From 9d4baee172a5ef64eca21a86e48cc3c05db53339 Mon Sep 17 00:00:00 2001 From: Ben Jarmak <104460670+jarmak-nv@users.noreply.github.com> Date: Tue, 27 Feb 2024 08:39:27 -0500 Subject: [PATCH] Add CUDA 12.2 in nightlies (#483) Co-authored-by: Bradley Dice --- _includes/selector.html | 11 +++++++---- install/install.md | 6 ++++-- 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/_includes/selector.html b/_includes/selector.html index 018dce4b2ff..164e624f71c 100644 --- a/_includes/selector.html +++ b/_includes/selector.html @@ -370,8 +370,8 @@ // all possible values python_vers: ["3.9", "3.10"], - cuda_vers: ["11.2", "11.8", "12.0"], - pip_cuda_vers: ["11.2 - 11.8", "12.0"], + cuda_vers: ["11.2", "11.8", "12.0", "12.2"], + pip_cuda_vers: ["11.2 - 11.8", "12"], methods: ["Conda", "pip", "Docker"], releases: ["Stable", "Nightly"], img_loc: ["NGC", "Dockerhub"], @@ -627,9 +627,9 @@ }, getpipNotes() { var notes = []; - var install_location_notes = "cuDF, dask-cuDF, cuML, cuGraph, cuSpatial, cuProj, cuxfilter, cuCIM, and RAFT pip packages are hosted by NVIDIA
" + var install_location_notes = "RAPIDS pip packages are hosted by NVIDIA
" notes = [...notes, install_location_notes, - 'pip installation supports Python 3.9 and 3.10
']; + 'pip installation supports only Python 3.9 and 3.10
']; return notes.map(note => this.note_prefix + " " + note); }, @@ -668,6 +668,7 @@ var isDisabled = false; if (this.active_additional_packages.includes("TensorFlow") && (cuda_version !== "12.0")) isDisabled = true; if (this.active_method === "Docker" && cuda_version < "11.8") isDisabled = true; + if (this.active_release != "Nightly" && cuda_version === "12.2") isDisabled = true; return isDisabled; }, disableUnsupportedPython(python_version) { @@ -696,6 +697,8 @@ }, releaseClickHandler(e, release) { if (this.isDisabled(e.target)) return; + // Remove below after we support 12.2 in stable + if (this.active_cuda_ver === "12.2" && release === "Stable") this.active_cuda_ver = "12.0"; this.active_release = release; }, imgTypeClickHandler(e, type) { diff --git a/install/install.md b/install/install.md index 3dee7f4b9f0..ae63e0f0d68 100644 --- a/install/install.md +++ b/install/install.md @@ -137,6 +137,7 @@ All provisioned systems need to be RAPIDS capable. Here's what is required: - [CUDA 11.5](https://developer.nvidia.com/cuda-11-5-0-download-archive){: target="_blank"} with Driver 495.29.05 or newer - [CUDA 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive){: target="_blank"} with Driver 520.61.05 or newer - [CUDA 12.0](https://developer.nvidia.com/cuda-12-0-1-download-archive){: target="_blank"} with Driver 525.60.13 or newer **see CUDA 12 section below for notes on usage** +- [CUDA 12.2](https://developer.nvidia.com/cuda-12-2-2-download-archive){: target="_blank"} with Driver 535.86.10 or newer **see CUDA 12 section below for notes on usage** **Note**: RAPIDS is tested with and officially supports the versions listed above. Newer CUDA and driver versions may also work with RAPIDS. See [CUDA compatibility](https://docs.nvidia.com/deploy/cuda-compatibility/index.html) for details. @@ -144,7 +145,7 @@ All provisioned systems need to be RAPIDS capable. Here's what is required: ### **Docker and Conda** -- CUDA 12 conda packages and Docker images currently support CUDA 12.0 +- Stable CUDA 12 conda packages and Docker images currently support CUDA 12.0. Additionally, nightly versions now support CUDA 12.2 - CUDA 11 conda packages and Docker images can be used on a system with a CUDA 12 driver because they include their own CUDA toolkit ### **pip** @@ -334,8 +335,9 @@ Windows users can now tap into GPU accelerated data science on their local machi 1. Install WSL2 and the Ubuntu 22.04 package [using Microsoft's instructions](https://docs.microsoft.com/en-us/windows/wsl/install){: target="_blank"}. 2. Install the [latest NVIDIA Drivers](https://www.nvidia.com/download/index.aspx){: target="_blank"} on the Windows host. 3. Log in to the WSL2 Linux instance. -4. Follow [this helpful developer guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl2){: target="_blank"} and then install the WSL-specific [CUDA 11.8](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} or [CUDA 12.0](https://developer.nvidia.com/cuda-12-0-1-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} Toolkit without drivers into the WSL2 instance. +4. Follow [this helpful developer guide](https://docs.nvidia.com/cuda/wsl-user-guide/index.html#cuda-support-for-wsl2){: target="_blank"} and then install the WSL-specific [CUDA 11](https://developer.nvidia.com/cuda-11-8-0-download-archive?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} or [CUDA 12](https://developer.nvidia.com/cuda-downloads?target_os=Linux&target_arch=x86_64&Distribution=WSL-Ubuntu&target_version=2.0&target_type=deb_local){: target="_blank"} Toolkit without drivers into the WSL2 instance. - The installed CUDA Toolkit version must match the pip wheel version (`-cu11` or `-cu12`) + - Any CUDA 12 CTK will work with RAPIDS `-cu12` pip packages 5. Install RAPIDS pip packages on the WSL2 Linux Instance using the [release selector](#selector) commands. 6. Run this code to check that the RAPIDS installation is working: ```