Distributed Computing with Workers

For distributed computing, such as cross-validating a model or tuning some hyper parameters, Cloudera Data Science Workbench provides basic support for leveraging multiple engine instances from a single run. Any R or Python engine can spawn other engines, known as workers, and give them code to execute when they start up. Worker output is displayed in the main console to allow you to debug your code. These workers are terminated when the session exits.

For more significant distributed computing needs, using Cloudera Distribution of Apache Spark 2 from within Cloudera Data Science Workbench is strongly recommended.

Spawning Workers

Select a language from the code samples below to launch workers:

R

library("cdsw") 
workers <- launch.workers(n=2, cpu=0.2, memory=0.5, env="", code="print('Hello from a CDSW Worker')")

Python

import cdsw
workers = cdsw.launch_workers(n=2, cpu=0.2, memory=0.5, code="print 'Hello from a CDSW Worker'")

Worker Network Communication

Workers are a low-level feature to help use higher level libraries that can operate across multiple nodes. As such, you will generally want to use workers only to launch the backends for these libraries.

To help you get your workers or distributed computing framework components talking to one another, every worker engine run includes an environmental variable CDSW_MASTER_IP with the fully addressable IP of the master engine. Every engine has a dedicated IP access with no possibility of port conflicts.

For instance, the following are trivial examples of two worker engines talking to the master engine.

R

From the master engine, the following master.r script will launch two workers and accept incoming connections from them.

# master.r

library("cdsw")

# Launch two CDSW workers. These are engines that will run in 
# the same project, execute a given code or script, and exit.
workers <- launch.workers(n=2, cpu=0.2, memory=0.5, env="", script="worker.r")

# Accept two connections, one from each worker. Workers will
# execute worker.r.
for(i in c(1,2)) {
  # Receive a message from each worker and return a response.
  con <- socketConnection(host="0.0.0.0", port = 6000, blocking=TRUE, server=TRUE, open="r+")
  data <- readLines(con, 1)
  print(paste("Server received:", data))
  writeLines("Hello from master!", con)
  close(con)
}

The workers will execute the following worker.r script and respond to the master.

# worker.r

print(Sys.getenv("CDSW_MASTER_IP"))
con <- socketConnection(host=Sys.getenv("CDSW_MASTER_IP"), port = 6000, blocking=TRUE, server=FALSE, open="r+")
write_resp <- writeLines("Hello from Worker", con)
server_resp <- readLines(con, 1)
print(paste("Worker received:  ", server_resp))
close(con)

Python

From the master engine, the following master.py script will launch two workers and accept incoming connections from them.

# master.py

import cdsw, socket

# Launch two CDSW workers. These are engines that will run in 
# the same project, execute a given code or script, and exit.
workers = cdsw.launch_workers(n=2, cpu=0.2, memory=0.5, script="worker.py")

# Listen on TCP port 6000
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.bind(("0.0.0.0", 6000))
s.listen(1)

# Accept two connections, one from each worker. Workers will
# execute worker.py.
conn, addr = s.accept()
for i in range(2):
    # Receive a message from each worker and return a response.
    data = conn.recv(20)
    if not data: break
    print "Master received:", data
    conn.send("Hello From Server!")
conn.close()

The workers will execute the following worker.py script and respond to the master.

# worker.py

import os, socket

# Open a TCP connection to the master.
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
s.connect((os.environ["CDSW_MASTER_IP"], 6000))

# Send some data and receive a response.
s.send("Hello From Worker!")
data = s.recv(1024)
s.close()

print "Worker received:", data