Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Torch.fx: Practical Program Capture and Transformation for Deep Learning
  in Python   Modern deep learning frameworks provide imperative, eager execution
programming interfaces embedded in Python to provide a productive development
experience. However, deep learning practitioners sometimes need to capture and
transform program structure for performance optimization, visualization,
analysis, and hardware integration. We study the different designs for program
capture and transformation used in deep learning. By designing for typical deep
learning use cases rather than long tail ones, it is possible to create a
simpler framework for program capture and transformation. We apply this
principle in torch.fx, a program capture and transformation library for PyTorch
written entirely in Python and optimized for high developer productivity by ML
practitioners. We present case studies showing how torch.fx enables workflows
previously inaccessible in the PyTorch ecosystem.

2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th