Firstly, I would like to offer a summary table explaining the main steps necessary to install Scipy on your Apple Silicon (Arm / M1). Please refer to the following HTML table:
| Step | Action |
|---|---|
| Step 1 | Install Homebrew |
| Step 2 | Update Homebrew and Install Dependencies |
| Step 3 | Install PyEnv |
| Step 4 | Create Virtual Environment |
| Step 5 | Install Scipy |
Now let’s walk through these steps more thoroughly.
Make sure you have Homebrew installed which acts as your package manager for all open-source software. You can use the command:
brew install
. Then update Homebrew using the command
brew update
. Following this, you need to install essential dependencies such as numpy and other Python libraries.
The next step is to install PyEnv, which will manage your Python versions. Use the command
brew install pyenv
for this. Post installation, create a virtual environment using
pyenv virtualenv xxx
, replace ‘xxx’ with the version of Python you wish to use. This creates an isolated environment ensuring no dependencies conflict with each other.
Finally, to install Scipy, activate the newly created environment and then use
pip install scipy
.
This process should ensure a successful installation of Scipy even in an ARM architecture like that of the Apple Silicon (M1). For comprehensive learning about installing programming tools on Apple Silicon (Arm/M1), I recommend articles from Medium[^1^] or Dev.to[^2^] that concisely summarize the entire spectrum.
[^1^]:https://medium.com/macoclock/how-to-install-scipy-on-m1-mac-13934f8b37e7
[^2^]:https://dev.to/qtangs/installing-programming-tools-for-apple-silicon-m1–58ogUnderstanding the difference between Intel chips and Apple’s Silicon brings to the front a new era for Mac users particularly when installing packages such as SciPy. While our focus is on “how to install SciPy on Apple Silicon,” it becomes pivotal to grasp what changes these two different chips bring about in the realm of software compatibility and performance.
Comparison: Apple Silicon vs Intel Chips
Performance & Efficiency
- Apple Silicon: The highlight of Apple Silicon (Arm/M1) lies in its unified memory architecture which results in significantly higher performance. Large datasets can be transferred at lightning speed among various components because CPU, GPU, and other cores share the same memory pool.
- Intel Chips: In contrast, Intel chips follow a segregated memory structure meaning that the CPU has separate RAM and GPU has its own graphics memory. This setup tends to slow down data transfer rate and affects the performance.
Power Consumption
- Apple Silicon: Arm/M1 also enjoys intrinsic advantage regarding power efficiency due to its ARM-based design engineered specifically for lower power consumption without compromising on performance.
- Intel Chips: Intel chips, being x86-based, are generally more power-hungry which diminishes battery life of portable devices like laptops.
| Apple Silicon | Intel Chips | |
|---|---|---|
| Performance | Better Performance due to unified memory structure | Inferior Performance due to segregated memory structure |
| Power Consumption | Lower Power Consumption | Higher Power Consumption |
Note: Despite Apple Silicon’s promising features, it poses challenges in the way we install software packages traditionally designed and optimized for x86 architecture of Intel Chips.
Now let’s move on to our main concern – installing SciPy library on Apple Silicon.
How to Install SciPy on Apple Silicon?
To install Python libraries like SciPy on Apple Silicon, using Conda is a practical solution.
Make sure you have Homebrew installed on your system. If not, you can install by running:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
Then install miniforge conda distribution:
brew install miniforge
Once you’ve set up miniforge, create a new conda environment for SciPy:
conda create --name scipy_env
Activate the new environment:
conda activate scipy_env
Now, you are ready to install scipy in your newly created environment:
conda install scipy
You’ve now successfully installed SciPy on an Apple Silicon machine!
Remember, software installation processes may look different with the shift from Intel to Apple Silicon. Understanding the fundamental differences, however, equips us better to navigate this change. The ability to adapt and optimize software usage according to evolving hardware architectures is indeed the mark of a professional coder!
Resources:
Port Your Python Runtime,
The shortcomings of M1 Macs,
Numpy’s special instructions for Apple Silicon Users.
The ARM / M1 architecture in the latest Macs represents a seismic shift from Intel’s x86 architectures that we’ve been accustomed to. This change brings along promises of significantly improved power efficiency and performance. However, it also introduces certain challenges, especially for developers who often rely on various libraries and tools which may not yet be fully compatible with the ARM64 architecture. Scipy is one such library many Python developers depend upon.
To install Scipy on Apple Silicon (ARM / M1), you’d usually use pip, but unfortunately, support for Apple Silicon (M1) isn’t entirely seamless at the moment. You might face errors related to the unavailability of certain wheels for the platform. Fear not, as there are multiple workarounds available to ensure you can still get Scipy up and running on your M1 machine.
Method 1: Use Homebrew
Homebrew now supports native compilation on Apple Silicon. To begin with, make sure you have XCode command-line tools installed:
xcode-select --install
Install Homebrew by executing the following script:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
Now, install OpenBLAS:
brew install openblas
You’re then set to create a new environment in Python where you can freely utilize SciPy:
python3 -m venv scipy-env source scipy-env/bin/activate pip install --upgrade pip wheel setuptools cython OPENBLAS="$(brew --prefix openblas)" pip install --no-use-pep517 scipy
This will install SciPy using OpenBLAS for computation tasks.
Method 2: Use Miniforge or Anaconda distribution
Miniforge is another way. Which gives us access to the
conda
package manager designed specifically for M1. The simple installation step would be:
bash Miniforge3-MacOSX-arm64.sh
Afterwards, create a new conda environment and install scipy:
conda create -n new_env python=3.8 conda activate new_env conda install scipy
In conclusion, while the new ARM / M1 Macs bring significant performance enhancements, you may initially encounter some challenges getting certain libraries and packages to work seamlessly. But with patience and the right methodologies, you can adeptly navigate these minor setbacks and unlock the full potential of your new gear!
Certainly, I’d be happy to provide you with an analytical and engaging step-wise guide on how to install Scipy on Apple Silicon. This process involves several stages, beginning with the installation of prerequisites and ending with the actual installation of Scipy.
The very first step is the installation of Homebrew, a package manager for macOS. Homebrew makes it effortless to download, compile, and manage open-source software on your Mac. Use the mentioned Terminal command below to install Homebrew:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
Next, after having Homebrew in place, we have to make certain that all Apple command line tools are present. This ensures that there will be no conflicts that can hamper package installations. The Terminal command here is:
xcode-select --install
Then it’s time to install Python. As Scipy is a Python library for scientific computing, naturally we require Python installed in our environment. We can use the brew command, which utilizes Homebrew to do that:
brew install python
We also need to ensure that pip3, the package installer for Python, is present in our system. If not, we install it using the following command:
brew postinstall python3
Now we’re ready for installing virtual environments. A Python “Virtual Environment” isolates the Python dependencies per project making it easier to handle individual project configurations. Use these commands to install and create a virtual environment:
pip3 install virtualenv virtualenv env source env/bin/activate
After ensuring that our environment is activated, we move forward to install the three essential packages that Scipy relies on: NumPy, Cython, and Pybind11. These are crucial since Scipy is built on these three packages.
pip3 install cython pybind11 pip3 install numpy
Finally, now that we’ve prepared our environment, we can download and install Scipy. Remember, this should be done while still inside the previously active virtual environment. You can perform this operation by executing the following Terminal commands:
git clone https://github.com/scipy/scipy.git cd scipy python setup.py install
Congrats! You’ve managed to install Scipy on your Apple Silicon (Arm/M1) within a virtual environment. To exit the environment when you’ve finished running Scipy applications or exercises, use `deactivate` command:
deactivate
Remember, the above steps ensure that the Scipy installation doesn’t conflict with other Python versions or libraries in your machine, thanks to the isolated environment that we’ve created with virtualenv. This makes managing and maintaining your project configuration much more manageable.
For more information regarding scipy installation, feel free to check their official installation guide.
As a professional coder who has juggled numerous code installations on different systems, I recognize the potential for bumps on the road. One prime example is installing Scipy on Apple Silicon (ARM / M1).
Installing Scipy specifically on the newer iterations of Apple computers hosting the ARM or M1 chip will likely encounter unique difficulties primarily because most Python packages including Scipy are not yet natively compiled to run on these processors.
Take note, however, that these issues are not insurmountable, and with elaborate analysis and strategic implementation of solutions, you’ll have Scipy running on your machine in no time.
Firstly, it’s important to understand that the default Python interpreter may work suboptimally due to dependency problems on ARM machines. It is recommended to install a separate Python interpreter from miniforge that is specifically optimized for your architecture.
Use
arch -arm64 /bin/zsh -c "$(curl -fsSL https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh)"
This command downloads and installs a Python interpreter designed for the ARM architecture.
Secondly, know that there can be challenges with simple
pip install scipy
command, which may lead to errors commonly relating to incompatibilities in building the necessary wheels. The ideal solution would be installing the package directly from conda-forge by using the command
conda install -c conda-forge scipy
It specifies the channel as conda-forge, which ensures the M1-optimized version of the SciPy library.
Lastly, a recurring issue often involves corrupt files or missing dependencies, for which I’d recommend using miniforge’s conda clean command:
conda clean --all
To provide a crucial layer of clarity, here’s a visual to aid the process:
| Issue | Solution |
|---|---|
| Python Interpreter Compatibility | Install miniforge python interpreter with ARM optimizations. |
| Pip Installation Fails | Use conda to install scipy from the conda-forge channel. |
| Corrupt Files/Missing Dependencies | Execute the ‘conda clean’ command to clear any corrupted cached files. |
And remember, coding is an iterative process full of trial and error. Armed with this information, I’m hopeful you’ll get scipy up and running on your Apple silicon machine in a breeze. Check out the official miniforge installer documentation for more detailed instructions.Installing Scipy on Arm / M1 machines like Apple Silicon can be tricky due to compatibility issues with the hardware architecture. However, there are various workarounds for this problem that you can employ to successfully install this package on your machine. Here’s a complete guide on how to install Scipy on an M1 Mac:
#### Using Homebrew and Pip
Homebrew is a free and open-source software package management system. It simplifies the installation of software on macOS and Linux. After installing Homebrew, we’ll use pip, Python’s recommended tool for installing packages.
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
This command will install homebrew on your machine. Next, we need to update homebrew and add a few lines in our terminal profile. If you’re using zsh profiler, just replace .bash_profile with .zshrc.
echo 'eval $(/opt/homebrew/bin/brew shellenv)' >> /Users/{your_user_name}/.bash_profile
eval $(/opt/homebrew/bin/brew shellenv)
We also have to install Python through homebrew:
brew install python
After installing Python, we can use pip to install scipy:
pip3 install scipy
#### Using Miniforge
Another workaround for installing Scipy on Arm / M1 Machines is by using Miniforge. It’s a community-led Conda installer specifically designed for ARM architectures. We can download it from their GitHub repository.
Once downloaded, open a Terminal window and navigate to the folder where Miniforge is downloaded then run this command to install it:
chmod +x Miniforge3-MacOSX-arm64.sh ./Miniforge3-MacOSX-arm64.sh
After successfully installing Miniforge, open a new Terminal window (or source your updated bashrc/zshrc) and create a new Conda environment as follows:
conda create -n myenv python=3.8 conda activate myenv
Now within this environment, install scipy:
conda install scipy
#### Using Rosetta 2
Apple provides a technology called Rosetta 2 that allows apps built for Intel processor to run on Apple Silicon. To use Rosetta 2, first we need to install it:
softwareupdate --install-rosetta
Then, open terminal using Rosetta (right-click Terminal -> Get Info -> Open using Rosetta).
From here, we can install scipy normally using pip:
pip3 install scipy
Remember, each method might have different scenery depending on your own configuration or some other occurring issues during the process. So adapt accordingly. Whichever method you choose, by following these instructions closely, you should be able to have Scipy up and running on an M1 Mac.
When you’re working on installing Scipy on Apple silicon, namely the ARM or M1 variants, it is common to encounter certain errors during the process. However, here we will overview some of these common challenges, and propose a set of resolutions to mitigate them.
Error: scipy-base fails to build when installing via pip
This error typically occurs because scipy hasn’t been well-optimized for Apple’s recent Silicon processors, like the M1. There could be incompatibility between pre-built binary wheels and the new architecture.
Resolution:
To resolve this, try using
miniforge
or
conda
to manage your Python environment, and install scipy this way:
#!bash conda install -c conda-forge scipy
By installing Miniforge or Conda, which are macOS-specific distributions of Python and other packages, you can ensure a smoother installation process by utilizing their pre-built binary packages that support the new Apple Silicon architecture.
Error: fatal error: ‘numpy/arrayobject.h’ file not found
The numpy headers are required for building scipy from source. This error stems from PYTHONPATH environment variable not being set or modified correctly during the installation process.
Resolution:
Ensure the correct version of numpy is installed and then export the correct path as shown below:
#!bash pip install numpy==1.19.4 export PYTHONPATH="/path/to/venv/lib/python3.8/site-packages"
In the above command, “/path/to/venv” refers to the location of your virtual environment. You should replace it with the actual path in your system.
Error: Python.h: No such file or directory
This error indicates that Python development headers are missing. They are required for compiling scipy from source.
Resolution:
You need to install Xcode Command Line Developer Tools to get the correct headers:
#!bash xcode-select --install
Above command will download and install the Xcode Command Line Developer Tools package, which includes necessary compilers and libraries for software development on macOS.
In essence, it’s important to remember that installing scientific computing packages like scipy on a new architecture may come with its unique set of challenges. By employing strategies like using Miniforge or Conda to utilise pre-built binaries, ensuring the correct numpy libraries and headers are installed, and downloading required development tools, we can successfully install scipy on Apple Silicon (ARM/M1).
Installing SciPy on Apple Silicon Mac (M1) and optimizing its performance are two critical aspects that I’ll cover in this section. To install packages of Python or any library such as SciPy on an M1 Mac, you ideally need to use a version of Python that’s compiled for the ARM architecture.
Installing SciPy on Apple Silicon Macs
First, let’s discuss how to install the binary distribution of Python that Homebrew provides, which is set up to work with the new Apple Silicon chipsets:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
After Homebrew installation, you can then install Python with:
brew install python
We also recommend using ‘pyenv’ for managing Python versions in your Mac. Once you’ve installed Homebrew and Python, here’s the process to install SciPy using pip:
pip3 install scipy
Optimizing Performance of SciPy under Apple Silicon
Once SciPy is installed, optimizing its performance relies on three main points: Using optimized libraries such as OpenBLAS and LAPACK, compiling code natively for Apple Silicon using Arm instructions and multiprocessing when possible.
Using optimized libraries. The speed of numpy and scipy largely depends on the underlying implementation of BLAS and LAPACK. So make sure that your scipy is linked to an optimized BLAS/LAPACK library. You can check this by:
import numpy numpy.__config__.show()
Compiling Code Natively for Apple Silicon. Compiled languages, like C, often perform better than interpreted languages like Python because they compile code to machine language, which runs directly on the processor. However, to take full advantage of the M1 processor, the software must be compiled specifically for it.
Forcing native compilation for Apple Silicon is complicated and out of the scope of this answer, but if you are really pushing the limit, consult the guide from Apple on porting your macOS Apps to Apple Silicon.
Multiprocessing. The M1 chip has 8 cores (4 high-performance cores + 4 energy-efficient cores). By making use of parallel programming via multiprocessing, you can improve the performance of your scripts drastically. This will allow tasks to be performed in parallel across multiple cores.
For example, here’s a simple way to run a function in parallel using pool from the multiprocessing module:
from multiprocessing import Pool
def f(x):
return x * x
with Pool(5) as p:
print(p.map(f, [1, 2, 3]))
These optimizations in combination should help you harness the power of your new Apple Silicon Mac to its full potential while working on SciPy projects.
Installing Scipy on Apple Silicon (ARM / M1) could initially seem like a daunting process. But in reality, given the correct set of guidelines and instructions, we can easily perform this task.
Firstly, we need to ensure that Python 3.8 or later is installed on our system. We can verify this by running
python3 --version
in terminal. If we have an older version, upgrade Python to meet the prerequisites.
Next, we require Homebrew, which is a package manager for macOS. This can be installed by typing
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
into the terminal. Post installation, it’s advisable to add Homebrew to your PATH.
Following the Python and Homebrew prerequisites, we arrive at the crucial step of installing the Miniforge console. For this, copy and run this command line
curl -LJO https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh bash Miniforge3-MacOSX-arm64.sh
.
Moreover, an integral requirement is to have a version of pip that supports Apple Silicon. You can achieve this by executing
pip install --upgrade pip
from the terminal after activating your newly created Miniforge conda environment with
conda activate
.
Finally, once these requirements are met, you can smoothly install Scipy by simply keying in the command
pip install scipy
.
Through these structured steps, we can successfully install Scipy on Apple Silicon (ARM/M1), thus making our system ready for Python-related experimental endeavors. Understanding these steps will also empower us with skills beyond just limited to this, which include the ability to setup a conducive development environment on MacBook Pro or MacBook Air machines with the M1 chip.
More details and information related to installing Python packages specifically designed for Apple Silicon, such as Numpy and Scipy, can be found on popular developer communities like StackOverflow and official documentation websites like Python’s Official Documentation Website.
Flexibility and compatibility issues faced by early adopters of Apple Silicon shall soon be alleviated as more open-source developers strive to provide support for the new architecture.
As a coder, my analysis indicates that this process may be a glimpse of what the future holds in store regarding software installations with newer hardware architectures coming into play. For aspiring developers using Apple M1 chips, it would serve well to familiarize themselves with these processes and guidelines.