spaCy/website/docs/usage/projects.md

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Projects	3	Intro & Workflow intro Directory & Assets directory Custom Projects custom Integrations integrations

🪐 Project templates

Our projects repo includes various project templates for different NLP tasks, models, workflows and integrations that you can clone and run. The easiest way to get started is to pick a template, clone it and start modifying it!

spaCy projects let you manage and share end-to-end spaCy workflows for different use cases and domains, and orchestrate training, packaging and serving your custom models. You can start off by cloning a pre-defined project template, adjust it to fit your needs, load in your data, train a model, export it as a Python package and share the project templates with your team. spaCy projects can be used via the new spacy project command. For an overview of the available project templates, check out the projects repo. spaCy projects also integrate with many other cool machine learning and data science tools to track and manage your data and experiments, iterate on demos and prototypes and ship your models into production.

Introduction and workflow

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spaCy projects make it easy to integrate with many other awesome tools in the data science and machine learning ecosystem to track and manage your data and experiments, iterate on demos and prototypes and ship your models into production.

Manage and version your data Create labelled training data Visualize and demo your models Serve your models and host APIs Distributed and parallel training Track your experiments and results

1. Clone a project template

Cloning under the hood

To clone a project, spaCy calls into git and uses the "sparse checkout" feature to only clone the relevant directory or directories.

The spacy project clone command clones an existing project template and copies the files to a local directory. You can then run the project, e.g. to train a model and edit the commands and scripts to build fully custom workflows.

$ python -m spacy clone some_example_project

By default, the project will be cloned into the current working directory. You can specify an optional second argument to define the output directory. The --repo option lets you define a custom repo to clone from, if you don't want to use the spaCy projects repo. You can also use any private repo you have access to with Git.

2. Fetch the project assets

project.yml

assets:
  - dest: 'assets/training.spacy'
    url: 'https://example.com/data.spacy'
    checksum: '63373dd656daa1fd3043ce166a59474c'

Assets are data files your project needs – for example, the training and evaluation data or pretrained vectors and embeddings to initialize your model with. Each project template comes with a project.yml that defines the assets to download and where to put them. The spacy project assets will fetch the project assets for you:

cd some_example_project
python -m spacy project assets

3. Run a command

project.yml

commands:
  - name: preprocess
    help: "Convert the input data to spaCy's format"
    script:
      - 'python -m spacy convert assets/train.conllu corpus/'
      - 'python -m spacy convert assets/eval.conllu corpus/'
    deps:
      - 'assets/train.conllu'
      - 'assets/eval.conllu'
    outputs:
      - 'corpus/train.spacy'
      - 'corpus/eval.spacy'

Commands consist of one or more steps and can be run with spacy project run. The following will run the command preprocess defined in the project.yml:

$ python -m spacy project run preprocess

Commands can define their expected dependencies and outputs using the deps (files the commands require) and outputs (files the commands create) keys. This allows your project to track changes and determine whether a command needs to be re-run. For instance, if your input data changes, you want to re-run the preprocess command. But if nothing changed, this step can be skipped. You can also set --force to force re-running a command, or --dry to perform a "dry run" and see what would happen (without actually running the script).

4. Run a workflow

project.yml

workflows:
  all:
    - preprocess
    - train
    - package

Workflows are series of commands that are run in order and often depend on each other. For instance, to generate a packaged model, you might start by converting your data, then run spacy train to train your model on the converted data and if that's successful, run spacy package to turn the best model artifact into an installable Python package. The following command run the workflow named all defined in the project.yml, and execute the commands it specifies, in order:

$ python -m spacy project run all

Using the expected dependencies and outputs defined in the commands, spaCy can determine whether to re-run a command (if its inputs or outputs have changed) or whether to skip it. If you're looking to implement more advanced data pipelines and track your changes in Git, check out the Data Version Control (DVC) integration. The spacy project dvc command generates a DVC config file from a workflow defined in your project.yml so you can manage your spaCy project as a DVC repo.

Project directory and assets

project.yml

The project.yml defines the assets a project depends on, like datasets and pretrained weights, as well as a series of commands that can be run separately or as a workflow – for instance, to preprocess the data, convert it to spaCy's format, train a model, evaluate it and export metrics, package it and spin up a quick web demo. It looks pretty similar to a config file used to define CI pipelines.

https://github.com/explosion/spacy-boilerplates/blob/master/ner_fashion/project.yml

Section	Description
variables	A dictionary of variables that can be referenced in paths, URLs and scripts. For example, {NAME} will use the value of the variable NAME.
directories	An optional list of directories that should be created in the project for assets, training outputs, metrics etc. spaCy will make sure that these directories always exist.
assets	A list of assets that can be fetched with the project assets command. url defines a URL or local path, dest is the destination file relative to the project directory, and an optional checksum ensures that an error is raised if the file's checksum doesn't match.
workflows	A dictionary of workflow names, mapped to a list of command names, to execute in order. Workflows can be run with the project run command.
commands	A list of named commands. A command can define an optional help message (shown in the CLI when the user adds --help) and the script, a list of commands to run. The deps and outputs let you define the created file the command depends on and produces, respectively. This lets spaCy determine whether a command needs to be re-run because its dependencies or outputs changed. Commands can be run as part of a workflow, or separately with the project run command.

Dependencies and outputs

Each command defined in the project.yml can optionally define a list of dependencies and outputs. These are the files the commands requires and creates. For example, a command for training a model may depend on a config.cfg and the training and evaluation data, and it will export a directory model-best, containing the best model, which you can then re-use in other commands.

### project.yml
commands:
  - name: train
    help: 'Train a spaCy model using the specified corpus and config'
    script:
      - 'python -m spacy train ./corpus/training.spacy ./corpus/evaluation.spacy ./configs/config.cfg -o training/'
    deps:
      - 'configs/config.cfg'
      - 'corpus/training.spacy'
      - 'corpus/evaluation.spacy'
    outputs:
      - 'training/model-best'

Re-running vs. skipping

Under the hood, spaCy uses a project.lock lockfile that stores the details for each command, as well as its dependencies and outputs and their checksums. It's updated on each run. If any of this information changes, the command will be re-run. Otherwise, it will be skipped.

If you're running a command and it depends on files that are missing, spaCy will show you an error. If a command defines dependencies and outputs that haven't changed since the last run, the command will be skipped. This means that you're only re-running commands if they need to be re-run. Commands can also set no_skip: true if they should never be skipped – for example commands that run tests. Commands without outputs are also never skipped. To force re-running a command or workflow, even if nothing changed, you can set the --force flag.

Note that spacy project doesn't compile any dependency graphs based on the dependencies and outputs, and won't re-run previous steps automatically. For instance, if you only run the command train that depends on data created by preprocess and those files are missing, spaCy will show an error – it won't just re-run preprocess. If you're looking for more advanced data management, check out the Data Version Control (DVC) integration integration. If you're planning on integrating your spaCy project with DVC, you can also use outputs_no_cache instead of outputs to define outputs that won't be cached or tracked.

Files and directory structure

The project.yml can define a list of directories that should be created within a project – for instance, assets, training, corpus and so on. spaCy will make sure that these directories are always available, so your commands can write to and read from them. Project directories will also include all files and directories copied from the project template with spacy project clone. Here's an example of a project directory:

project.yml

directories: ['assets', 'configs', 'corpus', 'metas', 'metrics', 'notebooks', 'packages', 'scripts', 'training']

### Example project directory
├── project.yml          # the project settings
├── project.lock         # lockfile that tracks inputs/outputs
├── assets/              # downloaded data assets
├── configs/             # model config.cfg files used for training
├── corpus/              # output directory for training corpus
├── metas/               # model meta.json templates used for packaging
├── metrics/             # output directory for evaluation metrics
├── notebooks/           # directory for Jupyter notebooks
├── packages/            # output directory for model Python packages
├── scripts/             # directory for scripts, e.g. referenced in commands
├── training/            # output directory for trained models
└── ...                  # any other files, like a requirements.txt etc.

If you don't want a project to create a directory, you can delete it and remove its entry from the project.yml – just make sure it's not required by any of the commands. Custom templates can use any directories they need – the only file that's required for a project is the project.yml.

---

Custom scripts and projects

The project.yml lets you define any custom commands and run them as part of your training, evaluation or deployment workflows. The script section defines a list of commands that are called in a subprocess, in order. This lets you execute other Python scripts or command-line tools. Let's say you've written a few integration tests that load the best model produced by the training command and check that it works correctly. You can now define a test command that calls into pytest, runs your tests and uses pytest-html to export a test report:

### project.yml
commands:
  - name: test
    help: 'Test the trained model'
    script:
      - 'pip install pytest pytest-html'
      - 'python -m pytest ./scripts/tests --html=metrics/test-report.html'
    deps:
      - 'training/model-best'
    outputs:
      - 'metrics/test-report.html'
    no_skip: true

Adding training/model-best to the command's deps lets you ensure that the file is available. If not, spaCy will show an error and the command won't run. Setting no_skip: true means that the command will always run, even if the dependencies (the trained model) hasn't changed. This makes sense here, because you typically don't want to skip your tests.

Writing custom scripts

Your project commands can include any custom scripts – essentially, anything you can run from the command line. Here's an example of a custom script that uses typer for quick and easy command-line arguments that you can define via your project.yml:

About Typer

typer is a modern library for building Python CLIs using type hints. It's a dependency of spaCy, so it will already be pre-installed in your environment. Function arguments automatically become positional CLI arguments and using Python type hints, you can define the value types. For instance, batch_size: int means that the value provided via the command line is converted to an integer.

### scripts/custom_evaluation.py
import typer

def custom_evaluation(batch_size: int = 128, model_path: str, data_path: str):
    # The arguments are now available as positional CLI arguments
    print(batch_size, model_path, data_path)

if __name__ == "__main__":
    typer.run(custom_evaluation)

In your project.yml, you can then run the script by calling python scripts/custom_evaluation.py with the function arguments. You can also use the variables section to define reusable variables that will be substituted in commands, paths and URLs. In this example, the BATCH_SIZE is defined as a variable will be added in place of {BATCH_SIZE} in the script.

Calling into Python

If any of your command scripts call into python, spaCy will take care of replacing that with your sys.executable, to make sure you're executing everything with the same Python (not some other Python installed on your system). It also normalizes references to python3, pip3 and pip.

### project.yml
variables:
  BATCH_SIZE: 128

commands:
  - name: evaluate
    script:
      - 'python scripts/custom_evaluation.py {BATCH_SIZE} ./training/model-best ./corpus/eval.json'
    deps:
      - 'training/model-best'
      - 'corpus/eval.json'

Cloning from your own repo

The spacy project clone command lets you customize the repo to clone from using the --repo option. It calls into git, so you'll be able to clone from any repo that you have access to, including private repos.

$ python -m spacy project your_project --repo https://github.com/you/repo

At a minimum, a valid project template needs to contain a project.yml. It can also include other files, like custom scripts, a requirements.txt listing additional dependencies, training configs and model meta templates, or Jupyter notebooks with usage examples.

It's typically not a good idea to check large data assets, trained models or other artifacts into a Git repo and you should exclude them from your project template by adding a .gitignore. If you want to version your data and models, check out Data Version Control (DVC), which integrates with spaCy projects.

Working with private assets

For many projects, the datasets and weights you're working with might be company-internal and not available via a public URL. In that case, you can specify the destination paths and a checksum, and leave out the URL. When your teammates clone and run your project, they can place the files in the respective directory themselves. The spacy project assets command will alert about missing files and mismatched checksums, so you can ensure that others are running your project with the same data.

### project.yml
assets:
  - dest: 'assets/private_training_data.json'
    checksum: '63373dd656daa1fd3043ce166a59474c'
  - dest: 'assets/private_vectors.bin'
    checksum: '5113dc04e03f079525edd8df3f4f39e3'

Integrations

Data Version Control (DVC) {#dvc}

Data assets like training corpora or pretrained weights are at the core of any NLP project, but they're often difficult to manage: you can't just check them into your Git repo to version and keep track of them. And if you have multiple steps that depend on each other, like a preprocessing step that generates your training data, you need to make sure the data is always up-to-date, and re-run all steps of your process every time, just to be safe.

Data Version Control (DVC) is a standalone open-source tool that integrates into your workflow like Git, builds a dependency graph for your data pipelines and tracks and caches your data files. If you're downloading data from an external source, like a storage bucket, DVC can tell whether the resource has changed. It can also determine whether to re-run a step, depending on whether its input have changed or not. All metadata can be checked into a Git repo, so you'll always be able to reproduce your experiments.

To set up DVC, install the package and initialize your spaCy project as a Git and DVC repo. You can also customize your DVC installation to include support for remote storage like Google Cloud Storage, S3, Azure, SSH and more.

pip install dvc   # Install DVC
git init          # Initialize a Git repo
dvc init          # Initialize a DVC project

The spacy project dvc command creates a dvc.yaml config file based on a workflow defined in your project.yml. Whenever you update your project, you can re-run the command to update your DVC config. You can then manage your spaCy project like any other DVC project, run dvc add to add and track assets and dvc repro to reproduce the workflow or individual commands.

$ python -m spacy project dvc [workflow name]

DVC currently expects a single workflow per project, so when creating the config with spacy project dvc, you need to specify the name of a workflow defined in your project.yml. You can still use multiple workflows, but only one can be tracked by DVC.

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Prodigy {#prodigy}

Prodigy is a modern annotation tool for creating training data for machine learning models, developed by us. It integrates with spaCy out-of-the-box and provides many different annotation recipes for a variety of NLP tasks, with and without a model in the loop. If Prodigy is installed in your project, you can start the annotation server from your project.yml for a tight feedback loop between data development and training.

The following example command starts the Prodigy app using the ner.correct recipe and streams in suggestions for the given entity labels produced by a pretrained model. You can then correct the suggestions manually in the UI. After you save and exit the server, the full dataset is exported in spaCy's format and split into a training and evaluation set.

Example usage

$ python -m spacy project run annotate

### project.yml
variables:
  PRODIGY_DATASET: 'ner_articles'
  PRODIGY_LABELS: 'PERSON,ORG,PRODUCT'
  PRODIGY_MODEL: 'en_core_web_md'

commands:
  - name: annotate
  - script:
      - 'python -m prodigy ner.correct {PRODIGY_DATASET} ./assets/raw_data.jsonl {PRODIGY_MODEL} --labels {PRODIGY_LABELS}'
      - 'python -m prodigy data-to-spacy ./corpus/train.json ./corpus/eval.json --ner {PRODIGY_DATASET}'
      - 'python -m spacy convert ./corpus/train.json ./corpus/train.spacy'
      - 'python -m spacy convert ./corpus/eval.json ./corpus/eval.spacy'
  - deps:
      - 'assets/raw_data.jsonl'
  - outputs:
      - 'corpus/train.spacy'
      - 'corpus/eval.spacy'

You can use the same approach for other types of projects and annotation workflows, including text classification, dependency parsing, part-of-speech tagging or fully custom recipes – for instance, an A/B evaluation workflow that lets you compare two different models and their results.

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Streamlit {#streamlit}

Streamlit is a Python framework for building interactive data apps. The spacy-streamlit package helps you integrate spaCy visualizations into your Streamlit apps and quickly spin up demos to explore your models interactively. It includes a full embedded visualizer, as well as individual components.

$ pip install spacy_streamlit

Using spacy-streamlit, your projects can easily define their own scripts that spin up an interactive visualizer, using the latest model you trained, or a selection of models so you can compare their results. The following script starts an NER visualizer and takes two positional command-line argument you can pass in from your config.yml: a comma-separated list of model paths and an example text to use as the default text.

### scripts/visualize.py
import spacy_streamlit
import sys

DEFAULT_TEXT = sys.argv[2] if len(sys.argv) >= 3 else ""
MODELS = [name.strip() for name in sys.argv[1].split(",")]
spacy_streamlit.visualize(MODELS, DEFAULT_TEXT, visualizers=["ner"])

Example usage

$ python -m spacy project run visualize

### project.yml
commands:
  - name: visualize
    help: "Visualize the model's output interactively using Streamlit"
    script:
      - 'streamlit run ./scripts/visualize.py ./training/model-best "I like Adidas shoes."'
    deps:
      - 'training/model-best'

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FastAPI {#fastapi}

FastAPI is a modern high-performance framework for building REST APIs with Python, based on Python type hints. It's become a popular library for serving machine learning models and you can use it in your spaCy projects to quickly serve up a trained model and make it available behind a REST API.

# TODO: show an example that addresses some of the main concerns for serving ML (workers etc.)

Example usage

$ python -m spacy project run visualize

### project.yml
commands:
  - name: serve
    help: "Serve the trained model with FastAPI"
    script:
      - 'python ./scripts/serve.py ./training/model-best'
    deps:
      - 'training/model-best'
    no_skip: true

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Ray {#ray}

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Weights & Biases {#wandb}