From 16f026d01259a1823ba873129910966c2d613145 Mon Sep 17 00:00:00 2001 From: pmitev Date: Mon, 6 May 2024 13:40:20 +0200 Subject: [PATCH] NVIDIA DLF --- .../img/pytorch-nvidia.png | Bin .../nvidia-deep-learning-frameworks.md | 25 +++++++++--------- mkdocs.yml | 1 + 3 files changed, 14 insertions(+), 12 deletions(-) rename docs/software/{directly-from-IG => }/img/pytorch-nvidia.png (100%) rename docs/software/{directly-from-IG => }/nvidia-deep-learning-frameworks.md (87%) diff --git a/docs/software/directly-from-IG/img/pytorch-nvidia.png b/docs/software/img/pytorch-nvidia.png similarity index 100% rename from docs/software/directly-from-IG/img/pytorch-nvidia.png rename to docs/software/img/pytorch-nvidia.png diff --git a/docs/software/directly-from-IG/nvidia-deep-learning-frameworks.md b/docs/software/nvidia-deep-learning-frameworks.md similarity index 87% rename from docs/software/directly-from-IG/nvidia-deep-learning-frameworks.md rename to docs/software/nvidia-deep-learning-frameworks.md index 246da00b2..00d7d84f7 100644 --- a/docs/software/directly-from-IG/nvidia-deep-learning-frameworks.md +++ b/docs/software/nvidia-deep-learning-frameworks.md @@ -1,16 +1,15 @@ # NVIDIA Deep Learning Frameworks -https://www.uppmax.uu.se/support/user-guides/nvidia-deep-learning-frameworks/ -Here is how easy one can use an NVIDIA environment for deep learning with all the following tools preset. A screenshot of that page is shown below. +Here is how easy one can use an NVIDIA [environment](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/rel_22-03.html) for deep learning with all the following tools preset. A screenshot of that page is shown below. -pytorch - -Pull the container (6.5GB). +![web screenshot](./img/pytorch-nvidia.png) +First - pull the container (6.5GB). +```bash singularity pull docker://nvcr.io/nvidia/pytorch:22.03-py3 - +``` Get an interactive shell. - +```bash singularity shell --nv ~/external_1TB/tmp/pytorch_22.03-py3.sif Singularity> python3 @@ -34,9 +33,9 @@ True # test torch >>> torch.zeros(1).to('cuda') tensor([0.], device='cuda:0') - +``` From the container shell, check what else is available... - +```bash Singularity> nvcc -V nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation @@ -60,15 +59,16 @@ Singularity> jupyter-lab [I 13:35:46.616 LabApp] http://hostname:8888/?token=d6e865a937e527ff5bbccfb3f150480b76566f47eb3808b1 [I 13:35:46.616 LabApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation). ... - +``` You can use this container to add more packages. - +```singularity Bootstrap: docker From: nvcr.io/nvidia/pytorch:22.03-py3 ... +``` Just keep in mind that "upgrading" the build-in torch package might install a package that is compatible with less GPU architectures and it might not work anymore on your hardware. - +```bash Singularity> python3 -c "import torch; print(torch.__version__); print(torch.cuda.is_available()); print(torch.cuda.get_arch_list()); torch.zeros(1).to('cuda')" 1.10.0+cu102 @@ -76,3 +76,4 @@ True ['sm_37', 'sm_50', 'sm_60', 'sm_70'] NVIDIA A100-PCIE-40GB with CUDA capability sm_80 is not compatible with the current PyTorch installation. The current PyTorch install supports CUDA capabilities sm_37 sm_50 sm_60 sm_70. +``` \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml index 4c422d839..6571024e7 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -67,6 +67,7 @@ nav: - MetONTIIME: software/metontiime.md - Tracer: software/tracer.md - Machine Learning: + - NVIDIA DLF: software/nextflow.md - TensorFlow: software/tensorflow.md - Programming languages: - Julia: software/julia.md