GPU infrastructure planning in Prompt Engineering / GenAI - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to specify the number of GPUs for training.

Prompt Engineering / GenAI

trainer = Trainer(model=model, args=TrainingArguments(per_device_train_batch_size=16, [1]=4))

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

An_gpu

Bnum_gpus

Cgpu_count

Ddevice_count

Attempts:

3 left

💡 Hint

Common Mistakes

Using num_gpus or gpu_count which are not standard argument names.

The correct argument to specify the number of GPUs in many training frameworks is n_gpu.

2fill in blank

medium

Complete the code to move the model to the GPU device.

Prompt Engineering / GenAI

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = model.[1](device)

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

Acuda

Bto

Cmove

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Using cuda as a method, which is not correct syntax here.

The to method moves the model to the specified device, such as GPU.

3fill in blank

hard

Fix the error in the code to correctly check GPU availability.

Prompt Engineering / GenAI

if torch.cuda.[1]() > 0:
    print('GPUs are available')

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

Adevice_count

Bis_available

Cavailable

Dcount

Attempts:

3 left

💡 Hint

Common Mistakes

Using is_available which returns a boolean, not a count.

The correct method to get the number of GPUs is torch.cuda.device_count().

4fill in blank

hard

Fill both blanks to create a dictionary mapping GPU IDs to their memory usage in MB.

Prompt Engineering / GenAI

gpu_memory = {i: torch.cuda.memory_allocated(i) // (1024 * 1024) for i in {{BLANK_2}}(torch.cuda.device_count())}

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

Ai

Brange

Ctorch

Ddevice

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch or device incorrectly in the loop.

We use i as the device ID and range to iterate over all GPU indices.

5fill in blank

hard

Fill all three blanks to set up distributed training with the correct backend and initialize the process group.

Prompt Engineering / GenAI

import torch.distributed as dist

dist.init_process_group(backend=[1], init_method='env://', world_size=[2], rank=[3])

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

A'nccl'

Bworld_size

Crank

D'gloo'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'gloo' backend for GPU training which is slower.

The backend for GPU distributed training is 'nccl'. The world_size and rank are variables representing total processes and current process ID.

Practice

1. Why is it important to plan GPU infrastructure before starting a GenAI project?

easy

A. To reduce the size of the AI model automatically

B. To ensure the GPU has enough memory and speed for the AI model

C. Because GPUs are always cheaper than CPUs

D. To avoid using any GPUs and rely only on CPUs

Solution

1. Step 1: Understand GPU role in AI projects

   GPUs speed up AI model training and need enough memory to handle data.

2. Step 2: Importance of matching GPU specs to model needs

   Choosing a GPU with insufficient memory or speed will slow down or fail the project.

3. Final Answer:

   To ensure the GPU has enough memory and speed for the AI model -> Option B

4. Quick Check:

   GPU specs must match AI needs = D [OK]

Hint: Match GPU memory and speed to your AI model size [OK]

Common Mistakes:

• Thinking CPUs can replace GPUs for heavy AI tasks
• Assuming all GPUs have the same performance
• Ignoring GPU memory limits

2. Which of the following is the correct way to check GPU memory using Python's PyTorch library?

easy

A. torch.cuda.memory_size()

B. torch.gpu.memory.total()

C. torch.cuda.get_device_properties(0).total_memory

D. torch.device.memory()

Solution

1. Step 1: Recall PyTorch GPU memory query syntax

   The correct method is torch.cuda.get_device_properties(device_id).total_memory.

2. Step 2: Check each option for correctness

   Only torch.cuda.get_device_properties(0).total_memory uses the correct PyTorch function and attribute.

3. Final Answer:

   torch.cuda.get_device_properties(0).total_memory -> Option C

4. Quick Check:

   Correct PyTorch GPU memory call = C [OK]

Hint: Use torch.cuda.get_device_properties(0).total_memory to check GPU memory [OK]

Common Mistakes:

• Using non-existent PyTorch functions
• Confusing device and memory functions
• Missing the device index argument

3. Given this Python code snippet using PyTorch, what will be printed?

import torch
if torch.cuda.is_available():
    mem = torch.cuda.get_device_properties(0).total_memory
    print(mem > 8_000_000_000)
else:
    print(False)

medium

A. True if GPU memory is more than 8GB, else False

B. Always True

C. Always False

D. Raises an error if no GPU

Solution

1. Step 1: Understand the code logic

   The code checks if a GPU is available, then compares its memory to 8GB (8 billion bytes).

2. Step 2: Determine output based on GPU memory

   If GPU memory is greater than 8GB, it prints True; otherwise, False. If no GPU, prints False.

3. Final Answer:

   True if GPU memory is more than 8GB, else False -> Option A

4. Quick Check:

   GPU memory check > 8GB = A [OK]

Hint: Check GPU memory size condition to predict output [OK]

Common Mistakes:

• Assuming always True regardless of GPU
• Expecting error if no GPU instead of False
• Confusing bytes with gigabytes

4. Identify the error in this GPU memory check code and select the fix:

import torch
if torch.cuda.is_available():
    mem = torch.cuda.get_device_properties().total_memory
    print(mem)
else:
    print('No GPU')

medium

A. Add device index 0 in get_device_properties: get_device_properties(0)

B. Replace torch.cuda.is_available() with torch.has_cuda()

C. Use torch.cuda.memory_allocated() instead of get_device_properties()

D. No error, code is correct

Solution

1. Step 1: Check get_device_properties usage

   The function requires a device index argument, e.g., 0 for the first GPU.

2. Step 2: Identify the fix

   Adding (0) fixes the error. Other options are incorrect or unnecessary.

3. Final Answer:

   Add device index 0 in get_device_properties: get_device_properties(0) -> Option A

4. Quick Check:

   Missing device index causes error = B [OK]

Hint: Always provide device index to get_device_properties() [OK]

Common Mistakes:

• Omitting device index argument
• Using non-existent torch.has_cuda()
• Confusing memory functions

5. You plan to train a large GenAI model requiring 24GB GPU memory. Your local GPUs have 16GB each. Which is the best GPU infrastructure planning choice?

hard

A. Ignore memory limits and expect training to succeed

B. Reduce the model size to fit 16GB GPU and train locally

C. Train on CPU only to avoid GPU memory limits

D. Use multiple GPUs with model parallelism or switch to cloud GPUs with 24GB+ memory

Solution

1. Step 1: Analyze GPU memory requirement vs available hardware

   The model needs 24GB, but local GPUs have only 16GB, so one GPU is insufficient.

2. Step 2: Consider solutions for insufficient GPU memory

   Using multiple GPUs with model parallelism or cloud GPUs with enough memory solves the problem effectively.

3. Final Answer:

   Use multiple GPUs with model parallelism or switch to cloud GPUs with 24GB+ memory -> Option D

4. Quick Check:

   Match GPU memory to model needs with parallelism or cloud = A [OK]

Hint: Use multi-GPU or cloud GPUs for models needing more memory [OK]

Common Mistakes:

• Trying to train large models on insufficient GPU memory
• Ignoring cloud GPU options
• Assuming CPU can replace GPU for large models