Update dist-train.md
diff --git a/docs-site/docs/dist-train.md b/docs-site/docs/dist-train.md
index 3fb14e0..7ce7bfc 100644
--- a/docs-site/docs/dist-train.md
+++ b/docs-site/docs/dist-train.md
@@ -34,7 +34,7 @@
the normal SGD optimizer for updating the model parameters. The following
example illustrates the usage of `DistOpt` for training a CNN model over the
MNIST dataset. The full example is available
-[here](https://github.com/apache/singa/blob/master/examples/autograd/doc_dist_train.py).
+[here](https://github.com/apache/singa/blob/master/examples/autograd/mnist_dist.py).
### Example Code
@@ -153,11 +153,11 @@
1. Put all the above training codes in a function
```python
-def train_mnist_cnn(nccl_id=None, gpu_num=None, gpu_per=None):
+def train_mnist_cnn(nccl_id=None, gpu_num=None, num_gpus=None):
...
```
-2. Create `doc_dist_multiprocess.py`
+2. Create `mnist_multiprocess.py`
```python
if __name__ == '__main__':
@@ -200,7 +200,7 @@
sgd = opt.DistOpt(sgd, nccl_id=nccl_id, gpu_num=gpu_num, num_gpus=num_gpus)
```
-3. Run `doc_dist_multiprocess.py`
+3. Run `mnist_multiprocess.py`
```sh
python doc_dist_multiprocess.py 2
@@ -246,7 +246,7 @@
It works for both single node and multiple nodes as long as there are multiple
GPUs.
-1. Create `doc_dist_train.py`
+1. Create `mnist_dist.py`
```python
if __name__ == '__main__':
@@ -263,10 +263,10 @@
3. Launch the training via `mpiexec`
```sh
-mpiexec --hostfile host_file python doc_dist.py
+mpiexec --hostfile host_file python mnist_dist.py
```
-It could result in several times speed up compared to the single GPU training.
+It could result in speed up compared to the single GPU training.
```
Starting Epoch 0:
