How to Install TensorFlow from Source in Anaconda?

Installing TensorFlow from source in an Anaconda environment can be both powerful and complex. This step-by-step guide will walk you through the process, ensuring that you set up everything correctly to leverage the latest TensorFlow features directly from the source code.

Prerequisites

Before we begin, ensure that you have the following:

Python 3.8: TensorFlow can be built and used with Python 3.8 or higher. Conda Environment: Anaconda is required to manage and create environments. Git: To clone the TensorFlow repository. Bazel: TensorFlow's build system.

Step 1: Setting Up the Anaconda Environment

The first step is to create and activate a new Anaconda environment specifically for TensorFlow.

Create a new conda environment:

conda create --name tf-source python3.8

Activate the environment:

conda activate tf-source

This sets up a fresh environment for TensorFlow, ensuring that all packages are installed in isolation.

Step 2: Installing Required Packages

TensorFlow requires several packages. Install them with:

conda install numpy wheel

These packages are necessary for basic functionality and ensuring smooth integration with TensorFlow.

Step 3: Installing Bazel

Bazel is a build tool used by TensorFlow. Installation instructions can be found on the Bazel installation page. Here’s how to install Bazel via conda:

conda install -c conda-forge bazel

Ensure that Bazel is properly installed and configured for your build environment.

Step 4: Cloning the TensorFlow Repository

Clone the TensorFlow GitHub repository and checkout the desired version:

git clone cd tensorflow

Check out the specific tag or branch you want to build:

git checkout desired_version

This step ensures that you are working with the correct version of TensorFlow, suitable for your needs.

Step 5: Configuring the Build

Run the configuration script:

./configure

You will be prompted to answer several configuration questions. These questions are related to your system's CUDA (for GPU support) and other options. Ensure that all relevant dependencies are addressed to avoid errors during the build process.

Step 6: Building TensorFlow

Build TensorFlow using Bazel. Here’s a command to build the CPU version:

bazel build --configopt //tensorflow/tools/pip_package:build_pip_package

If you want GPU support, replace --configopt with --configcuda. Building with GPU support requires CUDA and cuDNN installed on your system.

Step 7: Creating a Wheel Package

After building TensorFlow, create a wheel package:

bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg

This step generates the pip package that you will install.

Step 8: Installing the Package

Install the generated wheel package in your Anaconda environment:

pip install /tmp/tensorflow_pkg/tensorflow-*.whl

This command installs the wheel file from the directory where it was generated.

Step 9: Verifying the Installation

To verify that TensorFlow is installed correctly, run a Python session and import TensorFlow:

python -c "import tensorflow as tf; print(_sum(([1000, 1000])))"

This simple test imports TensorFlow and runs a basic operation to validate the installation.

Additional Notes

Dependencies: Ensure all required dependencies are installed, especially if you are building with GPU support. TensorFlow works seamlessly with CUDA, cuDNN, and other optional packages.

Documentation: Refer to the official TensorFlow build from source guide for more detailed information and troubleshooting. The GitHub repository is also a valuable resource for any open issues or community discussions.

This process should help you successfully install TensorFlow from source in your Anaconda environment. If you encounter any issues, check the TensorFlow GitHub repository for further assistance.