利用 NVIDIA RAPIDS 加速技术提升 AI 应用
AI Application Boost with NVIDIA RAPIDS Acceleration
- 1. Introduction
- 1. Course content
- 2. CPU vs GPU
- 3. GPU and CUDA
- 4. RAPIDS
- 2. cuDF
- 1. cuDF - intuition
- 2. Installation
- 3. Pandas and cuDF
- 4. Basic commands 1
- 5. Basic commands 2
- 6. Basic commands 3
- 7. Basic commands 4
- 8. Integration with cuPy
- 9. Other data convertions
- 10. User defined functions 1
- 11. User defined functions 2
- 12. User defined functions 3
- 13. Performance comparison 1
- 14. Performance comparison 2
- 15. Performance comparison 3
- 3. cuML
- 1. cuML - intution
- 2. Preparing the environment
- 3. Regression with scikit-learn
- 4. Regression with cuML
- 5. Ridge regression
- 6. Parameter tuning
- 7. Performance comparison 1
- 8. Performance comparison 2
- 4. Complete project
- 1. Installations and libraries
- 2. Census dataset
- 3. Categorical features 1
- 4. Categorical features 2
- 5. Additional pre-processing
- 6. Logistic regression and kNN
- 7. Random Forest and SVM
- 9. Homework solution 1
- 10. Homework solution 2
- 5. DASK
- 1. DASK - intuition
- 2. Creating a local cluster
- 3. Arrays in distributed GPUs
- 4. DASK and cuDF
- 5. DASK and cuML 1
- 6. DASK and cuML 2
- 6. Final remarks
- 1. Final remarks
- 2. BONUS