RuntimeError
torch.cuda.OutOfMemoryError
Stack trace
RuntimeError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 8.00 GiB total capacity; 6.50 GiB already allocated; 256.00 MiB free; 6.75 GiB reserved in total by PyTorch)
Why it happens
When generating embeddings in batches on a GPU, the batch size or model size can exceed the available GPU memory. This causes PyTorch to raise a CUDA out of memory error because it cannot allocate enough memory for the tensors required during embedding computation.
Detection
Monitor GPU memory usage during embedding batch processing using tools like nvidia-smi or PyTorch's memory_allocated() to detect when memory limits are approached before the error occurs.
Causes & fixes
Batch size is too large for the available GPU memory.
Reduce the batch size for embedding generation to fit within the GPU memory limits.
Multiple processes or models are using the GPU simultaneously, reducing available memory.
Ensure exclusive GPU access or reduce concurrent GPU workloads to free memory for embedding tasks.
Model or embedding dimension is too large for the GPU capacity.
Use a smaller embedding model or reduce embedding dimensionality to lower memory consumption.
GPU memory fragmentation due to previous allocations not being freed.
Restart the Python process or clear GPU cache with torch.cuda.empty_cache() before running embeddings.
Code: broken vs fixed
import os
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2').cuda()
texts = ['text1', 'text2', 'text3', 'text4', 'text5', 'text6', 'text7', 'text8']
batch_size = 8 # Too large for GPU memory
for i in range(0, len(texts), batch_size):
batch = texts[i:i+batch_size]
inputs = tokenizer(batch, padding=True, truncation=True, return_tensors='pt').to('cuda')
outputs = model(**inputs) # RuntimeError: CUDA out of memory hereimport os
import torch
from transformers import AutoTokenizer, AutoModel
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2').cuda()
texts = ['text1', 'text2', 'text3', 'text4', 'text5', 'text6', 'text7', 'text8']
batch_size = 2 # Reduced batch size to fit GPU memory
for i in range(0, len(texts), batch_size):
batch = texts[i:i+batch_size]
inputs = tokenizer(batch, padding=True, truncation=True, return_tensors='pt').to('cuda')
outputs = model(**inputs)
print(f'Processed batch {i//batch_size + 1}')Workaround
Catch the RuntimeError, clear GPU cache with torch.cuda.empty_cache(), and retry with a smaller batch size dynamically.
Prevention
Implement dynamic batch sizing based on available GPU memory and monitor memory usage to avoid over-allocation during embedding generation.