--- title: Projects new: 3 menu: - ['Intro & Workflow', 'intro'] - ['Directory & Assets', 'directory'] - ['Custom Projects', 'custom'] - ['Integrations', 'integrations'] --- > #### 🪐 Project templates > > Our [`projects`](https://github.com/explosion/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`](/api/cli#project) command. For an overview of the available project templates, check out the [`projects`](https://github.com/explosion/projects) repo. spaCy projects also [integrate](#integrations) 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 {#intro} 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 {#clone} > #### 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`](/api/cli#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. ```cli python -m spacy project 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`](https://github.com/explosion/projects) repo. You can also use any private repo you have access to with Git. ### 2. Fetch the project assets {#assets} > #### project.yml > > ```yaml > 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`](/api/cli#project-assets) will fetch the project assets for you: ```cli $ cd some_example_project $ python -m spacy project assets ``` ### 3. Run a command {#run} > #### project.yml > > ```yaml > 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`](/api/cli#project-run). The following will run the command `preprocess` defined in the `project.yml`: ```cli $ python -m spacy project run preprocess ``` Commands can define their expected [dependencies and outputs](#deps-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 {#run-workfow} > #### project.yml > > ```yaml > 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`](/api/cli#train) to train your model on the converted data and if that's successful, run [`spacy package`](/api/cli#package) to turn the best model artifact into an installable Python package. The following command runs the workflow named `all` defined in the `project.yml`, and executes the commands it specifies, in order: ```cli $ python -m spacy project run all ``` Using the expected [dependencies and outputs](#deps-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](#dvc). The [`spacy project dvc`](/api/cli#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 {#directory} ### project.yml {#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. ```yaml 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](#project-files) 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`](/api/cli#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`](/api/cli#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`](/api/cli#project-run) command. | ### Dependencies and outputs {#deps-outputs} Each command defined in the `project.yml` can optionally define a list of dependencies and outputs. These are the files the command requires and creates. For example, a command for training a model may depend on a [`config.cfg`](/usage/training#config) 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. ```yaml ### project.yml commands: - name: train help: 'Train a spaCy model using the specified corpus and config' script: - 'python -m spacy train ./configs/config.cfg -o training/ --paths.train ./corpus/training.spacy --paths.dev ./corpus/evaluation.spacy' 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`](/api/cli#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](#dvc) 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 {#project-files} 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`](/api/cli#project-clone). Here's an example of a project directory: > #### project.yml > > > ```yaml > directories: ['assets', 'configs', 'corpus', 'metas', 'metrics', 'notebooks', 'packages', 'scripts', 'training'] > ``` ```yaml ### 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](#custom) can use any directories they need – the only file that's required for a project is the `project.yml`. --- ## Custom scripts and projects {#custom} 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`](https://docs.pytest.org/en/latest/), runs your tests and uses [`pytest-html`](https://github.com/pytest-dev/pytest-html) to export a test report: ```yaml ### 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 {#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`](https://typer.tiangolo.com/) for quick and easy command-line arguments that you can define via your `project.yml`: > #### About Typer > > [`typer`](https://typer.tiangolo.com/) 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. ```python ### 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`. ```yaml ### 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 {#custom-repo} The [`spacy project clone`](/api/cli#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. ```cli python -m spacy project clone your_project --repo https://github.com/you/repo ``` At a minimum, a valid project template needs to contain a [`project.yml`](#project-yml). It can also include [other files](/usage/projects#project-files), like custom scripts, a `requirements.txt` listing additional dependencies, [training configs](/usage/training#config) 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) (DVC), which integrates with spaCy projects. ### Working with private assets {#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`](/api/cli#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. ```yaml ### project.yml assets: - dest: 'assets/private_training_data.json' checksum: '63373dd656daa1fd3043ce166a59474c' - dest: 'assets/private_vectors.bin' checksum: '5113dc04e03f079525edd8df3f4f39e3' ``` ## Integrations {#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](https://dvc.org) (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](https://dvc.org/doc/install/macos#install-with-pip) to include support for remote storage like Google Cloud Storage, S3, Azure, SSH and more. ```bash $ pip install dvc # Install DVC $ git init # Initialize a Git repo $ dvc init # Initialize a DVC project ``` DVC enables usage analytics by default, so if you're working in a privacy-sensitive environment, make sure to [**opt-out manually**](https://dvc.org/doc/user-guide/analytics#opting-out). The [`spacy project dvc`](/api/cli#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`](https://dvc.org/doc/command-reference/add) to add and track assets and [`dvc repro`](https://dvc.org/doc/command-reference/repro) to reproduce the workflow or individual commands. ```cli $ python -m spacy project dvc [workflow_name] ``` DVC currently expects a single workflow per project, so when creating the config with [`spacy project dvc`](/api/cli#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. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat mattis pretium. --- ### Prodigy {#prodigy} [Prodigy](https://prodi.gy) 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](https://prodi.gy/docs/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`](https://prodi.gy/docs/recipes#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 > > ```cli > $ python -m spacy project run annotate > ``` ```yaml ### 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](https://prodi.gy/docs/recipes#textcat), [dependency parsing](https://prodi.gy/docs/recipes#dep), [part-of-speech tagging](https://prodi.gy/docs/recipes#pos) or fully [custom recipes](https://prodi.gy/docs/custom-recipes) – for instance, an A/B evaluation workflow that lets you compare two different models and their results. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat mattis pretium. --- ### Streamlit {#streamlit}
[Streamlit](https://streamlit.io) is a Python framework for building interactive data apps. The [`spacy-streamlit`](https://github.com/explosion/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. ```bash $ pip install spacy_streamlit ```
![](../images/spacy-streamlit.png) Using [`spacy-streamlit`](https://github.com/explosion/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](/usage/visualizers#ent) 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. ```python ### 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 > > ```cli > $ python -m spacy project run visualize > ``` ```yaml ### 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' ``` Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat mattis pretium. --- ### FastAPI {#fastapi} [FastAPI](https://fastapi.tiangolo.com/) is a modern high-performance framework for building REST APIs with Python, based on Python [type hints](https://fastapi.tiangolo.com/python-types/). 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. ```python # TODO: show an example that addresses some of the main concerns for serving ML (workers etc.) ``` > #### Example usage > > ```cli > $ python -m spacy project run serve > ``` ```yaml ### 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 ``` Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat mattis pretium. --- ### Ray {#ray} --- ### Weights & Biases {#wandb}