximum performance on NVIDIA CPU platforms.

The `nvpl` package is a meta-package which provides all NVPL libraries from a release. NVPL component library packages may also be installed individually without the meta-package.

- [NVPL Homepage](https://developer.nvidia.com/nvpl/)
- [NVPL Documentation](https://docs.nvidia.com/nvpl/)
- [NVPL Downloads](https://developer.nvidia.com/nvpl-downloads/)
BoxUtils 0.1.11
A utility package for performing Box file and folder operations with a service account

# BoxUtils

A class using the box_sdk_gen python SDK for doing basic file and folder operations.

## Configuration Info Locations



Key information is stored in the developer console for the selected app in Platform Apps.

The User ID and Enterprise ID is found on the General. Settings/App Info section.

The service account which folders/files should be shared with is located on the General Settings/Service Account Info section.

The app configuration json file can be downloaded under the Configuration/App Settings section.

Two files are required for initialization of the class

1. box.env - contains information about  the JWT user id and the enterprise id

  ```
  # JWT Settings
  JWT_USER_ID = 397515XXXX
  ENTERPRISE_ID = 686XXX
  ```

2. box.config.json - contains the JWT credentials. This is downloaded from the box dev console.

  ```
  # Sample app configuration file
  {
    "boxAppSettings": {
      "clientID": "1u3gto5in5gff7ve8031tx8x6kl8xxxx",
      "clientSecret": "********************************",
      "appAuth": {
        "publicKeyID": "",
        "privateKey": "",
        "passphrase": ""
      }
    },
    "enterpriseID": "686XXX"
  }
  ```

## Sample usage

You can find the full sample usage script in the [sample_usage.py](sample_usage.py) file.

Note you may need to customize the folder ids for 
your account.

To run the sample usage script, use the following command:

```bash
source .venv/bin/activate
python BoxUtils/sample_usage.py
```

## For package creation and publishing

Be sure to update the setup.py with version information.

```bash
# create the dist
python -m build

# upload the package
# credentials for pypi.org are kept in ~/.pypirc
twine upload dist/*

```
paya 0.1.4
A minimum rest client based on deboa http client for rust
Requires Python: >=3.8
Keywords: rest, client, http

# paya

A minimum rest client based on deboa http client for rust

## Installation

```bash
pip install paya
```

## Usage

```python
import asyncio
import json
import paya
from paya import send


async def main():
    req = paya.Paya("https://jsonplaceholder.typicode.com").get("/todos/1")
    res = await send(req)
    data = json.loads(res.body())
    print(data)

    req = (
        req.post("/posts")
        .header("Content-Type", "application/json")
        .body(
            json.dumps(
                {"title": "Some title", "body": "Some body", "userId": 1}
            ).encode("utf-8")
        )
    )
    res = await send(req)
    data = json.loads(res.body())
    print(data)

    req = req.delete("/posts/1")
    res = await send(req)
    data = json.loads(res.body())
    print(data)

asyncio.run(main())
```

## License

MIT


## Author

Rogerio Pereira Araujo <rogerio.araujo@gmail.com>
nvpl-lapack 0.3.2
NVPL LAPACK
Keywords: nvidia, nvpl, hpc, LAPACK, linear algebra

# NVPL LAPACK
The NVPL LAPACK library is a component of the NVIDIA Performance Libraries (NVPL), a collection of high performance mathematical libraries optimized for the NVIDIA Grace ARMv9.0-A architecture and future NVIDIA aarch64 CPUs.

These CPU-only libraries have no dependency on CUDA, and are drop in replacements for standard C and Fortran mathematical APIs allowing HPC applications to achieve maximum performance on NVIDIA CPU platforms.

- [NVPL Homepage](https://developer.nvidia.com/nvpl/)
- [NVPL Documentation](https://docs.nvidia.com/nvpl/)
- [NVPL Downloads](https://developer.nvidia.com/nvpl-downloads/)