fix grammar, update illustration

doc/design/dist/README.md

@@ -27,18 +27,17 @@ Their relation is illustrated in the following graph:
 
 The master process will:
 
-- Shard dataset into [tasks](#task) and dispatch tasks to trainers.
+- Partition a dataset into [tasks](#task) and dispatch tasks to trainers.
 - Keep track of training progress on the dataset with [task queue](#task-queue). A training job will iterate on the dataset for a full pass until it goes into next pass.
 
-Now we will explain the concepts mentioned above:
 
 #### Task 
 
-A task is a piece of sharded data to be trained. The total number of tasks will be much bigger than the total number of trainers. The number of data instances inside a task will be much bigger than the mini-batch size.
+A task is a data shard to be trained. The total number of tasks will be much bigger than the total number of trainers. The number of data instances inside a task will be much bigger than the mini-batch size.
 
 #### Task Queue
 
-Master process has three task queues to track training progress. As illustrated in the graph below, Job A and Job B both have one master process. Each master process has three task queues.
+The master process has three task queues to track training progress. As illustrated in the graph below, Job A and Job B both have one master process. Each master process has three task queues.
 
 <img src="src/paddle-task-queues.png"/>
 
@@ -52,20 +51,20 @@ The life cycle of a single task is illustrated below:
 
 1. When a new pass of training starts, all tasks will be placed in the todo queue.
 1. The master process will dispatch few tasks to each trainer at a time, puts them in the pending queue and waits for completion.
-1. The trainer will work on it's tasks and tell the master process once a task is completed. The master process will dispatch a new task to that trainer.
-1. If a task timeout. the master process will move it back to the todo queue. The timeout count will increase by one. If the timeout count is above an threashold, the task is likely to cause a trainer to crash, so it will be discarded.
-1. The master process will move completed task to the done queue. When the todo queue is empty, the master process will start a new pass by moving all tasks in the done queue to todo queue and resetting the timeout counter of all tasks to zero.
+1. The trainer will work on its tasks and tell the master process once a task is completed. The master process will dispatch a new task to that trainer.
+1. If a task timeout. the master process will move it back to the todo queue. The timeout count will increase by one. If the timeout count is above a threshold, the task is likely to cause a trainer to crash, so it will be discarded.
+1. The master process will move completed task to the done queue. When the todo queue is empty, the master process will start a new pass by moving all tasks in the done queue to todo queue and reset the timeout counter of all tasks to zero.
 
 ### Trainer Process
 
 The trainer process will:
 
-- Receive the tasks from the master.
-- Work on the tasks: alculate and upload gradient to the parameter servers, and update local model by downloading new parameters from the parameter servers.
+- Receive tasks from the master.
+- Work on the tasks: calculate and upload gradient to parameter servers, and update local model by downloading new parameters from parameter servers.
 
 ### Parameter Server Process
 
-Parameter server processes hold the parameters collabratively. The parameters are sharded on different parameter servers.
+Parameter server processes hold the parameters collaboratively. The parameters are partitioned on different parameter servers.
 
 The parameter server will:
 
@@ -76,21 +75,21 @@ The parameter server will:
 
 The communication pattern between the trainers and the parameter servers depends on the category of optimization algorithm:
 
-- Synchronous Stochastic Gradient Decent (sync-SGD)
+- Synchronous Stochastic Gradient Descent (sync-SGD)
 
 	Parameter server will wait for all trainer finish n-th mini-batch calculation and send their gradients before broadcasting new parameters to every trainer. Every trainer will wait for the new parameters before starting n+1-th mini-batch.
   
-- Asynchronous Stochastic Gradient Decent (async-SGD)
+- Asynchronous Stochastic Gradient Descent (async-SGD)
 
 	There will no synchronization between different trainers, and parameter server updates its parameter as soon as it receives new gradient:
 
-	- Each trainer uploads its accumulated gradient every **n** mini-batches.
-	- Every **m** mini-batches, the trainer downloads new parameters from parameter server.
-	- **n** and **m** do not have to be equal.
+	- Each trainer uploads its accumulated gradient every n mini-batches.
+	- Every m mini-batches, the trainer downloads new parameters from parameter server.
+	- n and m do not have to be equal.
 
 ## Fault Tolerant
 
-The training job will pause if the master process is dead, or any of the parameter server process is dead. They will be started by [Kubernetes](https://kubernetes.io/) and recover in few minutes. Please refer to [fault recovery](#fault-recovery).
+The training job will pause if the master processes is dead, or any of the parameter server process is dead. They will be started by [Kubernetes](https://kubernetes.io/) and recover in few minutes. Please refer to [fault recovery](#fault-recovery).
 
 The training job will continue to make progress if there is at least one training process running. The strategy depends on the type of optimization algorithm:
 
@@ -104,9 +103,9 @@ The training job will continue to make progress if there is at least one trainin
 
 ## Fault Recovery
 
-PaddlePaddle uses [etcd](https://github.com/coreos/etcd) to keep track of the states of processes. Because etcd is a distributed reliable key-value store, the restarted process can recover its states from etcd. The model parameter are periodically saved into distributed file system, so a restarted parameter server can recover its parameters from the saved file.
+PaddlePaddle uses [etcd](https://github.com/coreos/etcd) to keep track of the states of processes. Because etcd is a distributed reliable key-value store, the restarted process can recover its states from etcd. The model parameters are periodically saved into distributed file system, so a restarted parameter server can recover its parameters from the saved file.
 
-Now we will introduce how each process recovers from failure, the graph below provides an illustration:
+Now we will introduce how each process recovers from a failure, the graph below shows how etcd is used:
 
 <img src="src/paddle-etcd.png"/>
 
@@ -115,7 +114,7 @@ Now we will introduce how each process recovers from failure, the graph below pr
 When the master is started by the Kubernetes, it executes the following steps at startup:
 
 1. Grabs a unique *master* lock in etcd, which prevents concurrent master instantiations.
-1. Recovers the task queues from etcd if they already exists, otherwise the master will create them.
+1. Recovers the task queues from etcd if they already exist, otherwise, the master will create them.
 1. Watches the trainer prefix keys `/trainer/` on etcd to find the live trainers.
 1. Starts dispatching the tasks to the trainers.
 
@@ -127,8 +126,8 @@ When the master process is dead for any reason, Kubernetes will restart it. It w
 
 When the trainer is started by the Kubernetes, it executes the following steps at startup:
 
-1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until count of parameter servers reaches the desired count.
-1. Generates an unique ID, and sets key `/trainer/<unique ID>` with its contact address as value. The key will be deleted when the lease expires, so the master will be aware of the trainer being online and offline.
+1. Watches the available parameter server prefix keys `/ps/` on etcd and waits until the count of parameter servers reaches the desired count.
+1. Generates a unique ID, and sets key `/trainer/<unique ID>` with its contact address as value. The key will be deleted when the lease expires, so the master will be aware of the trainer being online and offline.
 1. Waits for tasks from the master to start training.
 
 If trainer's etcd lease expires, it will try set key `/trainer/<unique ID>` again so that the master process can discover the trainer again.
@@ -138,7 +137,7 @@ If trainer's etcd lease expires, it will try set key `/trainer/<unique ID>` agai
 When the parameter server is started by Kubernetes, it executes the following steps at startup:
 
 1. Read desired total number of parameter servers from etcd `/ps_desired`
-1. Search though etcd keys `/ps/<index>` (`/ps/0`, `/ps/1`, ...) to find the first non-existant key whose index is smaller than the total number of parameter servers. Set the key using a transaction to avoid concurrent writes. The parameter server's index is inferred from the key name.
+1. Search through etcd keys `/ps/<index>` (`/ps/0`, `/ps/1`, ...) to find the first non-existant key whose index is smaller than the total number of parameter servers. Set the key using a transaction to avoid concurrent writes. The parameter server's index is inferred from the key name.
 
 	The desired number of parameter servers is 3: