Build Rdm From Source For Mac

Opening Darwin was not simple. Apple’s development work on Mac OS X is managed using XBS, a complex and publicly undocumented internal build system; since nobody outside of Apple has access to the system, it was very difficult to build functional installs of Darwin from the source code as changes were introduced.

Hi everyone!As you may recall, we’re currently working on the cross-platform implementation of RDM. To give you some added insights on what’s happening behind the scenes, I sat down with Richard Markiewicz, our software architect working on the Mac and mobile version of RDM, at our new office in Montreal. I asked him to share some of experiences and thoughts about this exciting project.Below are my questions, and Richard’s answers:

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What’s your experience regarding Mac environment integration and mobile platforms? What projects have you worked on in the past?

I’ve been a staunch Mac user at home since the early nineties, when I received a Macintosh IIci as a gift. However, my first opportunity to develop professionally for an Apple platform didn’t come until 2008 with the launch of the iOS SDK. Since then I’ve worked on multiple successful iPhone and iPad applications targeting both enterprise and personal users, including the very popular Bixou.On the desktop side, I’ve most recently been responsible for bringing an existing .NET enterprise application to Linux, Android and iOS while achieving over 90% code reuse between platforms.

Regarding RDM, what would you like to achieve in the next few months on the cross-platform implementation?

I’d like to get something with a useful set of features built, shipped and in the hands of users. RDM is an incredibly complex and powerful application once you start looking under the hood – the amount of functionality is mind-boggling. If we tried to ship a first version for Mac or mobile incorporating everything from RDM, it would take a very long time! So I’d like to start with an initial release incorporating some of the core functionality, which we can then build on over time. Certainly I’d like to get something out there within a few months.

What was the first thing you’ve worked on when you started the project? What’s your approach?

I’ve started with a lot of prototyping. RDM has a large existing codebase that is already stable and well tested. I want to tap into that as much as possible, so I’ve been investing design time upfront to make sure we can take as much of the existing code as possible and adapt it to reuse across multiple platforms. This will bring dividends down the road where we can focus on making a great native UI experiences, rather than spend time re-implementing existing functionality.Of course we aim to get as close to 100% code reuse as we can, but it’s not always possible. So another benefit of a good prototyping stage is to identify where we will need to develop platform specific implementations up front.

Is integrating RDM from a Microsoft environment to a Mac environment and mobile platforms any different from any other projects you’ve worked on?

One of the biggest differences in this project is my workflow and the tools that I get to use. Despite being a long time Mac user at home and for personal projects, I’ve always worked chiefly on Windows. So this is the first time I’m able to use a Mac as my primary work environment – which makes me very happy.

How is it going so far? What’s your biggest challenge you’ve encountered?

So far, it’s going very well. One of the biggest challenges is getting the user experience right – for Mac OS X and iOS, Apple provides very strict guidelines for how interfaces should look and behave: first and many third-party apps are very good at following those guidelines. If an application doesn’t look or behave right, it instantly stands out. So a big challenge is achieving a balance between the powerful features and customization in RDM while providing an elegant, intuitive interface.Traditional cross-platform toolkits follow a write-once / run anywhere model, where applications look alien on every device. The tools we’re using provide access to the native SDKs and UI controls of every platform, which allows us to re-use the important business logic of RDM while providing a high-performance, native application.

What tools do you use? What kind of coding (.NET, C#, etc…)?

My workstation is a retina display Macbook Pro, running the latest Mac OS X. I use Parallels Desktop to run Windows 8 in a virtualized environment – it’s a great setup that cleanly integrates the two platforms and allows for easy sharing of files and programs between the two.RDM is written in C# and runs on the .NET Framework. To get maximum reuse of that existing code, I’m tapping the excellent open-source Mono project that provides a cross-platform, .NET compatible runtime. This also gives me access to some excellent additional tools including MonoMac, MonoTouch and Mono for Android – allowing us to bind our C# code to the native UI toolkits as well as package the application for their respective platforms.Most of my coding, debugging and UI development is done on MonoDevelop and XCode on the Mac however, I also use Visual Studio on Windows. I’m also using a whole slew of scripting and command line utilities to make my workflow more straightforward.Do you have a question or comment for Richard? Then please post a comment box below. Richard is very busy, but has assured me that he will answer everyone!And if you haven’t already, please feel free to subscribe to our blog, so that you can receive the latest news on the cross-platform implementation. I will keep you all updated with the latest news!Thanks!

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, 1.8 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++.

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

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

Install Anaconda
Install CUDA, if your machine has a CUDA-enabled GPU.
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:

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

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

Prerequisites

Install Anaconda[#anaconda]
Install CUDA, if your machine has a CUDA-enabled GPU.
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. Pearl whirlpool tub replacement parts. 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

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.

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.