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Fix Windows-native CPU-only install path and Python version support in pip guide #2448

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Fix Windows-native CPU-only install path and Python version support in pip guide #2448

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88 changes: 31 additions & 57 deletions site/en/install/pip.md
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Original file line number Diff line number Diff line change
Expand Up @@ -50,11 +50,10 @@ step-by-step instructions.
[TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-)

```bash
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"
# Native Windows is CPU-only. For GPU support, use Windows WSL2 above.
python -m pip install tensorflow
# Verify the installation:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
python -c "import tensorflow as tf; print(tf.reduce_sum(tf.random.normal([1000, 1000])))"
```

* {Windows WSL2}
Expand Down Expand Up @@ -133,14 +132,14 @@ enable compute capabilities by [building TensorFlow from source](./source.md).

* Ubuntu 16.04 or higher (64-bit)
* macOS 12.0 (Monterey) or higher (64-bit) *(no GPU support)*
* Windows Native - Windows 7 or higher (64-bit) *(no GPU support after TF 2.10)*
* Windows Native - Windows 10 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

* Python 3.9–3.12
* Python 3.10–3.13
* pip version 19.0 or higher for Linux (requires `manylinux2014` support) and
Windows. pip version 20.3 or higher for macOS.
* Windows Native Requires
Expand Down Expand Up @@ -300,7 +299,7 @@ The following NVIDIA® software are only required for GPU support.

Check if your Python environment is already configured:

Note: Requires Python 3.9–3.11, and pip >= 20.3 for MacOS.
Note: Requires Python 3.10–3.13, and pip >= 20.3 for MacOS.

```bash
python3 --version
Expand Down Expand Up @@ -335,13 +334,13 @@ The following NVIDIA® software are only required for GPU support.
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](https://tensorflow.org/install/pip#windows-[wsl2]),
[TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2),
or install `tensorflow-cpu` and, optionally, try the
[TensorFlow-DirectML-Plugin](https://github.com/microsoft/tensorflow-directml-plugin#tensorflow-directml-plugin-)

## 1. System requirements
### 1. System requirements

* Windows 7 or higher (64-bit)
* Windows 10 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:
Expand Down Expand Up @@ -375,50 +374,29 @@ The following NVIDIA® software are only required for GPU support.
[long paths are enabled](https://superuser.com/questions/1119883/windows-10-enable-ntfs-long-paths-policy-option-missing)
on Windows.

### 3. Install Miniconda

[Miniconda](https://docs.conda.io/en/latest/miniconda.html)
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.
### 3. Create a virtual environment with [venv](https://docs.python.org/3/library/venv.html){:.external}

Download the
[Miniconda Windows Installer](https://repo.anaconda.com/miniconda/Miniconda3-latest-Windows-x86_64.exe).
Double-click the downloaded file and follow the instructions on the screen.
The venv module is part of Python's standard library and is the officially
recommended way to create virtual environments.

### 4. Create a conda environment
Note: Starting with TensorFlow 2.21, Python 3.9 is no longer supported.
Install a supported Python version, for example, 3.10-3.13.

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

```bash
conda create --name tf python=3.9
python -m venv tf
```

You can deactivate and activate it with the following commands.
You can activate it with the following command.

```bash
conda deactivate
conda activate tf
tf\Scripts\activate
```

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](https://www.nvidia.com/Download/index.aspx)
if you have not.

Then install the CUDA, cuDNN with conda.

```bash
conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
```
Make sure that the virtual environment is activated for the rest of the installation.

### 6. Install TensorFlow
### 4. Install TensorFlow

TensorFlow requires a recent version of pip, so upgrade your pip
installation to be sure you're running the latest version.
Expand All @@ -429,16 +407,21 @@ The following NVIDIA® software are only required for GPU support.

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.
Note: Native Windows is CPU-only. For NVIDIA GPU support, install
[TensorFlow in WSL2](https://tensorflow.org/install/pip#windows-wsl2).

```bash
# Anything above 2.10 is not supported on the GPU on Windows Native
pip install "tensorflow<2.11"
# For CPU users on native Windows
pip install tensorflow
# Or install the smaller CPU-only package:
# pip install tensorflow-cpu
```

### 7. Verify the installation
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.

### 5. Verify the installation

Verify the CPU setup:

Expand All @@ -448,15 +431,6 @@ The following NVIDIA® software are only required for GPU support.

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

Verify the GPU setup:

```bash
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
Expand Down
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