FrameWise is an advanced AI framework selection tool designed to help developers, researchers, and organizations identify the most optimal AI framework for their projects. Leveraging key evaluation metrics such as throughput, latency, scalability, security, ease of use, model support, and cost efficiency, FrameWise ensures a data-driven and structured approach to decision-making for your AI initiatives.
FrameWise stands out by providing a comprehensive solution for AI framework selection, ensuring you make an informed decision that aligns with your technical and business goals. Whether you're working on machine learning models, NLP applications, or deep learning frameworks, FrameWise has you covered.
The primary goal of FrameWise is to simplify the AI framework selection process by providing:
- Data-driven recommendations tailored to your project needs.
- A structured evaluation of popular frameworks like SGLang, NVIDIA NIM, vLLM, Mistral.rs, and FastChat.
- An intuitive interface for customizing your framework evaluation.
- In-Depth Use Case Analysis: Tailor recommendations based on your specific project requirements.
- Comprehensive Framework Comparison: Evaluate and compare top AI frameworks.
- Criteria-Based Selection: Optimize selection using metrics like throughput, latency, scalability, security, and more.
- Customizable Input: Add and evaluate unique use cases not included in the default list.
- User-Friendly Interface: Powered by Streamlit for an intuitive and seamless user experience.
| Name | Quick Description | When to Use/Best Use Case | Link to Reference Docs |
|---|---|---|---|
| vLLM | High-throughput, low-latency LLM inference with memory optimizations | Fast and memory-efficient local LLM inference | vLLM Docs |
| FastChat | Multi-model chat interface and inference server | Chat applications or multi-model APIs | FastChat Docs |
| Mistral.rs | Rust-based lightweight inference for Mistral models | Lightweight, high-performance Rust-based deployments | Mistral.rs Docs |
| Ollama | Local model inference for macOS | Mac-based LLM inference with an intuitive interface | Ollama Docs |
| SGLang | Scalable and optimized LLM inference library | Large-scale, optimized inference for custom workflows | SGLang Docs |
| Transformers/Pipeline | Hugging Face pipeline API for LLM inference | Easy-to-use, quick implementation of pre-trained models | Transformers Docs |
| Transformers/Tokenizer | Tokenization utilities for Hugging Face models | Preprocessing inputs for efficient model usage | Tokenizer Docs |
| llama.cpp | CPU-based optimized inference for LLaMA models | Low-resource environments without GPU acceleration | llama.cpp Docs |
| ONNX Runtime | Cross-platform optimized inference runtime for ONNX models | Deploying ONNX models in production | ONNX Runtime Docs |
| PyTorch | Inference framework with TorchScript and C++ runtime | Custom PyTorch model deployment in production | PyTorch Docs |
| TensorFlow Serving | High-performance serving system for TensorFlow models | TensorFlow models in production | TensorFlow Serving Docs |
| DeepSpeed-Inference | Optimized inference for large models | Ultra-large model inference with low latency | DeepSpeed Docs |
| NVIDIA Triton | Multi-framework inference server | Scalable deployments of diverse models | Triton Docs |
| NVIDIA TensorRT | Optimized GPU inference runtime | GPU-accelerated inference | TensorRT Docs |
| NVIDIA Inference Microservice (NIM) | Lightweight microservice for NVIDIA-based model inference | Scalable NVIDIA-based cloud deployments | NIM Docs |
| OpenVINO | Intel-optimized inference toolkit | Optimized execution on Intel hardware | OpenVINO Docs |
| DJL (Deep Java Library) | Java-based inference framework | Java-based applications requiring inference support | DJL Docs |
| Ray Serve | Distributed inference and serving system | Deploying distributed models at scale | Ray Serve Docs |
| KServe | Kubernetes-native model inference server | Deploying on Kubernetes with scaling needs | KServe Docs |
| TorchServe | PyTorch model serving for scalable inference | PyTorch-based scalable deployments | TorchServe Docs |
| Hugging Face Inference API | Cloud-based inference API | Using Hugging Face-hosted models for inference | Hugging Face API Docs |
| AWS SageMaker | Managed cloud service for model deployment | Fully managed cloud-based ML model inference | SageMaker Docs |
| Google Vertex AI | Unified platform for model deployment | Enterprise-grade ML model serving | Vertex AI Docs |
| Apache TVM | Model compilation for efficient inference | Optimizing models for hardware-agnostic inference | Apache TVM Docs |
| TinyML | Framework for low-power ML inference | Ultra-low power edge-based applications | TinyML Docs |
| LiteRT | Google's high-performance runtime for on-device AI, formerly TensorFlow Lite | On-device AI inference with minimal latency | LiteRT Docs |
| DeepSparse | Inference runtime specializing in sparse models | Accelerating sparse models for efficient inference | DeepSparse Docs |
| ONNX.js | JavaScript library for running ONNX models in browsers | Browser-based AI inference | ONNX.js Docs |
| TFLite | TensorFlow's lightweight solution for mobile and embedded devices | Deploying TensorFlow models on mobile and edge devices | TFLite Docs |
| Core ML | Apple's framework for integrating machine learning models into apps | iOS and macOS app development with ML capabilities | Core ML Docs |
| SNPE (Snapdragon Neural Processing Engine) | Qualcomm's AI inference engine for mobile devices | AI acceleration on Snapdragon-powered devices | SNPE Docs |
| MACE (Mobile AI Compute Engine) | Deep learning inference framework optimized for mobile platforms | Deploying AI models on Android, iOS, Linux, and Windows devices | MACE Docs |
| NCNN | High-performance neural network inference framework optimized for mobile platforms | Deploying AI models on mobile devices | NCNN Docs |
| LiteML | Lightweight, mobile-focused AI inference library | On-device ML for lightweight applications | LiteML Docs |
| Banana | Serverless GPU-based inference deployment | Fast and cost-effective LLM or vision model inference | Banana Docs |
| Gradient Inference | Managed inference service from Paperspace | Cloud-based model inference for scalable AI solutions | Gradient Docs |
| H2O AI Cloud | Open-source platform for ML and AI deployment | Building, deploying, and managing enterprise AI | H2O AI Cloud Docs |
| Inferentia | AWS hardware-optimized inference accelerator | High-performance inference with reduced cost | Inferentia Docs |
| RunPod | Scalable GPU cloud for AI inference | Affordable, high-performance GPU-based inference environments | RunPod Docs |
| Deci AI | Platform for optimizing and deploying deep learning models | Optimizing models for cost-efficient deployment | Deci AI Docs |
| RedisAI | AI Serving over Redis | Real-time AI inference with Redis integration | RedisAI Docs |
| MLflow | Open-source platform for managing ML lifecycles | Experiment tracking, model registry, and inference deployment | MLflow Docs |
| ONNX Runtime Web | ONNX inference runtime for browsers | Browser-based inference for ONNX models | ONNX Runtime Web Docs |
| Raspberry Pi Compute | On-device AI inference for Raspberry Pi | Deploying lightweight AI models on edge devices | Raspberry Pi AI Docs |
| Colossal-AI | Unified system for distributed training and inference | Large-scale distributed model training and inference | Colossal-AI Docs |
| Azure Machine Learning Endpoint | Scalable inference with Azure cloud | Cloud-based enterprise-grade inference | Azure ML Docs |
| BigDL | Distributed deep learning and inference library | Accelerating distributed inference on Apache Spark | BigDL Docs |
| Amazon SageMaker Neo | Optimize models for inference on multiple platforms | Cost and latency optimization for multi-platform AI deployment | Neo Docs |
| Hugging Face Text Generation Inference | Optimized inference server for text generation models | Scaling text generation workloads | HF Text Gen Inference Docs |
| Deploy.ai | Simple inference deployment service | Fast model deployment without managing infrastructure | Deploy.ai Docs |
| Snorkel Flow | Data-centric AI platform with deployment capabilities | Building and deploying high-quality AI solutions | Snorkel Flow Docs |
| Azure Functions for ML | Serverless ML inference on Microsoft Azure | On-demand, event-driven model inference | Azure Functions Docs |
| AWS Lambda for ML | Serverless inference with AWS Lambda | Event-driven AI model inference | AWS Lambda Docs |
| Dask-ML | Scalable machine learning and inference with Dask | Parallel and distributed inference for large datasets | Dask-ML Docs |
Get started with FrameWise in just a few simple steps:
- Python 3.11 or higher
- Git (optional, for cloning the repository)
git clone https://github.com/KingLeoJr/FrameWise.git
cd FrameWisepython -m venv venvActivate the virtual environment:
- On Windows:
venv\Scripts\activate
- On macOS/Linux:
source venv/bin/activate
pip install -r requirements.txtCreate a .env file in the project root directory and add your API key:
API_KEY=your_api_key_here
Launch the Streamlit app:
streamlit run app.pyNavigate to http://localhost:8501 in your browser.
- Select a Use Case: Choose from predefined use cases or enter your own.
- Submit: Click "Submit" to analyze and compare frameworks.
- View Results: See recommendations and a breakdown of evaluation criteria.
We welcome contributions to FrameWise! Here’s how you can get involved:
- Fork the repository.
- Create a new branch:
git checkout -b feature/YourFeatureName
- Commit your changes:
git commit -m 'Add your feature' - Push your changes:
git push origin feature/YourFeatureName
- Open a pull request.
FrameWise is licensed under the MIT License. See the LICENSE file for details.
- Thanks to the Streamlit team for their incredible framework.
- Gratitude to the open-source community for their invaluable contributions.
FrameWise is a tool that helps you select the most suitable AI framework for your project by evaluating frameworks based on key metrics.
FrameWise supports popular AI frameworks like SGLang, NVIDIA NIM, vLLM, Mistral.rs, and FastChat.
FrameWise evaluates frameworks using metrics such as throughput, latency, scalability, security, ease of use, model support, and cost efficiency.
Yes! FrameWise allows you to input and evaluate custom use cases.
Follow the Installation steps above to set up FrameWise on your machine.
Check out the Contributing section to learn how you can contribute to the project.
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FrameWise is your one-stop solution for finding the perfect AI framework for your next project. Get started today and streamline your AI development process!
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