semvec/pypi-packages huggingface dataset:
mkl-random 1.3.1
NumPy-based Python interface to Intel (R) MKL Random Number Generation functionality
Requires Python: <3.15,>=3.9
Keywords: MKL, VSL, true randomness, pseudorandomness, Philox, MT-19937, SFMT-19937, MT-2203, ARS-5, R-250, MCG-31

## `mkl_random` -- a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (OneMKL) Random Number Generation functionality
[![Conda package using conda-forge](https://github.com/IntelPython/mkl_random/actions/workflows/conda-package-cf.yml/badge.svg)](https://github.com/IntelPython/mkl_random/actions/workflows/conda-package-cf.yml)

`mkl_random` started as a part of Intel® Distribution for Python optimizations to NumPy.

Per NumPy's community suggestions, voiced in https://github.com/numpy/numpy/pull/8209, it is being released as a
stand-alone package.

Prebuilt `mkl_random` can be installed into conda environment from Intel's channel using:

```
  conda install -c https://software.repos.intel.com/python/conda mkl_random
```

or from conda forge channel:

```
   conda install -c conda-forge mkl_random
```

---

To install `mkl_random` PyPI package please use following command:

```
   python -m pip install -i https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_random
```

If command above installs NumPy package from the Pypi, please use following command to install Intel optimized NumPy wheel package from Intel Pypi Cloud:

```
   python -m pip install -i https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_random numpy==<numpy_version>
```

Where `<numpy_version>` should be the latest version from https://software.repos.intel.com/python/conda/

---

`mkl_random` is not fixed-seed backward compatible drop-in replacement for `numpy.random`, meaning that it implements sampling from the same distributions as `numpy.random`.

For distributions directly supported in Intel® OneMKL, `method` keyword is supported:

```
   mkl_random.standard_normal(size=(10**5, 10**3), method='BoxMuller')
```

Additionally, `mkl_random` exposes different basic random number generation algorithms available in MKL. For example to use `SFMT19937` use

```
   mkl_random.RandomState(77777, brng='SFMT19937')
```

For generator families, such that `MT2203` and Wichmann-Hill, a particular member of the family can be chosen by specifying ``brng=('WH', 3)``, etc.

The list of supported by `mkl_random.RandomState` constructor `brng` keywords is as follows:

  * 'MT19937'
  * 'SFMT19937'
  * 'WH' or ('WH', id)
  * 'MT2203' or ('MT2203', id)
  * 'MCG31'
  * 'R250'
  * 'MRG32K3A'
  * 'MCG59'
  * 'PHILOX4X32X10'
  * 'NONDETERM'
  * 'ARS5'

---

To build `mkl_random` from sources on Linux:
  - install a recent version of MKL, if necessary;
  - execute `source /path_to_oneapi/mkl/latest/env/vars.sh`;
  - execute `python -m pip install .`
finetune-lab 0.6.0
FineTune Lab SDK - Training, inference, batch testing, and analytics for LLMs
Requires Python: >=3.8

# FineTune Lab SDK

Python SDK for FineTune Lab - Training, inference, batch testing, and analytics for LLMs.

## Installation

```bash
# API client only (lightweight)
pip install finetune-lab

# With training dependencies (requires GPU)
pip install finetune-lab[training]
```

## Quick Start - API Client

```python
from finetune_lab import FinetuneLabClient

client = FinetuneLabClient(api_key="wak_your_api_key_here")
# Or set FINETUNE_LAB_API_KEY environment variable

# Inference
response = client.predict(
    model="gpt-4",
    messages=[{"role": "user", "content": "Hello!"}]
)
print(response.content)

# Batch Testing
test = client.batch_test.run(
    model_id="gpt-4",
    test_suite_id="suite_abc123"
)
print(f"Test started: {test.test_id}")

# Check status
status = client.batch_test.status(test.test_id)
print(f"Progress: {status.completed}/{status.total_prompts}")
```

## Training (Requires GPU)

### Supervised Fine-Tuning (SFT)

```python
from finetune_lab import train_sft

# Just paste your config ID
train_sft("train_abc123")
```

### Direct Preference Optimization (DPO)

```python
from finetune_lab import train_dpo

train_dpo("train_xyz456")
```

### RLHF Training

```python
from finetune_lab import train_rlhf

train_rlhf("train_def789")
```

## How It Works

1. Upload your dataset in FineTune Lab
2. Configure training parameters (or use templates)
3. Click "Generate Training Package"
4. Get your config ID (e.g., `train_abc123`)
5. Paste the 2-line snippet in HF Spaces/Colab/Kaggle
6. Training starts automatically!

## Features

- Automatic config and dataset loading from public API
- Pre-built training scripts for SFT, DPO, and RLHF
- Support for ChatML and ShareGPT dataset formats
- LoRA-enabled parameter-efficient fine-tuning
- Compatible with HuggingFace Transformers ecosystem

## API Key Scopes

Your API key must have the appropriate scope for each operation:

| Operation | Required Scope |
|-----------|---------------|
| `predict()` | `production` or `all` |
| `batch_test.*` | `testing` or `all` |
| `analytics.*` | `production` or `all` |

## Requirements

**API Client only (lightweight):**
- Python 3.8+
- requests

**Training (full dependencies):**
- Python 3.8+
- PyTorch 2.0+
- HuggingFace Transformers
- CUDA-capable GPU (recommended)

## License

MIT License
crossbar 25.12.1
Crossbar.io multi-protocol (WAMP/WebSocket, REST/HTTP, MQTT) application router for microservices.
Requires Python: >=3.11
Keywords: autobahn, autobahn.ws, crossbar, oracle, postgres, postgresql, pubsub, realtime, rfc6455, router, rpc, wamp, websocket

# Crossbar.io

[![PyPI](https://img.shields.io/pypi/v/crossbar.svg)](https://pypi.python.org/pypi/crossbar)
[![Python](https://img.shields.io/pypi/pyversions/crossbar.svg)](https://pypi.python.org/pypi/crossbar)
[![CI](https://github.com/crossbario/crossbar/workflows/main/badge.svg)](https://github.com/crossbario/crossbar/actions?query=workflow%3Amain)
[![Docs](https://readthedocs.org/projects/crossbar/badge/?version=latest)](https://crossbar.readthedocs.io/en/latest/)
[![License](https://img.shields.io/pypi/l/crossbar.svg)](https://github.com/crossbario/crossbar/blob/master/LICENSE)
[![Downloads](https://img.shields.io/pypi/dm/crossbar.svg)](https://pypi.python.org/pypi/crossbar)

---

*Decentralized Middleware*

[Crossbar.io](https://crossbar.io) is an open source networking platform
for distributed and microservice applications. It implements the open
Web Application Messaging Protocol (WAMP), is feature rich, scalable,
robust and secure. Let Crossbar.io take care of the hard parts of
messaging so you can focus on your app\'s features.

## Resources

-   Download from [PyPI](https://pypi.org/project/crossbar/)
-   Read more on the [Project Homepage](https://crossbar.io)
-   Jump into the [Getting
    Started](https://crossbar.io/docs/Getting-Started/)
-   Read the reference [Documentation](https://crossbar.io/docs/)
-   Join the [User forum](https://crossbar.discourse.group/)
-   Ask a question on
    [StackOverflow](https://stackoverflow.com/questions/ask?tags=crossbar,wamp)
-   Read our [Legal
    Notes](https://github.com/crossbario/crossbar/blob/master/legal/README.md)

## Docker images

-   [amd64](https://hub.docker.com/r/crossbario/crossbar)
-   [armv7](https://hub.docker.com/r/crossbario/crossbar-armhf)
-   [armv8](https://hub.docker.com/r/crossbario/crossbar-aarch64)

## JSON Schema for Crossbar.io Configuration File Format

We now have a JSON Schema file available for **config.json**, if you\'re
using VSCode you can make use of this by adding the following to your
VSCode settings; (File -\> Preferences -\> Settings)

``` json
"json.schemas": [
    {
        "fileMatch": [
            "/config.json",
            "/.config.json"
        ],
        "url": "https://raw.githubusercontent.com/crossbario/crossbar/master/crossbar.json"
    }
],
```

Alternatively, the generic approach is to insert a \"\$schema\" line at
the top of your file;

``` json
{
    "$schema": "https://raw.githubusercontent.com/crossbario/crossbar/master/crossbar.json",
    "version": 2,
```

This file is currently experimental, but it should give you contextual
auto-completion on all Crossbar **config.json** syntax, use CTRL+Space
in VSCode to activate IntelliSense.

------------------------------------------------------------------------

*Copyright (C) 2013-2021 typedef int GmbH. All rights reserved. WAMP,
Crossbar.io and XBR are trademarks of typedef int GmbH.*