Yibing Liu
7 years ago
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# Contributor Covenant Code of Conduct
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## Our Pledge
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In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.
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## Our Standards
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Examples of behavior that contributes to creating a positive environment include:
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* Using welcoming and inclusive language
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* Being respectful of differing viewpoints and experiences
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* Gracefully accepting constructive criticism
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* Focusing on what is best for the community
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* Showing empathy towards other community members
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Examples of unacceptable behavior by participants include:
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* The use of sexualized language or imagery and unwelcome sexual attention or advances
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* Trolling, insulting/derogatory comments, and personal or political attacks
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* Public or private harassment
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* Publishing others' private information, such as a physical or electronic address, without explicit permission
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* Other conduct which could reasonably be considered inappropriate in a professional setting
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## Our Responsibilities
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Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
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Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
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## Scope
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This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
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## Enforcement
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Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at paddle-dev@baidu.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
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Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.
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## Attribution
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This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]
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[homepage]: http://contributor-covenant.org
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[version]: http://contributor-covenant.org/version/1/4/
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# Fluid Distributed Training
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## Introduction
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In this article, we'll explain how to config and run distributed training jobs with PaddlePaddle Fluid in a bare metal cluster.
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## Preparations
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### Get your cluster ready
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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.
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### Have PaddlePaddle installed
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PaddlePaddle must be installed on all nodes. If you have GPU cards on your nodes, be sure to properly install drivers and CUDA libraries.
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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).
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### Update training script
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#### Non-cluster training script
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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.
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This demo's non-cluster version with fluid API is as follows:
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``` python
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import paddle.v2 as paddle
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import paddle.v2.fluid as fluid
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y_predict = fluid.layers.fc(input=x, size=1, act=None)
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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cost = fluid.layers.square_error_cost(input=y_predict, label=y)
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avg_cost = fluid.layers.mean(x=cost)
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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BATCH_SIZE = 20
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.uci_housing.train(), buf_size=500),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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fluid.io.save_persistables(exe, "./fit_a_line.model/")
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fluid.io.load_persistables(exe, "./fit_a_line.model/")
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for data in train_reader():
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avg_loss_value, = exe.run(fluid.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[avg_cost])
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if avg_loss_value[0] < 10.0:
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exit(0) # if avg cost less than 10.0, we think our code is good.
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exit(1)
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```
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We created a simple fully connected neural networks training program and handed it to the fluid executor to run for 100 passes.
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Now let's try to convert it to a distributed version to run in a cluster.
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#### Introducing parameter server
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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.
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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).
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Now we need to create program for both trainers and parameter servers, the question is how?
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#### Slice the program
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Fluid provides a tool called "Distribute Transpiler" to automatically convert the non-cluster program into cluster program.
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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.
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Optimize OPs and gradient parameters can be found from the return values of optimizer's minimize function.
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To put them together:
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``` python
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... #define the program, cost, and create sgd optimizer
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optimize_ops, params_grads = sgd_optimizer.minimize(avg_cost) #get optimize OPs and gradient parameters
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t = fluid.DistributeTranspiler() # create transpiler instance
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# 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
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t.transpile(optimize_ops, params_grads, pservers=pserver_endpoints, trainers=2)
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... #create executor
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# in pserver, run this
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exe.run(fluid.default_startup_program())
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#current_endpoint here means current pserver IP:PORT you wish to run on
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exe.run(t.get_pserver_program(current_endpoint, optimize_ops))
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# in trainer, run this
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... # define data reader
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exe.run(fluid.default_startup_program())
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for pass_id in range(100):
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for data in train_reader():
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exe.run(t.get_trainer_program())
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```
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### E2E demo
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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:
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``` bash
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PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=PSERVER python notest_dist_fit_a_line.py
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```
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*please note we assume that your parameter server runs at 192.168.1.2:6174*
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Wait until the prompt `Server listening on 192.168.1.2:6174`
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Then in 2 of your trainer node run this:
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``` bash
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PSERVERS=192.168.1.2:6174 SERVER_ENDPOINT=192.168.1.2:6174 TRAINING_ROLE=TRAINER python notest_dist_fit_a_line.py
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```
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*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*
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Now you have 2 trainers and 1 parameter server up and running.
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