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You are deploying a DOCA application that needs to interact with the host operating system for certain tasks. What are the potential challenges and solutions for achieving this interaction securely and efficiently?
A. Challenges: Difficulty in debugging and troubleshooting issues across the host-DPU boundary. Solutions: Using comprehensive logging and tracing mechanisms, implementing remote debugging tools, and establishing clear communication channels between the host and DPU components.
B. Challenges: Resource contention between the host and DPU applications. Solutions: Using proper resource allocation and prioritization mechanisms, such as cgroups and QOS policies, to prevent resource starvation.
C. Challenges: Limited direct access to host resources, security concerns, and potential performance overhead. Solutions: Using DOCA Comm Channel for control message exchange, utilizing shared memory for data transfer, and employing secure APIs for host interaction.
D. Challenges: Kernel module compatibility issues and potential conflicts with host drivers. Solutions: Using standard Linux APIs whenever possible, avoiding direct kernel module modifications, and testing thoroughly for compatibility.
E. Solutions: Direct Memory Access on non secured memory for performance
Answer: A,B,C,D
Explanation:
Interacting with the host OS poses several challenges, including limited access, security concerns, and potential conflicts. The solutions involve using secure communication channels, standard APIs, comprehensive debugging mechanisms, and resource allocation policies. Direct Memory access on non-secured memory is not a solution for secure and efficient communication.
NEW QUESTION # 14
You have successfully pulled a TensorFlow container from NGC and now need to run it on your stand-alone GPU-enabled server.
Which command should you use to ensure that the container has access to all available GPUs?
A. docker run --gpus all nvcr.io/nvidia/tensorflow:<tag>
B. kubectl create pod --gpu=all nvcr.io/nvidia/tensorflow:<tag>
C. docker start nvcr.io/nvidia/tensorflow:<tag>
D. docker run nvcr.io/nvidia/tensorflow:<tag>
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When running a GPU-enabled container directly on a server with Docker, the flag--gpus allis required to allow the container access to all GPUs on the host system. This ensures that the TensorFlow container can utilize GPU resources fully. The other options either do not specify GPU access correctly or are Kubernetes- specific commands.
NEW QUESTION # 15
A BCM pipeline deployed using containers is failing to launch with a 'CUDA driver version is insufficient for CUDA runtime version' error.
What is the root cause and solution?
A. The container image has an incorrect CUDA runtime version. Rebuild the container image with a CUDA runtime version compatible with the host driver.
B. The host machine's NVIDIA driver is older than the CUDA runtime version used in the container. Upgrade the host's NVIDIA driver.
C. The container is not requesting GPU resources correctly. Ensure the container runtime is configured to allow GPU access.
D. A and B
E. The 'nvidia-container-runtime' is not properly configured. Verify the configuration and restart the container runtime.
Answer: D
Explanation:
The error indicates a mismatch between host driver and container runtime versions. Upgrading the driver or rebuilding the container with a compatible runtime are the solutions.
NEW QUESTION # 16
After installing BCM, you notice that it's not displaying any GPU metrics. You've verified that the NVIDIA GPU Operator is installed and functioning correctly. What is the MOST likely cause of this issue?
A. The BCM server is not properly configured to communicate with the Kubernetes API server.
B. The NVIDIA Data Center GPU Manager (DCGM) is not properly configured or running on the GPU nodes.
C. The NVIDIA drivers are outdated.
D. The BCM agent is not installed on the GPU nodes.
E. The GPU nodes do not have internet access.
Answer: B
Explanation:
BCM relies on DCGM to collect GPU metrics. If DCGM is not properly configured or running, BCM will not be able to retrieve the necessary data to display GPU metrics. While the other options could potentially cause issues, a misconfigured DCGM is the most common reason for this specific symptom.
NEW QUESTION # 17
A cloud engineer is looking to provision a virtual machine for machine learning using the NVIDIA Virtual Machine Image (VMI) and Rapids.
What technology stack will be set up for the development team automatically when the VMI is deployed?
A. Cent OS, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI
B. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver, Rapids
C. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver
D. Ubuntu Server, Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The NVIDIA Virtual Machine Image (VMI) for machine learning provisioning automatically sets up anUbuntu Serverenvironment with essential components including Docker-CE, NVIDIA Container Toolkit, CSP CLI, NGC CLI, NVIDIA Driver, andRapids-a suite of GPU-accelerated data science and analytics libraries. This comprehensive stack enables immediate development and deployment of ML workloads.
NEW QUESTION # 18
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