Get started

Install spaCy

Operating system
Package manager
Trained pipelines
Select pipeline for
# Note M1 GPU support is experimental, see Thinc issue #792python -m venv .envsource .env/bin/activatesource .env/bin/activate.env\Scripts\activatepython -m venv .envsource .env/bin/activatesource .env/bin/activate.env\Scripts\activateconda create -n venvconda activate venvpip install -U pip setuptools wheelpip install -U pip setuptools wheelpip install -U spacyconda install -c conda-forge spacyconda install -c conda-forge cupyconda install -c conda-forge spacy-transformersgit clone spaCypip install -r requirements.txtpip install --no-build-isolation --editable .# packages only available via pippip install spacy-lookups-datapython -m spacy download ca_core_news_smpython -m spacy download zh_core_web_smpython -m spacy download hr_core_news_smpython -m spacy download da_core_news_smpython -m spacy download nl_core_news_smpython -m spacy download en_core_web_smpython -m spacy download fi_core_news_smpython -m spacy download fr_core_news_smpython -m spacy download de_core_news_smpython -m spacy download el_core_news_smpython -m spacy download it_core_news_smpython -m spacy download ja_core_news_smpython -m spacy download ko_core_news_smpython -m spacy download lt_core_news_smpython -m spacy download mk_core_news_smpython -m spacy download xx_ent_wiki_smpython -m spacy download nb_core_news_smpython -m spacy download pl_core_news_smpython -m spacy download pt_core_news_smpython -m spacy download ro_core_news_smpython -m spacy download ru_core_news_smpython -m spacy download sl_core_news_smpython -m spacy download es_core_news_smpython -m spacy download sv_core_news_smpython -m spacy download uk_core_news_sm

Installation instructions

spaCy is compatible with 64-bit CPython 3.7+ and runs on Unix/Linux, macOS/OS X and Windows. The latest spaCy releases are available over pip and conda.


Using pip, spaCy releases are available as source packages and binary wheels. Before you install spaCy and its dependencies, make sure that your pip, setuptools and wheel are up to date.

When using pip it is generally recommended to install packages in a virtual environment to avoid modifying system state:

spaCy also lets you install extra dependencies by specifying the following keywords in brackets, e.g. spacy[ja] or spacy[lookups,transformers] (with multiple comma-separated extras). See the [options.extras_require] section in spaCy’s setup.cfg for details on what’s included.

lookupsInstall spacy-lookups-data for data tables for lemmatization and lexeme normalization. The data is serialized with trained pipelines, so you only need this package if you want to train your own models.
transformersInstall spacy-transformers. The package will be installed automatically when you install a transformer-based pipeline.
cuda, …Install spaCy with GPU support provided by CuPy for your given CUDA version. See the GPU installation instructions for details and options.
appleInstall thinc-apple-ops to improve performance on an Apple M1.
ja, ko, thInstall additional dependencies required for tokenization for the languages.


Thanks to our great community, we’ve been able to re-add conda support. You can also install spaCy via conda-forge:

For the feedstock including the build recipe and configuration, check out this repository. Note that we currently don’t publish any pre-releases on conda.

Upgrading spaCy

When updating to a newer version of spaCy, it’s generally recommended to start with a clean virtual environment. If you’re upgrading to a new major version, make sure you have the latest compatible trained pipelines installed, and that there are no old and incompatible packages left over in your environment, as this can often lead to unexpected results and errors. If you’ve trained your own models, keep in mind that your train and runtime inputs must match. This means you’ll have to retrain your pipelines with the new version.

spaCy also provides a validate command, which lets you verify that all installed pipeline packages are compatible with your spaCy version. If incompatible packages are found, tips and installation instructions are printed. It’s recommended to run the command with python -m to make sure you’re executing the correct version of spaCy.

Run spaCy with GPU

As of v2.0, spaCy comes with neural network models that are implemented in our machine learning library, Thinc. For GPU support, we’ve been grateful to use the work of Chainer’s CuPy module, which provides a numpy-compatible interface for GPU arrays.

spaCy can be installed for a CUDA-compatible GPU by specifying spacy[cuda], spacy[cuda102], spacy[cuda112], spacy[cuda113], etc. If you know your CUDA version, using the more explicit specifier allows CuPy to be installed via wheel, saving some compilation time. The specifiers should install cupy.

Once you have a GPU-enabled installation, the best way to activate it is to call spacy.prefer_gpu or spacy.require_gpu() somewhere in your script before any pipelines have been loaded. require_gpu will raise an error if no GPU is available.

Compile from source

The other way to install spaCy is to clone its GitHub repository and build it from source. That is the common way if you want to make changes to the code base. You’ll need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip and git installed. The compiler part is the trickiest. How to do that depends on your system. See notes on Ubuntu, macOS / OS X and Windows for details.

To install with extras:

How to install compilers and related build tools:

  • Ubuntu: Install system-level dependencies via apt-get: sudo apt-get install build-essential python-dev git
  • macOS / OS X: Install a recent version of XCode, including the so-called “Command Line Tools”. macOS and OS X ship with Python and Git preinstalled.
  • Windows: Install a version of the Visual C++ Build Tools or Visual Studio Express that matches the version that was used to compile your Python interpreter.

Using build constraints when compiling from source

If you install spaCy from source or with pip for platforms where there are not binary wheels on PyPI, you may need to use build constraints if any package in your environment requires an older version of numpy.

If numpy gets downgraded from the most recent release at any point after you’ve compiled spacy, you might see an error that looks like this:

To fix this, create a new virtual environment and install spacy and all of its dependencies using build constraints. Build constraints specify an older version of numpy that is only used while compiling spacy, and then your runtime environment can use any newer version of numpy and still be compatible. In addition, use --no-cache-dir to ignore any previously cached wheels so that all relevant packages are recompiled from scratch:

Our build constraints currently specify the oldest supported numpy available on PyPI for x86_64 and aarch64. Depending on your platform and environment, you may want to customize the specific versions of numpy. For other platforms, you can have a look at SciPy’s oldest-supported-numpy package to see what the oldest recommended versions of numpy are.

(Warning: don’t use pip install -c constraints.txt instead of PIP_CONSTRAINT, since this isn’t applied to the isolated build environments.)

Additional options for developers

Some additional options may be useful for spaCy developers who are editing the source code and recompiling frequently.

  • Install in editable mode. Changes to .py files will be reflected as soon as the files are saved, but edits to Cython files (.pxd, .pyx) will require the pip install command below to be run again. Before installing in editable mode, be sure you have removed any previous installs with pip uninstall spacy, which you may need to run multiple times to remove all traces of earlier installs.

  • Build in parallel. Starting in v3.4.0, you can specify the number of build jobs with the environment variable SPACY_NUM_BUILD_JOBS:

  • For editable mode and parallel builds with python instead of pip (no longer recommended):

Visual Studio Code extension

spaCy extension demo

The spaCy VSCode Extension provides additional tooling and features for working with spaCy’s config files. Version 1.0.0 includes hover descriptions for registry functions, variables, and section names within the config as an installable extension.

  1. Install a supported version of Python on your system (>=3.7)
  2. Install the Python Extension for Visual Studio Code
  3. Create a virtual python environment
  4. Install all python requirements (spaCy >= 3.4.0 & pygls >= 1.0.0)
  5. Install spaCy extension for Visual Studio Code
  6. Select your python environment
  7. You are ready to work with .cfg files in spaCy!

Building an executable

The spaCy repository includes a Makefile that builds an executable zip file using pex (Python Executable). The executable includes spaCy and all its package dependencies and only requires the system Python at runtime. Building an executable .pex file is often the most convenient way to deploy spaCy, as it lets you separate the build from the deployment process.

You can configure the build process with the following environment variables:

SPACY_EXTRASAdditional Python packages to install alongside spaCy with optional version specifications. Should be a string that can be passed to pip install. See Makefile for defaults.
PYVERThe Python version to build against. This version needs to be available on your build and runtime machines. Defaults to 3.8.
WHEELHOUSEDirectory to store the wheel files during compilation. Defaults to ./wheelhouse.

Run tests

spaCy comes with an extensive test suite. In order to run the tests, you’ll usually want to clone the repository and build spaCy from source. This will also install the required development dependencies and test utilities defined in the requirements.txt.

Alternatively, you can find out where spaCy is installed and run pytest on that directory. Don’t forget to also install the test utilities via spaCy’s requirements.txt:

Calling pytest on the spaCy directory will run only the basic tests. The flag --slow is optional and enables additional tests that take longer.

Troubleshooting guide

This section collects some of the most common errors you may come across when installing, loading and using spaCy, as well as their solutions. Also see the Discussions FAQ Thread, which is updated more frequently and covers more transitory issues.

This usually means that the trained pipeline you’re trying to download does not exist, or isn’t available for your version of spaCy. Check the compatibility table to see which packages are available for your spaCy version. If you’re using an old version, consider upgrading to the latest release. Note that while spaCy supports tokenization for a variety of languages, not all of them come with trained pipelines. To only use the tokenizer, import the language’s Language class instead, for example from import French.

This error means that the spaCy module can’t be located on your system, or in your environment. Make sure you have spaCy installed. If you’re using a virtual environment, make sure it’s activated and check that spaCy is installed in that environment – otherwise, you’re trying to load a system installation. You can also run which python to find out where your Python executable is located.

As of spaCy v1.7, all trained pipelines can be installed as Python packages. This means that they’ll become importable modules of your application. If this fails, it’s usually a sign that the package is not installed in the current environment. Run pip list or pip freeze to check which pipeline packages you have installed, and install the correct package if necessary. If you’re importing a package manually at the top of a file, make sure to use the full name of the package.

This error may occur when running the spacy command from the command line. spaCy does not currently add an entry to your PATH environment variable, as this can lead to unexpected results, especially when using a virtual environment. Instead, spaCy adds an auto-alias that maps spacy to python -m spacy. If this is not working as expected, run the command with python -m, yourself – for example python -m spacy download en_core_web_sm. For more info on this, see the download command.

While this could technically have many causes, including spaCy being broken, the most likely one is that your script’s file or directory name is “shadowing” the module – e.g. your file is called, or a directory you’re importing from is called spacy. So, when using spaCy, never call anything else spacy.

If your training data only contained new entities and you didn’t mix in any examples the model previously recognized, it can cause the model to “forget” what it had previously learned. This is also referred to as the “catastrophic forgetting problem”. A solution is to pre-label some text, and mix it with the new text in your updates. You can also do this by running spaCy over some text, extracting a bunch of entities the model previously recognized correctly, and adding them to your training examples.

If you’re training models, writing them to disk, and versioning them with git, you might encounter this error when trying to load them in a Windows environment. This happens because a default install of Git for Windows is configured to automatically convert Unix-style end-of-line characters (LF) to Windows-style ones (CRLF) during file checkout (and the reverse when committing). While that’s mostly fine for text files, a trained model written to disk has some binary files that should not go through this conversion. When they do, you get the error above. You can fix it by either changing your core.autocrlf setting to "false", or by committing a .gitattributes file to your repository to tell Git on which files or folders it shouldn’t do LF-to-CRLF conversion, with an entry like path/to/spacy/model/** -text. After you’ve done either of these, clone your repository again.