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You are setting up a new A1 server with multiple NVIDIA GPUs for deep learning workloads. You need to install the NVIDIA Container Toolkit. After installing the necessary packages using your distribution's package manager, what is the most crucial next step to ensure proper integration with Docker?
A. Run 'nvidia-ctk runtime configure -runtime=docker' .
B. Reboot the server to load the new kernel modules.
C. Manually create vetc/docker/daemon.json' and add 'nvidia' as a default runtime.
D. Restart the Docker daemon (e.g., 'systemctl restart docker'
E. Run 'nvidia-smr to verify GPU detection. This automatically configures the container runtime.
Answer: A
Explanation:
The 'nvidia-ctk runtime configure -runtime=docker' command is essential to configure Docker to use the NVIDIA Container Runtime. This integrates the NVIDIA drivers and libraries within the Docker environment, allowing containers to access the GPUs. While restarting Docker is often helpful, 'nvidia-ctk' performs the necessary configuration steps. Rebooting is not always necessary after just installing a package, and manually editing 'daemon.json' is generally not the recommended approach. 'nvidia-smi' simply reports the status of the GPUs, it doesn't configure the runtime.
NEW QUESTION # 156
You are configuring a BlueField-3 DPLJ for a cloud-native application using Kubernetes. You want to offload container networking using OVS (Open vSwitch). Which of the following configuration steps are NECESSARY to integrate the BlueField-3 DPIJ with the Kubernetes cluster for network offload? (Select TWO)
A. Configure the BlueField DPIJ to act as a DHCP server for the Kubernetes pods.
B. Configure the Kubernetes CNI (Container Network Interface) to use the OVS bridge managed by the BlueField DPIJ.
C. Manually create OVS bridges and vPorts on the BlueField DPU using 'ovs-vsctr.
D. Deploy the NVIDIA BlueField Kubernetes Operator to manage the DPIJ lifecycle and networking configurations.
E. Install the Mellanox OFED drivers on all Kubernetes worker nodes.
Answer: B,D
Explanation:
The NVIDIA BlueField Kubernetes Operator is essential for automating the management and configuration of the DPIJ within the Kubernetes environment. This includes creating and managing OVS bridges. Integrating the Kubernetes CNI to use the OVS bridge managed by the BlueField DPIJ allows pod networking traffic to be offloaded to the DPU. Installing Mellanox OFED everywhere isn't needed with the operator. While you could manually create the bridges (E), the operator is the preferred method. The DPIJ acting as a DHCP server (D) is not a requirement for simple network offload.
NEW QUESTION # 157
After upgrading the NGC CLI using 'pip install -upgrade nvidia-cli' , some commands are no longer working as expected, producing errors related to missing modules. What is the most likely reason for this issue and how can you resolve it?
A. The host's operating system must be re-imaged.
B. The NGC CLI upgrade introduced breaking changes. Review the NGC CLI release notes and update your scripts accordingly.
C. The system's PATH variable has not been updated to reflect the new NGC CLI installation location. Update the PATH variable accordingly.
D. The Python environment used by the NGC CLI might be broken or inconsistent. Create a new virtual environment and reinstall the NGC CLI in the new environment.
E. The upgrade process might have corrupted the NGC CLI installation. Reinstall the package using 'pip install -force-reinstall nvidia-cli'.
Answer: B,D
Explanation:
Breaking changes in the NGC CLI upgrade (B) are a possibility, requiring script updates. An inconsistent Python environment (C) can also cause issues after an upgrade. Reinstalling (A) or updating the PATH (D) might not resolve the issue if the environment itself is the problem. OS re-imaging is highly unnecessary (E).
NEW QUESTION # 158
After successfully installing the NVIDIA Container Toolkit and configuring the Docker runtime, you attempt to run a container that requires GPU access. However, the container fails to start with an error indicating that no GPUs are detected. You've verified that 'nvidia-smi' works on the host. Which of the following could be potential causes for this issue? (Select all that apply)
A. The NVIDIA drivers are not compatible with the kernel version running on the host.
B. The '-gpus all' flag was not included when running the 'docker run' command.
C. The container image itself is missing the necessary CUDA libraries.
D. The NVIDIA Container Toolkit package is corrupted and needs to be reinstalled.
E. The Docker daemon was not restarted after configuring the NVIDIA runtime.
Answer: A,B,E
Explanation:
Several factors can cause this issue. A driver-kernel incompatibility (A) prevents the NVIDIA drivers from properly communicating with the hardware. Forgetting to restart the Docker daemon (B) means the configuration changes applied by 'nvidia-ctk' are not active. The '-gpus all' (or equivalent) flag (C) is mandatory to explicitly request GPU resources for the container. Corrupted toolkit (D) would likely present installation failures earlier. Missing CUDA libraries (E) would likely lead to runtime errors within the container, not a failure to detect the GPUs in the first place.
NEW QUESTION # 159
You're debugging performance issues in a distributed training job. 'nvidia-smi' shows consistently high GPU utilization across all nodes, but the training speed isn't increasing linearly with the number of GPUs. Network bandwidth is sufficient. What is the most likely bottleneck?
A. The global batch size has exceeded the optimal point for the model, reducing per-sample accuracy and slowing convergence.
B. NCCL is not configured optimally for the network topology, leading to high communication overhead.
C. CUDA Graphs is not being utilized.
D. Inefficient data loading and preprocessing pipeline, causing GPUs to wait for data.
E. The learning rate is not adjusted appropriately for the increased batch size across multiple GPUs.
Answer: A,B,D,E
Explanation:
If GPUs are highly utilized but scaling is poor, the bottleneck is likely not GPU compute itself. Inefficient data pipelines mean GPUs spend time idle waiting for data. Suboptimal NCCL configurations result in communication overhead negating the benefit of more GPUs. Incorrect learning rate with larger batch size will impact covergence. Batch sizes can affect convergence and model effectiveness. While CUDA Graphs improves performance, the other answers are more pertinent to the question.
NEW QUESTION # 160
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