Fix yaml resources definition
@@ -32,12 +32,12 @@ each subsection in this guide describes how to define the the distributed traini
## Distributed experiments with Tensorflow
To [distribute Tensorflow](https://www.tensorflow.org/deploy/distributed) experiment,
-the user needs to defines cluster, which is a set of tasks that participate in the distributed execution.
+the user needs to define a cluster, which is a set of tasks that participate in the distributed execution.
Tensorflow defines 3 different types of tasks: master, worker, and parameter server.
To define a cluster in Polyaxon with a master, 3 workers, and 1 parameter server,
-add a tensorflow subsection to environment section of your polyaxonfile:
+add a tensorflow subsection to the environment section of your polyaxonfile:
```yaml
...
@@ -50,10 +50,10 @@ environment:
n_ps: 1
```
-You can have more control over the created tasks by polyaxon, by defining the resources of each task,
+You can have more control over the created tasks by defining the resources of each task
the same way we defined the resources for the master.
-Here's an example where we define a resources for the master, workers and parameter server.
+Here's an example where we define resources for the master, workers and parameter server.
```yaml
@@ -157,27 +157,6 @@ default_worker_resources:
Same logic applies to the parameter servers with the `default_ps_resources` and `ps_resources`.
-## Distributed experiments with MXNet
-
-In a similar fashion, MXNet defines 3 types of tasks: scheduler, workers, and parameter servers.
-
-To define an MXNet cluster in Polyaxon with a scheduler, 3 workers, and 1 parameter server,
-add an mxnet subsection to the environment section of your polyaxonfile:
-
-```yaml
-...
-
-environment:
- ...
-
- mxnet:
- n_workers: 3
- n_ps: 1
-```
-
-You can customize the resources of each task the same way it was explained in the tensorflow section.
-
-
## Distributed experiments with MXNet
In a similar fashion, [MXNet](https://mxnet.incubator.apache.org/faq/multi_devices.html#distributed-training-with-multiple-machines) defines 3 types of tasks: scheduler, workers, and parameter servers.
@@ -203,7 +182,7 @@ You can customize the resources of each task the same way it was explained in th
[Distributed Pytorch](http://pytorch.org/tutorials/intermediate/dist_tuto.html) is also similar but only defines a master task (worker with rank 0) and a set of worker tasks.
-To define an Pytorch cluster in Polyaxon with master, 3 workers,
+To define a Pytorch cluster in Polyaxon with a master, 3 workers,
add a pytorch subsection to the environment section of your polyaxonfile:
