HI Niketan,
You are right. I was actually testing 2 seperate code in the same environment.
Maybe that's why having this high stats were coming. Please find the stats in
standalone environment.
SystemML Statistics:
Total elapsed time: 0.000 sec.
Total compilation time: 0.000 sec.
Total execution time: 0.000 sec.
Number of compiled Spark inst: 0.
Number of executed Spark inst: 0.
Cache hits (Mem, WB, FS, HDFS): 2/0/0/0.
Cache writes (WB, FS, HDFS): 0/0/0.
Cache times (ACQr/m, RLS, EXP): 0.000/0.001/0.000/0.000 sec.
HOP DAGs recompiled (PRED, SB): 0/0.
HOP DAGs recompile time: 0.000 sec.
Spark ctx create time (lazy): 0.000 sec.
Spark trans counts (par,bc,col):0/0/0.
Spark trans times (par,bc,col): 0.000/0.000/0.000 secs.
Total JIT compile time: 0.0 sec.
Total JVM GC count: 168.
Total JVM GC time: 0.345 sec.
Heavy hitter instructions (name, time, count):
-- 1) ba+* 0.010 sec 1
-- 2) rand 0.007 sec 2
-- 3) createvar 0.001 sec 3
-- 4) rmvar 0.000 sec 3
-- 5) cpvar 0.000 sec 1
Thanks a lot!
Arijit
________________________________
From: Niketan Pansare <npan...@us.ibm.com>
Sent: Friday, May 12, 2017 8:04:41 PM
To: dev@systemml.incubator.apache.org
Subject: Re: Improve SystemML execution speed in Spark
Hi Arijit
The second statistics is bit surprising. Is it possible for you to share
the exact setup may be via git: bash script (for example run.sh with Jvm
sizes, number of executors, etc), pyspark script (containing mlcontext
code) and DML script. This will help me reproduce those numbers on my
cluster.
May be Matthias can comment on buildTree part.
Thanks
Niketan
> On May 12, 2017, at 5:07 AM, arijit chakraborty <ak...@hotmail.com>
wrote:
>
> Hi Niketan,
>
>
> Sorry for asking the nuisance question. Please find the output from
"setStatistics(True)" for my model.
>
>
> SystemML Statistics:
>
> Total elapsed time: 0.000 sec.
>
> Total compilation time: 0.000 sec.
>
> Total execution time: 0.000 sec.
>
> Number of compiled Spark inst: 583.
>
> Number of executed Spark inst: 29.
>
> Cache hits (Mem, WB, FS, HDFS): 180563/0/0/3.
>
> Cache writes (WB, FS, HDFS): 36070/0/0.
>
> Cache times (ACQr/m, RLS, EXP): 4.349/0.077/0.729/0.000 sec.
>
> HOP DAGs recompiled (PRED, SB): 0/496.
>
> HOP DAGs recompile time: 4.976 sec.
>
> Functions recompiled: 46.
>
> Functions recompile time: 3.016 sec.
>
> Spark ctx create time (lazy): 0.008 sec.
>
> Spark trans counts (par,bc,col):29/0/3.
>
> Spark trans times (par,bc,col): 0.021/0.000/4.178 secs.
>
> ParFor loops optimized: 1.
>
> ParFor optimize time: 0.158 sec.
>
> ParFor initialize time: 0.070 sec.
>
> ParFor result merge time: 0.001 sec.
>
> ParFor total update in-place: 0/4446/53253
>
> Total JIT compile time: 1.083 sec.
>
> Total JVM GC count: 212.
>
> Total JVM GC time: 0.844 sec.
>
> Heavy hitter instructions (name, time, count):
>
> -- 1) dev 7.381 sec 1
>
> -- 2) buildTree_t10 5.524 sec 1
>
> -- 3) buildTree_t11 5.423 sec 1
>
> -- 4) buildTree_t9 5.225 sec 1
>
> -- 5) rangeReIndex 4.551 sec 60268
>
> -- 6) findBestSplitSC_t10 3.737 sec 15
>
> -- 7) findBestSplitSC_t11 3.635 sec 15
>
> -- 8) findBestSplitSC_t9 3.410 sec 12
>
> -- 9) append 1.509 sec 197
>
> -- 10) leftIndex 0.994 sec 53253
>
>
>
> "buildTree" part is taking quite a bit of time.
>
>
> I also tested the following basic code. This is also taking high time.
>
>
> A = matrix(1, 10,10)
> B = matrix(1,5,10)
> C = B %*% A
>
>
> The log output is the following
>
>
> SystemML Statistics:
> Total elapsed time: 0.000 sec.
> Total compilation time: 0.000 sec.
> Total execution time: 0.000 sec.
> Number of compiled Spark inst: 0.
> Number of executed Spark inst: 270.
> Cache hits (Mem, WB, FS, HDFS): 518/0/0/114.
> Cache writes (WB, FS, HDFS): 414/0/0.
> Cache times (ACQr/m, RLS, EXP): 32.094/0.003/0.034/0.000 sec.
> HOP DAGs recompiled (PRED, SB): 0/132.
> HOP DAGs recompile time: 0.165 sec.
> Spark ctx create time (lazy): 0.000 sec.
> Spark trans counts (par,bc,col):58/54/118.
> Spark trans times (par,bc,col): 0.040/0.225/32.187 secs.
> Total JIT compile time: 4.431 sec.
> Total JVM GC count: 1136.
> Total JVM GC time: 15.553 sec.
> Heavy hitter instructions (name, time, count):
> -- 1) append 23.917 sec 60
> -- 2) sp_rblk 8.201 sec 54
> -- 3) sp_ctable 1.915 sec 54
> -- 4) sp_sample 1.597 sec 54
> -- 5) sp_mapmm 0.995 sec 54
> -- 6) sp_seq 0.195 sec 54
> -- 7) rmvar 0.071 sec 916
> -- 8) rangeReIndex 0.010 sec 72
> -- 9) createvar 0.010 sec 576
> -- 10) rmempty 0.007 sec 54
>
>
> I can see JVM GC time is high (is pretty low in above case) & append is
taking time (even though we are not appending anything).
>
>
> Can you please help me to understand why this can be the case?
>
>
>
>
> Thanks a lot!
>
> Arijit
>
> ________________________________
> From: arijit chakraborty <ak...@hotmail.com>
> Sent: Friday, May 12, 2017 2:32:07 AM
> To: dev@systemml.incubator.apache.org
> Subject: Re: Improve SystemML execution speed in Spark
>
> Hi Niketan,
>
>
> Thank you for your suggestion!
>
>
> I tried what you suggested.
>
>
> ## Changed it here:
>
>
> from pyspark.sql import SQLContext
> import systemml as sml
> sqlCtx = SQLContext(sc)
> ml = sml.MLContext(sc).setStatistics(True)
>
>
> # And then :
>
>
> scriptUrl = "C:/systemml-0.13.0-incubating-bin/scripts/model_code.dml"
> %%time
> script = sml.dml(scriptUrl).input(bdframe_train =train_data ,
bdframe_test = test_data).output("check_func")
>
> beta = ml.execute(script).get("check_func").toNumPy()
>
> pd.DataFrame(beta).head(1)
>
>
>
> It gave me output:
>
>
> Wall time: 16.3 s
>
>
>
> But how I can get this "time is spent in converters" or "some instruction
in SystemML"?
>
>
> Just want to add I'm running this code through jupyter notebook.
>
>
> Thanks again!
>
>
> Arijit
>
> ________________________________
> From: Niketan Pansare <npan...@us.ibm.com>
> Sent: Friday, May 12, 2017 2:02:52 AM
> To: dev@systemml.incubator.apache.org
> Subject: Re: Improve SystemML execution speed in Spark
>
> Ok, then the next step would be to set statistics:
>>> ml = sml.MLContext(sc).setStatistics(True)
>
> It will help you identify whether the time is spent in converters or some
instruction in SystemML.
>
> Also, since dataframe creation is lazy, you may to do persist() followed
by an action such as count() to ensure you are measuring it correctly.
>
>> On May 11, 2017, at 1:27 PM, arijit chakraborty <ak...@hotmail.com>
wrote:
>>
>> Thank you Niketan for your reply! I was actually putting the timer in
the dml code part. Rest of the portion were almost instantaneous. The dml
code part was taking time. And I could not able to figure out why it could
be.
>>
>>
>> Thanks again!
>>
>> Arijit
>>
>> ________________________________
>> From: Niketan Pansare <npan...@us.ibm.com>
>> Sent: Thursday, May 11, 2017 1:33:15 AM
>> To: dev@systemml.incubator.apache.org
>> Subject: Re: Improve SystemML execution speed in Spark
>>
>> Hi Arijit,
>>
>> Can you please put timing counters around below code to understand 20-30
seconds you observe:
>> 1. Creation of SparkContext:
>> sc = SparkContext("local[*]", "test")
>> 2. Converting pandas to Pyspark dataframe:
>>> train_data= pd.read_csv("data1.csv")
>>> test_data = pd.read_csv("data2.csv")
>>> train_data = sqlCtx.createDataFrame(pd.DataFrame(train_data))
>>> test_data = sqlCtx.createDataFrame(pd.DataFrame(test_data))
>>
>>
>> Also, you can pass pandas data frame directly to MLContext :)
>>
>> Thanks
>>
>> Niketan
>>
>>> On May 10, 2017, at 10:31 AM, arijit chakraborty <ak...@hotmail.com>
wrote:
>>>
>>> Hi,
>>>
>>>
>>> I'm creating a process in SystemML, and running it through spark. I'm
running the code in the following way:
>>>
>>>
>>> # Spark Specifications:
>>>
>>>
>>> import os
>>> import sys
>>> import pandas as pd
>>> import numpy as np
>>>
>>> spark_path = "C:\spark"
>>> os.environ['SPARK_HOME'] = spark_path
>>> os.environ['HADOOP_HOME'] = spark_path
>>>
>>> sys.path.append(spark_path + "/bin")
>>> sys.path.append(spark_path + "/python")
>>> sys.path.append(spark_path + "/python/pyspark/")
>>> sys.path.append(spark_path + "/python/lib")
>>> sys.path.append(spark_path + "/python/lib/pyspark.zip")
>>> sys.path.append(spark_path + "/python/lib/py4j-0.10.4-src.zip")
>>>
>>> from pyspark import SparkContext
>>> from pyspark import SparkConf
>>>
>>> sc = SparkContext("local[*]", "test")
>>>
>>>
>>> # SystemML Specifications:
>>>
>>>
>>> from pyspark.sql import SQLContext
>>> import systemml as sml
>>> sqlCtx = SQLContext(sc)
>>> ml = sml.MLContext(sc)
>>>
>>>
>>> # Importing the data
>>>
>>>
>>> train_data= pd.read_csv("data1.csv")
>>> test_data = pd.read_csv("data2.csv")
>>>
>>>
>>>
>>> train_data = sqlCtx.createDataFrame(pd.DataFrame(train_data))
>>> test_data = sqlCtx.createDataFrame(pd.DataFrame(test_data))
>>>
>>>
>>> # Finally executing the code:
>>>
>>>
>>> scriptUrl = "C:/systemml-0.13.0-incubating-bin/scripts/model_code.dml"
>>>
>>> script = sml.dml(scriptUrl).input(bdframe_train =train_data ,
bdframe_test = test_data).output("check_func")
>>>
>>> beta = ml.execute(script).get("check_func").toNumPy()
>>>
>>> pd.DataFrame(beta).head(1)
>>>
>>> The datasize are 1000 & 100 rows for train and test respectively. I'm
testing it on small dataset during development. Later will test in larger
dataset. I'm running on my local system with 4 cores.
>>>
>>> The problem is, if I run the model in R, it's taking fraction of
second. But when I'm running like this, it's taking around 20-30 seconds.
>>>
>>> Could anyone please suggest me how to improve the execution speed? In
case there are any other way I can execute the code, which can improve the
execution speed.
>>>
>>> Also, thank you all you guyz for releasing the 0.14 version. There are
fewimprovements we found extremely helpful.
>>>
>>> Thank you!
>>> Arijit
>>>
>>
>
