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pytorch memory track code
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# Pytorch-Memory-Utils
These codes can help you to detect your GPU memory during training with Pytorch.
A blog about this tool and explain the details : https://oldpan.me/archives/pytorch-gpu-memory-usage-track
# Usage:
Put ``modelsize_estimate.py`` or ``gpu_mem_track.py`` under your current working directory and import them.
## The following is the print content.
- Calculate the memory usage of a single model
```
Model Sequential : params: 0.450304M
Model Sequential : intermedite variables: 336.089600 M (without backward)
Model Sequential : intermedite variables: 672.179200 M (with backward)
```
- Track the amount of GPU memory usage
```markdown
# 30-Apr-21-20:25:29-gpu_mem_track.txt
GPU Memory Track | 30-Apr-21-20:25:29 | Total Tensor Used Memory:0.0 Mb Total Used Memory:0.0 Mb
At main.py line 10: <module> Total Tensor Used Memory:0.0 Mb Total Allocated Memory:0.0 Mb
+ | 1 * Size:(64, 64, 3, 3) | Memory: 0.1406 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 1 * Size:(128, 128, 3, 3) | Memory: 0.5625 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 1 * Size:(256, 128, 3, 3) | Memory: 1.125 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 1 * Size:(512, 256, 3, 3) | Memory: 4.5 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 3 * Size:(256, 256, 3, 3) | Memory: 6.75 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 8 * Size:(512,) | Memory: 0.0156 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 2 * Size:(64,) | Memory: 0.0004 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 7 * Size:(512, 512, 3, 3) | Memory: 63.0 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 4 * Size:(256,) | Memory: 0.0039 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 1 * Size:(128, 64, 3, 3) | Memory: 0.2812 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 2 * Size:(128,) | Memory: 0.0009 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
+ | 1 * Size:(64, 3, 3, 3) | Memory: 0.0065 M | <class 'torch.nn.parameter.Parameter'> | torch.float32
At main.py line 12: <module> Total Tensor Used Memory:76.4 Mb Total Allocated Memory:76.4 Mb
+ | 1 * Size:(60, 3, 512, 512) | Memory: 180.0 M | <class 'torch.Tensor'> | torch.float32
+ | 1 * Size:(40, 3, 512, 512) | Memory: 120.0 M | <class 'torch.Tensor'> | torch.float32
+ | 1 * Size:(30, 3, 512, 512) | Memory: 90.0 M | <class 'torch.Tensor'> | torch.float32
At main.py line 18: <module> Total Tensor Used Memory:466.4 Mb Total Allocated Memory:466.4 Mb
+ | 1 * Size:(120, 3, 512, 512) | Memory: 360.0 M | <class 'torch.Tensor'> | torch.float32
+ | 1 * Size:(80, 3, 512, 512) | Memory: 240.0 M | <class 'torch.Tensor'> | torch.float32
At main.py line 23: <module> Total Tensor Used Memory:1066.4 Mb Total Allocated Memory:1066.4 Mb
- | 1 * Size:(40, 3, 512, 512) | Memory: 120.0 M | <class 'torch.Tensor'> | torch.float32
- | 1 * Size:(120, 3, 512, 512) | Memory: 360.0 M | <class 'torch.Tensor'> | torch.float32
At main.py line 29: <module> Total Tensor Used Memory:586.4 Mb Total Allocated Memory:586.4 Mb
```
## How to use
### Track the amount of GPU memory usage
simple example:
```python
import torch
from torchvision import models
from gpu_mem_track import MemTracker
device = torch.device('cuda:0')
gpu_tracker = MemTracker() # define a GPU tracker
gpu_tracker.track() # run function between the code line where uses GPU
cnn = models.vgg19(pretrained=True).features.to(device).eval()
gpu_tracker.track() # run function between the code line where uses GPU
dummy_tensor_1 = torch.randn(30, 3, 512, 512).float().to(device) # 30*3*512*512*4/1024/1024 = 90.00M
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HUANG Fei · @thu-coai · China
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Jiarui Fang（方佳瑞） · ByteDance · China
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:10beaf0151a0d43f, topic:gpu
matched fp:10beaf0151a0d43f, topic:pytorch
matched fp:10beaf0151a0d43f, topic:memory