Skip to content

Local Installation Setup for Machine Learning and AI Course#

This guide is for the latest stable version of TensorFlow. For the preview build (nightly), use the pip package named tf-nightly. Refer to these tables for older TensorFlow version requirements. For the CPU-only build, use the pip package named tensorflow-cpu.

Here are the quick versions of the install commands if you have the base software installed. Scroll down for the step-by-step instructions.

Note: Starting with TensorFlow 2.10, Linux CPU-builds for Aarch64/ARM64 processors are built, maintained, tested and released by a third party: AWS. Installing the tensorflow package on an ARM machine installs AWS's tensorflow-cpu-aws package. They are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. See this blog post for more information about this collaboration.

conda install -c conda-forge cudatoolkit=11.8.0
python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.13.*

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:


pip install scikit-learn pandas pillow jupyter

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# Verify install:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"


# There is currently no official GPU support for MacOS.
python3 -m pip install tensorflow

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter
# Verify install:
python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Windows Native#

Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow or tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
# Anything above 2.10 is not supported on the GPU on Windows Native
python -m pip install "tensorflow<2.11"

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter
# Verify install:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

Windows WSL2#

Note: TensorFlow with GPU access is supported for WSL2 on Windows 10 19044 or higher. This corresponds to Windows 10 version 21H2, the November 2021 update. You can get the latest update from here: Download Windows 10. For instructions, see Install WSL2 and NVIDIA’s setup docs for CUDA in WSL.

conda install -c conda-forge cudatoolkit=11.8.0
python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.13.*

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# Verify install:
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

CPU#

Note: Starting with TensorFlow 2.10, Windows CPU-builds for x86/x64 processors are built, maintained, tested and released by a third party: Intel. Installing the Windows-native tensorflow or tensorflow-cpu package installs Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. See this blog post for more information about this collaboration.

python3 -m pip install tensorflow

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Verify install:#

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Nightly#

python3 -m pip install tf-nightly

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Verify install:#

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

Hardware requirements#

The following GPU-enabled devices are supported:

  • NVIDIA® GPU card with CUDA® architectures 3.5, 5.0, 6.0, 7.0, 7.5, 8.0 and higher. See the list of CUDA®-enabled GPU cards.
  • For GPUs with unsupported CUDA® architectures, or to avoid JIT compilation from PTX, or to use different versions of the NVIDIA® libraries, see the Linux build from source guide.
  • Packages do not contain PTX code except for the latest supported CUDA® architecture; therefore, TensorFlow fails to load on older GPUs when CUDA_FORCE_PTX_JIT=1 is set. (See Application Compatibility for details.)

System requirements#

  • Ubuntu 16.04 or higher (64-bit) or any other Linux System (64-bit)
  • macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support)
  • Windows Native - Windows 7 or higher (64-bit) (no GPU support after TF 2.10)
  • Windows WSL2 - Windows 10 19044 or higher (64-bit)

Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards.

Software requirements#

The following NVIDIA® software are only required for GPU support.

Step-by-step instructions#

#

Linux#

#

1. System requirements#

  • Ubuntu 16.04 or higher (64-bit)

TensorFlow only officially support Ubuntu. However, the following instructions may also work for other Linux distros.

Note: Starting with TensorFlow 2.10, Linux CPU-builds for Aarch64/ARM64 processors are built, maintained, tested and released by a third party: AWS. Installing the tensorflow package on an ARM machine installs AWS's tensorflow-cpu-aws package. They are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. See this blog post for more information about this collaboration.

#

2. Install Miniconda#

Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.

You can use the following command to install Miniconda. During installation, you may need to press enter and type "yes".

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

You may need to restart your terminal or source ~/.bashrc to enable the conda command. Use conda -V to test if it is installed successfully.

#

3. Create a conda environment#

Create a new conda environment named tf with the following command.

conda create --name tf python=3.9

You can deactivate and activate it with the following commands.

conda deactivate
conda activate tf

Make sure it is activated for the rest of the installation.

#

4. GPU Setup#

You can skip this section if you only run TensorFlow on the CPU.

First install the NVIDIA GPU driver if you have not. You can use the following command to verify it is installed.

nvidia-smi

Then install CUDA and cuDNN with conda and pip.

conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==8.6.0.163

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Configure the system paths. You can do it with the following command every time you start a new terminal after activating your conda environment.

CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH

For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$CUDNN_PATH/lib:$CONDA_PREFIX/lib/:$LD_LIBRARY_PATH' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

#

5. Install TensorFlow#

TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.

pip install --upgrade pip

# To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Then, install TensorFlow with pip.

Note: Do not install TensorFlow with conda. It may not have the latest stable version. pip is recommended since TensorFlow is only officially released to PyPI.

pip install tensorflow==2.13.*

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

#

6. Verify Install#

Verify the CPU setup:

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If a tensor is returned, you've installed TensorFlow successfully.

Verify the GPU setup:

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

If a list of GPU devices is returned, you've installed TensorFlow successfully.

#

Ubuntu 22.04#

In Ubuntu 22.04, you may encounter the following error:

Can't find libdevice directory ${CUDA_DIR}/nvvm/libdevice.
...
Couldn't invoke ptxas --version
...
InternalError: libdevice not found at ./libdevice.10.bc [Op:__some_op]

To fix this error, you will need to run the following commands.

# Install NVCC
conda install -c nvidia cuda-nvcc=11.3.58
# Configure the XLA cuda directory
mkdir -p $CONDA_PREFIX/etc/conda/activate.d
printf 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib/\n' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
# Copy libdevice file to the required path
mkdir -p $CONDA_PREFIX/lib/nvvm/libdevice
cp $CONDA_PREFIX/lib/libdevice.10.bc $CONDA_PREFIX/lib/nvvm/libdevice/

#

MacOS#

#

1. System requirements#

  • macOS 10.12.6 (Sierra) or higher (64-bit)

Note: While TensorFlow supports Apple Silicon (M1), packages that include custom C++ extensions for TensorFlow also need to be compiled for Apple M1. Some packages, like tensorflow_decision_forests publish M1-compatible versions, but many packages don't. To use those libraries, you will have to use TensorFlow with x86 emulation and Rosetta.

Currently there is no official GPU support for running TensorFlow on MacOS. The following instructions are for running on CPU.

#

2. Check Python version#

Check if your Python environment is already configured:

Note: Requires Python 3.8–3.11, and pip >= 20.3 for MacOS.

python3 --version
python3 -m pip --version

#

3. Install Miniconda#

Miniconda is the recommended approach for installing TensorFlow. It creates a separate environment to avoid changing any installed software in your system.

Note: Miniconda is not a requirement for running TensorFlow on CPU. If you'd prefer to create a virtual environment using venv or Virtualenv, you can do so. Just adjust the installation steps accordingly, and then follow the instructions to install TensorFlow using pip.

Install Miniconda:

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -o Miniconda3-latest-MacOSX-x86_64.sh
bash Miniconda3-latest-MacOSX-x86_64.sh

You may need to restart your terminal or source ~/.bashrc to enable the conda command. Use conda -V to test if it is installed successfully.

#

4. Create a conda environment#

Create a new conda environment named tf with the following command.

conda create --name tf python=3.9

You can deactivate and activate it with the following commands.

conda deactivate
conda activate tf

Make sure it is activated for the rest of the installation.

#

5. Install TensorFlow#

TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.

pip install --upgrade pip

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Then, install TensorFlow with pip.

Note: Do not install TensorFlow with conda. It may not have the latest stable version. pip is recommended since TensorFlow is only officially released to PyPI.

pip install tensorflow

# To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

#

6. Verify Install#

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If a tensor is returned, you've installed TensorFlow successfully.

#

Windows Native#

Caution: TensorFlow 2.10 was the last TensorFlow release that supported GPU on native-Windows. Starting with TensorFlow 2.11, you will need to install TensorFlow in WSL2, or install tensorflow-cpu and, optionally, try the TensorFlow-DirectML-Plugin

1. System requirements#

  • Windows 7 or higher (64-bit)

Note: Starting with TensorFlow 2.10, Windows CPU-builds for x86/x64 processors are built, maintained, tested and released by a third party: Intel. Installing the windows-native tensorflow or tensorflow-cpu package installs Intel's tensorflow-intel package. These packages are provided as-is. Tensorflow will use reasonable efforts to maintain the availability and integrity of this pip package. There may be delays if the third party fails to release the pip package. See this blog post for more information about this collaboration.

#

2. Install Microsoft Visual C++ Redistributable#

Install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017, and 2019. Starting with the TensorFlow 2.1.0 version, the msvcp140_1.dll file is required from this package (which may not be provided from older redistributable packages). The redistributable comes with Visual Studio 2019 but can be installed separately:

  1. Go to the Microsoft Visual C++ downloads.
  2. Scroll down the page to the Visual Studio 2015, 2017 and 2019 section.
  3. Download and install the Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019 for your platform.

Make sure long paths are enabled on Windows.

#

3. Install Miniconda#

Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.

Download the Miniconda Windows Installer. Double-click the downloaded file and follow the instructions on the screen.

#

4. Create a conda environment#

Create a new conda environment named tf with the following command.

conda create --name tf python=3.9

You can deactivate and activate it with the following commands.

conda deactivate
conda activate tf

Make sure it is activated for the rest of the installation.

#

5. GPU setup#

You can skip this section if you only run TensorFlow on CPU.

First install NVIDIA GPU driver if you have not.

Then install the CUDA, cuDNN with conda.

conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0

#

6. Install TensorFlow#

TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version.

pip install --upgrade pip

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Then, install TensorFlow with pip.

Note: Do not install TensorFlow with conda. It may not have the latest stable version. pip is recommended since TensorFlow is only officially released to PyPI.

# Anything above 2.10 is not supported on the GPU on Windows Native
pip install "tensorflow<2.11"

To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

#

7. Verify install#

Verify the CPU setup:

python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If a tensor is returned, you've installed TensorFlow successfully.

Verify the GPU setup:

python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

If a list of GPU devices is returned, you've installed TensorFlow successfully.

#

Windows WSL2#

#

1. System requirements#

  • Windows 10 19044 or higher (64-bit). This corresponds to Windows 10 version 21H2, the November 2021 update.

See the following documents to:

#

2. Install Miniconda#

Miniconda is the recommended approach for installing TensorFlow with GPU support. It creates a separate environment to avoid changing any installed software in your system. This is also the easiest way to install the required software especially for the GPU setup.

You can use the following command to install Miniconda. During installation, you may need to press enter and type "yes".

curl https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -o Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

You may need to restart your terminal or source ~/.bashrc to enable the conda command. Use conda -V to test if it is installed successfully.

#

3. Create a conda environment#

Create a new conda environment named tf with the following command.

conda create --name tf python=3.9

You can deactivate and activate it with the following commands.

conda deactivate
conda activate tf

Make sure it is activated for the rest of the installation.

#

4. GPU Setup#

You can skip this section if you only run TensorFlow on the CPU.

First install the NVIDIA GPU driver if you have not. You can use the following command to verify it is installed.

nvidia-smi

Then install CUDA and cuDNN with conda and pip.

conda install -c conda-forge cudatoolkit=11.8.0
pip install nvidia-cudnn-cu11==8.6.0.163
To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Configure the system paths. You can do it with following command everytime your start a new terminal after activating your conda environment.

CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDNN_PATH/lib:$CONDA_PREFIX/lib/

For your convenience it is recommended that you automate it with the following commands. The system paths will be automatically configured when you activate this conda environment.

mkdir -p $CONDA_PREFIX/etc/conda/activate.d
echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDNN_PATH/lib:$CONDA_PREFIX/lib/' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh

#

5. Install TensorFlow#

TensorFlow requires a recent version of pip, so upgrade your pip installation to be sure you're running the latest version. 

pip install --upgrade pip
To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

Then, install TensorFlow with pip.

Note: Do not install TensorFlow with conda. It may not have the latest stable version. pip is recommended since TensorFlow is only officially released to PyPI.

pip install tensorflow==2.13.*
To install additional packages required for the course such as scikit-learn, pandas, pillow, and Jupyter, use the following command:

pip install scikit-learn pandas pillow jupyter

#

6. Verify Install#

Verify the CPU setup:

python3 -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

If a tensor is returned, you've installed TensorFlow successfully.

Verify the GPU setup:

python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"

If a list of GPU devices is returned, you've installed TensorFlow successfully.

Package location#

A few installation mechanisms require the URL of the TensorFlow Python package. The value you specify depends on your Python version.

VersionURL
Linux
Python 3.8 GPU support https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.8 CPU-only https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.13.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.9 GPU support https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.9 CPU-only https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.10 GPU support https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Python 3.10 CPU-only https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
macOS (CPU-only)
Python 3.8 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.13.0-cp38-cp38-macosx_10_15_x86_64.whl
Python 3.9 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.13.0-cp39-cp39-macosx_10_15_x86_64.whl
Python 3.10 https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.13.0-cp310-cp310-macosx_10_15_x86_64.whl
Windows
Python 3.8 CPU-only https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.13.0-cp38-cp38-win_amd64.whl
Python 3.9 CPU-only https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.13.0-cp39-cp39-win_amd64.whl
Python 3.10 CPU-only https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.13.0-cp310-cp310-win_amd64.whl