123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199 |
- meta:
- grid: 1 2 2 3
- gutter: 1
- class-container: container pb-3
- classes:
- class-img-top: pt-2 w-75 d-block mx-auto fixed-height-img
- projects:
- - name: Classy Vision Integration
- section_title: Classy Vision
- description: Classy Vision is a new end-to-end, PyTorch-based framework for
- large-scale training of state-of-the-art image and video classification models.
- The library features a modular, flexible design that allows anyone to train
- machine learning models on top of PyTorch using very simple abstractions.
- website: https://github.com/facebookresearch/ClassyVision/blob/main/tutorials/ray_aws.ipynb
- repo: https://github.com/facebookresearch/ClassyVision
- image: ../images/classyvision.png
- - name: Dask Integration
- section_title: Dask
- description: Dask provides advanced parallelism for analytics, enabling performance
- at scale for the tools you love. Dask uses existing Python APIs and data
- structures to make it easy to switch between Numpy, Pandas,
- Scikit-learn to their Dask-powered equivalents.
- website: dask-on-ray
- repo: https://github.com/dask/dask
- image: ../images/dask.png
- - name: Flambé Integration
- section_title: Flambé
- description: Flambé is a machine learning experimentation framework built to
- accelerate the entire research life cycle. Flambé’s main objective is to
- provide a unified interface for prototyping models, running experiments
- containing complex pipelines, monitoring those experiments in real-time,
- reporting results, and deploying a final model for inference.
- website: https://github.com/asappresearch/flambe
- repo: https://github.com/asappresearch/flambe
- image: ../images/flambe.png
- - name: Flyte Integration
- section_title: Flyte
- description: Flyte is a Kubernetes-native workflow automation platform for complex,
- mission-critical data and ML processes at scale. It has been battle-tested
- at Lyft, Spotify, Freenome, and others and is truly open-source.
- website: https://flyte.org/
- repo: https://github.com/flyteorg/flyte
- image: ../images/flyte.png
- - name: Horovod Integration
- section_title: Horovod
- description: Horovod is a distributed deep learning training framework for
- TensorFlow, Keras, PyTorch, and Apache MXNet. The goal of
- Horovod is to make distributed deep learning fast and easy to use.
- website: https://horovod.readthedocs.io/en/stable/ray_include.html
- repo: https://github.com/horovod/horovod
- image: ../images/horovod.png
- - name: Hugging Face Transformers Integration
- section_title: Hugging Face Transformers
- description: State-of-the-art Natural Language Processing for
- Pytorch and TensorFlow 2.0. It integrates with Ray for distributed
- hyperparameter tuning of transformer models.
- website: https://huggingface.co/transformers/master/main_classes/trainer.html#transformers.Trainer.hyperparameter_search
- repo: https://github.com/huggingface/transformers
- image: ../images/hugging.png
- - name: Intel Analytics Zoo Integration
- section_title: Intel Analytics Zoo
- description: Analytics Zoo seamlessly scales TensorFlow, Keras and PyTorch
- to distributed big data (using Spark, Flink & Ray).
- website: https://analytics-zoo.github.io/master/#ProgrammingGuide/rayonspark/
- repo: https://github.com/intel-analytics/analytics-zoo
- image: ../images/zoo.png
- - name: NLU Integration
- section_title: John Snow Labs' NLU
- description: The power of 350+ pre-trained NLP models, 100+ Word Embeddings,
- 50+ Sentence Embeddings, and 50+ Classifiers in 46 languages
- with 1 line of Python code.
- website: https://nlu.johnsnowlabs.com/docs/en/predict_api#modin-dataframe
- repo: https://github.com/JohnSnowLabs/nlu
- image: ../images/nlu.png
- - name: Ludwig Integration
- section_title: Ludwig AI
- description: Ludwig is a toolbox that allows users to train and test deep learning
- models without the need to write code. With Ludwig, you can train a deep learning
- model on Ray in zero lines of code, automatically leveraging Dask on Ray for data
- preprocessing, Horovod on Ray for distributed training, and Ray Tune for
- hyperparameter optimization.
- website: https://medium.com/ludwig-ai/ludwig-ai-v0-4-introducing-declarative-mlops-with-ray-dask-tabnet-and-mlflow-integrations-6509c3875c2e
- repo: https://github.com/ludwig-ai/ludwig
- image: ../images/ludwig.png
- - name: MARS Integration
- section_title: MARS
- description: Mars is a tensor-based unified framework for large-scale data
- computation which scales Numpy, Pandas and Scikit-learn. Mars can scale in to
- a single machine, and scale out to a cluster with thousands of machines.
- website: mars-on-ray
- repo: https://github.com/mars-project/mars
- image: ../images/mars.png
- - name: Modin Integration
- section_title: Modin
- description: Scale your pandas workflows by changing one line of code.
- Modin transparently distributes the data and computation so that all you need
- to do is continue using the pandas API as you were before installing Modin.
- website: https://github.com/modin-project/modin
- repo: https://github.com/modin-project/modin
- image: ../images/modin.png
- - name: Prefect Integration
- section_title: Prefect
- description: Prefect is an open source workflow orchestration platform in Python.
- It allows you to easily define, track and schedule workflows in Python. This
- integration makes it easy to run a Prefect workflow on a Ray cluster in a
- distributed way.
- website: https://github.com/PrefectHQ/prefect-ray
- repo: https://github.com/PrefectHQ/prefect-ray
- image: ../images/prefect.png
- - name: PyCaret Integration
- section_title: PyCaret
- description: PyCaret is an open source low-code machine learning library in Python
- that aims to reduce the hypothesis to insights cycle time in a ML experiment.
- It enables data scientists to perform end-to-end experiments quickly
- and efficiently.
- website: https://github.com/pycaret/pycaret
- repo: https://github.com/pycaret/pycaret
- image: ../images/pycaret.png
- - name: PyTorch Lightning Integration
- section_title: PyTorch Lightning
- description: PyTorch Lightning is a popular open-source library that provides a
- high level interface for PyTorch. The goal of PyTorch Lightning is to structure
- your PyTorch code to abstract the details of training, making AI research
- scalable and fast to iterate on.
- website: https://github.com/ray-project/ray_lightning_accelerators
- repo: https://github.com/ray-project/ray_lightning_accelerators
- image: ../images/pytorch_lightning_small.png
- - name: RayDP Integration
- section_title: Spark on Ray (RayDP)
- description: RayDP ("Spark on Ray") enables you to easily use Spark inside a
- Ray program. You can use Spark to read the input data, process the data using
- SQL, Spark DataFrame, or Pandas (via Koalas) API, extract and transform features
- using Spark MLLib, and use RayDP Estimator API for distributed training
- on the preprocessed dataset.
- website: https://github.com/Intel-bigdata/oap-raydp
- repo: https://github.com/Intel-bigdata/oap-raydp
- image: ../images/intel.png
- - name: Scikit Learn Integration
- section_title: Scikit Learn
- description: Scikit-learn is a free software machine learning library for
- the Python programming language. It features various classification,
- regression and clustering algorithms including support vector machines,
- random forests, gradient boosting, k-means and DBSCAN, and is designed to
- interoperate with the Python numerical and scientific libraries NumPy and SciPy.
- website: https://docs.ray.io/en/master/joblib.html
- repo: https://github.com/scikit-learn/scikit-learn
- image: ../images/scikit.png
- - name: Seldon Alibi Integration
- section_title: Seldon Alibi
- description: Alibi is an open source Python library aimed at machine learning model
- inspection and interpretation. The focus of the library is to provide high-quality
- implementations of black-box, white-box, local and global explanation methods for
- classification and regression models.
- website: https://github.com/SeldonIO/alibi
- repo: https://github.com/SeldonIO/alibi
- image: ../images/seldon.png
- - name: Sematic Integration
- section_title: Sematic
- description: Sematic is an open-source ML pipelining tool written in Python.
- It enables users to write end-to-end pipelines that can seamlessly transition between
- your laptop and the cloud, with rich visualizations, traceability,
- reproducibility, and usability as first-class citizens. This integration
- enables dynamic allocation of Ray clusters within Sematic pipelines.
- website: https://docs.sematic.dev/integrations/ray
- repo: https://github.com/sematic-ai/sematic
- image: ../images/sematic.png
- - name: spaCy Integration
- section_title: spaCy
- description: spaCy is a library for advanced Natural Language Processing in Python
- and Cython. It's built on the very latest research, and was designed from
- day one to be used in real products.
- website: https://pypi.org/project/spacy-ray/
- repo: https://github.com/explosion/spacy-ray
- image: ../images/spacy.png
- - name: XGBoost Integration
- section_title: XGBoost
- description: XGBoost is a popular gradient boosting library for classification
- and regression. It is one of the most popular tools in data science and
- workhorse of many top-performing Kaggle kernels.
- website: https://github.com/ray-project/xgboost_ray
- repo: https://github.com/ray-project/xgboost_ray
- image: ../images/xgboost_logo.png
- - name: LightGBM Integration
- section_title: LightGBM
- description: LightGBM is a high-performance gradient boosting library for
- classification and regression. It is designed to be distributed and efficient.
- website: https://github.com/ray-project/lightgbm_ray
- repo: https://github.com/ray-project/lightgbm_ray
- image: ../images/lightgbm_logo.png
- - name: Volcano Integration
- section_title: Volcano
- description: Volcano is system for running high-performance workloads
- on Kubernetes. It features powerful batch scheduling capabilities required by ML
- and other data-intensive workloads.
- website: https://github.com/volcano-sh/volcano/releases/tag/v1.7.0
- repo: https://github.com/volcano-sh/volcano/
- image: ./images/volcano.png
|