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eco-gallery.yml
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eco-gallery.yml
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meta:
section-titles: true
container: container pb-12
column: col-md-12 px-2 py-2
img-top-cls: pt-10 w-50 d-block mx-auto
buttons:
classes: btn-outline-info btn-block
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 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://docs.ray.io/en/master/joblib.html
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: 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