135 lines
6.2 KiB
Markdown
135 lines
6.2 KiB
Markdown
# Python installation (Windows)
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## Goal
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Install your very own Python and keep everything under control.
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## Windows
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We need to download a suitable Python version from [https://www.python.org](https://www.python.org). In the case we want to use PyTorch [check here](https://pytorch.org/get-started/locally/#windows-python) or TensorFlow [check here](https://www.tensorflow.org/install/pip#software_requirements) we need to check if the Python version is okay.
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I downloaded this file:
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[https://www.python.org/ftp/python/3.11.7/python-3.11.7-amd64.exe](https://www.python.org/ftp/python/3.11.7/python-3.11.7-amd64.exe)
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Select "Add python.exe to Path" and then press "Install Now".
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We need to update pip first:
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```shell
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python.exe -m pip install --upgrade pip
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```
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### Numba
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{: .topic-optional}
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This is an optional topic!
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The question is: Is numba broken in the moment or not? First we install numpy and then numba. We will observe if numba tried to change the numpy version.
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```shell
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pip install numpy
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```
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```shell
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pip install numba
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```
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If so then you have to decide if you want to remove numba and the downgraded numpy version before continuing. I would remove it!
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```shell
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pip uninstall numpy numba
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```
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|Package||
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|numba|[Numba is an open source JIT compiler that translates a subset of Python and NumPy code into fast machine code.](https://numba.pydata.org/)|
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## Basic Packages you want to install
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```shell
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pip3 install numpy scipy pandas flake8 pep8-naming black matplotlib seaborn ipython jupyterlab mypy dataclasses-json dataconf ipympl pywavelets scikit-image opencv-python scikit-learn tqdm argh sympy jsmin pybind11 pybind11-stubgen pigar asciichartpy natsort ipykernel
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```
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|Package||
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|numpy|[The fundamental package for scientific computing with Python](https://numpy.org/)|
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|scipy|[Fundamental algorithms for scientific computing in Python](https://scipy.org/)|
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|pandas|[pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool](https://pandas.pydata.org/)|
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|flake8|[Your Tool For Style Guide Enforcement](https://flake8.pycqa.org/en/latest/)|
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|pep8-naming|[Check your code against PEP 8 naming conventions](https://pypi.org/project/pep8-naming/)|
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|black|[The uncompromising code formatter](https://black.readthedocs.io/en/stable/index.html)|
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|matplotlib|[Visualization with Python](https://matplotlib.org/)|
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|seaborn|[seaborn: statistical data visualization](https://seaborn.pydata.org/)|
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|ipython|[IPython provides a rich architecture for interactive computing](https://ipython.org/)|
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|jupyterlab|[JupyterLab is the latest web-based interactive development environment for notebooks, code, and data.](https://jupyter.org/)|
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|mypy|[Mypy is an optional static type checker for Python that aims to combine the benefits of dynamic (or "duck") typing and static typing.](http://mypy-lang.org/")|
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|dataclasses-json|[Easily serialize Data Classes to and from JSON](https://github.com/lidatong/dataclasses-json)|
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|dataconf|[Simple dataclasses configuration management for Python with hocon/json/yaml/properties/env-vars/dict support](https://github.com/zifeo/dataconf)|
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|ipympl|[Matplotlib Jupyter Integration](https://github.com/matplotlib/ipympl)|
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|pywavelets|[PyWavelets is open source wavelet transform software for Python.](https://pywavelets.readthedocs.io/en/latest/)|
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|scikit-learn|[Simple and efficient tools for predictive data analysis](https://scikit-learn.org/)|
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|scikit-image|[Image processing in Python](https://scikit-image.org/)|
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|opencv-python|[Open Source Computer Vision Library](https://opencv.org/)|
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|tqdm|[A Fast, Extensible Progress Bar for Python and CLI](https://github.com/tqdm/tqdm)|
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|argh|[Building a command-line interface?](https://pythonhosted.org/argh/) Found yourself uttering “argh!” while struggling with the API of argparse? Don’t want to lose its power but don’t need the complexity?|
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|sympy|[SymPy is a Python library for symbolic mathematics.](https://www.sympy.org/en/index.html)|
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|jsmin|[JavaScript minifier](https://github.com/tikitu/jsmin/)|
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|pybind11|[Seamless operability between C++11 and Python](https://github.com/pybind/pybind11)|
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|pybind11-stubgen|For mypy|
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|pigar|[A tool to generate requirements.txt for Python project, and more than that.](https://github.com/damnever/pigar)|
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|asciichartpy|[Nice-looking lightweight console ASCII line charts](https://github.com/kroitor/asciichart)|
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|natsort|[Simple yet flexible natural sorting in Python.](https://github.com/SethMMorton/natsort)|
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|ipykernel|IPython Kernel for Jupyter|
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### PyTorch
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[Check here](https://pytorch.org/get-started/locally/#start-locally) for a installation configurator.
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**Note: Even if you don't want to use TensorFlow, you want to install it too due to the tensorboard package.** Tensorboard allows to track the progress during learning.
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#### CPU only
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```shell
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pip3 install torch torchvision torchaudio torchtext
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```
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#### NVidia GPU
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```shell
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pip3 install torch torchvision torchaudio torchtext --index-url https://download.pytorch.org/whl/cu121
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```
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|Package||
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|torch|[PyTorch is a Python package that provides two high-level features: a. Tensor computation (like NumPy) with strong GPU acceleration b. Deep neural networks built on a tape-based autograd system](https://pytorch.org/)|
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|torchvision|[The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision.](https://github.com/pytorch/vision)|
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|torchaudio|[The aim of torchaudio is to apply PyTorch to the audio domain.](https://github.com/pytorch/audio)|
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|torchtext|[Models, data loaders and abstractions for language processing, powered by PyTorch](https://github.com/pytorch/text)|
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### TensorFlow
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#### CPU only
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```shell
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pip install tensorflow
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```
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#### NVidia GPU
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```shell
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python3 -m pip install tensorflow[and-cuda]
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```
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|Package||
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|tensorflow|[TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices.](https://www.tensorflow.org/)|
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