Compute resource management in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to request 4 CPUs for a Kubernetes pod.

MLOps

resources:
  requests:
    cpu: [1]

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

A4

B2

C8

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory units instead of CPU

Specifying CPU as a string with units like '4m'

Setting cpu: 4 requests 4 CPUs for the pod.

2fill in blank

medium

Complete the command to check current GPU usage on a node.

MLOps

nvidia-smi [1]

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

A--query-gpu=memory.used

B--list-gpus

C--help

D--version

Attempts:

3 left

💡 Hint

Common Mistakes

Using --list-gpus which only lists GPUs

Using --version which shows version info

The --query-gpu=memory.used option shows GPU memory usage.

3fill in blank

hard

Fix the error in the YAML to limit memory to 8Gi.

MLOps

resources:
  limits:
    memory: [1]

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

A8000Mi

B8GB

C8Gi

D8g

Attempts:

3 left

💡 Hint

Common Mistakes

Using 8GB which is invalid

Using lowercase g instead of Gi

Memory limits in Kubernetes use binary SI units like Gi, so 8Gi is correct.

4fill in blank

hard

Fill both blanks to create a resource request for 2 CPUs and 4Gi memory.

MLOps

resources:
  requests:
    cpu: [1]
    memory: [2]

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

A2

B4Gi

C8Gi

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing CPU and memory units

Using incorrect memory units like GB

Requesting 2 CPUs and 4Gi memory uses cpu: 2 and memory: 4Gi.

5fill in blank

hard

Fill all three blanks to define a pod with 1 CPU request, 2Gi memory request, and 1 GPU request.

MLOps

resources:
  requests:
    cpu: [1]
    memory: [2]
    nvidia.com/gpu: [3]

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

A1

B2Gi

D4Gi

Attempts:

3 left

💡 Hint

Common Mistakes

Using memory units for CPU or GPU

Incorrect GPU key name

The pod requests 1 CPU, 2Gi memory, and 1 GPU using the correct keys and values.

Practice

1. What is the main purpose of compute resource management in MLOps?

easy

A. To write machine learning model code

B. To store data permanently on disk

C. To create user interfaces for ML applications

D. To control CPU, memory, and GPU usage for efficient job execution

Solution

1. Step 1: Understand resource management role

   Compute resource management controls hardware resources like CPU, memory, and GPU.

2. Step 2: Identify its purpose in MLOps

   It ensures jobs run efficiently and avoid crashes by managing these resources.

3. Final Answer:

   To control CPU, memory, and GPU usage for efficient job execution -> Option D

4. Quick Check:

   Resource management = control CPU, memory, GPU [OK]

Hint: Think about what hardware resources need managing [OK]

Common Mistakes:

• Confusing resource management with coding tasks
• Thinking it manages data storage only
• Assuming it builds user interfaces

2. Which command correctly allocates GPU resources for a job in Kubernetes?

easy

A. kubectl run job --gpu=2

B. kubectl run job --requests=nvidia.com/gpu=2

C. kubectl run job --memory=2Gi

D. kubectl run job --cpu=2

Solution

1. Step 1: Recall Kubernetes resource request syntax

   Kubernetes uses resource requests like --requests=nvidia.com/gpu=2 to allocate GPUs.

2. Step 2: Match correct GPU allocation command

   kubectl run job --requests=nvidia.com/gpu=2 uses the correct syntax for GPU requests in Kubernetes.

3. Final Answer:

   kubectl run job --requests=nvidia.com/gpu=2 -> Option B

4. Quick Check:

   GPU allocation uses --requests=nvidia.com/gpu [OK]

Hint: Look for --requests with nvidia.com/gpu key [OK]

Common Mistakes:

• Using --gpu directly (not valid syntax)
• Confusing memory or CPU flags with GPU
• Missing the resource request keyword

3. Given this Kubernetes pod spec snippet, what is the CPU limit set for the container?

resources:
  limits:
    cpu: "4"
  requests:
    cpu: "2"

medium

A. 4 CPUs

B. 6 CPUs

C. No CPU limit set

D. 2 CPUs

Solution

1. Step 1: Identify CPU limit in pod spec

   The 'limits' section sets the maximum CPU usage, here cpu: "4" means 4 CPUs.

2. Step 2: Understand difference between requests and limits

   Requests are minimum guaranteed (2 CPUs), limits are max allowed (4 CPUs).

3. Final Answer:

   4 CPUs -> Option A

4. Quick Check:

   CPU limit = 4 CPUs [OK]

Hint: Limits set max CPU, requests set minimum [OK]

Common Mistakes:

• Confusing requests with limits
• Ignoring quotes around CPU values
• Assuming no limit means unlimited

4. You see this error when submitting a job: Insufficient cpu resources. What is the most likely cause?

medium

A. The job is missing GPU allocation

B. The job has no CPU requests set

C. The job requests more CPU than available on the cluster

D. The job memory limit is too high

Solution

1. Step 1: Interpret the error message

   'Insufficient cpu resources' means requested CPU exceeds cluster capacity.

2. Step 2: Identify cause from options

   The job requests more CPU than available on the cluster matches the error cause: job requests more CPU than available.

3. Final Answer:

   The job requests more CPU than available on the cluster -> Option C

4. Quick Check:

   Insufficient CPU = request > available [OK]

Hint: Error means requested CPU > cluster CPU [OK]

Common Mistakes:

• Assuming missing CPU requests cause this error
• Confusing CPU and GPU errors
• Blaming memory limits for CPU shortage

5. You want to run multiple ML training jobs on a GPU cluster. Which strategy best manages GPU resources to avoid conflicts?

hard

A. Allocate GPUs explicitly per job and release after completion

B. Run all jobs without GPU limits and share GPUs freely

C. Assign CPU limits only and ignore GPU allocation

D. Use only CPU resources to avoid GPU conflicts

Solution

1. Step 1: Understand GPU resource management needs

   Explicit allocation prevents multiple jobs from using the same GPU simultaneously.

2. Step 2: Evaluate options for best practice

   Allocate GPUs explicitly per job and release after completion correctly allocates and releases GPUs per job to avoid conflicts.

3. Final Answer:

   Allocate GPUs explicitly per job and release after completion -> Option A

4. Quick Check:

   Explicit GPU allocation avoids conflicts [OK]

Hint: Always allocate and release GPUs per job [OK]

Common Mistakes:

• Ignoring GPU allocation causing conflicts
• Assuming CPU limits control GPU usage
• Avoiding GPUs when cluster has them