pinferencia
dslinter
Our great sponsors
| pinferencia | dslinter | |
|---|---|---|
| 21 | 2 | |
| 556 | 17 | |
| 0.0% | - | |
| 0.0 | 4.2 | |
| about 1 year ago | almost 2 years ago | |
| Python | Python | |
| Apache License 2.0 | GNU General Public License v3.0 only |
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.
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
dslinter
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[P][R] Announcing `dslinter` -- a Pylint plugin for assessing Python-written ML project code quality
Check out our repo for more information: https://github.com/SERG-Delft/dslinter
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Announcing `dslinter` -- a Pylint plugin for assessing Python-written ML project code quality
It would be a massive help if you could run `dslinter` on your machine learning project in the industry setting and share the text and the json output with us. You can simply use pip to install dslinter and run dslinter. The steps and commands can be found here: https://github.com/SERG-Delft/dslinter/blob/main/STEPS_TO_FOLLOW.md . The running time of the dslinter should be approximately 1 minute for a project with 10000 lines. The whole process should take no longer than 10 minutes. The process is anonymous and we will remove any sensitive information before the results are published. We can also arrange a meeting and go through the process together if you prefer. If you have time to check whether the output linting message can help you with the project development, we are more than happy to hear the feedback! Please don't hesitate to contact me if you have any questions : )
What are some alternatives?
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
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, run, benchmark and optimize AI, ML and other applications and systems across diverse and continuously changing models, data, software and hardware
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
readsql - Convert SQL to most human readable format
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
libsa4py - LibSA4Py: Light-weight static analysis for extracting type hints and features
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
mllint - `mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.
polyaxon - MLOps Tools For Managing & Orchestrating The Machine Learning LifeCycle
serving - A flexible, high-performance serving system for machine learning models
serve - Serve, optimize and scale PyTorch models in production
pyro - Deep universal probabilistic programming with Python and PyTorch