Before Training a Neural Network: Understanding the Environment

Introduction

Training a neural network for scientific research is not just about writing a model and running the code. Before reaching that stage, we need to establish a working software environment. Whether we are training a neural network for image restoration or a physics-informed neural network (PINN) to solve differential equations, software dependencies and hardware compatibility can become a source of frustration.

While reproducing an existing deep-learning project, I spent considerable time understanding how the software components fit together. This post explains the essential concepts so that researchers can spend less time debugging their environment and more time working on their scientific problems.

1. Why Do We Need a Virtual Environment?

An operating system contains many software packages, each with its own dependencies and version requirements. Different research projects may require different versions of the same library. Installing everything into one Python environment can cause dependency conflicts.

A virtual environment provides an isolated space for a particular project. Think of it as a small box inside a bigger box. The bigger box is your operating system, while the smaller box contains the Python packages and dependencies required by your project.

I use micromamba to create and manage these environments. For example: 

micromamba create -n pinn python=3.10
micromamba activate pinn
python --version

Python 3.10 is only an example here. The appropriate version depends on the requirements of the framework and the research code. Always check the project's installation instructions before choosing a version.

2. Conda, Micromamba, and pip

These tools help us manage software, but they serve slightly different purposes:

Some scientific packages are convenient to install through Conda-compatible repositories, while others are commonly installed using pip. Both package managers can be used in the same environment, but their installation order and dependency resolution matter.

A practical approach is to create an isolated environment, install the main framework and its compatible dependencies, and then add the remaining packages. Once everything works, record the environment so it can be reconstructed later.

3. CPU, GPU, NVIDIA Drivers, and CUDA

A CPU handles general-purpose computation, while a GPU can accelerate highly parallel operations such as the tensor computations used in neural networks. However, using a GPU requires more than simply having one installed in your computer.

TensorFlow and PyTorch provide high-level interfaces for building and training neural networks. They handle many low-level computational details, so researchers usually do not need to write CUDA code themselves.

CUDA is NVIDIA's platform for GPU computing. The NVIDIA driver allows applications to communicate with the GPU, while CUDA libraries provide functionality used by GPU-enabled software. cuDNN supplies GPU-accelerated routines used by deep-learning frameworks.

These components must be compatible. Installing CUDA independently does not guarantee that TensorFlow or PyTorch will use the GPU. Compatibility depends on the framework version, its supported CUDA and cuDNN libraries, the driver, and the GPU hardware.

After installation, verify that the framework detects the GPU. For TensorFlow: 

import tensorflow as tf

print(tf.__version__)
print(tf.config.list_physical_devices("GPU"))

For PyTorch: 

import torch

print(torch.__version__)
print(torch.cuda.is_available())

A detected GPU is a useful first check, although it does not guarantee that every operation will execute successfully or efficiently on it.

4. Why Do Package Versions Matter?

Python libraries depend on one another. An incompatible NumPy version may cause errors in a framework or another scientific package. A newer framework release may also change its API or drop support for older software.

This is particularly important when reproducing published research. The original code may have been developed using specific versions of Python, TensorFlow or PyTorch, NumPy, and other dependencies. Upgrading everything immediately can introduce new problems.

Start with the versions specified by the project. Once the original implementation works, test upgrades separately rather than changing several components at once.

To inspect your installed Python packages, run:

python -m pip list
python -m pip check

The first command lists installed packages. The second checks declared dependencies for conflicts or missing requirements. Neither command detects every possible runtime error or GPU compatibility issue, so the application must still be tested.

For reproducibility, you can record your pip-installed packages: 

python -m pip freeze > requirements.txt

This records package versions visible to pip, but it does not fully capture the system driver, GPU hardware, or every dependency installed through Conda-compatible repositories. For a more complete record, preserve the environment specification and document the hardware and driver versions too.

5. What Changes When Training a PINN?

A physics-informed neural network introduces another important consideration: automatic differentiation.

Unlike a conventional supervised neural network trained only against labelled examples, a PINN can incorporate the residual of a differential equation into its loss function. For example, solving a partial differential equation may require first- or second-order derivatives of the network output with respect to spatial or temporal coordinates.

Deep-learning frameworks use automatic differentiation to compute these derivatives. The framework must correctly track the operations involved in the computation. If the required derivatives are disconnected from the computational graph, the residual calculation may fail or return incorrect results.

GPU acceleration can help with large workloads, but it is not automatically faster for every PINN. Small models, high-order derivatives, memory requirements, and the structure of the differential equation all influence performance.

Before scaling up a PINN experiment, test the complete workflow on a small problem: evaluate the network, calculate the required derivatives, construct the PDE residual, compute the loss, and verify that gradients can be calculated with respect to the trainable parameters.

Conclusion

A reliable deep-learning workflow begins before the first training epoch. Isolating dependencies, choosing compatible package versions, understanding GPU requirements, and verifying automatic differentiation can prevent hours of unnecessary debugging.

For scientific machine learning, the goal is not simply to make the code run. It is to establish an environment that can be reproduced, inspected, and trusted when running experiments.

My advice is simple: get a minimal example working first, record the environment, and only then move to the full scientific problem.

Further Reading