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Install TensorFlow in the same Python environment your Jupyter notebook uses, then select that environment’s kernel. For a local setup, create and activate a virtual environment, install TensorFlow with pip, register the environment with Jupyter if needed, and test the import from a notebook cell.
Before you install: choose a compatible Python environment
TensorFlow’s supported Python versions vary by release and platform. Check the current TensorFlow pip installation guide and its version and compatibility guidance for the TensorFlow release and operating system you plan to use. Do not rely on an old version table: compatibility information can change.
Use a dedicated virtual environment to keep TensorFlow’s dependencies separate from other projects. TensorFlow’s guide recommends Python’s built-in venv and pip for installation. The commands below use python; on some systems, use python3 instead.
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Install TensorFlow and connect it to Jupyter
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Create and activate a virtual environment
Open a terminal in your project directory and create the environment:
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python -m venv .venvActivate it using the command for your shell:
- Windows Command Prompt:
.venvScriptsactivate - Windows PowerShell:
.venvScriptsActivate.ps1 - macOS or Linux:
source .venv/bin/activate
- Windows Command Prompt:
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Install TensorFlow in the activated environment
Upgrade pip, then install the standard TensorFlow package:
python -m pip install --upgrade pip python -m pip install tensorflowFor GPU use, follow the platform-specific instructions in the official TensorFlow guide. Its documented
tensorflow[and-cuda]pip path applies to supported Linux and Windows WSL2 setups; it is not a universal GPU command. GPU support also depends on compatible hardware, drivers, and software. -
Register the environment as a Jupyter kernel if needed
If Jupyter is running from a different Python installation or environment, install
ipykernelusing the activated environment’s interpreter and register it:Outdated Drivers Are Slowing You Down
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--nameif you already have a kernel namedtf. The display name is what you will see in Jupyter. The IPython kernel installation guide explains that a separate Python version or virtual environment needs its own kernel installation.Rank #3
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Select the TensorFlow kernel
In Jupyter, open the notebook’s kernel selector and choose Python (TensorFlow), or the display name you used. Jupyter kernels determine which programming-language environment executes notebook cells; installing a package in another environment will not make it available to the selected kernel. See Jupyter’s explanation of kernels.
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Verify the installation in a notebook cell
Run:
import tensorflow as tf print(tf.__version__) tf.reduce_sum(tf.random.normal([1000, 1000]))The version should print without an import error, and the operation should return a tensor. This confirms that TensorFlow imports and runs in the selected kernel; it does not establish that a GPU is available.
Choose the right installation path for your platform
| Platform | Installation guidance | GPU considerations |
|---|---|---|
| Linux | Use a virtual environment and the pip instructions in TensorFlow’s guide. TensorFlow officially supports Ubuntu; instructions may work on other Linux distributions. | The guide documents tensorflow[and-cuda] for supported configurations. ARM64 Linux CPU packages are maintained and released by AWS as a third-party package. |
| macOS | Use the documented CPU installation route and verify current Python compatibility. | TensorFlow’s documentation states there is currently no official GPU support for TensorFlow on macOS. |
| Windows native | The native Windows CPU route is available through the documented pip instructions. | TensorFlow 2.10 was the last release with native-Windows GPU support. For newer GPU use, TensorFlow directs users to WSL2. |
| Windows with WSL2 | Follow the TensorFlow WSL2 instructions for your release and Python version. | The guide documents CPU and GPU paths for WSL2. Its current GPU instructions give Windows 10 version 19044 or higher as a baseline; compatible NVIDIA drivers and software are also required. |
For a hosted notebook rather than a local install, TensorFlow identifies Google Colab as a Jupyter notebook environment that requires no local setup. The steps here are for setting up a local Python environment.
Fix “ModuleNotFoundError: No module named ‘tensorflow’” in Jupyter
If TensorFlow imports in a terminal but not in a notebook, the notebook is likely running a different Python environment from the one where you installed the package. Check the notebook’s interpreter in a cell:
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import sys
print(sys.executable)Compare the printed path with the Python environment where you installed TensorFlow. If they differ, register the TensorFlow environment with ipykernel using the commands above, then select its kernel in Jupyter. If the paths match, confirm TensorFlow installation completed in that environment and consult TensorFlow’s current platform and Python compatibility guidance.
Check GPU visibility separately
After confirming that TensorFlow imports, check whether it detects a GPU:
tf.config.list_physical_devices('GPU')An empty list means the current TensorFlow process does not see a GPU. A successful CPU calculation does not prove GPU support. Verify that your operating system, TensorFlow release, GPU hardware, driver, and required software meet the official platform-specific requirements before changing the installation.
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