Mastering Dependency Resolution for TensorFlow Projects

If you take only one thing from this article, let it be this: never install project dependencies into your global Python installation. Always use a virtual environment.

A virtual environment is an isolated Python environment that allows you to install packages for a specific project without affecting other projects or your system's Python. Python's built-in venv module is all you need to get started.

1. Create the Environment:

Navigate to your project folder in the terminal and run:

python -m venv .venv

This creates a .venv directory containing a private copy of Python and pip. It's a good practice to add .venv to your .gitignore file.

2. Activate the Environment:

Before you install anything, you must "activate" the environment. The command differs by operating system:

# On macOS and Linux
source .venv/bin/activate

# On Windows (Command Prompt)
.venv\Scripts\activate.bat

# On Windows (PowerShell)
.venv\Scripts\Activate.ps1

You’ll know it’s active because your shell prompt will be prefixed with (.venv). Now, any pip install command will install packages into this isolated space.

Strategy 2: Pinning Dependencies for Reproducibility

Creating a virtual environment solves the isolation problem. But how do you ensure a colleague (or your future self) can perfectly recreate this environment? The answer is pinning your dependency versions.

A requirements.txt file lists all the packages your project needs. Instead of just listing names, you should specify the exact versions.

After you've installed all your project's dependencies (e.g., pip install tensorflow==2.15.0 pandas matplotlib), run this command:

pip freeze > requirements.txt

This will generate a file that looks something like this:

absl-py==1.4.0
astunparse==1.6.3
gast==0.5.4
grpcio==1.60.0
h5py==3.10.0
keras==2.15.0
numpy==1.26.2
pandas==2.1.4
tensorflow==2.15.0
...

Now, anyone can recreate your exact environment with one command:

pip install -r requirements.txt

This simple practice is the cornerstone of reproducible machine learning.

Strategy 3: Advanced Tooling with Poetry and Conda

While venv and pip are a great start, more complex projects can benefit from more powerful tools.

Poetry for Superior Dependency Resolution

Poetry is a modern Python dependency manager. It uses a pyproject.toml file to define dependencies and a sophisticated resolver to find a compatible set of packages. It then generates a poetry.lock file, which is an even more robust version of a pinned requirements.txt.

Key advantages of Poetry:

• Smarter Resolver: It's much better at resolving complex, conflicting transitive dependencies than pip.
• Locked and Repeatable: The poetry.lock file guarantees byte-for-byte identical installs across all machines.
• Integrated Tooling: It manages virtual environments for you automatically.

Conda for System-Level Management

What about CUDA and cuDNN? This is where Conda shines. Conda is both a package manager and an environment manager. Its biggest advantage is that it can manage non-Python packages.

This means you can define your TensorFlow version, Python version, and your CUDA toolkit version all in one file, typically an environment.yml:

name: tf_env
channels:
  - conda-forge
  - nvidia # Important for CUDA packages
dependencies:
  - python=3.10
  - pip
  - tensorflow=2.15.0=*cuda120*
  - cudatoolkit=12.0
  - cudnn=8.9
  - pandas
  - scikit-learn

With this file, you can create a complete, self-contained environment with a single command: conda env create -f environment.yml. For GPU users, this is often the most straightforward path to a working setup.

The CUDA Conundrum: Taming the GPU

If you're not using Conda or a pre-built Docker container, getting the GPU to work is the final boss of dependency management. Here’s the manual checklist:

1. Check Your Driver: Run nvidia-smi in your terminal. This tells you your driver version and the maximum CUDA version it supports.
2. Consult the TF Build Chart: Go to the official TensorFlow website and find the "Tested build configurations" page. This table is your source of truth. It will tell you which versions of Python, CUDA, and cuDNN were used to build your target TensorFlow version.
3. Install Correctly: Download and install the exact versions of the CUDA Toolkit and cuDNN specified in the chart.
4. Verify: After installation, run a simple Python script to check if TensorFlow can see your GPU:

   import tensorflow as tf
   print("Num GPUs Available: ", len(tf.config.list_physical_devices('GPU')))

   If the output is greater than 0, congratulations!

Conclusion: From Frustration to Flow

Dependency management might seem like a chore, but it's an investment that pays massive dividends. A stable, reproducible environment frees you to focus on what really matters: designing, training, and deploying incredible machine learning models.

Start with venv and pinned requirements.txt files for every project. As your needs grow, explore tools like Poetry for complex Python dependencies or Conda for all-in-one system management. By adopting these practices, you'll trade hours of frustrating debugging for a smooth and predictable development workflow.