Distributed training basics in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Parameter Server Role

In a distributed training setup using a parameter server architecture, what is the primary role of the parameter server?

A It manages the hardware resources like GPUs and CPUs.

B It performs data preprocessing before sending batches to workers.

C It stores and updates the global model parameters shared among workers.

D It handles user authentication and access control for the training cluster.

💡 Hint

Think about where the model weights are kept and updated during training.

The parameter server holds the global model parameters and updates them as workers send gradients. It acts as a central point for synchronizing model state.

💻 Command Output

intermediate

Output of Distributed Training Node Status Command

What is the expected output of the command kubectl get pods -l app=distributed-train if three training pods are running successfully?

MLOps

kubectl get pods -l app=distributed-train

A

NAME                 READY   STATUS    RESTARTS   AGE
train-worker-0       0/1     Pending   0          10m
train-worker-1       0/1     Pending   0          10m
train-worker-2       0/1     Pending   0          10m

B

NAME                 READY   STATUS    RESTARTS   AGE
train-worker-0       1/1     Running   0          10m
train-worker-1       1/1     Running   0          10m
train-worker-2       1/1     Running   0          10m

C Error from server (NotFound): pods "train-worker-0" not found

D No resources found in default namespace.

💡 Hint

Look for pods with status 'Running' and readiness '1/1'.

The command lists pods with label app=distributed-train. If three pods are running successfully, each will show 1/1 READY and Running STATUS.

❓ Configuration

advanced

Correct Distributed Training Configuration Snippet

Which configuration snippet correctly sets up a distributed training job using Horovod with 4 worker replicas in Kubernetes?

A

apiVersion: batch/v1
kind: Job
metadata:
  name: horovod-job
spec:
  parallelism: 4
  template:
    spec:
      containers:
      - name: horovod
        image: horovod/horovod:latest
        command: ["horovodrun", "-np", "4", "python", "train.py"]
      restartPolicy: Never

B

apiVersion: batch/v1
kind: Job
metadata:
  name: horovod-job
spec:
  replicas: 4
  template:
    spec:
      containers:
      - name: horovod
        image: horovod/horovod:latest
        command: ["horovodrun", "-np", "4", "python", "train.py"]
      restartPolicy: Always

C

apiVersion: apps/v1
kind: Deployment
metadata:
  name: horovod-deployment
spec:
  replicas: 4
  template:
    spec:
      containers:
      - name: horovod
        image: horovod/horovod:latest
        command: ["horovodrun", "-np", "1", "python", "train.py"]
      restartPolicy: Always

D

apiVersion: batch/v1
kind: Job
metadata:
  name: horovod-job
spec:
  parallelism: 1
  template:
    spec:
      containers:
      - name: horovod
        image: horovod/horovod:latest
        command: ["horovodrun", "-np", "4", "python", "train.py"]
      restartPolicy: Never

💡 Hint

Check the correct field for parallelism in a Kubernetes Job and the restart policy.

For a Kubernetes Job, parallelism controls how many pods run in parallel. The command must request 4 processes. Restart policy should be Never for batch jobs.

❓ Troubleshoot

advanced

Diagnosing Worker Node Failure in Distributed Training

During distributed training, one worker node repeatedly crashes with an error related to 'connection refused' when trying to reach the parameter server. What is the most likely cause?

A The parameter server is not running or not reachable on the expected network address.

B The worker node has insufficient GPU memory to run the training batch.

C The worker node's disk is full, preventing checkpoint saving.

D The training script has a syntax error causing the crash.

💡 Hint

Connection refused usually means network or service availability issues.

'Connection refused' means the worker tried to connect but no service was listening at the address. This usually means the parameter server is down or network settings block access.

🔀 Workflow

expert

Correct Sequence for Distributed Training Job Deployment

Arrange the steps in the correct order to deploy a distributed training job on a Kubernetes cluster using a containerized training image.

A 3,1,2,4

B 2,1,3,4

C 1,3,2,4

D 1,2,3,4

💡 Hint

Think about the logical order from building to running and monitoring.

You must first build the image, then push it to a registry, then create the Kubernetes resources that use the image, and finally monitor the job.