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It looks like (dask/dask-ml#295 (comment)) I ran this notebook locally because it only uses a (very small) subset of the dataset. I don't recall ever using the complete Criteo dataset, or even a significant fraction of it
But you use dask.distrebuted
Also
Data is only 370gb in zipped file
In this link
https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html#criteo_tb
I mostly used Distributed for it's useful dashboard (for debugging/profiling/etc). I didn't focus on actually scaling to entire Criteo dataset; IIRC this simple use case illustrated some problems in Dask-ML.
My metric for "big data" is any data that's too large to fit in RAM. 370GB is certainly more RAM than the 16GB my local machine has.
I see
Is dask distributed is free
And will read data from lobsvm format?
Is dask distributed is free
Yes. Free as in beer (i.e, doesn't cost money) and free as in speech (the source is freely available).
And will read data from lobsvm format?
Yes. Dask-ML is a wrapper around scikit-learn, and they have a function for read in libsvm: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_svmlight_file.html. It'd be pretty simple to wrap that function with Dask:
from sklearn.datasets import load_svmlight_file
def read_chunk(filename):
X, y = load_svmlight_file(filename)
return X, y # scipy.sparse matrix, raw ndarray
from distributed import Client
client = Client()
filenames = ["criteo-day-1.svmlight", ...]
Xs_ys = client.map(read_chunk, filenames)
# Xs_ys will be tasked to the cluster, and will perform work in the background
# continue with rest of notebook
This code is untested.
Great code thanks
So dusk can help in both cases to read original RTB Criteo file or libsvm format
Only short question:
In your code above - load_svmlight_file meanse to read any svmlib format or specific svmlight format
Again thank a lot taking care
if somebody tried to run locally ?
like
https://github.com/rambler-digital-solutions/criteo-1tb-benchmark