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You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects. Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?
A. NVLink provides a direct, high-bandwidth, low-latency connection between GPUs, which is generally superior to PCle Gen4 for deep learning training involving frequent inter-GPU communication.
B. PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
C. NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.
D. The choice between PCle Gen4 and NVLink depends solely on the type of deep learning framework being used; TensorFlow requires PCle Gen4, while PyTorch benefits from NVLink.
E. PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.
Answer: A
Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.
NEW QUESTION # 48
You're configuring a BlueField-3 DPU-based server for high-performance storage. You want to utilize NVMe-oF (NVMe over Fabrics) to access remote NVMe SSDs. What is the primary benefit of using a BlueField DPU in this NVMe-oF setup compared to a traditional server with a standard NIC?
A. BlueField DPU allows hot-swapping of NVMe SSDs without interrupting the NVMe-oF connection.
B. BlueField DPU eliminates the need for a separate NVMe-oF target server.
C. BlueField DPU automatically configures the NVMe-oF target without any manual intervention.
D. BlueField DPU provides built-in hardware encryption for all NVMe-oF traffic.
E. BlueField DPU offloads the NVMe-oF protocol processing, reducing CPU overhead on the host server.
Answer: E
Explanation:
The key advantage of using a BlueField DPU in an NVMe-oF setup is its ability to offload the NVMe-oF protocol processing. This significantly reduces the CPU overhead on the host server, allowing it to dedicate more resources to other tasks. While some DPUs might offer features like hardware encryption, the primary benefit is protocol offload.
NEW QUESTION # 49
You are developing a distributed deep learning application that uses multiple GPUs across several Docker containers running on different physical servers. How do you ensure that each container can access and utilize the GPUs on its respective host?
A. Create a custom Docker network and configure each container to use the network's gateway as the default GPU device.
B. Manually configure each container to use the 'CUDA VISIBLE DEVICES' environment variable to specify the GPUs it should use on its respective host.
C. Install the same version of NVIDIA drivers on all host machines and configure a network file system (NFS) to share the CUDA libraries between the containers.
D. Use Docker Swarm and specify GPU resource constraints in the 'docker-compose.yml' file to allocate GPUs to each service.
E. Ensure the NVIDIA Container Toolkit is installed and configured on each host machine, and use a container orchestration platform like Kubernetes to manage the deployment and GPU assignment.
Answer: E
Explanation:
The most robust solution for distributed GPU utilization is to leverage a container orchestration platform like Kubernetes (B) along with the NVIDIA Container Toolkit. Kubernetes handles scheduling, resource allocation (including GPUs), and networking across multiple nodes.
The NVIDIA Container Toolkit ensures that each container can access the GPUs on its host. While (C) is useful, it's not sufficient for multi-server deployments. Docker Swarm (D) can work but lacks the sophisticated GPU scheduling capabilities of Kubernetes. NFS sharing (A) is unnecessary and can introduce performance bottlenecks. A custom Docker network (E) doesn't directly address GPU access.
NEW QUESTION # 50
After installing the NGC CLI, you attempt to run 'ngc config set' and encounter the following error: 'Error: API key is invalid or missing'.
What are the most likely causes of this issue and how can you resolve them?
A. The host does not have network access to NGC.
B. The NGC CLI configuration file is corrupted. Delete the file (A.ngc/config.json') and reconfigure the CLI.
C. The NGC API key is incorrect or has expired. Verify the API key in your NVIDIA account and update the configuration using 'ngc config set'.
D. The NGC CLI is not properly installed. Reinstall the package using 'pip install -upgrade nvidia-cli'
E. The NGC service is down. Check the NVIDIA NGC status page for any known outages.
Answer: A,B,C
Explanation:
The most likely cause is an invalid API key (B) or a corrupted configuration file (C), or the host lacks network access (E). Reinstalling the package (A) might not resolve the issue if the problem lies with the API key or config file. While NGC service outages (D) are possible, they are less common.
NEW QUESTION # 51
After installing NGC CLI using pip, you encounter 'ngc' command not found error even though pip install reported successful. What can be the cause?
A. The NGC CLI installation was corrupted. Run 'pip install -force-reinstall nvidia-cli'
B. NGC CLI only works inside Docker containers.
C. The host's operating system is not supported by NGC CLI.
D. The shell needs to be reloaded or a new terminal session initiated for PATH changes to take effect.
E. The python executable where NGC CLI got installed is not in the system PATH.
Answer: D,E
Explanation:
The most common reason the 'ngc' command isn't found is that the python environment's executable path isn't in the system PATH (A). A quick fix to ensure environment variables are updated in your current shell is to reload the shell or start a new session (C).
NEW QUESTION # 52
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