cognita
pinferencia
| cognita | pinferencia | |
|---|---|---|
| 4 | 21 | |
| 2,817 | 558 | |
| 68.5% | 0.4% | |
| 8.9 | 0.0 | |
| 1 day ago | over 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.
cognita
- FLaNK AI Weekly for 29 April 2024
- Show HN: Cognita – open-source RAG framework for modular applications
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Dream – A Distributed RAG Experimentation Framework
Hi, I've come across an open-source API-driven RAG framework launched recently. It's different from other frameworks in a lot of context. Give it a try and let me know your thoughts: https://github.com/truefoundry/cognita
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
-
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.
-
what is the easiest way to deploy a nlp model?
Check this out https://github.com/underneathall/pinferencia
What are some alternatives?
warc-gpt - WARC + AI - Experimental Retrieval Augmented Generation Pipeline for Web Archive Collections.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
llmware - Unified framework for building enterprise RAG pipelines with small, specialized models
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
serving - A flexible, high-performance serving system for machine learning models
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.
serve - Serve, optimize and scale PyTorch models in production
pyro - Deep universal probabilistic programming with Python and PyTorch
papers-with-data - A curated list of papers that released datasets along with their work
ray-llm - RayLLM - LLMs on Ray