I am using Julia 0.4 for this purpose, if that's what is meant by "0.4 only".
On Wednesday, October 14, 2015 at 9:53:09 AM UTC+5:30, Jacob Quinn wrote: > > Oh yes, I forgot to mention that the CSV/DataStreams code is 0.4 only. > Definitely interested to hear about any results/experiences though. > > -Jacob > > On Tue, Oct 13, 2015 at 10:11 PM, Yichao Yu <[email protected] > <javascript:>> wrote: > >> On Wed, Oct 14, 2015 at 12:02 AM, Grey Marsh <[email protected] >> <javascript:>> wrote: >> > @Jacob, I tried your approach. Somehow it got stuck in the "@time ds = >> > DataStreams.DataTable(f)" line. After 15 minutes running, julia is using >> > ~500mb and 1 cpu core with no sign of end. The memory use has been >> almost >> > same for the whole duration of 15 minutes. I'm letting it run, hoping >> that >> > it finishes after some time. >> > >> > From your run, I can see it needs 12gb memory which is higher than my >> > machine memory of 8gb. could it be the problem? >> >> 12GB is the total number of memory ever allocated during the timing. A >> lot of them might be intermediate results that are freed by the GC. >> Also, from the output of @time, it looks like 0.4. >> >> > >> > On Wednesday, October 14, 2015 at 2:28:09 AM UTC+5:30, Jacob Quinn >> wrote: >> >> >> >> I'm hesitant to suggest, but if you're in a bind, I have an >> experimental >> >> package for fast CSV reading. The API has stabilized somewhat over the >> last >> >> week and I'm planning a more broad release soon, but I'd still >> consider it >> >> alpha mode. That said, if anyone's willing to give it a drive, you >> just need >> >> to >> >> >> >> Pkg.add("Libz") >> >> Pkg.add("NullableArrays") >> >> Pkg.clone("https://github.com/quinnj/DataStreams.jl";) >> >> Pkg.clone("https://github.com/quinnj/CSV.jl";) >> >> >> >> With the original file referenced here I get: >> >> >> >> julia> reload("CSV") >> >> >> >> julia> f = >> CSV.Source("/Users/jacobquinn/Downloads/train.csv";null="NA") >> >> CSV.Source: "/Users/jacobquinn/Downloads/train.csv" >> >> delim: ',' >> >> quotechar: '"' >> >> escapechar: '\\' >> >> null: "NA" >> >> schema: >> >> >> DataStreams.Schema(UTF8String["ID","VAR_0001","VAR_0002","VAR_0003","VAR_0004","VAR_0005","VAR_0006","VAR_0007","VAR_0008","VAR_0009" >> >> … >> >> >> "VAR_1926","VAR_1927","VAR_1928","VAR_1929","VAR_1930","VAR_1931","VAR_1932","VAR_1933","VAR_1934","target"],[Int64,DataStreams.PointerString,Int64,Int64,Int64,DataStreams.PointerString,Int64,Int64,DataStreams.PointerString,DataStreams.PointerString >> >> … >> >> >> Int64,Int64,Int64,Int64,Int64,Int64,Int64,Int64,DataStreams.PointerString,Int64],145231,1934) >> >> dateformat: Base.Dates.DateFormat(Base.Dates.Slot[],"","english") >> >> >> >> >> >> julia> @time ds = DataStreams.DataTable(f) >> >> 43.513800 seconds (694.00 M allocations: 12.775 GB, 2.55% gc time) >> >> >> >> >> >> You can convert the result to a DataFrame with: >> >> >> >> function DataFrames.DataFrame(dt::DataStreams.DataTable) >> >> cols = dt.schema.cols >> >> data = Array(Any,cols) >> >> types = DataStreams.types(dt) >> >> for i = 1:cols >> >> data[i] = DataStreams.column(dt,i,types[i]) >> >> end >> >> return DataFrame(data,Symbol[symbol(x) for x in dt.schema.header]) >> >> end >> >> >> >> >> >> -Jacob >> >> >> >> On Tue, Oct 13, 2015 at 2:40 PM, feza <[email protected]> wrote: >> >>> >> >>> Finally was able to load it, but the process consumes a ton of >> memory. >> >>> julia> @time train = readtable("./test.csv"); >> >>> 124.575362 seconds (376.11 M allocations: 13.438 GB, 10.77% gc time) >> >>> >> >>> >> >>> >> >>> On Tuesday, October 13, 2015 at 4:34:05 PM UTC-4, feza wrote: >> >>>> >> >>>> Same here on a 12gb ram machine >> >>>> >> >>>> _ >> >>>> _ _ _(_)_ | A fresh approach to technical computing >> >>>> (_) | (_) (_) | Documentation: http://docs.julialang.org >> >>>> _ _ _| |_ __ _ | Type "?help" for help. >> >>>> | | | | | | |/ _` | | >> >>>> | | |_| | | | (_| | | Version 0.5.0-dev+429 (2015-09-29 09:47 >> UTC) >> >>>> _/ |\__'_|_|_|\__'_| | Commit f71e449 (14 days old master) >> >>>> |__/ | x86_64-w64-mingw32 >> >>>> >> >>>> julia> using DataFrames >> >>>> >> >>>> julia> train = readtable("./test.csv"); >> >>>> ERROR: OutOfMemoryError() >> >>>> in resize! at array.jl:452 >> >>>> in readnrows! at >> >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:164 >> >>>> in readtable! at >> >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:767 >> >>>> in readtable at >> >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:847 >> >>>> in readtable at >> >>>> C:\Users\Mustafa\.julia\v0.5\DataFrames\src\dataframe\io.jl:893 >> >>>> >> >>>> >> >>>> >> >>>> >> >>>> >> >>>> On Tuesday, October 13, 2015 at 3:47:58 PM UTC-4, Yichao Yu wrote: >> >>>>> >> >>>>> >> >>>>> On Oct 13, 2015 2:47 PM, "Grey Marsh" <[email protected]> wrote: >> >>>>> >> >>>>> Which julia version are you using. There's sime gc tweak on 0.4 for >> >>>>> that. >> >>>>> >> >>>>> > >> >>>>> > I was trying to load the training dataset from springleaf >> marketing >> >>>>> > response on Kaggle. The csv is 921 mb, has 145321 row and 1934 >> columns. My >> >>>>> > machine has 8 gb ram and julia ate 5.8gb+ memory after that I >> stopped julia >> >>>>> > as there was barely any memory left for OS to function properly. >> It took >> >>>>> > about 5-6 minutes later for the incomplete operation. I've >> windows 8 64bit. >> >>>>> > Used the following code to read the csv to Julia: >> >>>>> > >> >>>>> > using DataFrames >> >>>>> > train = readtable("C:\\train.csv") >> >>>>> > >> >>>>> > Next I tried to to load the same file in python: >> >>>>> > >> >>>>> > import pandas as pd >> >>>>> > train = pd.read_csv("C:\\train.csv") >> >>>>> > >> >>>>> > This took ~2.4gb memory, about a minute time >> >>>>> > >> >>>>> > Checking the same in R again: >> >>>>> > df = read.csv('E:/Libraries/train.csv', as.is = T) >> >>>>> > >> >>>>> > This took 2-3 minutes and consumes 3.5gb mem on the same machine. >> >>>>> > >> >>>>> > Why such discrepancy and why Julia even fails to load the csv >> before >> >>>>> > running out of memory? If there is any better way to get the file >> loaded in >> >>>>> > Julia? >> >>>>> > >> >>>>> > >> >> >> >> >> > >> > >
