GPU support in containers in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the GPU runtime when running a Docker container.

MLOps

docker run --gpus [1] nvidia/cuda:11.0-base nvidia-smi

Drag options to blanks, or click blank then click option'

Acpu

Bnone

Call

Ddefault

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'none' disables GPU access.

Using 'cpu' is invalid for GPU runtime.

Using 'default' does not specify GPU usage.

Using --gpus all allows the container to access all GPUs on the host.

2fill in blank

medium

Complete the Dockerfile line to install NVIDIA CUDA toolkit inside the container.

MLOps

RUN apt-get update && apt-get install -y [1]

Drag options to blanks, or click blank then click option'

Apython3

Bgit

Cnginx

Dcuda-toolkit-11-0

Attempts:

3 left

💡 Hint

Common Mistakes

Installing unrelated packages like python3 or nginx.

Forgetting to update apt-get before installing.

Installing cuda-toolkit-11-0 provides CUDA support inside the container.

3fill in blank

hard

Fix the error in the Docker run command to enable GPU support.

MLOps

docker run --runtime=[1] nvidia/cuda:11.0-base nvidia-smi

Drag options to blanks, or click blank then click option'

Adocker

Bnvidia

Cdefault

Dgpu

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'docker' or 'default' runtimes disables GPU support.

Using 'gpu' is not a valid runtime name.

The correct runtime for NVIDIA GPU support is nvidia.

4fill in blank

hard

Fill both blanks to create a Docker Compose service with GPU support.

MLOps

services:
  gpu-service:
    image: nvidia/cuda:11.0-base
    deploy:
      resources:
        reservations:
          devices:
            - driver: [1]
              count: [2]
              capabilities: [gpu]

Drag options to blanks, or click blank then click option'

Anvidia

Ball

C2

Ddefault

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'default' as driver disables GPU support.

Using 'all' as count is invalid; count expects a number.

The device driver should be nvidia and count specifies how many GPUs to reserve, e.g., 2.

5fill in blank

hard

Fill all three blanks to write a Dockerfile snippet that sets environment variables for CUDA and runs a GPU test.

MLOps

ENV CUDA_VERSION=[1]
ENV PATH=/usr/local/cuda-[2]/bin:${PATH}
RUN nvidia-smi --query-gpu=name,memory.total --format=csv > [3]

Drag options to blanks, or click blank then click option'

A11.0

C/tmp/gpu_info.csv

D/var/log/gpu.log

Attempts:

3 left

💡 Hint

Common Mistakes

Mismatching CUDA versions in ENV variables.

Saving output to a non-writable or unrelated file path.

Set CUDA version to 11.0, update PATH accordingly, and save GPU info to /tmp/gpu_info.csv.

Practice

1. What is the main purpose of enabling GPU support in containers?

easy

A. To reduce the container's memory usage

B. To increase the container's disk space

C. To enable network access inside the container

D. To allow containers to use the host's GPU for faster computing

Solution

1. Step 1: Understand GPU role in containers

   GPUs speed up computing tasks by handling parallel processing efficiently.

2. Step 2: Identify GPU support purpose

   Enabling GPU support allows containers to access the host's GPU hardware for faster computation.

3. Final Answer:

   To allow containers to use the host's GPU for faster computing -> Option D

4. Quick Check:

   GPU support = faster computing [OK]

Hint: GPU support means using host GPU inside container [OK]

Common Mistakes:

• Confusing GPU support with disk or memory changes
• Thinking GPU enables network access
• Assuming GPU support reduces container size

2. Which Docker command flag is used to enable GPU support when running a container?

easy

A. --gpus

B. --enable-gpu

C. --gpu-access

D. --use-gpu

Solution

1. Step 1: Recall Docker GPU flag syntax

   The official Docker flag to enable GPU support is --gpus.

2. Step 2: Verify other options

   Options like --enable-gpu, --gpu-access, and --use-gpu are incorrect or do not exist.

3. Final Answer:

   --gpus -> Option A

4. Quick Check:

   Docker GPU flag = --gpus [OK]

Hint: Docker GPU flag is exactly --gpus [OK]

Common Mistakes:

• Using incorrect flag names like --enable-gpu
• Confusing GPU flag with network or volume flags
• Omitting the flag entirely

3. What will be the output of the command docker run --gpus all nvidia/cuda:11.0-base nvidia-smi if the host has a compatible NVIDIA GPU and drivers installed?

medium

A. Displays the NVIDIA GPU status and driver information

B. Shows an error: 'nvidia-smi command not found'

C. Runs the container but shows no GPU information

D. Fails with 'GPU not accessible' error

Solution

1. Step 1: Understand the command purpose

   The command runs a container with full GPU access and executes nvidia-smi to show GPU info.

2. Step 2: Check host requirements

   If the host has compatible NVIDIA GPU and drivers, nvidia-smi runs successfully inside the container.

3. Final Answer:

   Displays the NVIDIA GPU status and driver information -> Option A

4. Quick Check:

   Host GPU + drivers + --gpus = nvidia-smi output [OK]

Hint: If host GPU ready, nvidia-smi shows GPU info inside container [OK]

Common Mistakes:

• Assuming nvidia-smi is missing inside official CUDA image
• Ignoring host driver requirements
• Expecting GPU info without --gpus flag

4. You run docker run --gpus all nvidia/cuda:11.0-base nvidia-smi but get the error: 'docker: Error response from daemon: could not select device driver'. What is the most likely cause?

medium

A. The container command syntax is incorrect

B. The Docker image does not support GPUs

C. The NVIDIA Container Toolkit is not installed on the host

D. The host has no internet connection

Solution

1. Step 1: Analyze the error message

   The error indicates Docker cannot find a GPU device driver to assign to the container.

2. Step 2: Identify missing component

   This usually happens if the NVIDIA Container Toolkit (nvidia-docker2) is not installed or configured on the host.

3. Final Answer:

   The NVIDIA Container Toolkit is not installed on the host -> Option C

4. Quick Check:

   Missing NVIDIA toolkit = device driver error [OK]

Hint: Device driver error means NVIDIA Container Toolkit missing [OK]

Common Mistakes:

• Blaming Docker image for GPU support
• Assuming syntax error causes this message
• Thinking internet is required for this error

5. You want to run a container with access to only GPUs 0 and 1 on a host with 4 GPUs. Which Docker run command correctly limits GPU access?

hard

A. docker run --gpus 2 nvidia/cuda:11.0-base nvidia-smi

B. docker run --gpus 'device=0,1' nvidia/cuda:11.0-base nvidia-smi

C. docker run --gpus all nvidia/cuda:11.0-base nvidia-smi

D. docker run --gpus 'count=2' nvidia/cuda:11.0-base nvidia-smi

Solution

1. Step 1: Understand GPU selection syntax

   To limit to specific GPUs 0 and 1, Docker uses the --gpus 'device=0,1' syntax to specify GPU IDs.

2. Step 2: Evaluate options

   docker run --gpus 2 nvidia/cuda:11.0-base nvidia-smi requests any 2 GPUs but does not specify GPUs 0 and 1. docker run --gpus 'count=2' nvidia/cuda:11.0-base nvidia-smi uses invalid syntax count=2. docker run --gpus all nvidia/cuda:11.0-base nvidia-smi uses all GPUs.

3. Final Answer:

   docker run --gpus 'device=0,1' nvidia/cuda:11.0-base nvidia-smi -> Option B

4. Quick Check:

   Specify GPUs by device IDs with --gpus 'device=...' [OK]

Hint: Use --gpus 'device=0,1' to pick specific GPUs [OK]

Common Mistakes:

• Using --gpus 2 without device IDs
• Using invalid syntax like count=2
• Assuming --gpus all limits GPUs