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Why Deep Learning Projects Still Use Conda Despite Its Reputation Problems

Cowrie
Cowrie
Dev @ Bswen

Why Deep Learning Projects Still Use Conda Despite Its Reputation Problems

Every machine learning developer faces this dilemma: pip is cleaner and faster, but conda keeps showing up in deep learning repositories. Why do academic papers, ML tutorials, and production projects still use Conda despite its notorious installation failures and slow dependency resolution?

The answer lies in how deep learning projects handle GPU dependencies. Unlike standard Python applications, ML frameworks require specific CUDA versions, cuDNN libraries, and C++ scientific computing packages that pip simply cannot manage effectively.

The Problem with Pip and GPU Dependencies

Let’s be honest - trying to install TensorFlow or PyTorch with pip often ends in frustration:

Terminal window
# This fails more often than it succeeds
pip install tensorflow-gpu

You get cryptic errors about missing CUDA libraries, version mismatches, or compilation failures. The real issue? Deep learning frameworks don’t just depend on Python packages - they depend on complex binary libraries:

  • CUDA Toolkit (NVIDIA’s GPU parallel computing platform)
  • cuDNN (CUDA Deep Neural Network library)
  • cuBLAS (CUDA Basic Linear Algebra Subprograms)
  • cuFFT (CUDA Fast Fourier Transform)

These aren’t Python packages. They’re compiled C/C++ libraries that need to match exactly with your GPU hardware and driver versions.

Why Conda Excels at ML Dependency Management

Conda handles what pip can’t: it manages both Python and system-level dependencies in one cohesive environment. When you run:

Terminal window
conda create -n tf-gpu python=3.9 tensorflow-gpu cudatoolkit=11.2 cudnn=8.1.0

Conda does something pip can’t - it solves the entire dependency graph including:

  1. Python 3.9 from conda-forge
  2. TensorFlow GPU with the right CUDA bindings
  3. CUDA 11.2 Toolkit pre-compiled for your OS
  4. cuDNN 8.1.0 specifically built for CUDA 11.2
  5. All the C++ runtime libraries needed by TensorFlow

The key advantage? Binary distribution. Conda provides pre-compiled packages that work out of the box, eliminating the compilation nightmares that plague pip installations on Windows and macOS.

Real-World Scenario: Reproducing Research

This isn’t just about convenience - it’s about reproducibility. When I tried to reproduce a research paper recently, I encountered:

Terminal window
# Failed pip attempt
pip install tensorflow==2.9.0
ERROR: Could not build wheels for tensorflow-gpu, which is required to install pyproject.toml-based projects

The solution? Conda environment files that specified exact versions:

environment.yml
name: deep-learning-env
channels:
  - conda-forge
  - defaults
dependencies:
  - python=3.9
  - tensorflow-gpu=2.9.0
  - cudatoolkit=11.2
  - cudnn=8.1.0
  - pytorch=1.12.0
  - torchvision=0.13.0
  - pandas=1.4.0
  - numpy=1.23.0

This environment file ensures everyone on the team gets exactly the same libraries, including the non-Python dependencies that make or break deep learning projects.

Common Pitfalls of Pure Pip Workflows

Many developers try to use pip exclusively and run into these issues:

  • CUDA Version Mismatches: PyPI packages often expect different CUDA versions than what’s installed
  • Missing System Dependencies: Libraries like OpenCV or scikit-learn need system packages
  • Broken Binaries: Some PyPI packages have compilation issues on certain platforms
  • Environment Isolation: Virtualenvs don’t handle system packages well

Conda solves these by treating the entire system as part of the dependency graph, not just Python packages.

The Trade-Off

Conda isn’t perfect. It has legitimate issues:

  • Slow dependency resolution
  • Large base environments
  • Proprietary defaults channel vs open-source conda-forge
  • Complex channel management

But for deep learning projects, the trade-off is worth it. The ability to reliably install TensorFlow, PyTorch, or any other framework with all its GPU dependencies intact makes Conda indispensable in the ML ecosystem.

Conclusion

Deep learning projects rely on Conda because it solves the unique challenge of managing GPU dependencies and scientific computing packages that standard Python package managers cannot handle. When your project depends on CUDA version matching, cuDNN libraries, and C++ runtime dependencies, Conda’s binary distribution and comprehensive dependency management become essential rather than optional.

The next time you’re frustrated with Conda’s slow installation, remember: it’s not about package management - it’s about making GPU-accelerated machine learning actually work across different systems and hardware configurations.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!

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