MOJO

Mojo is designed to solve a variety of AI development challenges that no other language can, because Mojo is the first programming language built from the ground-up with MLIR (a compiler infrastructure that’s ideal for heterogeneous hardware, from CPUs and GPUs, to various AI ASICs).

A language for next-generation compiler technology

Nowadays, the standard technology for specialized machine learning accelerators is MLIR.

MLIR is a relatively new open-source compiler infrastructure started at Google (whose leads moved to Modular) that has been widely adopted across the machine learning accelerator community. MLIR’s strength is its ability to build domain specific compilers, particularly for weird domains that aren’t traditional CPUs and GPUs, such as AI ASICS, quantum computing systems, FPGAs, and custom silicon.

While many other projects now use MLIR, Mojo is the first major language designed expressly for MLIR, which makes Mojo uniquely powerful when writing systems-level code for AI workloads

A member of the Python family

The Mojo language has lofty goals: mojo want full compatibility with the Python ecosystem, mojo want predictable low-level performance and low-level control, and mojo need the ability to deploy subsets of code to accelerators. Additionally, we don’t want to create a fragmented software ecosystem—we don’t want Python users who adopt Mojo to draw comparisons to the painful migration from Python 2 to 3. These are no small goals!

Fortunately, while Mojo is a brand-new code base, it is not really starting from scratch conceptually. Embracing Python massively simplifies it's design efforts, because most of the syntax is already specified. it can instead focus it's efforts on building Mojo’s compilation model and systems programming features.

• The journey to the Clang compiler (a compiler for C, C++, Objective-C, CUDA, OpenCL, and others), which is a “compatible replacement” for GCC, MSVC and other existing compilers. It is hard to make a direct comparison, but the complexity of the Clang problem appears to be an order of magnitude bigger than implementing a compatible replacement for Python.
• The journey to the Swift programming language, which embraced the Objective-C runtime and language ecosystem, and progressively migrated millions of programmers (and huge amounts of code). With Swift, we learned lessons about how to be “run-time compatible” and cooperate with a legacy runtime.
• The mojo CLI provides all the tools you need for Mojo development, such as commands to run, compile, and package Mojo code. A list of all commands are listed below, and you can learn more about each one by adding the --help option to the command (for example, mojo package --help).However, you may omit the run and repl commands. That is, you can run a Mojo file by simply passing the filename to mojo:

mojo hello.mojo        

And you can start a REPL session by running mojo with no commands.

To update Mojo to the latest version, use the modular tool:

modular update mojo        

You can check your current version with mojo --version. For information about Mojo updates, see the Mojo changelog.

Commands

run — Builds and executes a Mojo file.

build — Builds an executable from a Mojo file.

repl — Launches the Mojo REPL.

debug — Launches the LLDB debugger with support for debugging Mojo programs.

package — Compiles a Mojo package.

format — Formats Mojo source files.