Bash Script Wait for GPU Memory before Running PyTorch Training Script
In deep learning projects, it's common to train models using PyTorch, a popular open-source machine learning library. However, training large models can require significant GPU memory, and if another process consumes all available memory, subsequent PyTorch training scripts may fail. To address this issue, we can write a Bash script that waits for sufficient GPU memory before running the PyTorch training script.
Prerequisites
Before we begin, ensure you have:
- A Linux system with Bash and NVIDIA CUDA installed
- PyTorch installed using pip or conda
Creating the Wait Script
Create a new file named 'wait.sh' in the '/scripts' directory:
#!/bin/bash
# Set the threshold for available GPU memory in MB
THRESHOLD=1000
# Function to check GPU memory usage
check_memory() {
nvidia-smi --query-gpu --format=csv --output=text | gre -E '^(.*)\s+Memory\s+used:\s+[0-9]\{1,\}MB$' | awk '{print $1}' | awk -F' ' '{print $2}' | awk '{print $1}' | awk '{print int($1/1024)}'
}
# Function to wait for GPU memory
wait_for_memory() {
while [ $(check_memory) -lt $THRESHOLD ]; do
echo "GPU memory usage: $(check_memory) MB"
sleep 10
done
echo "GPU memory usage: $(check_memory) MB"
echo "Sufficient GPU memory available. Starting PyTorch training..."
}
wait_for_memory
Explanation
The 'wait.sh' script checks the GPU memory usage and waits until it reaches the specified threshold. It uses the 'nvidia-smi' utility to get the GPU memory usage and calculates the value in MB.
Using the Wait Script
To use the wait script, call it before running the PyTorch training script:
./scripts/wait.sh && ./scripts/train.py