Here's new user interface design idea I propose. Any advices are welcome! https://wiki.apache.org/hama/Neuron
On Mon, Jun 29, 2015 at 4:38 PM, Edward J. Yoon <[email protected]> wrote: > Hey all, > > As you know, the lastest Apache Hama provides distributed training of > an Artificial Neural Network using its BSP computing engine. In > general, the training data is stored in HDFS and is distributed in > multiple machines. In Hama, two kinds of components are involved in > the training procedure: the master task and the groom task. The master > task is in charge of merging the model updating information and > sending model updating information to all the groom tasks. The groom > tasks is in charge of calculate the weight updates according to the > training data. > > The training procedure is iterative and each iteration consists of > two phases: update weights and merge update. In the update weights > phase, each groom task would first update the local model according to > the received message from the master task. Then they would compute the > weight updates locally with assigned data partitions (mini-batch SGD) > and finally send the updated weights to the master task. In the merge > update phase, the master task would update the model according to the > messages received from the groom tasks. Then it would distribute the > updated model to all groom tasks. The two phases will repeat > alternatively until the termination condition is met (reach a > specified number of iterations). > > The model is designed in a hierarchical way. The base class is more > abstract than the derived class, so that the structure of the ANN > model can be freely set by the user, as long as it is a layered model. > Therefore, the Perceptron, Auto-encoder, Linear and Logistic regressor > can all be uniformly represented by an ANN. > > However, as described in above, currently the data parallelism is > only used. Each node will have a copy of the model. In each iteration, > the computation is conducted on each node and a final aggregation is > conducted in one node. Then the updated model will be synchronized to > each node. So, the performance is one thing; the parameters should fit > into the memory of a single machine. > > Here is a tentative near future plan I propose for applications > needing large model with huge memory consumptions, moderate > computational power for one mini-batch, and lots of training data. The > main idea is use of Parameter Server to parallelize model creation and > distribute training across machines. Apache Hama framework assigns > each split of training data stored in HDFS to each BSP task. Then, the > BSP task assigns each of the N threads a small portion of work, much > smaller than 1/Nth of the total size of a mini-batch, and assigns new > portions whenever they are free. With this approach, faster threads do > more work than slower threads. Each thread asynchronously asks the > Parameter Server who stores the parameters in distributed machines for > an updated copy of its model, computes the gradients on the assigned > data, and sends updated gradients back to the parameter server. This > architecture is inspired by Google's DistBelief (Jeff Dean et al, > 2012). Finally, I have no concrete idea regarding programming > interface at the moment but I'll try to provide neuron-centric > programming model like Google's Pregel if possible. > > -- > Best Regards, Edward J. Yoon -- Best Regards, Edward J. Yoon
