---

name: backend-and-configuration description: "Install and select DeepXDE tensor backends and configure dtype, autodiff, random seed, XLA, GPU, and parallel settings." disable-model-invocation: true metadata: disco-role: operating license: LGPL 2.1

DeepXDE backend and configuration

Use this sub-skill when the task is about choosing or diagnosing a DeepXDE tensor backend, installing backend-specific dependencies, or configuring dde.config settings such as dtype, autodiff mode, random seed, XLA, and parallel scaling.

This construction verified DeepXDE with the PyTorch backend on CPU. TensorFlow, JAX, PaddlePaddle, GPU, Horovod, and MPI paths are supported by DeepXDE but were not runtime-verified here; treat them as optional/alternative paths until checked in the target environment.

Fast path

1. Select the backend before importing DeepXDE:

   DDE_BACKEND=pytorch python your_script.py
   

   or, inside Python before import deepxde:

   import os
   os.environ.setdefault("DDE_BACKEND", "pytorch")
   import deepxde as dde
   

2. Run the diagnostic helper when imports or devices are uncertain:

   python scripts/check_backend.py --backend pytorch
   python scripts/check_backend.py --backend tensorflow --json
   

3. Apply configuration immediately after importing DeepXDE and before constructing data, networks, or models:

   dde.config.set_default_float("float32")
   dde.config.set_random_seed(1234)
   dde.config.set_default_autodiff("reverse")
   

Runtime references

• Backend dependencies and selection order: references/backend-selection.md
• Configuration API behavior: references/configuration.md
• Failure triage: references/troubleshooting.md
• Safe diagnostic script: scripts/check_backend.py

Route out of this sub-skill

• PDE/ODE/IDE/FPDE geometry, boundary conditions, residuals, and gradients: ../pinn-problem-setup/SKILL.md
• Model.compile, training loops, callbacks, checkpoints, metrics, and predictions: ../training-workflows/SKILL.md
• DeepONet, MIONet, operator datasets, PI-DeepONet, or ZCS operator workflows: ../operator-learning/SKILL.md

Operating rules

• Do not rely on backend auto-detection in reproducible scripts. Set DDE_BACKEND explicitly or set the saved DeepXDE config.
• Backend selection is resolved during import. Changing DDE_BACKEND after import deepxde is too late for the current process.
• Prefer pytorch for CPU-safe examples in this generated skill unless the user explicitly requests another backend and the required package set is installed.
• Never claim GPU, Horovod, TensorFlow, JAX, or PaddlePaddle verification from this skill alone. Use the diagnostic script and environment-specific tests first.