Yes, Conda environments can run on a Raspberry Pi, but the standard ARM64 setup requires a compatible Pi and a 64-bit operating system. For most new projects, use Miniforge rather than Miniconda: it provides Conda and Mamba, has a dedicated Linux ARM64 installer, and is configured for conda-forge. A Raspberry Pi is useful for learning, small classical machine-learning projects, and edge inference; it is not a substitute for a desktop GPU or cloud training machine.
What you need before installing Conda
The usual ARM64 Conda path is suitable for Raspberry Pi 3, 4, or 5 models running 64-bit Raspberry Pi OS or Ubuntu. The processor being capable of 64-bit operation is not enough: the installed OS must also be 64-bit. A Pi with 32-bit Raspberry Pi OS cannot use the standard Linux-aarch64 Miniforge installer.
Raspberry Pi OS is available in 32-bit and 64-bit editions, and Raspberry Pi documents its OS and Python guidance here. The Raspberry Pi 64-bit OS announcement describes support for newer models including Pi 3, 4, and 5: Raspberry Pi OS 64-bit.
- Use a reliable storage device and leave room for environments, package caches, datasets, and models. There is no universal free-space minimum because usage varies substantially.
- For sustained workloads, use suitable power and cooling. Short scripts and prolonged ML workloads do not place the same demands on the hardware.
- Plan to use the Pi mainly for small experiments or deployment. Train larger models on a more capable machine and transfer the resulting model to the Pi when appropriate.
Check your Pi’s architecture
Before downloading an installer, inspect the OS, processor architecture, memory, storage, and Python version:
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cat /etc/os-release
uname -m
getconf LONG_BIT
python3 --version
free -h
df -hFor the standard ARM64 installer, uname -m should report aarch64 and getconf LONG_BIT should report 64. If the architecture is armv7l or armv6l, the running OS is 32-bit; do not try to force an ARM64 installer onto it. Install a 64-bit OS on a compatible Pi first. The Miniforge project lists supported installers and requirements at its requirements and installers page.
Choose Miniforge, Miniconda, or another environment
Miniconda and Miniforge both provide Conda-style environments, but their defaults and Raspberry Pi fit differ. Anaconda warns that its Linux ARM64 Miniconda builds may not be compatible with some Raspberry Pi systems because of compiler options aimed at server-class ARM processors. See Anaconda’s Miniconda system requirements.
| Option | Best for | Trade-off |
|---|---|---|
| Miniforge | Conda-based scientific Python on ARM64; the practical default for most Pi users. | Not every package is built for Linux ARM64, and environments use more storage than a minimal venv. |
| Miniconda | Existing workflows or a specific need for Anaconda’s ecosystem and repositories. | Anaconda notes possible Raspberry Pi compatibility issues for some ARM64 builds; repository licensing considerations may apply. |
venv and pip |
Lightweight applications using available Python wheels. | Compiled scientific dependencies and binary compatibility can take more work to resolve. |
apt |
OS-integrated packages maintained for the installed Raspberry Pi OS release. | Package versions may lag, and packages are not isolated per project by default. |
| Docker or remote development | Reproducible deployment or training and experimentation on another machine. | Docker adds resource overhead and requires ARM-compatible images; remote work needs network access. |
Raspberry Pi’s Python documentation recommends using OS packages or virtual environments rather than altering system Python: Raspberry Pi OS documentation. On modern Raspberry Pi OS, direct system-wide pip installs may be blocked by the externally managed environment mechanism. Do not override that protection as a routine fix.
Install Miniforge on 64-bit Raspberry Pi OS
- Update the OS.
sudo apt update sudo apt full-upgrade -y sudo rebootAfter reboot, repeat the architecture checks above. Continue only when the system reports
aarch64and 64-bit userspace. - Install basic download and archive tools.
sudo apt install -y wget curl bzip2 ca-certificatesIf a later package needs to be compiled, add tools such as
git,build-essential, andpkg-configthen; they are not prerequisites for every installation. - Download the current Linux-aarch64 installer from the official release page. Use Miniforge releases and choose a file named like
Miniforge3-<version>-Linux-aarch64.sh. The official conda-forge download page documents the general installer pattern. - Run the installer. Substitute the exact filename you downloaded:
bash Miniforge3-<version>-Linux-aarch64.shReview and accept the license, choose an installation directory, and allow shell initialization when prompted.
- Load the shell configuration and verify the commands.
source ~/.bashrc conda --version mamba --versionIf the commands are still unavailable, open a new terminal and check that the installer initialized the shell you use.
- Keep the base environment from activating automatically (optional).
conda config --set auto_activate_base falseThis leaves project environments explicit rather than placing every new terminal in
base.
Miniforge includes Conda and Mamba, its faster alternative for solving environment dependencies. Its documentation also explains shell initialization and environment creation: Miniforge README.
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Create and test a practical machine-learning environment
For learning, data preparation, and small classical-ML work, start with packages available through conda-forge:
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mamba create -n rpi-ml -c conda-forge
python=3.12 numpy pandas scipy scikit-learn matplotlib jupyterlabActivate the environment before installing or running project packages:
conda activate rpi-mlConda-forge lists scikit-learn for Linux ARM64 on its package page. Package builds and versions change, so availability for linux-aarch64 matters more than a version number copied from an older tutorial.
Check that the imports work:
python - <<'PY'
import sys
import numpy
import pandas
import sklearn
print("Python:", sys.version)
print("NumPy:", numpy.__version__)
print("pandas:", pandas.__version__)
print("scikit-learn:", sklearn.__version__)
PYGood first projects include classifying sensor readings, fitting a small regression model, clustering a modest dataset, or preprocessing time-series data. Training small classical models locally is realistic; dataset size, available RAM, and runtime still matter.
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Start JupyterLab from the activated environment with:
jupyter lab --ip=0.0.0.0 --no-browserThis listens beyond the local machine. Do not expose an unauthenticated Jupyter server to an untrusted network; use authentication and appropriate network protections, or bind to localhost if remote access is unnecessary.
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Save the environment for later
Record the packages you explicitly requested with:
conda env export --from-history > environment.ymlRecreate the environment with:
conda env create -f environment.ymlFor a fuller package snapshot, use conda env export > environment-lock.yml. A full export can be platform-specific and may not reproduce exactly on a different architecture.
What machine learning can a Raspberry Pi handle?
- Learning and experimentation: Python, scientific libraries, notebooks, and small datasets are suitable uses.
- Classical ML: Small regression, classification, clustering, and feature-extraction workloads can run locally, with speed and memory depending on the model and data.
- Neural-network inference: Small or quantized models may be usable with an appropriate runtime. Successful package installation alone does not guarantee useful performance.
- Large-scale training: Generally a poor fit because of CPU performance, memory, storage bandwidth, and the absence of an NVIDIA CUDA-capable GPU.
For PyTorch, conda-forge lists a Linux ARM64 package on its PyTorch package page, but that does not guarantee every feature, extension, or model will work on every Pi. If you want to try it, make a separate environment:
mamba create -n rpi-torch -c conda-forge
python=3.12 pytorch torchvision torchaudio
conda activate rpi-torch
python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"A normal Raspberry Pi does not become CUDA-capable through this install: its VideoCore GPU is not an NVIDIA CUDA device. Treat PyTorch as an optional, architecture- and workload-dependent CPU path unless you have separately supported accelerator software.
Do not assume that the newest TensorFlow package will install through Conda on ARM64. TensorFlow availability depends on the exact OS, Python version, architecture, and installation route. For edge inference, investigate TensorFlow Lite, ONNX Runtime, vendor runtimes, or the Raspberry Pi AI software stack against your specific hardware and model.
When to use an accelerator or another machine
Raspberry Pi’s current AI software documentation describes AI-model operation for Raspberry Pi 5 with 64-bit Raspberry Pi OS Trixie and supported Hailo accelerator options. Check the Raspberry Pi AI documentation for compatible hardware and software; this is not a promise that arbitrary neural models will run or be accelerated.
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The Pi 5 uses a quad-core 64-bit Arm Cortex-A76 CPU, according to the Raspberry Pi 5 announcement. For long workloads, adequate cooling and a reliable power supply are practical considerations; consult the Raspberry Pi 5 product brief for hardware specifications. Avoid assuming a performance or temperature figure without measurements for your setup.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Troubleshoot common installation problems
The installer reports the wrong architecture
Run uname -m. Use the Linux-aarch64 installer only when it returns aarch64. If it returns armv7l or armv6l, install a 64-bit OS on compatible hardware before continuing. An x86_64 result means the shell is running on an x86-64 system, not an ARM Pi environment.
Miniconda installs, but packages fail to resolve or run
Possible causes include a missing ARM64 build, a dependency published only for linux-64, an unsupported Python version, CPU-specific assumptions, or a package too demanding to compile locally. Try Miniforge and conda-forge, create a fresh environment with a compatible Python version, and check the package’s Linux ARM64 availability. If no suitable binary exists, consider an OS package, a venv, building on another ARM64 machine, or deploying from a remote development system.
The Conda solver is slow or dependencies conflict
Use Mamba for environment creation and avoid casually mixing many package channels. A consistent channel choice reduces the chance of incompatible binary combinations:
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mamba create -n rpi-ml -c conda-forge
python=3.12 numpy pandas scikit-learnpip reports an externally managed environment
This usually means pip is targeting system Python. Install inside your Conda environment, or create a standard virtual environment:
conda activate rpi-ml
python -m pip install package-nameAlternatively:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install package-nameRaspberry Pi’s guidance is to avoid modifying system Python; see its OS documentation.
Installation runs out of memory or storage
Close desktop applications, prefer prebuilt packages, and use a Pi with more RAM if the workload requires it. Building packages locally can be more demanding than installing binaries. For larger datasets and model files, a USB 3 drive or SSD can be more suitable than relying exclusively on a heavily used microSD card. To remove unused package caches, run:
conda clean --allThis removes cached packages and other unused cache data, not the active environments themselves; packages may need to be downloaded again later.
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PyTorch installs but inference is too slow
Reduce model size, quantize where the model and runtime support it, choose a purpose-built inference runtime, or move training to a more capable machine. For supported edge workloads, consider a compatible Pi 5 accelerator; accelerator support is specific to its hardware and software stack.
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