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No matter your OS, this step will look something like this: # Requires the latest pip Usually, pip comes already installed with Python 3.0 or above, but it’s usually a good idea to use the latest version. Once your venv is activated (we show the exact commands for this in the OS-specific sections below), you’ll need to install or update the latest version of pip, the standard package manager for Python. The venv module in Python is the recommended way of managing venvs, and it ships with the Python environment. Next, you’ll want to activate a virtual environment (venv), which is a Python environment that lets you keep your pre-installed Python interpreter, libraries and scripts separate from those installed in other venvs. Check out OS-specific resources for Windows, macOS and Linux for the steps to follow. First, you’ll need to install a Python development environment on your system - if you don’t already have one. We’ll present an overview of the process before getting into OS-specific instructions. The steps can be found at TensorFlow’s installation guide, but we’ve broken them down here to simplify the process based on your operating system. Installing the package itself is easy enough, but there are a few prerequisites to setting TensorFlow up properly. Let’s take a look at how to install TensorFlow so you can begin your ML journey with Python. In fact, up-and-coming engineers can employ TensorFlow in their own personal projects with relative ease. However, they designed TensorFlow to be applicable to a variety of use cases beyond those required by the tech giant. The team created the library to conduct research on ML and deep neural networks at Google. TensorFlow is an open-source software library originally developed by Google Brain researchers and developers. With that in mind, we’ve put together this guide on how to install TensorFlow. However, ML engineers will approach the task from a variety of development contexts. Many ML engineers are turning to libraries like Python’s TensorFlow to develop massive neural networks. This makes it an essential tool for Python developers, and it is typically included by default when you install Python on your system.With more than 77% of Fortune 1000 organizations running artificial intelligence workloads in production as of 2021, machine learning (ML) on the rise. In addition to managing package installation, pip also provides many other useful features, such as the ability to list installed packages, upgrade outdated packages, and create and manage Python environments. This can save you a lot of time and effort, as you don’t have to manually download and install each package yourself. You can easily download and install these packages on your computer, and use them in your Python projects by using pip. Python packages are code libraries that provide specific functionality or features, such as support for a particular data format, algorithm, or library. modules, libraries, and frameworks) that are available on the Python Package Index (PyPI). It is a tool that allows you to easily install, upgrade, and manage Python packages (i.e. To do so, you can refer to the documentation for pip or conda to learn how to install Python using these tools. This can be a convenient option if you want to manage multiple Python installations or environments on your system. Alternatively, you can also download and install Python using a package manager like pip or conda. |