Vulkan Kompute  

[wtm_mlop_cats] Vulkan Kompute – The General Purpose Vulkan Compute Framework. Blazing fast, lightweight, mobile-enabled, and optimized for advanced GPU data processing usecases. Features Single header library for simple import to your projectDocumentation leveraging doxygen and sphinxBYOV: Bring-your-own-Vulkan design to play nice with existing Vulkan applicationsNon-Vulkan core naming conventions to disambiguate Vulkan vs Kompute componentsFast development […]

CuPy

[wtm_mlop_cats] Features CuPy is an implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of cupy.ndarray, the core multi-dimensional array class, and many functions on it. It supports a subset of numpy.ndarray interface. Basic indexing (indexing by ints, slices, newaxes, and Ellipsis) Most of Advanced indexing (except for some indexing patterns with boolean masks) Data […]

Vaex  

[wtm_mlop_cats] Vaex is a python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion () objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, […]

CuML 

[wtm_mlop_cats] cuML is a suite of fast, GPU-accelerated machine learning algorithms designed for data science and analytical tasks. Our API mirrors Sklearn’s, and we provide practitioners with the easy fit-predict-transform paradigm without ever having to program on a GPU. As data gets larger, algorithms running on a CPU becomes slow and cumbersome. RAPIDS provides users […]

CuDF  

[wtm_mlop_cats] cuDF is a Python GPU DataFrame library (built on the Apache Arrow columnar memory format) for loading, joining, aggregating, filtering, and otherwise manipulating data. cuDF also provides a pandas-like API that will be familiar to data engineers & data scientists, so they can use it to easily accelerate their workflows without going into the […]

Tpot 

[wtm_mlop_cats] TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT will automate the most tedious part of machine learning by intelligently exploring thousands of possible pipelines to find the best one for your data. […]

Singa 

[wtm_mlop_cats] Apache SINGA is an Apache Top Level Project, focusing on distributed training of deep learning and machine learning models Features Create a model training job for supported tasks, with their own datasetsDeploy an ensemble of trained models for inferenceIntegrate model predictions in their apps over HTTP Official website Link Tutorial and documentation Click here […]

Ray 

[wtm_mlop_cats] Ray provides a simple, universal API for building distributed applications. Ray accomplishes this mission by: Providing simple primitives for building and running distributed applications. Enabling end users to parallelize single machine code, with little to zero code changes. Including a large ecosystem of applications, libraries, and tools on top of the core Ray to […]

Rapids 

[wtm_mlop_cats] The RAPIDS suite of open source software libraries and APIs gives you the ability to execute end-to-end data science and analytics pipelines entirely on GPUs. Licensed under Apache 2.0, RAPIDS is incubated by NVIDIA® based on extensive hardware and data science experience. RAPIDS utilizes NVIDIA CUDA® primitives for low-level compute optimization, and exposes GPU […]

Petastorm 

[wtm_mlop_cats] Petastorm is an open source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. It can also be used from pure […]