Re: Speeding up K-Means Clustering
Is it v0.9? Did you run in local mode? Try to set --driver-memory 4g
and repartition your data to match number of CPU cores such that the
data is evenly distributed. You need 1m * 50 * 8 ~ 400MB to storage
the data. Make sure there are enough memory for caching. -Xiangrui
On Thu, Jul 17, 2014 at 1:48 AM, Ravishankar Rajagopalan
viora...@gmail.com wrote:
I am trying to use MLlib for K-Means clustering on a data set with 1 million
rows and 50 columns (all columns have double values) which is on HDFS (raw
txt file is 28 MB)
I initially tried the following:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This took me nearly 850 seconds.
I tried using persist with MEMORY_ONLY option hoping that this would
significantly speed up the algorithm:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
parsedData3.persist(MEMORY_ONLY)
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This resulted in only a marginal improvement and took around 720 seconds.
Is there any other way to speed up the algorithm further?
Thank you.
Regards,
Ravi
Re: Speeding up K-Means Clustering
Hi Xiangrui,
Yes I am using Spark v0.9 and am not running it in local mode.
I did the memory setting using export SPARK_MEM=4G before starting the Spark
instance.
Also previously, I was starting it with -c 1 but changed it to -c 12 since
it is a 12 core machine. It did bring down the time taken to less than 200
seconds from over 700 seconds.
I am not sure how to repartition the data to match the CPU cores. How do I
do it?
Thank you.
Ravi
On Thu, Jul 17, 2014 at 3:17 PM, Xiangrui Meng men...@gmail.com wrote:
Is it v0.9? Did you run in local mode? Try to set --driver-memory 4g
and repartition your data to match number of CPU cores such that the
data is evenly distributed. You need 1m * 50 * 8 ~ 400MB to storage
the data. Make sure there are enough memory for caching. -Xiangrui
On Thu, Jul 17, 2014 at 1:48 AM, Ravishankar Rajagopalan
viora...@gmail.com wrote:
I am trying to use MLlib for K-Means clustering on a data set with 1
million
rows and 50 columns (all columns have double values) which is on HDFS
(raw
txt file is 28 MB)
I initially tried the following:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This took me nearly 850 seconds.
I tried using persist with MEMORY_ONLY option hoping that this would
significantly speed up the algorithm:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
parsedData3.persist(MEMORY_ONLY)
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This resulted in only a marginal improvement and took around 720 seconds.
Is there any other way to speed up the algorithm further?
Thank you.
Regards,
Ravi
Re: Speeding up K-Means Clustering
Please try
val parsedData3 =
data3.repartition(12).map(_.split(\t)).map(_.toDouble).cache()
and check the storage and driver/executor memory in the WebUI. Make
sure the data is fully cached.
-Xiangrui
On Thu, Jul 17, 2014 at 5:09 AM, Ravishankar Rajagopalan
viora...@gmail.com wrote:
Hi Xiangrui,
Yes I am using Spark v0.9 and am not running it in local mode.
I did the memory setting using export SPARK_MEM=4G before starting the
Spark instance.
Also previously, I was starting it with -c 1 but changed it to -c 12 since
it is a 12 core machine. It did bring down the time taken to less than 200
seconds from over 700 seconds.
I am not sure how to repartition the data to match the CPU cores. How do I
do it?
Thank you.
Ravi
On Thu, Jul 17, 2014 at 3:17 PM, Xiangrui Meng men...@gmail.com wrote:
Is it v0.9? Did you run in local mode? Try to set --driver-memory 4g
and repartition your data to match number of CPU cores such that the
data is evenly distributed. You need 1m * 50 * 8 ~ 400MB to storage
the data. Make sure there are enough memory for caching. -Xiangrui
On Thu, Jul 17, 2014 at 1:48 AM, Ravishankar Rajagopalan
viora...@gmail.com wrote:
I am trying to use MLlib for K-Means clustering on a data set with 1
million
rows and 50 columns (all columns have double values) which is on HDFS
(raw
txt file is 28 MB)
I initially tried the following:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This took me nearly 850 seconds.
I tried using persist with MEMORY_ONLY option hoping that this would
significantly speed up the algorithm:
val data3 = sc.textFile(hdfs://...inputData.txt)
val parsedData3 = data3.map( _.split('\t').map(_.toDouble))
parsedData3.persist(MEMORY_ONLY)
val numIterations = 10
val numClusters = 200
val clusters = KMeans.train(parsedData3, numClusters, numIterations)
This resulted in only a marginal improvement and took around 720
seconds.
Is there any other way to speed up the algorithm further?
Thank you.
Regards,
Ravi
