Commit bfa3e0ee authored by Michael Carilli's avatar Michael Carilli
Browse files

Adding simple example links for distributed

parent adaa9137
......@@ -10,6 +10,8 @@ multiproc.py contains the source code for `apex.parallel.multiproc`, a launch ut
#### [Example/Walkthrough](https://github.com/NVIDIA/apex/tree/master/examples/distributed)
#### [Imagenet Example w/Mixed Precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
#### [Imagenet example with Mixed Precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
#### [Simple example with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/FP16_Optimizer_simple/distributed_apex)
**distributed_data_parallel.py** shows an example using `FP16_Optimizer` with
`apex.parallel.DistributedDataParallel`.
The usage of `FP16_Optimizer` with distributed does not need to change from ordinary
single-process usage. Test via
```bash
bash run.sh
```
**distributed_data_parallel.py** shows an example using `FP16_Optimizer` with
`torch.nn.parallel.DistributedDataParallel`.
The usage of `FP16_Optimizer` with distributed does not need to change from ordinary
single-process usage. Test via
```bash
bash run.sh
```
# Basic Multiprocess Example based on pytorch/examples/mnist
# Multiprocess Example based on pytorch/examples/mnist
main.py demonstrates how to modify a simple model to enable multiprocess distributed data parallel
training using the module wrapper `apex.parallel.DistributedDataParallel`
......@@ -17,7 +17,9 @@ transfers to reduce the total number of transfers required.
#### [Source Code](https://github.com/NVIDIA/apex/tree/master/apex/parallel)
#### [Another Example: Imagenet with mixed precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
#### [Another example: Imagenet with mixed precision](https://github.com/NVIDIA/apex/tree/master/examples/imagenet)
#### [Simple example with FP16_Optimizer](https://github.com/NVIDIA/apex/tree/master/examples/FP16_Optimizer_simple/distributed_apex)
## Getting started
Prior to running please run
......
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