amazon-sagemaker-local-mode
pinferencia
| amazon-sagemaker-local-mode | pinferencia | |
|---|---|---|
| 1 | 21 | |
| 230 | 558 | |
| 1.3% | 0.4% | |
| 7.7 | 0.0 | |
| about 1 month ago | about 1 year ago | |
| Python | Python | |
| MIT No Attribution | 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.
amazon-sagemaker-local-mode
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Debugging Python Code in Amazon SageMaker Locally Using Visual Studio Code and PyCharm: A Step-by-Step Guide
git clone https://github.com/aws-samples/amazon-sagemaker-local-mode/ cd amazon-sagemaker-local-mode/general_pipeline_local_debug python3 -m venv .venv source .venv/bin/activate pip install jupyter jupyter lab
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?
quick-deploy - Optimize, convert and deploy machine learning models as fast inference API using Triton and ORT. Currently support Hugging Face transformers, PyToch, Tensorflow, SKLearn and XGBoost models.
server - The Triton Inference Server provides an optimized cloud and edge inferencing solution.
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
budgetml - Deploy a ML inference service on a budget in less than 10 lines of code.
mars - Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
aws-lambda-docker-serverless-inference - Serve scikit-learn, XGBoost, TensorFlow, and PyTorch models with AWS Lambda container images support.
llmware - Providing enterprise-grade LLM-based development framework, tools, and fine-tuned models.
nebuly - The user analytics platform for LLMs
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.