JAX installation with Nvidia CUDA and cudNN support (Fixing most common installation error)
This video is all you need to install JAX with Nvidia CUDA and cudNN support in your Python 3.x installation. This video covers the following:
1. Python 3.9
2. JAX with JAXLib 0.3.14
3. Cuda Toolkit 11.7
4. cuDNN 8.4 Installation
5. Conda Toolkit 11.7
6. Torch, TensorFlow and JAX with GPU Support
GitHub Resources:
https://github.com/prodramp/DeepWorks...
▬▬▬▬▬▬ ⏰ TUTORIAL TIME STAMPS ⏰ ▬▬▬▬▬▬
(00:00) Problem Introduction
(01:32) JAX CUDA+cudNN packages
(02:17) JAX can not find CUDA
(03:55) Most common installation error
(05:45) Clean JAX installation
(06:50) Final Installation validation
(07:38) End Credits
Connect
------------------
Prodramp LLC (@prodramp)
Website - https://prodramp.com
LinkedIn - / prodramp
GitHub- https://github.com/prodramp/
AngelList - https://angel.co/company/prodramp
Facebook - / prodramp
Content Creator: Avkash Chauhan (@avkashchauhan)
/ avkashchauhan
/ avkashchauhan
Tags:
#nvidia #cuda #jax #cnn #ml #lime #aicloud #h2oai #driverlessai #machinelearning #cloud #mlops #model #collaboration #deeplearning #modelserving #modeldeployment #pytorch #datarobot #datahub #streamlit #modeltesting #codeartifact #dataartifact #modelartifact #onnx #aws #kaggle #mapbox #lightgbm #xgboost #dataengineering #pandas #keras #tensorflow #tensorboard #cnn #prodramp #avkashchauhan #LIME #mli #xai #cuda #cuda-nn
