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
Nikos Karampatziakis. Cloud and Information Sciences Lab. Microsoft ... are in human readable form (text). â· Connects to the host:port VW is listening on ...
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 ...
Dec 16, 2011 - gt. Problem: Hessian can be too big (matrix of size dxd) .... terminate if either: the specified number of passes over the data is reached.
best-in-class algorithms such as Random Forest, Gradient Boosting and Deep Learning at scale. .... elegant web interface or fully scriptable R API from H2O CRAN package. · grid search for .... takes to cut the learning rate in half (e.g., 10â6 mea
Performance for SQL Based Applications. Then, if you have not already done so, ... In the Save As dialog box, save the file as plan1.sqlplan on your desktop. 6.
A Windows, Linux, or Mac OS X computer. ⢠Azure Storage Explorer. ⢠The lab files for this course. ⢠A Spark 2.0 HDInsight cluster. Note: If you have not already ...
Start Microsoft SQL Server Management Studio and connect to your database instance. 2. Click New Query, select the AdventureWorksLT database, type the ...
performed by writing code to manipulate data in R or Python, or by using some of the built-in modules ... https://cran.r-project.org/web/packages/dplyr/dplyr.pdf. ... You can also import custom R libraries that you have uploaded to Azure ML as R.
Developing SQL Databases. Lab 4 â Creating Indexes. Overview. A table named Opportunity has recently been added to the DirectMarketing schema within the database, but it has no constraints in place. In this lab, you will implement the required cons
create a new folder named iislogs in the root of your Azure Data Lake store. 4. Open the newly created iislogs folder. Then click Upload, and upload the 2008-01.txt file you viewed previously. Create a Job. Now that you have uploaded the source data
will create. The Azure ML Web service you will create is based on a dataset that you will import into. Azure ML Studio and is designed to perform an energy efficiency regression experiment. What You'll Need. To complete this lab, you will need the fo
Lab 2 â Using a U-SQL Catalog. Overview. In this lab, you will create an Azure Data Lake database that contains some tables and views for ongoing big data processing and reporting. What You'll Need. To complete the labs, you will need the following
The final Execute R/Python Script. 4. Edit the comment of the new Train Model module, and set it to Decision Forest. 5. Connect the output of the Decision Forest Regression module to the Untrained model (left) input of the new Decision Forest Train M
Page 1 ... A web browser and Internet connection. Create an Azure ... Now you're ready to start learning how to build data science and machine learning solutions.
In this lab, you will explore and visualize the data Rosie recorded. ... you will use the Data Analysis Pack in Excel to apply some statistical functions to Rosie's.
created previously. hbase org.apache.hadoop.hbase.mapreduce.LoadIncrementalHFiles. /data/storefile Stocks. 8. Wait for the MapReduce job to complete. Query the Bulk Loaded Data. 1. Enter the following command to start the HBase shell. hbase shell. 2.
videos and demonstrations in the module to learn more. 1. Search for the Evaluate Recommender module and drag it onto the canvas. Then connect the. Results dataset2 (right) output of the Split Data module to its Test dataset (left) input and connect
In this lab, you will create schemas and tables in the AdventureWorksLT database. Before starting this lab, you should view Module 1 â Designing a Normalized ...
Challenge 1: Add Constraints. You have been given the design for a ... add DEFAULT constraints to columns based on the requirements. Challenge 2: Test the ...
Review the following design requirements for your stored procedure: Stored Procedure: Reports. ... @Color (same data type as the Color column in the SalesLT.
A Microsoft Windows, Apple Macintosh, or Linux computer. Create an Azure Subscription. Note: If you already have a Microsoft Azure subscription, you can skip ...
Then in the Upload a new notebook dialog box, browse to select the notebook .... 9. On the browser tab containing the dashboard page for your Azure ML web ...