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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-application-boost-with-nvidia-rapids-acceleration/
课程评论：没有评论
**课程摘要：利用NVIDIA RAPIDS加速AI应用** 本课程将深入探讨NVIDIA RAPIDS平台，旨在帮助数据科学家和机器学习工程师显著提升AI应用的性能和效率。通过利用NVIDIA GPU的强大计算能力，RAPIDS能够将传统上需要数天才能完成的数据科学和机器学习任务缩短至几分钟。 **核心内容：** * **RAIDPS库的运用：** 课程将引导学员使用cuDF、cuPy和cuML等RAPIDS核心库，替换Pandas、NumPy和Scikit-learn等常用Python库。这些库能够直接在GPU上进行高效的数据处理和机器学习算法执行。 * **性能对比与加速：** 通过实验演示，课程将直观展示RAPIDS与传统Python库之间的性能差异，并揭示在特定场景下，RAPIDS可实现超过900倍的加速比。 * **端到端机器学习项目实践：** 学员将学习如何使用RAPIDS构建完整的机器学习项目，从数据加载、预处理，到模型训练、评估和预测，实现全流程的GPU加速。 * **DASK集成与多GPU/CPU并行：** 课程将介绍如何利用DASK库与RAPIDS协同工作，实现多GPU或多CPU的任务并行化，进一步提升计算效率。 **学习环境：** 本课程全程使用Python编程语言，并在Google Colab云端环境中进行教学。学员无需本地GPU硬件，即可利用Google提供的免费GPU资源进行课程学习和实践。 **课程价值：** 通过本课程的学习，学员将能够掌握利用RAPIDS加速AI工作流的关键技能，从而更快地迭代、优化模型，获得更精准的结果，并显著缩短模型开发和部署周期，最终实现更高的商业价值。
Data science and machine learning represent the largest computational sectors in the world, where modest improvements in the accuracy of analytical models can translate into billions of impact on the bottom line. Data scientists are constantly striving to train, evaluate, iterate, and optimize models to achieve highly accurate results and exceptional performance. With NVIDIA's powerful RAPIDS platform, what used to take days can now be accomplished in a matter of minutes, making the construction and deployment of high-value models easier and more agile. In data science, additional computational power means faster and more effective insights. RAPIDS harnesses the power of NVIDIA CUDA to accelerate the entire data science model training workflow, running it on graphics processing units (GPUs).In this course, you will learn everything you need to take your machine learning applications to the next level! Check out some of the topics that will be covered below:Utilizing the cuDF, cuPy, and cuML libraries instead of Pandas, Numpy, and scikit-learn; ensuring that data is processed and machine learning algorithms are executed with high performance on the GPU.Comparing the performance of classic Python libraries with RAPIDS. In some experiments conducted during the classes, we achieved acceleration rates exceeding 900x. This indicates that with certain databases and algorithms, RAPIDS can be 900 times faster!Creating a complete, step-by-step machine learning project using RAPIDS, from data loading to predictions.Using DASK for task parallelism on multiple GPUs or CPUs; integrated with RAPIDS for superior performance.Throughout the course, we will use the Python programming language and the online Google Colab. This way, you don't need to have a local GPU to follow the classes, as we will use the free hardware provided by Google.
