Working with big data#

New in version 1.2.

HyperSpy makes it possible to analyse data larger than the available memory by providing “lazy” versions of most of its signals and functions. In most cases the syntax remains the same. This chapter describes how to work with data larger than memory using the LazySignal class and its derivatives.

Creating Lazy Signals#

Lazy Signals from external data#

If the data is large and not loaded by HyperSpy (for example a hdf5.Dataset or similar), first wrap it in dask.array.Array as shown here and then pass it as normal and call as_lazy():

>>> import h5py 
>>> f = h5py.File("myfile.hdf5") 
>>> data = f['/data/path'] 

Wrap the data in dask and chunk as appropriate

>>> import dask.array as da 
>>> x = da.from_array(data, chunks=(1000, 100)) 

Create the lazy signal

>>> s = hs.signals.Signal1D(x).as_lazy() 

Loading lazily#

To load the data lazily, pass the keyword lazy=True. As an example, loading a 34.9 GB .blo file on a regular laptop might look like:

>>> s = hs.load("shish26.02-6.blo", lazy=True)  
>>> s 
<LazySignal2D, title: , dimensions: (400, 333|512, 512)>
>>> s.data 
dask.array<array-e..., shape=(333, 400, 512, 512), dtype=uint8, chunksize=(20, 12, 512, 512)>
>>> print(s.data.dtype, s.data.nbytes / 1e9) 
uint8 34.9175808

Change dtype to perform decomposition, etc.

>>> s.change_dtype("float")  
>>> print(s.data.dtype, s.data.nbytes / 1e9) 
float64 279.3406464

Loading the dataset in the original unsigned integer format would require around 35GB of memory. To store it in a floating-point format one would need almost 280GB of memory. However, with the lazy processing both of these steps are near-instantaneous and require very little computational resources.

New in version 1.4: close_file()

Currently when loading an hdf5 file lazily the file remains open at least while the signal exists. In order to close it explicitly, use the close_file() method. Alternatively, you could close it on calling compute() by passing the keyword argument close_file=True e.g.:

>>> s = hs.load("file.hspy", lazy=True) 
>>> ssum = s.sum(axis=0) 

Close the file

>>> ssum.compute(close_file=True)  

Lazy stacking#

Occasionally the full dataset consists of many smaller files. To combine them into a one large LazySignal, we can stack them lazily (both when loading or afterwards):

>>> siglist = hs.load("*.hdf5") 
>>> s = hs.stack(siglist, lazy=True) 

Or load lazily and stack afterwards:

>>> siglist = hs.load("*.hdf5", lazy=True) 

Make a stack, no need to pass 'lazy', as signals are already lazy

>>> s = hs.stack(siglist) 

Or do everything in one go:

>>> s = hs.load("*.hdf5", lazy=True, stack=True) 

Casting signals as lazy#

To convert a regular HyperSpy signal to a lazy one such that any future operations are only performed lazily, use the as_lazy() method:

>>> s = hs.signals.Signal1D(np.arange(150.).reshape((3, 50)))
>>> s
<Signal1D, title: , dimensions: (3|50)>
>>> sl = s.as_lazy()
>>> sl
<LazySignal1D, title: , dimensions: (3|50)>

Machine learning#

Warning

The machine learning features are in beta state.

Although most of them work as described, their operation may not always be optimal, well-documented and/or consistent with their in-memory counterparts.

Decomposition algorithms for machine learning often perform large matrix manipulations, requiring significantly more memory than the data size. To perform decomposition operation lazily, HyperSpy provides access to several “online” algorithms as well as dask’s lazy SVD algorithm. Online algorithms perform the decomposition by operating serially on chunks of data, enabling the lazy decomposition of large datasets. In line with the standard HyperSpy signals, lazy decomposition() offers the following online algorithms:

Available lazy decomposition algorithms in HyperSpy#

Algorithm

Method

“SVD” (default)

dask.array.linalg.svd()

“PCA”

sklearn.decomposition.IncrementalPCA

“ORPCA”

ORPCA

“ORNMF”

ORNMF

See also

decomposition() for more details on decomposition with non-lazy signals.

GPU support#

Lazy data processing on GPUs requires explicitly transferring the data to the GPU.

On linux, it is recommended to use the dask_cuda library (not supported on windows) to manage the dask scheduler. As for CPU lazy processing, if the dask scheduler is not specified, the default scheduler will be used.

>>> from dask_cuda import LocalCUDACluster 
>>> from dask.distributed import Client 
>>> cluster = LocalCUDACluster() 
>>> client = Client(cluster) 
>>> import cupy as cp 
>>> import dask.array as da

Create a dask array

>>> data = da.random.random(size=(20, 20, 100, 100))
>>> data
dask.array<random_sample, shape=(20, 20, 100, 100), dtype=float64, chunksize=(20, 20, 100, 100), chunktype=numpy.ndarray>

Convert the dask chunks from numpy array to cupy array

>>> data = data.map_blocks(cp.asarray) 
>>> data 
dask.array<random_sample, shape=(20, 20, 100, 100), dtype=float64, chunksize=(20, 20, 100, 100), chunktype=cupy.ndarray>

Create the signal

>>> s = hs.signals.Signal2D(data).as_lazy() 

Note

See the dask blog on Richardson Lucy (RL) deconvolution for an example of lazy processing on GPUs using dask and cupy

Model fitting#

Most curve-fitting functionality will automatically work on models created from lazily loaded signals. HyperSpy extracts the relevant chunk from the signal and fits to that.

The linear 'lstsq' optimizer supports fitting the entire dataset in a vectorised manner using dask.array.linalg.lstsq(). This can give potentially enormous performance benefits over fitting with a nonlinear optimizer, but comes with the restrictions explained in the linear fitting section.

Practical tips#

Despite the limitations detailed below, most HyperSpy operations can be performed lazily. Important points are:

Chunking#

Data saved in the HDF5 format is typically divided into smaller chunks which can be loaded separately into memory, allowing lazy loading. Chunk size can dramatically affect the speed of various HyperSpy algorithms, so chunk size is worth careful consideration when saving a signal. HyperSpy’s default chunking sizes are probably not optimal for a given data analysis technique. For more comprehensible documentation on chunking, see the dask array chunks and best practices docs. The chunks saved into HDF5 will match the dask array chunks in s.data.chunks when lazy loading. Chunk shape should follow the axes order of the numpy shape (s.data.shape), not the hyperspy shape. The following example shows how to chunk one of the two navigation dimensions into smaller chunks:

>>> import dask.array as da
>>> data = da.random.random((10, 200, 300))
>>> data.chunksize
(10, 200, 300)

>>> s = hs.signals.Signal1D(data).as_lazy()

Note the reversed order of navigation dimensions

>>> s
<LazySignal1D, title: , dimensions: (200, 10|300)>

Save data with chunking first hyperspy dimension (second array dimension)

>>> s.save('chunked_signal.zspy', chunks=(10, 100, 300)) 
>>> s2 = hs.load('chunked_signal.zspy', lazy=True) 
>>> s2.data.chunksize 
(10, 100, 300)

To get the chunk size of given axes, the get_chunk_size() method can be used:

>>> import dask.array as da
>>> data = da.random.random((10, 200, 300))
>>> data.chunksize
(10, 200, 300)
>>> s = hs.signals.Signal1D(data).as_lazy()
>>> s.get_chunk_size() # All navigation axes
((10,), (200,))
>>> s.get_chunk_size(0) # The first navigation axis
((200,),)

New in version 2.0.0.

Starting in version 2.0.0 HyperSpy does not automatically rechunk datasets as this can lead to reduced performance. The rechunk or optimize keyword argument can be set to True to let HyperSpy automatically change the chunking which could potentially speed up operations.

New in version 1.7.0.

For more recent versions of dask (dask>2021.11) when using hyperspy in a jupyter notebook a helpful html representation is available.

>>> import dask.array as da
>>> data = da.zeros((20, 20, 10, 10, 10))
>>> s = hs.signals.Signal2D(data).as_lazy()
>>> s 
../_images/chunks.png

This helps to visualize the chunk structure and identify axes where the chunk spans the entire axis (bolded axes).

Computing lazy signals#

Upon saving lazy signals, the result of computations is stored on disk.

In order to store the lazy signal in memory (i.e. make it a normal HyperSpy signal) it has a compute() method:

>>> s
<LazySignal2D, title: , dimensions: (10, 20, 20|10, 10)>
>>> s.compute() 
[########################################] | 100% Completed |  0.1s
>>> s 
<Signal2D, title: , dimensions: (10, 20, 20|10, 10)>

Lazy operations that affect the axes#

When using lazy signals the computation of the data is delayed until requested. However, the changes to the axes properties are performed when running a given function that modfies them i.e. they are not performed lazily. This can lead to hard to debug issues when the result of a given function that is computed lazily depends on the value of the axes parameters that may have changed before the computation is requested. Therefore, in order to avoid such issues, it is reccomended to explicitly compute the result of all functions that are affected by the axes parameters. This is the reason why e.g. the result of shift1D() is not lazy.

Dask Backends#

Dask is a flexible library for parallel computing in Python. All of the lazy operations in hyperspy run through dask. Dask can be used to run computations on a single machine or scaled to a cluster. The following example shows how to use dask to run computations on a variety of different hardware:

Single Threaded Scheduler#

The single threaded scheduler in dask is useful for debugging and testing. It is not recommended for general use.

>>> import dask
>>> import hyperspy.api as hs
>>> import numpy as np
>>> import dask.array as da

Set the scheduler to single-threaded globally

>>> dask.config.set(scheduler='single-threaded') 

Alternatively, you can set the scheduler to single-threaded for a single function call by setting the scheduler keyword argument to 'single-threaded'.

Or for something like plotting you can set the scheduler to single-threaded for the duration of the plotting call by using the with dask.config.set context manager.

>>> s.compute(scheduler="single-threaded") 

>>> with dask.config.set(scheduler='single-threaded'):
...     s.plot() 

Single Machine Schedulers#

Dask has two schedulers available for single machines.

  1. Threaded Scheduler:

    Fastest to set up but only provides parallelism through threads so only non python functions will be parallelized. This is good if you have largely numpy code and not too many cores.

  2. Processes Scheduler:

    Each task (and all of the necessary dependencies) are shipped to different processes. As such it has a larger set up time. This preforms well for python dominated code.

>>> import dask
>>> dask.config.set(scheduler='processes') 

Any hyperspy code will now use the multiprocessing scheduler

>>> s.compute()  

Change to threaded Scheduler, overwrite default

>>> dask.config.set(scheduler='threads') 
>>> s.compute() 

Distributed Scheduler#

Warning

Distributed computing is not supported for all file formats.

Distributed computing is limited to a few file formats, see the list of supported file format in RosettaSciIO documentation.

The recommended way to use dask is with the distributed scheduler. This allows you to scale your computations to a cluster of machines. The distributed scheduler can be used on a single machine as well. dask-distributed also gives you access to the dask dashboard which allows you to monitor your computations.

Some operations such as the matrix decomposition algorithms in hyperspy don’t currently work with the distributed scheduler.

>>> from dask.distributed import Client 
>>> from dask.distributed import LocalCluster 
>>> import dask.array as da
>>> import hyperspy.api as hs

>>> cluster = LocalCluster() 
>>> client = Client(cluster) 
>>> client 

Any calculation will now use the distributed scheduler

>>> s 
>>> s.plot() 
>>> s.compute() 

Running computation on remote cluster can be done easily using dask_jobqueue

>>> from dask_jobqueue import SLURMCluster 
>>> from dask.distributed import Client 
>>> cluster = SLURMCluster(cores=48,
...                        memory='120Gb',
...                        walltime="01:00:00",
...                        queue='research') 

Get 3 nodes

>>> cluster.scale(jobs=3) 
>>> client = Client(cluster) 
>>> client 

Any calculation will now use the distributed scheduler

>>> s = hs.data.two_gaussians()
>>> repeated_data = da.repeat(da.array(s.data[np.newaxis, :]),10, axis=0)
>>> s = hs.signals.Signal1D(repeated_data).as_lazy()
>>> summed = s.map(np.sum, inplace=False)
>>> s.compute() 

Limitations#

Most operations can be performed lazily. However, lazy operations come with a few limitations and constraints that we detail below.

Immutable signals#

An important limitation when using LazySignal is the inability to modify existing data (immutability). This is a logical consequence of the DAG (tree structure, explained in Behind the scenes – technical details), where a complete history of the processing has to be stored to traverse later.

In fact, lazy evaluation removes the need for such operation, since only additional tree branches are added, requiring very little resources. In practical terms the following fails with lazy signals:

>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s += 1 
Traceback (most recent call last):
  File "<ipython-input-6-1bd1db4187be>", line 1, in <module>
    s += 1
  File "<string>", line 2, in __iadd__
  File "/home/fjd29/Python/hyperspy3/hyperspy/signal.py", line 1591, in _binary_operator_ruler
    getattr(self.data, op_name)(other)
AttributeError: 'Array' object has no attribute '__iadd__'

However, when operating lazily there is no clear benefit to using in-place operations. So, the operation above could be rewritten as follows:

>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s = s + 1

Or even better:

>>> s = hs.signals.BaseSignal([0]).as_lazy()
>>> s1 = s + 1

Other minor differences#

  • Histograms for a LazySignal do not support knuth and blocks binning algorithms.

  • CircleROI sets the elements outside the ROI to np.nan instead of using a masked array, because dask does not support masking. As a convenience, nansum, nanmean and other nan* signal methods were added to mimic the workflow as closely as possible.

Saving Big Data#

The most efficient format supported by HyperSpy to write data is the ZSpy format, mainly because it supports writing concurrently from multiple threads or processes. This also allows for smooth interaction with dask-distributed for efficient scaling.

Behind the scenes – technical details#

Standard HyperSpy signals load the data into memory for fast access and processing. While this behaviour gives good performance in terms of speed, it obviously requires at least as much computer memory as the dataset, and often twice that to store the results of subsequent computations. This can become a significant problem when processing very large datasets on consumer-oriented hardware.

HyperSpy offers a solution for this problem by including LazySignal and its derivatives. The main idea of these classes is to perform any operation (as the name suggests) lazily (delaying the execution until the result is requested (e.g. saved, plotted)) and in a blocked fashion. This is achieved by building a “history tree” (formally called a Directed Acyclic Graph (DAG)) of the computations, where the original data is at the root, and any further operations branch from it. Only when a certain branch result is requested, the way to the root is found and evaluated in the correct sequence on the correct blocks.

The “magic” is performed by (for the sake of simplicity) storing the data not as numpy.ndarray, but dask.array.Array (see the dask documentation). dask offers a couple of advantages:

  • Arbitrary-sized data processing is possible. By only loading a couple of chunks at a time, theoretically any signal can be processed, albeit slower. In practice, this may be limited: (i) some operations may require certain chunking pattern, which may still saturate memory; (ii) many chunks should fit into the computer memory comfortably at the same time.

  • Loading only the required data. If a certain part (chunk) of the data is not required for the final result, it will not be loaded at all, saving time and resources.

  • Able to extend to a distributed computing environment (clusters). :dask.distributed (see the dask documentation) offers a straightforward way to expand the effective memory for computations to that of a cluster, which allows performing the operations significantly faster than on a single machine.