Install and Setup

Installation

DeepXDE requires one of the following backend-specific dependencies to be installed:

Then, you can install DeepXDE itself.

  • Install the stable version with pip:

    $ pip install deepxde

  • Install the stable version with conda:

    $ conda install -c conda-forge deepxde

  • For developers, you should clone the folder to your local machine and put it along with your project scripts:

    $ git clone https://github.com/lululxvi/deepxde.git

  • Other dependencies

Docker

The DeepXDE Docker image is configured to run DeepXDE with the GPU support. You need first to install NVIDIA Docker. Then you can run a Jupyter Notebook environment with GPU-enabled stable DeepXDE using:

$ nvidia-docker run -v $(pwd):/root/shared -w "/root/shared" -p 8888:8888 pescapil/deepxde:latest

The Dockerfile is based on Horovod Docker image with TensorFlow and PyTorch. To build a DeepXDE image, you can run:

$ git clone https://github.com/lululxvi/deepxde.git
$ cd deepxde/docker
$ docker build -f Dockerfile . -t deepxde

and then run your own DeepXDE image via:

$ nvidia-docker run -v $(pwd):/root/shared -w "/root/shared" -p 8888:8888 deepxde

Working with different backends

DeepXDE supports TensorFlow 1.x (tensorflow.compat.v1 in TensorFlow 2.x), TensorFlow 2.x, PyTorch, JAX, and PaddlePaddle backends. DeepXDE will choose the backend on the following options (high priority to low priority)

  • Use the DDE_BACKEND environment variable:

    • You can use DDE_BACKEND=BACKEND python pde.py to specify the backend. Currently BACKEND can be chosen from “tensorflow.compat.v1” (TensorFlow 1.x backend), “tensorflow” (TensorFlow 2.x backend), “pytorch” (PyTorch), “jax” (JAX), and “paddle” (PaddlePaddle).

    $ DDE_BACKEND=tensorflow.compat.v1 python pde.py

    $ DDE_BACKEND=tensorflow python pde.py

    $ DDE_BACKEND=pytorch python pde.py

    $ DDE_BACKEND=jax python pde.py

    $ DDE_BACKEND=paddle python pde.py

    • Or set the global environment variable DDE_BACKEND as BACKEND. In Linux, this usually can be done via export DDE_BACKEND=BACKEND; in Windows, set the environment variable DDE_BACKEND in System Settings

  • Modify the config.json file under “~/.deepxde”:

    • The file has the content such as {"backend": "tensorflow.compat.v1"}

    • You can also use python -m deepxde.backend.set_default_backend BACKEND to set the default backend

    • In Windows, you can find config.json file under “C:/Users/Username/.deepxde” directory

  • If no backend is selected as above, DeepXDE will automatically find an available backend.

Which backend should I choose?

Here is a comparison between different backends:

  • Different backends support slightly different features, and switch to another backend if DeepXDE raised a backend-related error.

    • Currently, the number of features supported is: PaddlePaddle ≈ TensorFlow 1.x > TensorFlow 2.x ≈ PyTorch > JAX.

    • Some features can be implemented easily (basically translating from one framework to another), and we welcome your contributions.

  • Different backends have different computational speed, and switch to another backend if the speed is an issue in your case.

    • We find that there is no backend that is always faster than the others.

    • The speed depends on the specific problem, the dataset size, your hardware, etc. In some cases, one backend could be significantly faster than others such as 4x faster.

    • If you are not sure which one is the fastest for your problem, in general, we recommend TensorFlow 2.x, because we find that in some cases TensorFlow 2.x > PyTorch > TensorFlow 1.x (> means faster).

TensorFlow 1.x backend

Export DDE_BACKEND as tensorflow.compat.v1 to specify TensorFlow 1.x backend. The required TensorFlow version is 2.2.0 or later. Essentially, TensorFlow 1.x backend uses the API tensorflow.compat.v1 in TensorFlow 2.x and disables the eager execution:

import tensorflow.compat.v1 as tf
tf.disable_eager_execution()

In addition, DeepXDE will set TF_FORCE_GPU_ALLOW_GROWTH to true to prevent TensorFlow take over the whole GPU memory.

TensorFlow 2.x backend

Export DDE_BACKEND as tensorflow to specify TensorFlow 2.x backend. The required TensorFlow version is 2.2.0 or later. In addition, DeepXDE will set TF_FORCE_GPU_ALLOW_GROWTH to true to prevent TensorFlow take over the whole GPU memory.

PyTorch backend

Export DDE_BACKEND as pytorch to specify PyTorch backend. The required PyTorch version is 1.9.0 or later. In addition, if GPU is available, DeepXDE will set the default tensor type to cuda, so that all the tensors will be created on GPU as default:

if torch.cuda.is_available():
    torch.set_default_tensor_type(torch.cuda.FloatTensor)

JAX backend

Export DDE_BACKEND as jax to specify JAX backend.

PaddlePaddle backend

Export DDE_BACKEND as paddle to specify PaddlePaddle backend. In addition, if GPU is available, DeepXDE will set the default device to GPU, so that all the tensors will be created on GPU as default:

if paddle.device.is_compiled_with_cuda():
    paddle.device.set_device("gpu")