conda cudf 升级_cuDF - 支持CUDA的DataFrame库

末蓝、 2023-01-09 14:18 120阅读 0赞

![Image 1][] cuDF - GPU DataFrames

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NOTE: For the latest stable README.md ensure you are on the master branch.

Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.

cuDF 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 details of CUDA programming.

For example, the following snippet downloads a CSV, then uses the GPU to parse it into rows and columns and run calculations:

import cudf, io, requests

from io import StringIO

url = "https://github.com/plotly/datasets/raw/master/tips.csv"

content = requests.get(url).content.decode('utf-8')

tips\_df = cudf.read\_csv(StringIO(content))

tips\_df\['tip\_percentage'\] = tips\_df\['tip'\] / tips\_df\['total\_bill'\] \* 100

\# display average tip by dining party size

print(tips\_df.groupby('size').tip\_percentage.mean())

Output:

size

1 21.729201548727808

2 16.571919173482897

3 15.215685473711837

4 14.594900639351332

5 14.149548965142023

6 15.622920072028379

Name: tip\_percentage, dtype: float64

For additional examples, browse our complete API documentation, or check out our more detailed notebooks.

Quick Start

Please see the Demo Docker Repository, choosing a tag based on the NVIDIA CUDA version you’re running. This provides a ready to run Docker container with example notebooks and data, showcasing how you can utilize cuDF.

Installation

CUDA/GPU requirements

CUDA 10.0+

NVIDIA driver 410.48+

Pascal architecture or better (Compute Capability >=6.0)

Conda

cuDF can be installed with conda (miniconda, or the full Anaconda distribution) from the rapidsai channel:

For cudf version == 0.13 :

\# for CUDA 10.0

conda install -c rapidsai -c nvidia -c numba -c conda-forge \\

cudf=0.13 python=3.6 cudatoolkit=10.0

\# or, for CUDA 10.1

conda install -c rapidsai -c nvidia -c numba -c conda-forge \\

cudf=0.13 python=3.6 cudatoolkit=10.1

\# or, for CUDA 10.2

conda install -c rapidsai -c nvidia -c numba -c conda-forge \\

cudf=0.13 python=3.6 cudatoolkit=10.2

For the nightly version of cudf :

\# for CUDA 10.0

conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \\

cudf python=3.6 cudatoolkit=10.0

\# or, for CUDA 10.1

conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \\

cudf python=3.6 cudatoolkit=10.1

\# or, for CUDA 10.2

conda install -c rapidsai-nightly -c nvidia -c numba -c conda-forge \\

cudf python=3.6 cudatoolkit=10.2

Note: cuDF is supported only on Linux, and with Python versions 3.6 or 3.7.

See the Get RAPIDS version picker for more OS and version info.

Build/Install from Source

Contributing

Contact

Find out more details on the RAPIDS site

![Image 1][] Open GPU Data Science

The RAPIDS suite of open source software libraries aim to enable execution of end-to-end data science and analytics pipelines entirely on GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

![Image 1][]

Apache Arrow on GPU

The GPU version of Apache Arrow is a common API that enables efficient interchange of tabular data between processes running on the GPU. End-to-end computation on the GPU avoids unnecessary copying and converting of data off the GPU, reducing compute time and cost for high-performance analytics common in artificial intelligence workloads. As the name implies, cuDF uses the Apache Arrow columnar data format on the GPU. Currently, a subset of the features in Apache Arrow are supported.

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