budgetml
pinferencia
Our great sponsors
| budgetml | pinferencia | |
|---|---|---|
| 4 | 21 | |
| 1,332 | 556 | |
| 0.2% | 0.0% | |
| 0.0 | 0.0 | |
| 2 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
budgetml
pinferencia
- Show HN: Pinferencia, Deploy Your AI Models with Pretty UI and REST API
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Stop Writing Flask to Serve/Deploy Your Model: Pinferencia is Here
Go visit: Pinferencia (underneathall.app) for detailed examples.
- Looking for a reference design pattern for an image to image microservice
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Google T5 Translation as a Service with Just 7 lines of Codes
**Pinferencia** makes it super easy to serve any model with just three extra lines.
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Pre-trained Model with Fine Tuning/Transfer Learning or Design and Train from Scratch?
Hi, recently I'm writing some tutorials involving HuggingFace's models for my project Pinferencia.
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[D] Pre-trained Model with Fine Tuning/Transfer Learning or Design and Train from Scratch?
Hi, I'm the creator of Pinferencia, recently I'm writer some tutorial involving HuggingFace's models.
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GPT2 — Text Generation Transformer: How to Use & How to Serve
If you haven't heard of Pinferencia go to its github page or its homepage to check it out, it's an amazing library help you deploy your model with ease.
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My first Udemy course on ML Ops deployment!
Please allow me to recommend another simple but serious deployment tools which is also compatible with triton, torchserve, kubeflow, tf serving: Pinferencia
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Easiest Way to Deploy HuggingFace Transformers
Never heard of Pinferencia? It’s not late. Go to its GitHub to take a look. Don’t forget to give it a star if you like it.
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what is the easiest way to deploy a nlp model?
Check this out https://github.com/underneathall/pinferencia
What are some alternatives?
zenml - ZenML 🙏: Build portable, production-ready MLOps pipelines. https://zenml.io.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
ck - Collective Mind (CM) is a simple, modular, cross-platform and decentralized workflow automation framework with a human-friendly interface and reusable automation recipes to make it easier to compose, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data, software and hardware
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
fastapi-template - Completely Scalable FastAPI based template for Machine Learning, Deep Learning and any other software project which wants to use Fast API as an API framework.
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
experta - Expert Systems for Python
serving - A flexible, high-performance serving system for machine learning models
tritony - Tiny configuration for Triton Inference Server
dslinter - `dslinter` is a pylint plugin for linting data science and machine learning code. We plan to support the following Python libraries: TensorFlow, PyTorch, Scikit-Learn, Pandas and NumPy.