
Dask-ML
Scalable machine learning in Python using Dask alongside popular machine learning libraries.

Accelerate data science workflows with open-source libraries on GPUs.

RAPIDS is a suite of open-source software libraries developed by NVIDIA for accelerating data science and analytics pipelines on GPUs. Built on CUDA, RAPIDS provides drop-in replacements for popular PyData tools like pandas and scikit-learn, allowing users to leverage GPU acceleration with minimal code changes. cuDF accelerates DataFrame operations, cuML optimizes machine learning algorithms, and cuGraph accelerates graph analytics. RAPIDS integrates with distributed computing frameworks like Apache Spark and Dask for scaling workloads across multiple GPUs and nodes. It supports various data formats and provides high-performance primitives for building custom analytics applications. RAPIDS democratizes access to accelerated data science, enabling data scientists and engineers to process large datasets faster and more efficiently.
RAPIDS is a suite of open-source software libraries developed by NVIDIA for accelerating data science and analytics pipelines on GPUs.
Explore all tools that specialize in train machine learning models. This domain focus ensures RAPIDS delivers optimized results for this specific requirement.
Explore all tools that specialize in graph analytics. This domain focus ensures RAPIDS delivers optimized results for this specific requirement.
RAPIDS offers drop-in replacements for popular data science libraries like pandas and scikit-learn, allowing users to accelerate their existing code with minimal modifications.
cuDF provides a GPU-accelerated DataFrame library that optimizes fundamental DataFrame operations, resulting in significant performance gains compared to CPU-based alternatives like pandas.
cuML optimizes machine learning algorithms for execution on GPUs, delivering 10-50x faster performance than CPU-based alternatives.
cuGraph accelerates graph algorithms for execution on GPUs, enabling users to process large graphs with millions of nodes without specialized software.
RAPIDS supports scaling workloads across multiple GPUs and nodes using Dask, allowing users to process even larger datasets.
Install the latest NVIDIA drivers compatible with CUDA.
Download and install miniforge using the provided script.
Create a conda environment with RAPIDS dependencies.
Activate the RAPIDS environment.
Install RAPIDS libraries using conda or pip.
Verify the installation by running sample code using cuDF, cuML, or cuGraph.
Consult the RAPIDS documentation for detailed instructions and troubleshooting.
All Set
Ready to go
Verified feedback from other users.
"Generally positive, with users praising the performance gains and ease of integration."
Post questions, share tips, and help other users.

Scalable machine learning in Python using Dask alongside popular machine learning libraries.

.NET Standard bindings for Google's TensorFlow, enabling C# and F# developers to build, train, and deploy machine learning models.

The notebook for reproducible research and collaborative data science.

Accelerate the Vision AI lifecycle with Agile ML and real-time automated labeling.

Master data science and AI through interactive, hands-on coding challenges and real-time AI pedagogical support.

A fully-managed, unified AI development platform for building and using generative AI, enhanced by Gemini models.

Build, deploy, and govern all types of AI across all your data with enterprise-grade security and scalability.