From the course: NVIDIA Certified Associate AI Infrastructure and Operations (NCA-AIIO) Cert Prep
Unlock this course with a free trial
Join today to access over 25,300 courses taught by industry experts.
Installing CUDA - NVIDIA Tutorial
From the course: NVIDIA Certified Associate AI Infrastructure and Operations (NCA-AIIO) Cert Prep
Installing CUDA
Installing CUDA is also very simple, there is clear documentation and detailed documentation available on NVIDIA website. You need a supported GPU from NVIDIA and you also need a supported operating system. So system requirement is CUDA capable GPU and a supported version of Linux and then you can install CUDA toolkit on top of it. It is supported on various versions of Linux and Windows, then it is supported on x86 whereas ARM platforms also plus some prerequisite installation you need GCC as compiler on that. You could download CUDA toolkit and get started with the installation so pretty straightforward and if you are using a pre-configured AMI or Azure Linux from cloud provider it may already be installed. check which version of CUDA is installed on your machine again the same command which is NVIDIA SMI. If you look here it talks and tells you that it is CUDA version 13 installed on it. Let me check the other machine which has multiple GPUs what is the version on top of it let me…
Practice while you learn with exercise files
Download the files the instructor uses to teach the course. Follow along and learn by watching, listening and practicing.
Contents
-
-
-
-
-
-
(Locked)
NVIDIA: Powering AI GPU innovation2m 37s
-
(Locked)
NVIDIA technology stack3m 12s
-
(Locked)
Layer 1: Physical layer3m 53s
-
(Locked)
GPU on a graphics card1m 57s
-
(Locked)
DGX platform2m 56s
-
(Locked)
DGX SuperPOD1m 57s
-
(Locked)
ConnectX1m 49s
-
(Locked)
BlueField DPUs2m 32s
-
(Locked)
NVIDIA reference architectures1m 38s
-
(Locked)
Understanding GPU cores5m
-
(Locked)
Comparing GPU cores4m 18s
-
(Locked)
NVIDIA DGX platform: Timeline4m 47s
-
(Locked)
DGX platform: Deployment options3m 38s
-
(Locked)
DGX A100 vs. H1004m 6s
-
(Locked)
Layer 2: Data movement and I/O acceleration59s
-
(Locked)
NVLink8m 5s
-
(Locked)
InfiniBand2m 5s
-
(Locked)
InfiniBand vs. Ethernet1m 43s
-
(Locked)
DMA and RDMA6m 30s
-
(Locked)
GPUDirect RDMA2m 44s
-
(Locked)
GPUDirect storage1m 45s
-
(Locked)
Quick comparison1m 56s
-
(Locked)
Layer 3: OS, driver, and virtualization2m 17s
-
(Locked)
GPU drivers4m 38s
-
(Locked)
GPU virtualization5m 8s
-
(Locked)
vGPU vs. MIG, part 17m 48s
-
(Locked)
vGPU vs. MIG, part 210m 59s
-
(Locked)
Layer 4: Core libraries6m 44s
-
(Locked)
Compute unified device architecture (CUDA)3m 12s
-
(Locked)
Installing CUDA2m 11s
-
(Locked)
NVIDIA collective communications library (NCCL)3m 41s
-
(Locked)
NVLink, NVSwitch, PCIe, RDMA vs. NCCL3m 44s
-
(Locked)
Layer 5: Monitoring and management2m 23s
-
(Locked)
NVIDIA-SMI4m 24s
-
(Locked)
Data Center GPU Manager (DCGM)7m 27s
-
(Locked)
Base Command Manager5m 33s
-
(Locked)
Which one to use?2m 3s
-
(Locked)
Layer 6: Applications and vertical solutions3m 48s
-
(Locked)
Summary2m 26s
-
(Locked)
NVIDIA AI Enterprise3m 2s
-
(Locked)
NVIDIA AI Factory2m 24s
-
(Locked)
-