Tensorflow: Each row in the training dataset contains 99% of the previous rows data - can I optimize it before running the training?
If you use Data API then you can cache the input. Also maybe TF's support for Kafka might be a help here as you could model it as a stream of data. 
Another approach would be to reuse some data between session calls. Then you would have to use resource variable (in the current Variable() spec it means using flag use_resource in the constructor). This way your csv could contain only minute level data and you would just add it to the variable - create a kind of circle-buffer from it.
I am searching for a way to make my training and testing data smaller in file size.
The model I want to end up with
I want to train a model that predicts whether or not a crypto coin price is making and x% (0.4 or so) jump within the next 10 minutes (i.e. I want the model to answer with a Yes or No). 
Every minute I will the model with the last 3 hours of price and volume data (that means 180 datapoints each containing 5 values open, close, high, low prices and volume).
My current training and testing sets are BIG
My training and testing sets are therefore row in a csv file where each row contains 5 x 180 = 900 numbers plus one label (Yes or No) and with about 100k rows I guess this is a very huge dataset.
But each row in csv contains mostly redundant data
But each "neighbor" row in the csv file only contain 1 new data point since every next row is only 1 minute "older" and therefore only has skipped the data point of oldest minute and instead introduced a new point of the next minute.
Is it possible to setup up the traing code so the csv file only needs to have the latest minute data point in each row?