semvec/pypi-packages huggingface dataset:
benchmark-4dn 0.5.28
Benchmark functions that returns total space, mem, cpu given input size and parameters for the CWL workflows
License: MIT
Requires Python: <4.0,>=3.5
Keywords: benchmark, cwl, common workflow language, docker, tibanna, bioinformatics, 4dn

The repo contains a benchmarking script for some of the CWL workflows used by 4DN-DCIC (https://github.com/4dn-dcic/pipelines-cwl), that returns total space, mem and CPUs required per given input size and a recommended AWS EC2 instance type.

### Example usage of benchmarking script
* importing the module
```python
from Benchmark import run as B
```

* md5
```python
app_name = 'md5'
input_json = {'input_size_in_bytes': {'input_file': 20000}}
B.benchmark(app_name, input_json)
```
```
{'aws': {'recommended_instance_type': 't2.xlarge', 'EBS_optimized': False, 'cost_in_usd': 0.188, 'EBS_optimization_surcharge': None, 'mem_in_gb': 16.0, 'cpu': 4}, 'total_size_in_GB': 14.855186462402344, 'total_mem_in_MB': 13142.84375, 'min_CPU': 4}
```

* fastqc-0-11-4-1
```python
app_name = 'fastqc-0-11-4-1'
input_json = {'input_size_in_bytes': {'input_fastq':20000},
              'parameters': {'threads': 2}}
B.benchmark(app_name, input_json)
```
```
{'recommended_instance_type': 't2.nano', 'EBS_optimized': False, 'cost_in_usd': 0.006, 'EBS_optimization_surcharge': None, 'mem_in_gb': 0.5, 'cpu': 1}
```

* bwa-mem
```python
app_name = 'bwa-mem'
input_json = {'input_size_in_bytes': {'fastq1':93520000,
                                      'fastq2':97604000,
                                      'bwa_index':3364568000},
              'parameters': {'nThreads': 4}}
B.benchmark(app_name, input_json)
```
```
{'aws': {'cost_in_usd': 0.188, 'EBS_optimization_surcharge': None, 'EBS_optimized': False, 'cpu': 4, 'mem_in_gb': 16.0, 'recommended_instance_type': 't2.xlarge'}, 'total_mem_in_MB': 12834.808349609375, 'total_size_in_GB': 15.502477258443832, 'min_CPU': 4}
```

To use Benchmark in from other places, install it as below.
```
pip install Benchmark-4dn
```
or
```
pip install git+git://github.com/SooLee/Benchmark.git
```


---

Note: From `0.5.3` we have a new function that takes in cpu and memory and returns a sorted list of instance dictionaries.
```
get_instance_types(cpu=1, mem_in_gb=0.5, instances=instance_list(), top=10, rank='cost_in_usd')
```

Keys in each instance dictionary:
```
'cost_in_usd', 'mem_in_gb', 'cpu', 'instance_type', 'EBS_optimized', 'EBS_optimization_surcharge'
```
mythic-lite 0.1.1
A lightweight, local AI chatbot system with text-to-speech capabilities
License: MIT
Requires Python: >=3.8
Keywords: ai, chatbot, llm, tts, asr, local, privacy, offline

# Mythic-Lite

## 🪄 What is Mythic? (the non-boring version)

Mythic is my playground for weird AI ideas.  
Think: Roblox clowns, cursed TikTok experiments, and NPCs with emotional damage.  

Yes, it’s half-baked. But half-baked on purpose.  
Runs locally, talks back, and might roast you if you let it.  

Right now it’s roleplaying as a 19th-century mercenary who talks funny.

## 🚀 Features

- **Local AI Processing**: Run completely offline using local LLM models
- **Text-to-Speech**: Natural voice synthesis with customizable voices
- **Speech Recognition**: Lightweight offline ASR system using Vosk for voice input
- **Conversation Memory**: Intelligent conversation management with automatic summarization
- **Beautiful CLI Interface**: Modern, intuitive command-line interface with rich output
- **Modular Architecture**: Separate workers for LLM, TTS, ASR, and summarization tasks
- **Rich Logging**: Comprehensive logging with configurable output formats
- **Environment Configuration**: Flexible configuration via environment variables
- **Automated Setup**: One-click environment setup with virtual environment and dependencies

## 🏗️ Architecture

Mythic-Lite uses a modular architecture with specialized workers:

- **Chatbot Orchestrator**: Coordinates all components and manages conversation flow
- **LLM Worker**: Handles language model inference and text generation
- **TTS Worker**: Manages text-to-speech synthesis
- **ASR Worker**: Handles automatic speech recognition for voice input
- **Summarization Worker**: Handles conversation summarization for memory management
- **Conversation Worker**: Manages conversation state and memory

## 📋 Requirements

- Python 3.8+
- Windows 10/11, Linux (Ubuntu 18.04+), or macOS 10.15+
- At least 8GB RAM (16GB+ recommended)
- Sufficient storage for model files (~4-8GB)
- Audio input/output capabilities


## 📁 Project Structure

```
mythic-lite/
├── src/mythic_lite/          # Main package source code
│   ├── core/                 # Core components (orchestrator, config, etc.)
│   ├── workers/              # Specialized AI workers (LLM, TTS, ASR, etc.)
│   ├── utils/                # Utilities (CLI, logging, etc.)
│   └── scripts/              # Setup and utility scripts
├── tests/                    # Test suite
├── docs/                     # Documentation
├── examples/                 # Usage examples
├── scripts/                  # Installation and startup scripts
├── pyproject.toml           # Modern Python packaging configuration
├── requirements.txt          # Runtime dependencies
├── requirements-dev.txt      # Development dependencies
└── README.md                # This file
```