Get Started#
Depending on your preferred environment you can install Ivy in various ways:
Installing using pip#
The easiest way to set up Ivy is to install it using pip with the following command:
pip install ivy
Keep in mind that this won’t install any framework other than NumPy! (PyTorch, TensorFlow, etc…)
Installing from source#
You can also install Ivy from source if you want to take advantage of the latest changes:
git clone https://github.com/ivy-llc/ivy.git
cd ivy
pip install --user -e .
When installing from source, we recommend installing ivy’s dev dependencies with the commands:
pip install -r requirements/requirements.txt
pip install -r requirements/optional.txt
There are also other ‘requirements/optional…’ files in the ‘requirements’ folder that can be install the dependencies for specific hardware, such as GPU machines or Apple silicon.
If you are planning to contribute, you want to run the tests, or you are looking for more in-depth instructions, it’s probably best to check out the Contributing - Setting Up page, where OS-specific and IDE-specific instructions and video tutorials to install Ivy are available!
Docker#
If you prefer to use containers, we also have pre-built Docker images with all the supported frameworks and some relevant packages already installed, which you can pull from:
docker pull ivyllc/ivy:latest
Ivy Folder#
When importing Ivy for the first time, a .ivy folder will be created in your
working directory. If you want to keep this folder in a different location,
you can set an IVY_ROOT environment variable with the path of your .ivy folder.
Issues and Questions#
If you find any issue or bug while using the tracer and/or the transpiler, please
raise an issue in GitHub and add the tracer
or the transpiler label accordingly. A member of the team will get back to you ASAP!