Ensuring Reproducibility in PyTorch with Multiple GPUs: Understanding torch.cuda.manual_seed_all

Purpose

• By setting a seed value, you ensure that the sequence of random numbers produced on all GPUs is identical for each run with the same seed. This is crucial for achieving reproducibility in your deep learning experiments, especially when:
  • Training models with random initializations for weights and biases.
  • Employing dropout layers or other random operations.
• This function is used to control the generation of random numbers within your PyTorch code when working with multiple GPUs (Graphics Processing Units).

Breakdown

• Arguments
  
  • seed (int): The integer value you want to use as the seed. This value determines the starting point for the random number generation algorithm.
• manual_seed_all: This function, part of torch.cuda, sets the seed for generating random numbers across all available GPUs in your system.
• torch.cuda: This module within PyTorch provides functionalities specifically for working with CUDA-enabled GPUs. It allows you to create and manage tensors residing on GPU memory, as well as perform computations using the GPU's parallel processing power.

How it Works

1. When you call torch.cuda.manual_seed_all(seed), PyTorch sets the internal state of the random number generator (RNG) on each GPU to the specified seed.
2. Subsequent calls to functions that rely on randomness, such as weight initialization, dropout layers, or random data shuffling, will now produce the same sequence of random numbers on all GPUs, as long as the seed remains the same.

Example

import torch

if torch.cuda.is_available():
    torch.cuda.manual_seed_all(42)  # Set the seed on all available GPUs

# Your PyTorch code using multiple GPUs here

• Determinism Limitations
  While torch.cuda.manual_seed_all helps with reproducibility, it's important to note that PyTorch doesn't guarantee complete determinism for all operations due to factors like non-deterministic CUDA operations and multi-threading.
• Multiple Processes
  If you're using multiple processes for training, you'll need to set the seed for each process independently to ensure reproducibility across processes as well. PyTorch offers additional functions like torch.manual_seed for this purpose.

---

import torch

# Check for CUDA availability
if torch.cuda.is_available():
    # Set seed for all GPUs
    torch.cuda.manual_seed_all(42)

# Set seed for CPU-based randomness (optional, but recommended for consistency)
torch.manual_seed(42)

# Set seed for Python's random module (optional, for custom random operations)
import random
random.seed(42)

# Your PyTorch code using multiple GPUs here

# Example usage with random number generation
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
random_tensor = torch.rand(2, 2, device=device)
print(random_tensor)

1. CUDA Seeding
   torch.cuda.manual_seed_all(42) sets the seed for all available GPUs.
2. CPU Seeding
   torch.manual_seed(42) sets the seed for the CPU's random number generator. This is optional but recommended for consistency, especially if you have mixed CPU/GPU operations.
3. Python Random Seeding
   random.seed(42) sets the seed for Python's built-in random module. This is useful if you use random operations outside of PyTorch's functionalities.
4. Device Handling
   The code checks for CUDA availability and assigns the appropriate device ("cuda" or "cpu") to the random_tensor.
5. Random Number Generation
   random_tensor = torch.rand(2, 2, device=device) creates a random tensor with dimensions (2, 2) placed on the chosen device.
6. Printing
   print(random_tensor) displays the generated random values.

Running this code multiple times with the same seed (42) will produce the same random tensor on all GPUs (if available) and the CPU. This ensures that the random number sequences used during training or other operations are consistent across runs.

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Individual GPU Seeding

• If you only need to control randomness on specific GPUs, you can use torch.cuda.set_device(gpu_id) to select the target GPU and then apply torch.cuda.manual_seed(seed) on that specific device.

import torch

if torch.cuda.is_available():
    # Set seed on specific GPU (e.g., GPU 0)
    torch.cuda.set_device(0)
    torch.cuda.manual_seed(42)

    # Repeat for other GPUs as needed

# Your PyTorch code using multiple GPUs here

Combined Seeding

• Combine torch.manual_seed(seed) for CPU-based randomness with torch.cuda.manual_seed_all(seed) for GPU randomness. This ensures consistency across both CPU and GPU operations.

import torch

if torch.cuda.is_available():
    # Set seed for CPU and all GPUs
    torch.manual_seed(42)
    torch.cuda.manual_seed_all(42)

# Your PyTorch code using multiple GPUs here

PyTorch Lightning's seed_everything

• If you're using PyTorch Lightning, consider its seed_everything(seed) function. This sets the seed for various sources of randomness, including CPU, GPUs, Python's random module, and libraries like NumPy (if available).

from pytorch_lightning import seed_everything

seed_everything(42)

# Your PyTorch Lightning code using multiple GPUs here

• Multi-process Training
  For distributed training across multiple processes, you'll need additional techniques like setting seeds within each process to achieve reproducibility.
• Deterministic CuDNN
  Consider setting torch.backends.cudnn.deterministic = True to enforce determinism during convolution operations, but be aware of potential performance trade-offs.