Reducing NumPy memory usage with lossless compression
If you’re running into memory issues because your NumPy arrays are too large, one of the basic approaches to reducing memory usage is compression. By changing how you represent your data, you can reduce memory usage and shrink your array’s footprint—often without changing the bulk of your code.
In this article we’ll cover:
- Reducing memory usage via smaller
- Sparse arrays.
- Some situations where these solutions won’t work.
Using smaller dtypes
When you create an array in NumPy, it has a data type, a
dtype that specifies what kind of array it is.
It might be an array of
uint8 (unsigned 8-bit integers) or
float64 (64-bit floating point numbers), and so on.
dtypes have different ranges of values they can represent:
- 16-bit uint range is 0-65535.
- 64-bit uint range is 0-18446744073709551615.
And they have different levels of memory usage; a 64-bit integer uses 4× memory than a 16-bit integer.
This gives us an opportunity to reduce memory usage: if your data is integers between 0 and 60K, there’s no point in using a 32-bit or 64-bit integer, you can use a 16-bit integer and use less memory.
>>> from numpy import ones >>> int64arr = ones((1024, 1024), dtype=np.uint64) >>> int16arr = ones((1024, 1024), dtype=np.uint16) >>> int64arr.nbytes 8388608 >>> int16arr.nbytes 2097152
As you would expect, a 16-bit array uses 25% of the RAM that a 64-bit array does.
dtype focuses on compression of individual cells in the array, sparse arrays focus on the overall structure of the array.
In particular, if your array is mostly zeros, why should you spend memory storing all those zeros?
A sparse array stores only the non-zero data, and all remaining data is assumed to be zero. There are different ways to implement sparseness, depending on the structure of your data, and we’ll focus on just one: coordinate-style.
Imagine a black and white picture of the stars: most of the background is black (i.e. zero), with occasional stars here and there. Instead of storing all the data, we can just say “at Y=123, X=500 there is a pixel with brightness 128”. Pixels that don’t get mentioned are assumed to have brightness 0. There is some overhead for recording X and Y for each star, but as long as most of the background is black this data structure will still save memory over the normal full array.
Example: reducing memory usage with a coordinate-style sparse array
In Python, the
sparse library provides an implementation of sparse arrays that is compatible with NumPy arrays.
It mostly focuses on coordinate-style arrays, which it calls
Here’s an example based on one from the Sparse documentation: we create an 2D array with uniform noise between 0 and 1, and set 90% of the pixels to black. If you think of it as a picture, this is similar to the star example we gave above, lots of zeros with occasional bright spots.
We can then compare memory usage of the original and
>>> import sparse, numpy as np >>> arr = np.random.random((1024, 1024)) >>> arr[arr < 0.9] = 0 >>> sparse_arr = sparse.COO(arr) >>> arr.nbytes 8388608 >>> sparse_arr.nbytes 2514648
The sparse array uses about 30% as much memory as the original array; only 10% of the array is non-zero, but there’s extra overhead from storing the X and Y coordinates.
When these strategies won’t work
Let’s imagine you have an image stored in an array.
You realize that given the nature of the image, you can use a 16-bit unsigned integer
dtype, and now you’ve limited the memory usage significantly.
Except—you want to use some functions from the excellent
And the thing about
scikit-image is that many of its functions will immediately convert the given array to a
dtype, if it isn’t already in that format.
So now you have the original 16-bit image and a new 64-bit image, for a total of 80 bits per pixel. In this situation you’re better off just storing the image as a 64-bit float in the first place, because at least 64-bit is better than 80-bit. Similar issues can apply to sparse arrays.
In short: even if you can find a smaller representation, the libraries you’re using might require a larger representation. Still, in many cases, just a tiny amount of changes to your code can reduce your memory usage, without changing your data at all.
Learn even more techniques for reducing memory usage—read the rest of the Larger-than-memory datasets guide for Python.
Wasting compute money on processes that use too much memory?
Your Python batch process is using too much memory, and you have no idea which part of your code is responsible.
You need a tool that will tell you exactly where to focus your optimization efforts, a tool designed for data scientists and scientists. Learn how the Fil memory profiler can help you.
How do you process large datasets with limited memory?
Get a free cheatsheet summarizing how to process large amounts of data with limited memory using Python, NumPy, and Pandas.
Plus, every week or so you’ll get new articles showing you how to process large data, and more generally improve you software engineering skills, from testing to packaging to performance: