Installation
Everything you need to know about installation of Supervisely SDK for Python
This part of the documentation covers the installation of Supervisely SDK for Python. The first step to using any software package is getting it properly installed.
Prerequisites
Python
You should use 🐍 Python 3.8 or greater, which can be installed either through the Anaconda package manager, Homebrew, or the Python website.
Libraries
Installation
If you're working with a custom Supervisely instance, please refer to the compatibility table below to ensure that you're using the correct version of the Python SDK, which supports your instance. Note: the latest version of the SDK always supports the latest version of Supervisely, so it's recommended to upgrade both from time to time.
Compatibility table
>=6.12.5
supervisely>=6.73.226
<=6.12.2
supervisely>=6.73.222
<=6.11.19
supervisely>=6.73.199
<=6.11.16
supervisely>=6.73.184
<=6.11.10
supervisely<=6.73.166
<=6.11.8
supervisely<=6.73.159
<=6.10.0
supervisely<=6.73.126
<=6.9.31
supervisely<=6.73.123
<=6.9.22
supervisely<=6.73.90
<=6.9.18
supervisely<=6.73.81
<=6.9.13
supervisely<=6.73.76
<=6.9.11
supervisely<=6.72.70
Pip
The latest stable version is available on PyPI. Either add supervisely
to your requirements.txt
file or install with pip:
To install a specific version, use the following command:
We are constantly updating our SDK by adding new features and fixing bugs. So if it is already installed on your dev environment, use the installation command with --upgrade
flag:
Source code
Supervisely is actively developed on GitHub, where the code is always available.
You can either clone the public repository:
Or, download the zipball:
Once you have a copy of the source, you can embed it in your own Python package, or install it into your site-packages easily:
VENV
Here is a tiny bash script, that you can place at the root of your repository (for example create_venv.sh
). It creates venv - “virtual” isolated Python installation and installs packages into that virtual installation. When you switch projects, you can simply create a new virtual environment and not have to worry about breaking the packages installed in the other environments. It is always recommended to use a virtual environment while developing Python applications.
Docker image
Supervisely SDK for python also has prebuilt docker image with everything already installed.
You can use the latest version
or some specific on that has completely the same tag as PIP releases, for example:
Here are the links to dockerfiles (base image, result image) where you can find the complete list of all recommended dependencies.
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