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+# Design Doc: Distributed Training
+
+## Objective
+
+In [this slides](https://www.slideshare.net/cxwangyi/paddlepaddle-a-complete-solution-for-businesses), we explained that we'd like PaddlePaddle running on general-purpose clusters like those managed by Kubernetes, so to address demands for AI from both Internet and non-Internet industries.
+
+This poses technical challenges to PaddlePaddle:
+
+1. Support fault-recovery.
+1. Support both offline and online training.
+1. [Serverless computing](https://en.wikipedia.org/wiki/Serverless_computing) of distributed training.
+
+
+## Training Job
+
+A training job will be created once user asks Paddle cloud to train a model. The training job is made up of different processes that collaboratively consume data and produce a trained model. There are three kinds of processes:
+
+1. the *master process*, which dispatches tasks to
+1. one or more *trainer processes*, which run distributed training and synchronize gradients/models via
+1. one or more *parameter server processes*, where each holds a shard of the global model.
+
+Their relation is illustrated in the following graph:
+
+
+
+### Master Process
+
+The master process will:
+
+- 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.
+
+
+#### Task
+
+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
+
+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.
+
+
+
+- The todo queue holds tasks to be dispatched. When a job starts, the master process fills in the todo queue with all tasks.
+- The pending queue holds tasks that are currently training by trainers.
+- the done queue holds tasks that are already trained.
+
+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 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 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 collaboratively. The parameters are partitioned on different parameter servers.
+
+The parameter server will:
+
+- Receive gradient from the trainers, update its parameters, and give the trainers the latest parameters.
+- Periodically save its parameters to distributed file system by overriding the previous save.
+
+### Optimization Algorithms
+
+The communication pattern between the trainers and the parameter servers depends on the category of optimization algorithm:
+
+- 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 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.
+
+## Fault Tolerant
+
+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:
+
+- sync-SGD
+
+ TODO
+
+- async-SGD
+
+ Since async-SGD does not require synchronization between mini-batches, the system will by definition make process if at least one trainer is running.
+
+## 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 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 a failure, the graph below shows how etcd is used:
+
+
+
+### Master Process
+
+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 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, and updates task queue using an etcd transaction to ensure lock is held during the update.
+
+The master process will kill itself if its etcd lease expires.
+
+When the master process is dead for any reason, Kubernetes will restart it. It will be online again with all states recovered from etcd in few minutes.
+
+### Trainer Process
+
+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 the count of parameter servers reaches the desired count.
+1. Generates a unique ID, and sets key `/trainer/` 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/` again so that the master process can discover the trainer again.
+
+### Parameter Server Process
+
+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 through etcd keys `/ps/` (`/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:
+
+ 
+
+ The third parameter server joined:
+
+ 
+
+1. The parameter server can load parameters if there are already saved parameters in the save path (inferred from its index).
+1. Now the parameter server is ready for the trainers' requests.
+
+If the parameter server's etcd lease expires, the parameter server will kill itself.
+
+
+## Dynamic Scaling
+
+### Trainer Scaling
+
+TODO
+
+### Parameter Server Scaling
+
+Not planned for v1.
+
+## Training Dataset Format
+
+TODO
+
+## User Interface
+
+TODO
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