As I embark on this 30-day journey of exploring MLX, I'm excited to share my experience with fellow iOS developers and Python newcomers alike. In this series, we'll delve into the world of machine learning (ML) and explore its applications in mobile app development.
Setting Up Your Environment
Before diving into the world of MLX, it's essential to set up your Python environment. If you're new to Python or haven't installed it on your system yet, now is the perfect time! On macOS Sonoma, I use brew to install the latest version of Python:
`shell
~ brew install python
`
Verify that Python is installed by running:
`shell
~ which python3
/opt/homebrew/bin/python3
`
Creating a Virtual Environment
As we'll be working on multiple projects, it's crucial to create a virtual environment for our MLX project. This will help keep dependencies organized and isolated from other projects. I recommend creating a new directory to host your virtual env:
`shell
~ mkdir mlx
~ cd mlx
`
Next, create and activate the virtual environment:
`shell
~ python3 -m venv env
~ source env/bin/activate
`
Installing MLX
Now that we have our virtual environment set up, let's install MLX using pip3:
`shell
pip3 install mlx
`
Testing Your Setup
To verify that everything is working as expected, follow the quick start guide to test your setup:
`shell
(env) ➜ mlx python
Python 3.12.2 (main, Feb 6 2024, 20:19:44) [Clang 15.0.0 (clang-1500.1.0.2.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import mlx.core as mx
>>> a = mx.array([1, 2, 3, 4])
>>> a.shape
(4,)
>>> a.dtype
mlx.core.int32
>>> b = mx.array([1.0, 2.0, 3.0, 4.0])
>>> b.dtype
mlx.core.float32
`
What's Next?
In the next blog post, we'll explore Swift APIs from MLX, which can be used to build on-device machine learning apps using the power of MLX library. Stay tuned!
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Let's get started with AI in mobile apps!