papers-with-data
pinferencia
| papers-with-data | pinferencia | |
|---|---|---|
| 2 | 21 | |
| 124 | 558 | |
| 0.0% | 0.4% | |
| 6.9 | 0.0 | |
| 5 months 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.
papers-with-data
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Elevate Your GitHub README Game
Over the past year, I’ve created a lot of GitHub repositories. Some of these repositories, such as my 10 Weeks of Plugins repo, are effectively aggregators — centralized locations that I can drive people to, almost like a personal Awesome list. Other repositories like VoxelGPT contain many moving pieces. Others still like Papers with Data are essentially minimum viable products for websites!
- CVPR 2023 Papers with Data Repo
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?
caer - High-performance Vision library in Python. Scale your research, not boilerplate.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
rtdl - Research on Tabular Deep Learning [Moved to: https://github.com/yandex-research/rtdl]
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
squirrel-core - A Python library that enables ML teams to share, load, and transform data in a collaborative, flexible, and efficient way :chestnut:
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
deeplake - Database for AI. Store Vectors, Images, Texts, Videos, etc. Use with LLMs/LangChain. Store, query, version, & visualize any AI data. Stream data in real-time to PyTorch/TensorFlow. https://activeloop.ai
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]
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.