Various security issues were addressed, for additional details on the med-high severity issues please review NVIDIA Product Security for more information.
Fixed a bug that can cause the InfoROM to be corrupted. This issue affects only Tesla and Quadro products.
Fixed an issue with MPS where the GPU can enter into a deadlock and eventually result in the GPU falling off the bus.
The x86_64 local and network installer driver packages (deb/rpm) for Tesla GPUs now include the end-user diagnostic utilities.
Installing the NVIDIA 375.66 Driver and CUDA 8 on AWS G2 Instances
Note that the existing CUDA 8 installer packages contain a version of the driver (375.26) that does not support the K520 GPU and thus additional steps are required to get started with using CUDA on the AWS EC2 G2 instances. One of the easier ways to install drivers and CUDA is to use the network installation package to install the NVIDIA 375.66 driver and the toolkit. For simplicity, these instructions will refer to the steps on an Ubuntu system using the APT package manager. The same instructions will apply when using other package managers such as yum or zypper.
Update the CUDA network repo keys using the following command
If you already have CUDA 8 installed on your instance and only need to update the NVIDIA driver, install the cuda-drivers meta-package. Then reboot the instance to complete the installation of the 375.66 NVIDIA driver.
If you also need to install the CUDA toolkit, then install the cuda-toolkit-8-0 meta-package to download and install CUDA 8.
# sudo apt-get -y install cuda-toolkit-8-0
Refer to the Linux Installation Guide for CUDA Toolkit for more information on using runfiles or local installers to install CUDA on various Linux distributions. The guide is located at the following URL: (http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
Tesla P100, Tesla P40, Tesla P4
Tesla K80, Tesla K40c, Tesla K40m, Tesla K40s, Tesla K40st, Tesla K40t, Tesla K20Xm, Tesla K20m, Tesla K20s, Tesla K20c, Tesla K10, Tesla K8
Various security issues were addressed, for additional details on the med-high severity issues please review NVIDIA Product Security for more information
Installing the NVIDIA 396.44 Driver and CUDA 9 on AWS G2 Instances
Note that the existing CUDA 9 installer packages contain a version of the driver (375.26) that does not support the K520 GPU and thus additional steps are required to get started with using CUDA on the AWS EC2 G2 instances. One of the easier ways to install drivers and CUDA is to use the network installation package to install the NVIDIA 396.26 driver and the toolkit. For simplicity, these instructions will refer to the steps on an Ubuntu system using the APT package manager. The same instructions will apply when using other package managers such as yum or zypper.
Update the CUDA network repo keys using the following command
If you already have CUDA 9 installed on your instance and only need to update the NVIDIA driver, install the cuda-drivers meta-package. Then reboot the instance to complete the installation of the 396.44 NVIDIA driver.
If you also need to install the CUDA toolkit, then install the cuda-toolkit-9-1 meta-package to download and install CUDA 9.2.
# sudo apt-get -y install cuda-toolkit-9-2
Refer to the Linux Installation Guide for CUDA Toolkit for more information on using runfiles or local installers to install CUDA on various Linux distributions. The guide is located at the following URL: (http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
Tesla P100, Tesla P40, Tesla P6, Tesla P4
Tesla K80, Tesla K520, Tesla K40c, Tesla K40m, Tesla K40s, Tesla K40st, Tesla K40t, Tesla K20Xm, Tesla K20m, Tesla K20s, Tesla K20c, Tesla K10, Tesla K8
Installing the NVIDIA 384.111 Driver and CUDA 8 on AWS G2 Instances
Note that the existing CUDA 8 installer packages contain a version of the driver (375.26) that does not support the K520 GPU and thus additional steps are required to get started with using CUDA on the AWS EC2 G2 instances. One of the easier ways to install drivers and CUDA is to use the network installation package to install the NVIDIA 390.12 driver and the toolkit. For simplicity, these instructions will refer to the steps on an Ubuntu system using the APT package manager. The same instructions will apply when using other package managers such as yum or zypper.
Update the CUDA network repo keys using the following command
If you already have CUDA 8 installed on your instance and only need to update the NVIDIA driver, install the cuda-drivers meta-package. Then reboot the instance to complete the installation of the 390.12 NVIDIA driver.
If you also need to install the CUDA toolkit, then install the cuda-toolkit-8-0 meta-package to download and install CUDA 8.
# sudo apt-get -y install cuda-toolkit-8-0
Refer to the Linux Installation Guide for CUDA Toolkit for more information on using runfiles or local installers to install CUDA on various Linux distributions. The guide is located at the following URL: (http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
Tesla P100, Tesla P40, Tesla P6, Tesla P4
Tesla K80, Tesla K520, Tesla K40c, Tesla K40m, Tesla K40s, Tesla K40st, Tesla K40t, Tesla K20Xm, Tesla K20m, Tesla K20s, Tesla K20c, Tesla K10, Tesla K8
Various security issues were addressed, for additional details on the med-high severity issues please review NVIDIA Product Security for more information
Fixed a bug in the NVIDIA GPU Boost algorithm that could cause the Tesla P100 SXM2 GPU to become unresponsive with a “GPU has fallen of the bus” error under certain workloads. In this state, the GPU is not available for any work
Added support for Tesla V100 GPUs
Added support for MPS on Tesla V100 GPUs
Added nvmlClocksThrottleReasonSwThermalSlowdown as a NVML throttle reason. This is shown as SW Thermal Slowdown in nvidia-smi -q
Added new «Memory Max Operating Temp» to nvidia-smi and SMBPBI to report the maximum memory temperature for Tesla V100
Added new «GPU Max Operating Temp» to nvidia-smi and SMBPBI to report the maximum GPU operating temperature for Tesla V100
Added CUDA support to allow JIT linking of binary compatible cubins
Fixed an issue in the driver that may cause certain applications using unified memory APIs to hang
Installing the NVIDIA 384.81 Driver and CUDA 8 on AWS G2 Instances
Note that the existing CUDA 8 installer packages contain a version of the driver (375.26) that does not support the K520 GPU and thus additional steps are required to get started with using CUDA on the AWS EC2 G2 instances. One of the easier ways to install drivers and CUDA is to use the network installation package to install the NVIDIA 384.81 driver and the toolkit. For simplicity, these instructions will refer to the steps on an Ubuntu system using the APT package manager. The same instructions will apply when using other package managers such as yum or zypper.
Update the CUDA network repo keys using the following command
If you already have CUDA 8 installed on your instance and only need to update the NVIDIA driver, install the cuda-drivers meta-package. Then reboot the instance to complete the installation of the 384.81 NVIDIA driver.
If you also need to install the CUDA toolkit, then install the cuda-toolkit-8-0 meta-package to download and install CUDA 8.
# sudo apt-get -y install cuda-toolkit-8-0
Refer to the Linux Installation Guide for CUDA Toolkit for more information on using runfiles or local installers to install CUDA on various Linux distributions. The guide is located at the following URL: (http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
Tesla P100, Tesla P40, Tesla P6, Tesla P4
Tesla K80, Tesla K520, Tesla K40c, Tesla K40m, Tesla K40s, Tesla K40st, Tesla K40t, Tesla K20Xm, Tesla K20m, Tesla K20s, Tesla K20c, Tesla K10, Tesla K8
