1. What's New in NVIDIA AI Enterprise
Changes in this release of NVIDIA AI Enterprise are as follows:
- New releases of the following software components of NVIDIA AI Enterprise
- GPU Operator: 1.11
- Network Operator: 1.2
- NVIDIA deep learning frameworks: 22.05-nvaie2.1
- NVIDIA vGPU software: 14.1
This release includes security updates for the NVIDIA Virtual GPU Manager and graphics driver for Linux- see Security Bulletin: NVIDIA GPU Display Driver - May 2022, which is listed on the NVIDIA Product Security page
- NVIDIA RAPIDS: 22.04-cuda11.4-ubuntu20.04-py3.8
- TAO Toolkit: 22.05
- Support for the following OS releases as a guest OS and in bare-metal deployments:
- Ubuntu 22.04 LTS
- Red Hat Enterprise Linux 9.0
- Support for GPU Operator and Network Operator with Ubuntu 22.04 LTS
- Support for GPU Operator with RedHat OpenShift on multicloud instances that support NVIDIA AI Enterprise
- Miscellaneous bug fixes
2. Supported Hardware and Software
For more information, refer to the NVIDIA AI Enterprise Product Support Matrix.
NVIDIA GPUs
- NVIDIA A100X
- NVIDIA A100 PCIe 40GB
- NVIDIA A100 HGX 40GB
- NVIDIA A100 PCIe 80GB
- NVIDIA A100 HGX 80GB
- NVIDIA A40
- NVIDIA A30X
- NVIDIA A30
- NVIDIA A10
- NVIDIA A16
- NVIDIA A2
- NVIDIA RTX A6000
- NVIDIA RTX A5500
- NVIDIA RTX A5000
- NVIDIA T4
- NVIDIA V100
For NVIDIA AI Enterprise Compatible servers, refer to the NVIDIA-certified systems page.
Multi-node scaling requires an Ethernet NIC that supports RoCE. For best performance, NVIDIA recommends using an NVIDIA® Mellanox® ConnectX®-6 Dx and an NVIDIA A100 GPU in each VM used for multi-node scaling. Refer to the Sizing guide and the Multi-Node Training solution guide for further information.
Hypervisor Software Supported
- VMware vSphere Hypervisor (ESXi) Enterprise Plus Edition 7.0 Update 3
- VMware vCenter Server 7.0 Update 3
- VMware vSphere 6.7 (only for NVIDIA T4 and NVIDIA V100 GPUs)
Guest Operating Systems Supported
- Ubuntu 22.04 LTS
- Ubuntu 20.04 LTS
- Red Hat Enterprise Linux 9.0
- Red Hat Enterprise Linux 8.4
- Red Hat OpenShift 4.9 and later
2.1. NVIDIA AI Enterprise Software Components
Software Component | NVIDIA Release |
---|---|
NVIDIA vGPU Software | 14.1:
|
NVIDIA GPU Operator | 1.11 |
NVIDIA Network Operator | 1.2 |
TensorFlow 2 | 22.02-tf2-nvaie-2.0-py3 |
TensorFlow 1 | 22.02-tf1-nvaie-2.0-py3 |
PyTorch | 22.02-nvaie-2.0-py3 |
NVIDIA Triton Inference Server | 22.02-nvaie-2.0-py3 and 22.02-nvaie-2.0-py3-sdk |
NVIDIA TensorRT | 22.02-nvaie-2.0-py3 |
NVIDIA RAPIDS | 22.04-cuda11.4-ubuntu20.04-py3.8 |
TAO Toolkit for Language Model (Conv AI) | 3.22.05-py3 |
TAO Toolkit for Conv AI | 3.22.05-py3 |
TAO Toolkit for CV | 3.22.05-tf1.15.4-py3-nvaie |
2.2. Switching the Mode of a GPU that Supports Multiple Display Modes
Some GPUs support displayless and display-enabled modes but must be used in NVIDIA AI Enterprise deployments in displayless mode.
The GPUs listed in the following table support multiple display modes. As shown in the table, some GPUs are supplied from the factory in displayless mode, but other GPUs are supplied in a display-enabled mode.
GPU | Mode as Supplied from the Factory |
---|---|
NVIDIA A40 | Displayless |
NVIDIA RTX A5000 | Display enabled |
NVIDIA RTX A6000 | Display enabled |
A GPU that is supplied from the factory in displayless mode, such as the NVIDIA A40 GPU, might be in a display-enabled mode if its mode has previously been changed.
To change the mode of a GPU that supports multiple display modes, use the displaymodeselector tool, which you can request from the NVIDIA Display Mode Selector Tool page on the NVIDIA Developer website.
Only the following GPUs support the displaymodeselector tool:
- NVIDIA A40
- NVIDIA RTX A5000
- NVIDIA RTX A6000
Other GPUs that support NVIDIA AI Enterprise do not support the displaymodeselector tool and, unless otherwise stated, do not require display mode switching.
2.3. Requirements for Using C-Series vCS vGPUs
Because C-Series vCS vGPUs have large BAR memory settings, using these vGPUs has some restrictions on VMware ESXi.
2.4. Requirements for Using vGPU on GPUs Requiring 64 GB or More of MMIO Space with Large-Memory VMs
Some GPUs require 64 GB or more of MMIO space. When a vGPU on a GPU that requires 64 GB or more of MMIO space is assigned to a VM with 32 GB or more of memory on ESXi , the VM’s MMIO space must be increased to the amount of MMIO space that the GPU requires.
For more information, refer to VMware Knowledge Base Article: VMware vSphere VMDirectPath I/O: Requirements for Platforms and Devices (2142307).
With ESXi 6.7 or later, no extra configuration is needed.
No extra configuration is needed.
The following table lists the GPUs that require 64 GB or more of MMIO space and the amount of MMIO space that each GPU requires.
2.5. Linux Only: Error Messages for Misconfigured GPUs Requiring Large MMIO Space
In a Linux VM, if the requirements for using C-Series vCS vGPUs or GPUs requiring large MMIO space in pass-through mode are not met, the following error messages are written to the VM's dmesg log during installation of the NVIDIA AI Enterprise graphics driver:
NVRM: BAR1 is 0M @ 0x0 (PCI:0000:02:02.0) [ 90.823015] NVRM: The system BIOS may have misconfigured your GPU. [ 90.823019] nvidia: probe of 0000:02:02.0 failed with error -1 [ 90.823031] NVRM: The NVIDIA probe routine failed for 1 device(s).
2.6. NVIDIA CUDA Toolkit Version Support
The releases in this release family of NVIDIA AI Enterprise support NVIDIA CUDA Toolkit 11.4.
For more information about NVIDIA CUDA Toolkit, see CUDA Toolkit 11.4 Documentation.
If you are using NVIDIA AI Enterprise with CUDA on Linux, avoid conflicting installation methods by installing CUDA from a distribution-independent runfile package. Do not install CUDA from a distribution-specific RPM or Deb package.
To ensure that the NVIDIA AI Enterprise graphics driver is not overwritten when CUDA is installed, deselect the CUDA driver when selecting the CUDA components to install.
For more information, see NVIDIA CUDA Installation Guide for Linux.
2.7. Support
is supported only on a subset of supported GPUs, VMware vSphere Hypervisor (ESXi) releases, and guest operating systems.
is supported for both time-sliced and MIG-backed vGPUs on all supported GPUs, hypervisor software releases, and guest operating systems.
- Unified memory
- Debuggers
- Profilers
Supported GPUs
- Tesla M6
- Tesla M10
- Tesla M60
- Tesla P4
- Tesla P6
- Tesla P40
Supported VMware vSphere Hypervisor (ESXi) Releases
Supported Guest OS Releases
Known Issues with Support
Use Case | Affected GPUs | Issue |
---|---|---|
Migration to or from a host running an NVIDIA AI Enterprise 14 release |
|
index.html#bug-3512790-session-freeze-linux-vm-migration-to-from-release-14 |
Migration between hosts with different ECC memory configuration | All GPUs that support | Migration of VMs configured with vGPU stops before the migration is complete |
2.8. Multiple vGPU Support
To support applications and workloads that are compute or graphics intensive, multiple vGPUs can be added to a single VM. The assignment of more than one vGPU to a VM is supported only on a subset of vGPUs and VMware vSphere Hypervisor (ESXi) releases.
Supported vGPUs
Only Q-series vGPUs that are allocated all of the physical GPU's frame buffer are supported.
Only Q-series and C-series time-sliced vGPUs that are allocated all of the physical GPU's frame buffer are supported. MIG-backed vGPUs are not supported.
Maximum vGPUs per VM
NVIDIA AI Enterprise supports up to a maximum of four vGPUs per VM on VMware vSphere Hypervisor (ESXi).
Supported Hypervisor Releases
Supported Hypervisor Releases
All hypervisors that support NVIDIA AI Enterprise are supported.
2.9. Peer-to-Peer CUDA Transfers over NVLink Support
Peer-to-peer CUDA transfers enable device memory between vGPUs on different GPUs that are assigned to the same VM to be accessed from within the CUDA kernels. NVLink is a high-bandwidth interconnect that enables fast communication between such vGPUs. Peer-to-Peer CUDA transfers over NVLink are supported only on a subset of vGPUs, VMware vSphere Hypervisor (ESXi) releases, and guest OS releases.
Supported vGPUs
Only Q-series vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support NVLink are supported.
Only Q-series and C-series time-sliced vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support NVLink are supported.
Supported Hypervisor Releases
Peer-to-Peer CUDA Transfers over NVLink are supported on all hypervisor releases that support the assignment of more than one vGPU to a VM. For details, see Multiple vGPU Support.
Supported Guest OS Releases
Linux only. Peer-to-Peer CUDA Transfers over NVLink are not supported on Windows.
Limitations
- PCIe is not supported.
- SLI is not supported.
2.10. Unified Memory Support
Unified memory is a single memory address space that is accessible from any CPU or GPU in a system. It creates a pool of managed memory that is shared between the CPU and GPU to provide a simple way to allocate and access data that can be used by code running on any CPU or GPU in the system. Unified memory is supported only on a subset of vGPUs and guest OS releases.
Supported vGPUs
Only Q-series vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support unified memory are supported.
Only time-sliced vGPUs that are allocated all of the physical GPU's frame buffer on physical GPUs that support unified memory are supported.
Supported Guest OS Releases
Linux only. Unified memory is not supported on Windows.
Limitations
2.11. NVIDIA GPU Operator Support
NVIDIA GPU Operator simplifies the deployment of NVIDIA AI Enterprise with software container platforms on immutable operating systems. An immutable operating system does not allow the installation of the NVIDIA AI Enterprise graphics driver directly on the operating system. NVIDIA GPU Operator is supported only on specific combinations of VMware vSphere Hypervisor (ESXi) release, container platform, and guest OS release.
VMware vSphere Hypervisor (ESXi) Release | Container Platform | Guest OS |
---|
VMware vSphere Hypervisor (ESXi) Release | Container Platform | Guest OS |
---|
3. NVIDIA AI Enterprise Supported Cloud Services
NVIDIA AI Enterprise is supported on several cloud services with bring-your-own-license (BYOL) licensing.
3.1. Amazon Web Services Elastic Compute Cloud (AWS EC2)
GPU | Supported AWS EC2 Instances | Supported Guest Operating Systems |
---|---|---|
NVIDIA T4 |
g4dn.xlarge g4dn.2xlarge g4dn.4xlarge g4dn.8xlarge g4dn.12xlarge g4dn.16xlarge |
Red Hat Enterprise Linux 8.4 Ubuntu 22.04 Ubuntu 20.04 |
NVIDIA V100 |
P3.2xlarge P3.8xlarge P3.16xlarge |
|
NVIDIA A10G |
g5.xlarge g5.2xlarge g5.4xlarge g5.8xlarge g5.12xlarge g5.16xlarge g5.24xlarge g5.48xlarge |
|
NVIDIA A100 |
p4d.24xlarge |
3.2. Google Cloud Platform (GCP)
GPU | Supported GCP Instances | Supported Guest Operating Systems |
---|---|---|
Tesla T4 |
Any custom machine type that can be created in a zone. |
Red Hat Enterprise Linux 8.4 Ubuntu 22.04 Ubuntu 20.04 |
Tesla V100 | ||
Tesla A100 |
a2-highgpu-1g a2-highgpu-2g a2-highgpu-4g a2-highgpu-8g a2-megagpu-16g |
3.3. Microsoft Azure
GPU | Supported Azure Instances | Supported Guest Operating Systems |
---|---|---|
NVIDIA V100 |
NC6s_v3 NC12s_v3 NC24s_v3 NC24rs_v3 ND40rs_v2 |
Red Hat Enterprise Linux 8.4 Ubuntu 22.04 Ubuntu 20.04 |
NVIDIA T4 |
NC4asT4_v3 NC8asT4_v3 NC16asT4_v3 NC64asT4_v3 |
|
NVIDIA A100 |
ND96asr_v4 ND96amsr_A100_v4 |
|
NVIDIA A10 |
NV6ads_A10_v5 NV12ads_A10_v5 NV18ads_A10_v5 NV36ads_A10_v5 NV36adms_A10_v5 NV72ads_A10_v5 |
3.4. NVIDIA GPU Optimized VMI on CSP Marketplace
For ease of use in the cloud, NVIDIA will also provide compute optimized and validated base Virtual Machine Instances (VMI) through CSP marketplaces. The VMI includes key technologies and software from NVIDIA for rapid deployment, management, and scaling of AI workloads in the modern hybrid cloud.
The VMI will have the following software pre-installed:
-
Ubuntu Server 20.04
-
NVIDIA driver 470TRD - 470.103.01
-
Docker-ce 20.10.12
-
NVIDIA Container Toolkit 1.8.1
-
NVIDIA Container Runtime 3.8.1
4. CPU Only Server Support
NVIDIA AI Enterprise supports deployments on CPU only servers that are part of the NVIDIA Certfied Systems list. Customers can deploy both GPU and CPU Only systems with VMware vSphere or Red Hat Enterprise Linux.
NVIDIA AI Enterprise will support the following CPU enabled frameworks:
-
TensorFlow
-
PyTorch
-
Triton Inference Server with FIL backend
-
NVIDIA RAPIDS with XGBoost and Dask
5. Known Product Limitations
Known product limitations for this release of NVIDIA AI Enterprise are described in the following sections.
5.1. Issues occur when the channels allocated to a vGPU are exhausted
Description
Issues occur when the channels allocated to a vGPU are exhausted and the guest VM to which the vGPU is assigned fails to allocate a channel to the vGPU. A physical GPU has a fixed number of channels and the number of channels allocated to each vGPU is inversely proportional to the maximum number of vGPUs allowed on the physical GPU.
When the channels allocated to a vGPU are exhausted and the guest VM fails to allocate a channel, the following errors are reported on the hypervisor host or in an NVIDIA bug report:
Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): Guest attempted to allocate channel above its max channel limit 0xfb Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): VGPU message 6 failed, result code: 0x1a Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): 0xc1d004a1, 0xff0e0000, 0xff0400fb, 0xc36f, Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): 0x1, 0xff1fe314, 0xff1fe038, 0x100b6f000, 0x1000, Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): 0x80000000, 0xff0e0200, 0x0, 0x0, (Not logged), Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): 0x1, 0x0 Jun 26 08:01:25 srvxen06f vgpu-3[14276]: error: vmiop_log: (0x0): , 0x0
Workaround
Use a vGPU type with more frame buffer, thereby reducing the maximum number of vGPUs allowed on the physical GPU. As a result, the number of channels allocated to each vGPU is increased.
5.2. Total frame buffer for vGPUs is less than the total frame buffer on the physical GPU
If error-correcting code (ECC) memory is enabled on a physical GPU that does not have HBM2 memory, the amount of frame buffer that is usable by vGPUs is further reduced. All types of vGPU are affected, not just vGPUs that support ECC memory.
On all GPUs that support ECC memory and, therefore, dynamic page retirement, additional frame buffer is allocated for dynamic page retirement. The amount that is allocated is inversely proportional to the maximum number of vGPUs per physical GPU. All GPUs that support ECC memory are affected, even GPUs that have HBM2 memory or for which ECC memory is disabled.
The approximate amount of frame buffer that NVIDIA AI Enterprise reserves can be calculated from the following formula:
max-reserved-fb = vgpu-profile-size-in-mb÷16 + 16 + ecc-adjustments + page-retirement-allocation + compression-adjustment
- max-reserved-fb
- The maximum total amount of reserved frame buffer in Mbytes that is not available for vGPUs.
- vgpu-profile-size-in-mb
- The amount of frame buffer in Mbytes allocated to a single vGPU. This amount depends on the vGPU type. For example, for the T4-16Q vGPU type, vgpu-profile-size-in-mb is 16384.
- ecc-adjustments
- The amount of frame buffer in Mbytes that is not usable by vGPUs when ECC is enabled on a
physical GPU that does not have HBM2 memory.
- If ECC is enabled on a physical GPU that does not have HBM2 memory ecc-adjustments is fb-without-ecc/16, which is equivalent to 64 Mbytes for every Gbyte of frame buffer assigned to the vGPU. fb-without-ecc is total amount of frame buffer with ECC disabled.
- If ECC is disabled or the GPU has HBM2 memory, ecc-adjustments is 0.
- page-retirement-allocation
- The amount of frame buffer in Mbytes that is reserved for dynamic page retirement.
- On GPUs based on the NVIDIA Maxwell GPU architecture, page-retirement-allocation = 4÷max-vgpus-per-gpu.
- On GPUs based on NVIDIA GPU architectures after the Maxwell architecture, page-retirement-allocation = 128÷max-vgpus-per-gpu
- max-vgpus-per-gpu
- The maximum number of vGPUs that can be created simultaneously on a physical GPU. This number varies according to the vGPU type. For example, for the T4-16Q vGPU type, max-vgpus-per-gpu is 1.
- compression-adjustment
-
The amount of frame buffer in Mbytes that is reserved for the higher compression overhead in vGPU types with 12 Gbytes or more of frame buffer on GPUs based on the Turing architecture.
compression-adjustment depends on the vGPU type as shown in the following table.
vGPU Type Compression Adjustment (MB) For all other vGPU types, compression-adjustment is 0.
5.3. Single vGPU benchmark scores are lower than pass-through GPU
Description
A single vGPU configured on a physical GPU produces lower benchmark scores than the physical GPU run in pass-through mode.
Aside from performance differences that may be attributed to a vGPU’s smaller frame buffer size, vGPU incorporates a performance balancing feature known as Frame Rate Limiter (FRL). On vGPUs that use the best-effort scheduler, FRL is enabled. On vGPUs that use the fixed share or equal share scheduler, FRL is disabled.
FRL is used to ensure balanced performance across multiple vGPUs that are resident on the same physical GPU. The FRL setting is designed to give good interactive remote graphics experience but may reduce scores in benchmarks that depend on measuring frame rendering rates, as compared to the same benchmarks running on a pass-through GPU.
5.4. VMs configured with large memory fail to initialize vGPU when booted
Description
When starting multiple VMs configured with large amounts of RAM (typically more than 32GB per VM), a VM may fail to initialize vGPU. In this scenario, the VM boots in VMware SVGA mode. The NVIDIA AI Enterprise GPU is present in Windows Device Manager but displays a warning sign, and the following device status:
Windows has stopped this device because it has reported problems. (Code 43)
The VMware vSphere VM’s log file contains these error messages:
6. Known Issues
6.1. The NVIDIA MOFED driver container fails to install the driver if Network Operator is installed
Description
The NVIDIA MOFED driver container fails to install the driver if Network Operator is installed. The installation fails because the container fails to unload the ib_core module. The rdma-core package is installed as part of the Red Hat CoreOS installation. This package loads the ib_core module if the system has Mellanox network interface cards (NICs).
Status
Open
Ref. #
3565857
6.2. Migration of VMs configured with vGPU stops before the migration is complete
Description
When a VM configured with vGPU is migrated to another host, the migration stops before it is complete.
This issue occurs if the ECC memory configuration (enabled or disabled) on the source and destination hosts are different. The ECC memory configuration on both the source and destination hosts must be identical.
Workaround
Before attempting to migrate the VM again, ensure that the ECC memory configuration on both the source and destination hosts are identical.
Status
Not an NVIDIA bug
Ref. #
200520027
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