Native learning reductions. Just like more complicated losses.
3
Other learning algorithms, as interest dictates.
4
Persistent Demonization
Goals for future from last year
1
Finish Scaling up. I want a kilonode program.
Some design considerations
Hadoop compatibility: Widely available, scheduling and robustness Iteration-firendly: Lots of iterative learning algorithms exist Minimum code overhead: Don’t want to rewrite learning algorithms from scratch Balance communication/computation: Imbalance on either side hurts the system
Some design considerations
Hadoop compatibility: Widely available, scheduling and robustness Iteration-firendly: Lots of iterative learning algorithms exist Minimum code overhead: Don’t want to rewrite learning algorithms from scratch Balance communication/computation: Imbalance on either side hurts the system Scalable: John has nodes aplenty
Current system provisions
Hadoop-compatible AllReduce Various parameter averaging routines Parallel implementation of Adaptive GD, CG, L-BFGS Robustness and scalability tested up to 1K nodes and thousands of node hours
Basic invocation on single machine
./spanning tree ../vw --total 2 --node 0 --unique id 0 -d $1 --span server localhost > node 0 2>&1 & ../vw --total 2 --node 1 --unique id 0 -d $1 --span server localhost killall spanning tree
Command-line options
--span server : Location of server for setting up spanning tree --unique id (=0): Unique id for cluster parallel job --total (=1): Total number of nodes used in cluster parallel job --node (=0): Node id in cluster parallel job
Basic invocation on a non-Hadoop cluster Spanning-tree server: Runs on cluster gateway, organizes communication ./spanning tree Worker nodes: Each worker node runs VW ./vw --span server --total --node --unique id -d
Basic invocation in a Hadoop cluster Spanning-tree server: Runs on cluster gateway, organizes communication ./spanning tree Map-only jobs: Map-only job launched on each node using Hadoop streaming hadoop jar $HADOOP HOME/hadoop-streaming.jar -Dmapred.job.map.memory.mb=2500 -input -output
Goals for future from last year. 1. Finish Scaling up. I want a kilonode program. 2. Native learning reductions. Just like more complicated losses. 3. Other learning algorithms, as interest dictates. 4. Persistent Demonization ...
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compose the set Dn. The whole data set now is denoted by Sn = {DLâªn,DU\n}. We call it semi-supervised data set. Initially S0 = D. After all unlabeled data are labeled, the data set is called genuine data set G,. G = Su = DLâªu. We define the cost
Oct 24, 2016 - In this paper, we combine state-of-the-art machine learn- ing methods with ... tribute to privacy since inference does not require commu- nicating user data to a ..... an Apache 2.0 license from github.com/tensorflow/models. For privac