How to install GQLAlchemy
There are two main ways of installing GQLAlchemy: with package managers such as pip and Poetry, and by building it from source.
To install GQLAlchemy, you will need the following:
- Python 3.8 - 3.11
pip install --user pymgclient
Python 3.11 users: On Windows, GQLAlchemy is not yet compatible with this Python version. Linux users can install GQLAlchemy without the DGL extra (due to its dependencies not supporting Python 3.11 yet). If this is currently a blocker for you, please let us know by opening an issue.
Install with pip
After you’ve installed the prerequisites, run the following command to install GQLAlchemy:
pip install gqlalchemy
With the above command, you get the default GQLAlchemy installation which doesn’t include import/export support for certain formats (see below). To get additional import/export capabilities, use one of the following install options:
pip install gqlalchemy[arrow] # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats pip install gqlalchemy[dgl] # DGL support (also includes torch) pip install gqlalchemy[docker] # Docker support pip install gqlalchemy[all] # All of the above
If you intend to use GQLAlchemy with PyTorch Geometric support, that library must be installed manually:
pip install gqlalchemy[torch_pyg] # prerequisite pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cpu.html"
If you are using the zsh terminal, surround
gqlalchemy[$extras] with quotes:
pip install 'gqlalchemy[arrow]'
Build from source
Clone or download the GQLAlchemy source code locally and run the following command to build it from source with Poetry:
poetry install --all-extras
poetry install --all-extras command installs GQLAlchemy with all extras
(optional dependencies). Alternatively, you can use the
-E option to define
what extras to install:
poetry install # No extras poetry install -E arrow # Support for the CSV, Parquet, ORC and IPC/Feather/Arrow formats poetry install -E dgl # DGL support (also includes torch) poetry install -E docker # Docker support
To run the tests, make sure you have an active Memgraph instance, and execute one of the following commands:
poetry run pytest . -k "not slow" # If all extras installed poetry run pytest . -k "not slow and not extras" # Otherwise
If you’ve installed only certain extras, it’s also possible to run their associated tests:
poetry run pytest . -k "arrow" poetry run pytest . -k "dgl" poetry run pytest . -k "docker"