I want to run a machine learning task on my Win 10 PC, 16GB RAM, Mathematica 11.3.0, but I am facing the following problems: training set size 10GB CSV file, with 700,000,000 x 2 datasets. Mathematica simply stops during import via Import or ReadList function. My idea is to split the input file into several smaller files that could be imported and to load the smaller files in a batch to feed the Predict function or perhabs a neural network. Any idea how to make it happen? Do you have a better idea?
Good hint, thank you. I am currently trying to import the data into PostgreSQL. This approach means that the Predict function or a neural network would get the Data in a stream with the consequence that not all data could be available in one moment of time (due to limited RAM).
Is it possible for Mathematica to build a ML model based on a stream of data?
That is very useful! Thank you very much. It seems that I can solve the problem with a database based stream of data and a neural network. I will make a try.
I am sorry for late response. Well, I have to admit that I am not a MongoDB expert. It is not possible for me to import the entire Csv file in Mongo. The import stops after ca. 1% (5,5 * 10^6) datasets. Now I am trying to parse CSV to Json, and I hope the Mongo import will lead to success with Json.
Thank You again to all participants for their contributions. After some trials I have found an efficient way to insert the big csv file in MongoDB via mongoimport.