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Yes—but only on a compatible 64-bit Raspberry Pi setup. A Raspberry Pi 3, 4, or 5 running a 64-bit operating system can use Conda-style environments for NumPy, pandas, SciPy, scikit-learn and selected deep-learning packages. For a new installation, Miniforge is usually the safer choice than Miniconda because it provides a dedicated Linux ARM64 installer and uses conda-forge, while Anaconda warns that some of its ARM64 Miniconda builds may target server-class ARM processors rather than Raspberry Pi.
The practical role of a Pi is small-model training, education, prototyping and edge inference—not replacing a desktop GPU or cloud machine for large neural-network training.
What you need before installing Conda
- Raspberry Pi 3, 4 or 5 with a 64-bit-capable processor. Pi 5 is the strongest general choice; Pi 3 works but is slower.
- 64-bit Raspberry Pi OS or Ubuntu for Raspberry Pi. A 64-bit processor running a 32-bit OS still cannot use the standard ARM64 installer.
- Reliable network access, adequate free storage and a suitable power supply.
- Cooling for sustained workloads, particularly on Raspberry Pi 5.
Raspberry Pi publishes 32-bit and 64-bit OS editions and advises against modifying the system Python installation. Check the Raspberry Pi OS documentation for current OS details. The Pi 5 uses a 2.4 GHz quad-core 64-bit Arm Cortex-A76 processor; sustained ML work should use appropriate power and cooling (Raspberry Pi 5 announcement).
Check architecture, OS and available resources
Run this checklist before downloading an installer:
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cat /etc/os-release
uname -m
getconf LONG_BIT
python3 --version
free -h
df -hFor the standard ARM64 Conda route, the important results are:
aarch64
64If uname -m reports armv7l or armv6l, your userspace is 32-bit. Do not force an ARM64 installer onto it; install a 64-bit OS on a compatible Pi instead. x86_64 means you are not currently on an ARM Raspberry Pi environment.
Miniconda, Miniforge, venv or apt?
| Option | Best use | Trade-offs |
|---|---|---|
| Miniforge | Conda environments on ARM64 | Dedicated Linux-aarch64 installer, conda-forge configuration and both conda and mamba; heavier than venv and package availability varies. |
| Miniconda | Existing Anaconda workflows or a required Anaconda repository | Familiar interface, but Anaconda documents possible Raspberry Pi incompatibilities for some Linux ARM64 builds. Repository licensing also depends on how Anaconda services are used; see Anaconda’s legal information. |
| venv + pip | Small applications using available wheels | Built into Python and lightweight, but compiled dependencies and version resolution can be more difficult. |
| apt | OS-integrated libraries | Maintained for your Raspberry Pi OS release, though versions can lag and isolation is limited. |
| Docker or a remote machine | Reproducible deployment or heavy training | Useful when the Pi is only the target device, but requires ARM-compatible images or another machine for training. |
Raspberry Pi OS Bookworm and later use an externally managed system Python. A direct system-wide pip install may therefore be blocked. Use apt, a Conda environment or a normal venv rather than overriding that protection.
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.
-
Install basic tools
sudo apt install -y wget curl bzip2 ca-certificatesIf a package later must compile locally, add
git build-essential pkg-configand any project-specific development headers. -
Download the official ARM64 installer
Use the official Miniforge releases page and choose the current file whose name ends in
Linux-aarch64.sh. Miniforge documents the ARM64 requirements and installer pattern at its requirements and installers page.Rank #2
Raspberry SC15184 Pi 4 Model B 2019 Quad Core 64 Bit WiFi Bluetooth (2GB)- Broadcom BCM2711, quad-core Cortex-A72 (ARM v8) 64-bit SoC @ 1. 5GHz
- 2. 4 GHz and 5. 0 GHz IEEE 802. 11b/g/n/ac wireless LAN, Bluetooth 5. 0, BLE
- 2 × USB 3. 0 ports, 2 x USB 2. 0 Ports
- 2 × micro HDMI ports supproting up to 4Kp60 video resolution
- Micro SD card slot for loading operating system and data storage
bash Miniforge3-<version>-Linux-aarch64.shReview the license, select an installation directory and allow shell initialization when prompted.
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Reload and verify
source ~/.bashrc conda --version mamba --versionIf the commands are not found, open a new terminal or inspect the shell-initialization message printed by the installer.
-
Keep projects out of the base environment
conda config --set auto_activate_base false
Miniconda follows a similar installation concept, but consult Anaconda’s Linux installer guide and system requirements if you specifically need it. Verify the downloaded installer according to the official instructions.
Create a practical machine-learning environment
For classical ML, create an isolated environment from conda-forge:
mamba create -n rpi-ml -c conda-forge
python=3.12 numpy pandas scipy scikit-learn matplotlib jupyterlab
conda activate rpi-mlYou can replace mamba with conda. Python 3.12 is an example, not a universal requirement; choose a version supported by the packages you need.
Confirm that imports work:
python - <<'PY'
import sys
import numpy, pandas, sklearn
print("Python:", sys.version)
print("NumPy:", numpy.__version__)
print("pandas:", pandas.__version__)
print("scikit-learn:", sklearn.__version__)
PYScikit-learn has a conda-forge ARM64 package (package page), making it a good first test. Suitable Pi workloads include regression, classification, decision trees, random forests, clustering, feature extraction and sensor-data analysis on small datasets.
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- 1GB, 2GB, 4GB or 8GB LPDDR4-3200 SDRAM (depending on model)
- 2.4 GHz and 5.0 GHz IEEE 802.11ac wireless, Bluetooth 5.0, BLE Gigabit Ethernet
- 2 USB 3.0 ports; 2 USB 2.0 ports.
- Raspberry Pi standard 40 pin GPIO header (fully backwards compatible with previous boards)
A tiny local training test
python - <<'PY'
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42, stratify=y
)
model = LogisticRegression(max_iter=500).fit(X_train, y_train)
print("Accuracy:", accuracy_score(y_test, model.predict(X_test)))
PYThis demonstrates a small classical model, not neural-network performance or a benchmark for your particular Pi.
Use JupyterLab safely
Start it locally with:
jupyter lab --ip=0.0.0.0 --no-browserExposing Jupyter beyond the local machine requires authentication, a strong token or password and sensible network controls. Do not treat an unauthenticated, network-wide Jupyter server as safe by default.
PyTorch and other neural-network packages
Conda-forge lists a Linux ARM64 PyTorch package (package page), but package availability does not guarantee that every model, extension or backend behaves identically on every Pi. CPU execution, memory use and model size remain constraints.
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python=3.12 pytorch torchvision torchaudio
conda activate rpi-torch
python -c "import torch; print(torch.__version__); print(torch.cuda.is_available())"On a normal Raspberry Pi, the VideoCore GPU is not an NVIDIA CUDA device, so a false CUDA result is expected. Do not assume that installing PyTorch creates GPU acceleration.
Do not promise that the newest TensorFlow package will install through Conda on Raspberry Pi. TensorFlow support depends on the exact OS, Python version, architecture and available wheel. For edge inference, TensorFlow Lite, ONNX Runtime, vendor runtimes or a supported accelerator stack may be more suitable than a full training framework.
What machine learning is realistic on a Pi?
- Good fit: learning Python and ML, tabular experiments, small datasets, feature engineering, classical-model training and CPU inference.
- Possible with care: small neural-network inference, especially with reduced or quantized models and an optimized runtime.
- Poor fit: training large neural networks, processing large datasets repeatedly or expecting CUDA-class throughput.
Raspberry Pi’s current AI documentation describes supported AI-model operation around Raspberry Pi 5, 64-bit Raspberry Pi OS Trixie and compatible Hailo accelerator options. See the Raspberry Pi AI documentation. An accelerator can improve supported edge-inference workloads, but it does not turn the Pi into a general-purpose training workstation.
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Storage, memory, power and cooling
There is no universal storage minimum: package caches, Jupyter, PyTorch, datasets and model files can exceed the installer size by a wide margin. Prefer fast, reliable storage; a USB 3 SSD is often more suitable for large datasets than a heavily used microSD card.
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Remove unused Conda caches when storage becomes tight:
conda clean --allThis removes cached packages and installers; it does not remove active environments, but future installs may need to download packages again. Close unnecessary applications and monitor memory during solves and builds. Sustained Pi 5 workloads should use suitable power delivery and cooling; determine throttling from your actual hardware and workload rather than assuming a temperature or performance figure.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reproducibility and deployment
Record a project’s requested dependencies:
conda env export --from-history > environment.yml
conda env create -f environment.ymlFor a more exact snapshot:
conda env export > environment-lock.ymlExact exports can contain platform-specific builds and may not recreate identically on another architecture. If the Pi is only a deployment target, train on a desktop or remote machine and copy a compact, compatible inference model to the Pi.
Troubleshooting common failures
“Wrong architecture” or the installer will not run
Check uname -m. Use the ARM64 installer only for aarch64. For armv7l or armv6l, move to a compatible 64-bit OS; for x86_64, you are on a different architecture.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Miniconda installs but packages fail
- Switch to Miniforge and use conda-forge consistently.
- Check that the package has a
linux-aarch64build. - Try a supported Python version in a fresh environment.
- Use
apt,venvor a prebuilt wheel when no Conda build exists. - Build on another ARM64 machine if local compilation is impractical.
The Conda solver is slow
Use Mamba and avoid casually mixing multiple channels:
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mamba create -n rpi-ml -c conda-forge python=3.12 numpy pandas scikit-learnpip reports an externally managed environment
Activate the Conda environment first:
conda activate rpi-ml
python -m pip install package-nameOr use a standard virtual environment:
python3 -m venv .venv
source .venv/bin/activate
python -m pip install package-nameDo not make --break-system-packages the default remedy; overriding OS-managed Python can damage system packages.
Compilation runs out of memory
Use a Pi with more RAM, close desktop programs, increase swap cautiously, prefer prebuilt binaries or compile on another ARM64 system. If a neural model is too slow, reduce or quantize it, use an optimized runtime or accelerator, and deploy only inference.
Should you buy a different Pi or an accelerator?
The Raspberry Pi 5 is the most appropriate current Pi for ARM64 ML experimentation. The keyboard-integrated Raspberry Pi 500 uses the same processor family but is less convenient for compact robotics, camera and GPIO deployments. Raspberry Pi’s AI Kit product brief describes supported accelerator hardware; buy one only when your model and software stack are supported. For ordinary scikit-learn or tabular work, the extra accelerator is unnecessary.
Historical launch prices for Pi 5 were $60 for 4 GB and $80 for 8 GB in September 2023, excluding local taxes; those figures are not current 2026 street prices. Check the official product page or approved resellers for current availability.
Frequently Asked Questions
Can I install Miniconda on a 32-bit Raspberry Pi OS installation?
No. The standard installer discussed here targets Linux ARM64. A compatible 64-bit Pi running a 32-bit OS must be reinstalled with a 64-bit operating system, or you should use apt and venv-based alternatives.
Is Miniforge free to use?
The Miniforge installer is an open-source project available from conda-forge. Miniconda and access to Anaconda-hosted repositories have separate terms, so organizations should review Anaconda’s current legal information.
Will PyTorch use the Raspberry Pi GPU?
Not through CUDA. A conda-forge ARM64 PyTorch package may run on the CPU, but the Pi’s VideoCore GPU is not an NVIDIA CUDA device. Supported Hailo hardware uses a separate, model-specific software path.
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