Python Anaconda Install Mac

Introduction

Install Opencv Python Anaconda Mac If you have a CDH cluster, you can install the Anaconda parcel using Cloudera Manager. The Anaconda parcel provides a static installation of Anaconda, based on Python 2.7, that can be used with Python and PySpark jobs on the cluster. Aug 27, 2020 Python Windows 10/7 Installation. The installation of Python can be done using two different ways that are downloading Python through python.org or downloading Python under Anaconda Installation. Both of these are mentioned below: Normal Installation using native Python.exe setup. For installing Python normally follow these steps.

Install Anaconda (Python 3.7) on Mac OSX Catalina. Nonthakon Jitchiranant. Graphical Installation of Anaconda. Installing Anaconda using a graphical installer is probably the easiest way to install Anaconda. 1 ‒ Go to the Anaconda Website and choose a Python 3.x graphical installer (A) or a Python 2.x graphical installer (B). If you aren’t sure which Python version you want to install, choose Python 3. Answer (1 of 10): You don’ t have to. At some point I got personally very annoyed with the conda installer. Pro:. Anaconda python is faster than vanilla python.

Anaconda is the most popular Python distribution among data scientists, mostly because it makes their life easier in multiple ways (which we will discuss below).

In this article, we will give you a high-level overview of the benefits of using the Anaconda Python distribution for machine learning, and we will guide you step-by-step on how to install it on your computer.

Let’s begin.

What is Anaconda Python?

Anaconda is a Python distribution maintained by Anaconda, Inc. (formerly Continuum Analytics), a company founded by two Python veterans: Peter Wang and Travis Oliphant.

Besides Python, it also supports R, and it does this cross-platform on Windows, macOS, and Linux.

What problem does Anaconda solve?

Most importantly, Anaconda tackles a frequent package management challenge faced by data scientists.

Package management with pip

Installing Python packages requires the use of a package manager, the most popular being pip. pip installs dependencies without checking whether they conflict with existing packages, so it might break them. Even worse, it can make them produce different results without the user’s knowledge.

To give an example, let’s say we are already using a machine learning package depending on NumPy (the most widely used numerical computation package for Python). Now, when we install some other package that depends on a different version of NumPy, it could break our machine learning library or generate inconsistent results.

Imagine having this in production code.

Package management with conda

For this reason, Anaconda uses its own package management system, conda. When installing or updating a package, it takes into account the whole environment and the user-defined package preferences. Then, it either tries to work out a configuration compatible with all dependencies or, at least, makes the conflicts explicit.

So, as a result, you do not have to be afraid that installing a new package could affect your entire data analytics workflow.

Package management, therefore, is a core feature of the Anaconda distribution. Besides this, however, there are further functionalities with their pros and cons.

We will review them in the next section.

The main differences between Anaconda and system Python

So, besides the different approach to package management, what are the main differences between the Anaconda distribution and the default system version of Python?

First, a side note. Very often, people compare Anaconda and pip as mutually exclusive alternatives, but as we will see, it is not that simple, as Anaconda is not only a package manager, but has other functionalities as well.

Here we will also compare Anaconda to pip, the Python default package manager.

Pros of using system Python

  • pip is the most straightforward way to install packages on Python
  • pip is often faster than conda (the package manager of Anaconda)
  • You get the most recent versions of each package
  • No company is involved

Cons of using system Python

  • Requires caution and possibly manual handling of dependencies
  • Relying on pip can be unstable and break dependencies
  • Cannot install non-Python packages

Pros of using Anaconda

  • Package management and dependency is more extensive as it can also track non-Python dependencies
  • It has its own native virtual environment management system which can integrate different Python versions
  • conda environments are isolated environments
  • Anaconda is a company, which ensures consistency and support for commercial applications
  • It comes with many useful and integrated applications for data science (most importantly Jupyter Notebook)
  • It is perhaps the most popular Python distribution nowadays, and the number one in data science development

Cons of using Anaconda

  • The package manager tends to be slow (although the Anaconda team has been actively working on this issue)
  • Often the packages are not the most recent version because they undergo a more thorough dependency checking
  • Not every package accessible through pip is also in conda, however some packages can be found in conda-forge, which is maintained by the user community
  • Installing packages using both pip and conda within the same environment can generate conflicts

Overall, we could say that it is best to use Anaconda for data-science related projects, and especially when managing a full ecosystem, including different Python versions and non-Python elements.

It might be better to stick to system Python, however, for building a customized and robust environment with a narrow focus on a particular Python version.

How to install Anaconda Python on macOS

The most straightforward way to install Anaconda is to download the appropriate installer from the official macOS download page. There you can download it by clicking on the link for your preferred version.

Although it is still possible to download the Python 2.7 version, we recommend going for Python 3.7, as Python 2 is no longer supported.

There are both a graphical and a command-line installer available. By default, you should be fine with the graphical version. In this tutorial, we also follow that path.

After downloading the package, run it and follow its instructions.

Most of the steps are straightforward; the two main things you can alter are

  • The install location
  • Whether to install the PyCharm IDE as well

If you do not have specific preferences regarding these options, you can use the default location and skip the PyCharm installation.

After going through all the steps, you should see the following screen.

Congratulations, you have successfully installed the Anaconda Python distribution!

Now, how should you proceed from here? In the next section, we will give you a quick overview of the things which you can do with Anaconda right away.

Getting started with Anaconda Python on Mac OS

Anaconda Navigator

You can use Anaconda both as a GUI (Anaconda Navigator) or through the command line (conda). If you are just getting started with it, we advise you to use the GUI version.

On macOS, you can open the Navigator by opening the Launchpad, and then clicking the Anaconda Navigator icon.

Applications coming with the Anaconda distribution

The home screen of the Navigator shows you the applications which come with the Anaconda distribution.

We do not have space to discuss them in detail, but here is a quick overview of them:

  • Jupyter Notebook: A popular web-based interactive environment frequently used by data scientists.
  • JupyterLab: The next-generation user interface extending Jupyter Notebook with multiple functionalities.
  • Qt Console: A lightweight terminal-like environment supporting many of the IPython-based GUI features of Jupyter Notebook.
  • Spyder: A Python IDE explicitly designed for data science purposes.
  • Glueviz: A linked-view data visualization package.
  • Orange: A component-based visual programming software package designed for data analytics and machine learning applications.
  • RStudio: An R IDE popular among data scientists using R.
  • Visual Studio Code: The popular IDE. If you launch it from the Navigator, it runs with the configurations of the currently selected environment.

Jupyter Notebook

If you are using Anaconda Python, you probably want to use it with Jupyter Notebook.

(If you are not familiar with Jupyter Notebook, you might want to try it, as it is a handy tool. We’ve also written a specific guide about it on our blog.)

You can start it directly by clicking on the “Launch” button in the Navigator’s Home screen (see above), or with the jupyter notebook command from the terminal.

This should open a new tab in your browser, similar to the one below. It will show you files in your home folder.

You can create a new notebook by clicking on the “New” button at the top-right.

This will open a new tab with the new notebook, where you can start working on your data science project.

Conclusion

In this article, we reviewed what the Anaconda Python distribution is and its main benefits.

Its core advantage (especially when compared to pip) is its robust package management system. It also provides an excellent environment management process and several tools (including Jupyter Notebook), all tailored for data science functionalities.

We also reviewed the main steps of installing Anaconda and starting the Navigator and Jupyter Notebook.

As you can see, Anaconda is a well thought through and carefully-designed distribution. It provides a sound development environment and frees up time you otherwise would have spent on unnecessary grunt work.

Are you interested in learning more about it? We can teach you the intricacies of Anaconda Python and how you can use it for machine learning and data science.

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  • How to install Anaconda Python on your Mac - June 15, 2020
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Start Locally

Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, 1.10 builds that are generated nightly. Please ensure that you have met the prerequisites below (e.g., numpy), depending on your package manager. Anaconda is our recommended package manager since it installs all dependencies. You can also install previous versions of PyTorch. Note that LibTorch is only available for C++.

Additional support or warranty for some PyTorch Stable and LTS binaries are available through the PyTorch Enterprise Support Program.

PyTorch can be installed and used on macOS. Depending on your system and compute requirements, your experience with PyTorch on a Mac may vary in terms of processing time. It is recommended, but not required, that your Mac have an NVIDIA GPU in order to harness the full power of PyTorch’s CUDAsupport.

Currently, CUDA support on macOS is only available by building PyTorch from source

Prerequisites

Python Anaconda Install Mac

macOS Version

PyTorch is supported on macOS 10.10 (Yosemite) or above.

Python

It is recommended that you use Python 3.5 or greater, which can be installed either through the Anaconda package manager (see below), Homebrew, or the Python website.

Package Manager

To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.

Anaconda

To install Anaconda, you can download graphical installer or use the command-line installer. If you use the command-line installer, you can right-click on the installer link, select Copy Link Address, and then use the following commands:

pip

Python 3

If you installed Python via Homebrew or the Python website, pip was installed with it. If you installed Python 3.x, then you will be using the command pip3.

Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary.

Installation

Anaconda

To install PyTorch via Anaconda, use the following conda command:

pip

To install PyTorch via pip, use one of the following two commands, depending on your Python version:

Verification

To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.

The output should be something similar to:

Building from source

For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.

You will also need to build from source if you want CUDA support.

Prerequisites

  1. Install Anaconda
  2. Install CUDA, if your machine has a CUDA-enabled GPU.
  3. Follow the steps described here: https://github.com/pytorch/pytorch#from-source

You can verify the installation as described above.

PyTorch can be installed and used on various Linux distributions. Depending on your system and compute requirements, your experience with PyTorch on Linux may vary in terms of processing time. It is recommended, but not required, that your Linux system has an NVIDIA GPU in order to harness the full power of PyTorch’s CUDAsupport..

Prerequisites

Supported Linux Distributions

PyTorch is supported on Linux distributions that use glibc >= v2.17, which include the following:

  • Arch Linux, minimum version 2012-07-15
  • CentOS, minimum version 7.3-1611
  • Debian, minimum version 8.0
  • Fedora, minimum version 24
  • Mint, minimum version 14
  • OpenSUSE, minimum version 42.1
  • PCLinuxOS, minimum version 2014.7
  • Slackware, minimum version 14.2
  • Ubuntu, minimum version 13.04

The install instructions here will generally apply to all supported Linux distributions. An example difference is that your distribution may support yum instead of apt. The specific examples shown were run on an Ubuntu 18.04 machine.

Python

Python 3.6 or greater is generally installed by default on any of our supported Linux distributions, which meets our recommendation.

Tip: By default, you will have to use the command python3 to run Python. If you want to use just the command python, instead of python3, you can symlink python to the python3 binary.

However, if you want to install another version, there are multiple ways:

  • APT

If you decide to use APT, you can run the following command to install it:

It is recommended that you use Python 3.6, 3.7 or 3.8, which can be installed via any of the mechanisms above .

If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.

Package Manager

To install the PyTorch binaries, you will need to use one of two supported package managers: Anaconda or pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python.

Anaconda

To install Anaconda, you will use the command-line installer. Right-click on the 64-bit installer link, select Copy Link Location, and then use the following commands:

You may have to open a new terminal or re-source your ~/.bashrc to get access to the conda command.

pip

Python 3

While Python 3.x is installed by default on Linux, pip is not installed by default.

Tip: If you want to use just the command pip, instead of pip3, you can symlink pip to the pip3 binary.

Installation

Anaconda

No CUDA

To install PyTorch via Anaconda, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Linux, Package: Conda and CUDA: None.Then, run the command that is presented to you.

With CUDA

To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Conda and the CUDA version suited to your machine. Often, the latest CUDA version is better.Then, run the command that is presented to you.

pip

No CUDA

To install PyTorch via pip, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Linux, Package: Pip and CUDA: None.Then, run the command that is presented to you.

With CUDA

To install PyTorch via pip, and do have a CUDA-capable system, in the above selector, choose OS: Linux, Package: Pip and the CUDA version suited to your machine. Often, the latest CUDA version is better.Then, run the command that is presented to you.

Verification

To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.

The output should be something similar to:

Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:

Building from source

For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.

Prerequisites

  1. Install Anaconda
  2. Install CUDA, if your machine has a CUDA-enabled GPU.
  3. Follow the steps described here: https://github.com/pytorch/pytorch#from-source

You can verify the installation as described above.

PyTorch can be installed and used on various Windows distributions. Depending on your system and compute requirements, your experience with PyTorch on Windows may vary in terms of processing time. It is recommended, but not required, that your Windows system has an NVIDIA GPU in order to harness the full power of PyTorch’s CUDAsupport.

Prerequisites

Supported Windows Distributions

PyTorch is supported on the following Windows distributions:

  • Windows 7 and greater; Windows 10 or greater recommended.
  • Windows Server 2008 r2 and greater

The install instructions here will generally apply to all supported Windows distributions. The specific examples shown will be run on a Windows 10 Enterprise machine

Python

Currently, PyTorch on Windows only supports Python 3.x; Python 2.x is not supported.

As it is not installed by default on Windows, there are multiple ways to install Python:

If you use Anaconda to install PyTorch, it will install a sandboxed version of Python that will be used for running PyTorch applications.

If you decide to use Chocolatey, and haven’t installed Chocolatey yet, ensure that you are running your command prompt as an administrator.

For a Chocolatey-based install, run the following command in an administrative command prompt:

Package Manager

To install the PyTorch binaries, you will need to use at least one of two supported package managers: Anaconda and pip. Anaconda is the recommended package manager as it will provide you all of the PyTorch dependencies in one, sandboxed install, including Python and pip.

Anaconda

To install Anaconda, you will use the 64-bit graphical installer for PyTorch 3.x. Click on the installer link and select Run. Anaconda will download and the installer prompt will be presented to you. The default options are generally sane.

pip

If you installed Python by any of the recommended ways above, pip will have already been installed for you.

Installation

Anaconda Python Install Location Mac

Anaconda

To install PyTorch with Anaconda, you will need to open an Anaconda prompt via Start Anaconda3 Anaconda Prompt.

No CUDA

To install PyTorch via Anaconda, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Conda and CUDA: None.Then, run the command that is presented to you.

Python Anaconda Install Mac

With CUDA

To install PyTorch via Anaconda, and you do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Conda and the CUDA version suited to your machine. Often, the latest CUDA version is better.Then, run the command that is presented to you.

pip

No CUDA

To install PyTorch via pip, and do not have a CUDA-capable system or do not require CUDA, in the above selector, choose OS: Windows, Package: Pip and CUDA: None.Then, run the command that is presented to you.

With CUDA

To install PyTorch via pip, and do have a CUDA-capable system, in the above selector, choose OS: Windows, Package: Pip and the CUDA version suited to your machine. Often, the latest CUDA version is better.Then, run the command that is presented to you.

Verification

To ensure that PyTorch was installed correctly, we can verify the installation by running sample PyTorch code. Here we will construct a randomly initialized tensor.

From the command line, type:

then enter the following code:

Install Anaconda And Python Mac

The output should be something similar to:

Additionally, to check if your GPU driver and CUDA is enabled and accessible by PyTorch, run the following commands to return whether or not the CUDA driver is enabled:

Building from source

For the majority of PyTorch users, installing from a pre-built binary via a package manager will provide the best experience. However, there are times when you may want to install the bleeding edge PyTorch code, whether for testing or actual development on the PyTorch core. To install the latest PyTorch code, you will need to build PyTorch from source.

Prerequisites

Install Python Anaconda Mac

  1. Install Anaconda
  2. Install CUDA, if your machine has a CUDA-enabled GPU.
  3. If you want to build on Windows, Visual Studio with MSVC toolset, and NVTX are also needed. The exact requirements of those dependencies could be found out here.
  4. Follow the steps described here: https://github.com/pytorch/pytorch#from-source

Install Anaconda Mac Python 3.8

You can verify the installation as described above.