https://github.com/dmlc/mxnet-memonger
Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets
https://github.com/dmlc/mxnet-memonger
Last synced: 3 months ago
JSON representation
Sublinear memory optimization for deep learning, reduce GPU memory cost to train deeper nets
- Host: GitHub
- URL: https://github.com/dmlc/mxnet-memonger
- Owner: dmlc
- License: apache-2.0
- Created: 2016-04-21T23:04:40.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2017-08-08T15:42:14.000Z (almost 8 years ago)
- Last Synced: 2025-04-02T11:54:27.655Z (3 months ago)
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 308
- Watchers: 18
- Forks: 63
- Open Issues: 9
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# MXNet Memory Monger
This project contains a 150 lines of python script to give sublinear memory plans of deep neural networks.
This allows you to trade computation for memory and get sublinear memory cost,
so you can train bigger/deeper nets with limited resources.
## Reference Paper
[Training Deep Nets with Sublinear Memory Cost](https://arxiv.org/abs/1604.06174) Arxiv 1604.06174
## How to Use
This code is based on [MXNet](https://github.com/dmlc/mxnet), a lightweight, flexible and efficient framework for deep learning.
- Configure your network as you normally will do using symbolic API
- Give hint to the allocator about the possible places that we need to bookkeep computations.
- Set attribute ```mirror_stage='True'```, see [example_resnet.py](example_resnet.py#L25)
- The memonger will try to find possible dividing points on the nodes that are annotated as mirror_stage.
- Call ```memonger.search_plan``` to get an symbolic graph with memory plan.
```python
import mxnet as mx
import memonger
# configure your network
net = my_symbol()
# call memory optimizer to search possible memory plan.
net_planned = memonger.search_plan(net)
# use as normal
model = mx.FeedForward(net_planned, ...)
model.fit(...)
```
## Write your Own Memory Optimizer
MXNet's symbolic graph support attribute to give hint on whether (mirror attribute) a result
can be recomputed or not. You can choose to re-compute instead of remembering a result
for less memory consumption. To set output of a symbol to be re-computable, use
```python
sym._set_attr(force_mirroring='True')
```
mxnet-memonger actually use the same way to do memory planning. You can simply write your own memory
allocator by setting the force_mirroring attribute in a smart way.