Hi nipype experts,
Sorry in advance if this question is too trivial. I am currently working on building a nipype workflow that performs small steps for manipulating neuroimaging related data. Each step is encoded in its own defined function. What I’ve done is to embed each of these functions as nodes using the Function utility interface. This works OK, but I am worried about the execution time of the workflow.
For instance, let’s say that my workflow consists of the folllowing steps:
read_input --> operation1 --> operation2 --> export_data
Here is a simple example doing this:
import numpy as np import nipype.pipeline.engine as pe import nipype.interfaces.utility as util wf = pe.Workflow(name="My-worfklow") # generate some data data=np.random.normal(size=(200000, 10)) # input node of the workflow node_input = pe.Node(util.IdentityInterface(fields = ['input_data']), name = "inputNode") def square(input_data): square_data = input_data**2 return square_data # node performing the square of each element of the data node_square = pe.Node(util.Function(input_names = ['input_data'], output_names = ['square_data'], function = square), name = "square") def sq_root(input_data): sqroot_data = np.sqrt(input_data) return sqroot_data # node performing the square root of each element of the data node_sqroot = pe.Node(util.Function(input_names = ['input_data'], output_names = ['sqroot_data'], function = sq_root, imports = ["import numpy as np"]), name = "squareRoot") # output node of the workflow output_node = pe.Node(util.IdentityInterface(fields = ['output_data']), name = "outputNode") wf.connect([(node_input, node_square, [('input_data','input_data')])]) wf.connect([(node_square, node_sqroot, [('square_data','input_data')])]) wf.connect([(node_sqroot, output_node, [('sqroot_data','output_data')])]) wf.inputs.inputNode.input_data = data wf.run()
The problem is that running this workflow is much slower than using the functions on the data directly (here only like 3 secs slower, but imagine using more complex operations). I get this and it makes sense, because each node is writing and reading from the hard disk, whereas if you use the functions directly on the data, this takes place on RAM and therefore is much faster.
I am just wondering whether is there a way of speeding things up in this kind of situations in nipype. I guess I could always gather all the small operations into one big function and use this as the only intermediate node in the workflow or even create an interface incorporating all the intermediate operations. The problem with doing this is that you lose traceability of what’s going on in the middle.
Have you ever encountered a similar problem? Any thoughts/recommendations?
Thanks a bunch!