diff --git a/develop/doc/_sources/howto/usage/cluster/fluid_cluster_train_en.md.txt b/develop/doc/_sources/howto/usage/cluster/fluid_cluster_train_en.md.txt index a64004a7c4ea12bc0d949d7f11f3e26af62bf912..11904a6f71bb6ce37417aeffb8e408ec65961b12 100644 --- a/develop/doc/_sources/howto/usage/cluster/fluid_cluster_train_en.md.txt +++ b/develop/doc/_sources/howto/usage/cluster/fluid_cluster_train_en.md.txt @@ -2,27 +2,27 @@ ## Introduction -In this article, we'll explain how to config and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster. +In this article, we'll explain how to configure and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster. ## Preparations -### Get your cluster ready +### Getting the cluster ready -Prepare your computer nodes in the cluster. Nodes in this cluster can be of any specification that runs PaddlePaddle, and with a unique IP address assigned to it. Make sure they can communicate with each other. +Prepare the compute nodes in the cluster. Nodes in this cluster can be of any specification that runs PaddlePaddle, and with a unique IP address assigned to it. Make sure they can communicate to each other. ### Have PaddlePaddle installed PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes, be sure to properly install drivers and CUDA libraries. -PaddlePaddle build and installation guide can be found from [here](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html). +PaddlePaddle build and installation guide can be found [here](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/build_and_install/index_en.html). -### Update training script +### Update the training script #### Non-cluster training script Let's take [Deep Learning 101](http://www.paddlepaddle.org/docs/develop/book/01.fit_a_line/index.html)'s first chapter: "fit a line" as an example. -This demo's non-cluster version with fluid API is as follows: +The non-cluster version of this demo with fluid API is as follows: ``` python import paddle.v2 as paddle @@ -65,25 +65,25 @@ for pass_id in range(PASS_NUM): exit(1) ``` -We created a simple fully connected neural networks training program and handed it to the fluid executor to run for 100 passes. +We created a simple fully-connected neural network training program and handed it to the fluid executor to run for 100 passes. -Now let's try to convert it to a distributed version to run in a cluster. +Now let's try to convert it to a distributed version to run on a cluster. #### Introducing parameter server -As you see from the non-cluster version of training script, there is only one role in it: the trainer, who does the computing as well as holding parameters. In cluster training, since multi-trainers are working on the same task, they need one centralized place to hold and distribute parameters. This centralized place is called the Parameter Server in PaddlePaddle. +As we can see from the non-cluster version of training script, there is only one role in the script: the trainer, that performs the computing as well as holds the parameters. In cluster training, since multi-trainers are working on the same task, they need one centralized place to hold and distribute parameters. This centralized place is called the Parameter Server in PaddlePaddle. -![parameter server architect](src/trainer.png) +![parameter server architecture](src/trainer.png) -Parameter Server in fluid does not only hold parameters but is also assigned with a part of the program. Trainers communicate with parameter servers via send/receive OPs. For more tech detail, please refer to this [document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/dist_refactor/distributed_architecture.md). +Parameter Server in fluid not only holds the parameters but is also assigned with a part of the program. Trainers communicate with parameter servers via send/receive OPs. For more technical details, please refer to [this document](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/design/dist_refactor/distributed_architecture.md). -Now we need to create program for both trainers and parameter servers, the question is how? +Now we need to create programs for both: trainers and parameter servers, the question is how? #### Slice the program -Fluid provides a tool called "Distribute Transpiler" to automatically convert the non-cluster program into cluster program. +Fluid provides a tool called "Distributed Transpiler" that automatically converts the non-cluster program into cluster program. -The idea behind this tool is to find optimize OPs and gradient parameters, slice the program into 2 pieces and connect them with send/receive OP. +The idea behind this tool is to find the optimize OPs and gradient parameters, slice the program into 2 pieces and connect them with send/receive OP. Optimize OPs and gradient parameters can be found from the return values of optimizer's minimize function. @@ -94,9 +94,9 @@ To put them together: optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) #get optimize OPs and gradient parameters -t = fluid.DistributeTranspiler() # create transpiler instance +t = fluid.DistributeTranspiler() # create the transpiler instance # slice the program into 2 pieces with optimizer_ops and gradient parameters list, as well as pserver_endpoints, which is a comma separated list of [IP:PORT] and number of trainers -t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) +t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2) ... #create executor @@ -119,7 +119,7 @@ for pass_id in range(100): ### E2E demo -Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py). In parameter server node run this in the command line: +Please find the complete demo from [here](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/v2/fluid/tests/book_distribute/notest_dist_fit_a_line.py). In parameter server node run the following in the command line: ``` bash PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py @@ -129,12 +129,12 @@ PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER Wait until the prompt `Server listening on 192.168.1.2:6174` -Then in 2 of your trainer node run this: +Then in 2 of your trainer nodes run this: ``` bash PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=TRAINER python notest_dist_fit_a_line.py ``` -*the reason you need to run this command twice in 2 nodes is: in the script we set the trainer count to be 2. You can change this setting on line 50* +*the reason you need to run this command twice in 2 nodes is because: in the script we set the trainer count to be 2. You can change this setting on line 50* Now you have 2 trainers and 1 parameter server up and running. diff --git a/develop/doc/howto/usage/cluster/fluid_cluster_train_en.html b/develop/doc/howto/usage/cluster/fluid_cluster_train_en.html index 4d3cfbcf3d624755de05bf5468176b7ef1e0fb5a..19956d25e420e2b9f7533c05a3b9628a4f67fe16 100644 --- a/develop/doc/howto/usage/cluster/fluid_cluster_train_en.html +++ b/develop/doc/howto/usage/cluster/fluid_cluster_train_en.html @@ -213,25 +213,25 @@
In this article, we’ll explain how to config and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster.
+In this article, we’ll explain how to configure and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster.
Prepare your computer nodes in the cluster. Nodes in this cluster can be of any specification that runs PaddlePaddle, and with a unique IP address assigned to it. Make sure they can communicate with each other.
+Prepare the compute nodes in the cluster. Nodes in this cluster can be of any specification that runs PaddlePaddle, and with a unique IP address assigned to it. Make sure they can communicate to each other.
PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes, be sure to properly install drivers and CUDA libraries.
-PaddlePaddle build and installation guide can be found from here.
+PaddlePaddle build and installation guide can be found here.
Let’s take Deep Learning 101‘s first chapter: “fit a line” as an example.
-This demo’s non-cluster version with fluid API is as follows:
+The non-cluster version of this demo with fluid API is as follows:
import paddle.v2 as paddle
import paddle.v2.fluid as fluid
@@ -272,29 +272,29 @@
exit(1)
We created a simple fully connected neural networks training program and handed it to the fluid executor to run for 100 passes.
-Now let’s try to convert it to a distributed version to run in a cluster.
+We created a simple fully-connected neural network training program and handed it to the fluid executor to run for 100 passes.
+Now let’s try to convert it to a distributed version to run on a cluster.
As you see from the non-cluster version of training script, there is only one role in it: the trainer, who does the computing as well as holding parameters. In cluster training, since multi-trainers are working on the same task, they need one centralized place to hold and distribute parameters. This centralized place is called the Parameter Server in PaddlePaddle.
-Parameter Server in fluid does not only hold parameters but is also assigned with a part of the program. Trainers communicate with parameter servers via send/receive OPs. For more tech detail, please refer to this document.
-Now we need to create program for both trainers and parameter servers, the question is how?
+As we can see from the non-cluster version of training script, there is only one role in the script: the trainer, that performs the computing as well as holds the parameters. In cluster training, since multi-trainers are working on the same task, they need one centralized place to hold and distribute parameters. This centralized place is called the Parameter Server in PaddlePaddle.
+Parameter Server in fluid not only holds the parameters but is also assigned with a part of the program. Trainers communicate with parameter servers via send/receive OPs. For more technical details, please refer to this document.
+Now we need to create programs for both: trainers and parameter servers, the question is how?
Fluid provides a tool called “Distribute Transpiler” to automatically convert the non-cluster program into cluster program.
-The idea behind this tool is to find optimize OPs and gradient parameters, slice the program into 2 pieces and connect them with send/receive OP.
+Fluid provides a tool called “Distributed Transpiler” that automatically converts the non-cluster program into cluster program.
+The idea behind this tool is to find the optimize OPs and gradient parameters, slice the program into 2 pieces and connect them with send/receive OP.
Optimize OPs and gradient parameters can be found from the return values of optimizer’s minimize function.
To put them together:
... #define the program, cost, and create sgd optimizer
optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) #get optimize OPs and gradient parameters
-t = fluid.DistributeTranspiler() # create transpiler instance
+t = fluid.DistributeTranspiler() # create the transpiler instance
# slice the program into 2 pieces with optimizer_ops and gradient parameters list, as well as pserver_endpoints, which is a comma separated list of [IP:PORT] and number of trainers
-t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
+t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
... #create executor
@@ -319,17 +319,17 @@
Please find the complete demo from here. In parameter server node run this in the command line:
+Please find the complete demo from here. In parameter server node run the following in the command line:
PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py
please note we assume that your parameter server runs at 192.168.1.2:6174
Wait until the prompt Server listening on 192.168.1.2:6174
Then in 2 of your trainer node run this:
+Then in 2 of your trainer nodes run this:
PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=TRAINER python notest_dist_fit_a_line.py
the reason you need to run this command twice in 2 nodes is: in the script we set the trainer count to be 2. You can change this setting on line 50
+the reason you need to run this command twice in 2 nodes is because: in the script we set the trainer count to be 2. You can change this setting on line 50
Now you have 2 trainers and 1 parameter server up and running.
IfElse
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