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989 lines
43 KiB
Markdown
989 lines
43 KiB
Markdown
---
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title: Projects
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new: 3
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menu:
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- ['Intro & Workflow', 'intro']
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- ['Directory & Assets', 'directory']
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- ['Custom Projects', 'custom']
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- ['Remote Storage', 'remote']
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- ['Integrations', 'integrations']
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---
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## Introduction and workflow {#intro hidden="true"}
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> #### 🪐 Project templates
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>
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> Our [`projects`](https://github.com/explosion/projects) repo includes various
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> project templates for different NLP tasks, models, workflows and integrations
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> that you can clone and run. The easiest way to get started is to pick a
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> template, clone it and start modifying it!
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spaCy projects let you manage and share **end-to-end spaCy workflows** for
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different **use cases and domains**, and orchestrate training, packaging and
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serving your custom pipelines. You can start off by cloning a pre-defined
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project template, adjust it to fit your needs, load in your data, train a
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pipeline, export it as a Python package, upload your outputs to a remote storage
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and share your results with your team. spaCy projects can be used via the new
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[`spacy project`](/api/cli#project) command and we provide templates in our
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[`projects`](https://github.com/explosion/projects) repo.
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<!-- TODO:
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<Project id="some_example_project">
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Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
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sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
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mattis pretium.
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</Project>
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-->
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spaCy projects make it easy to integrate with many other **awesome tools** in
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the data science and machine learning ecosystem to track and manage your data
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and experiments, iterate on demos and prototypes and ship your models into
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production.
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<Grid narrow cols={3}>
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<Integration title="DVC" logo="dvc" url="#dvc">Manage and version your data</Integration>
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<Integration title="Prodigy" logo="prodigy" url="#prodigy">Create labelled training data</Integration>
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<Integration title="Streamlit" logo="streamlit" url="#streamlit">Visualize and demo your pipelines</Integration>
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<Integration title="FastAPI" logo="fastapi" url="#fastapi">Serve your models and host APIs</Integration>
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<Integration title="Ray" logo="ray" url="#ray">Distributed and parallel training</Integration>
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<Integration title="Weights & Biases" logo="wandb" url="#wandb">Track your experiments and results</Integration>
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</Grid>
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### 1. Clone a project template {#clone}
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> #### Cloning under the hood
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>
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> To clone a project, spaCy calls into `git` and uses the "sparse checkout"
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> feature to only clone the relevant directory or directories.
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The [`spacy project clone`](/api/cli#project-clone) command clones an existing
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project template and copies the files to a local directory. You can then run the
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project, e.g. to train a pipeline and edit the commands and scripts to build
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fully custom workflows.
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<!-- TODO: update with real example project -->
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```cli
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python -m spacy project clone some_example_project
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```
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By default, the project will be cloned into the current working directory. You
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can specify an optional second argument to define the output directory. The
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`--repo` option lets you define a custom repo to clone from if you don't want
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to use the spaCy [`projects`](https://github.com/explosion/projects) repo. You
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can also use any private repo you have access to with Git.
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### 2. Fetch the project assets {#assets}
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> #### project.yml
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>
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> ```yaml
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> assets:
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> - dest: 'assets/training.spacy'
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> url: 'https://example.com/data.spacy'
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> checksum: '63373dd656daa1fd3043ce166a59474c'
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> - dest: 'assets/development.spacy'
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> git:
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> repo: 'https://github.com/example/repo'
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> branch: 'master'
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> path: 'path/development.spacy'
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> checksum: '5113dc04e03f079525edd8df3f4f39e3'
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> ```
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Assets are data files your project needs – for example, the training and
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evaluation data or pretrained vectors and embeddings to initialize your model
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with. Each project template comes with a `project.yml` that defines the assets
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to download and where to put them. The
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[`spacy project assets`](/api/cli#project-assets) will fetch the project assets
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for you:
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```cli
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$ cd some_example_project
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$ python -m spacy project assets
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```
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Asset URLs can be a number of different protocols: HTTP, HTTPS, FTP, SSH, and
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even cloud storage such as GCS and S3. You can also fetch assets using git, by
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replacing the `url` string with a `git` block. spaCy will use Git's "sparse
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checkout" feature to avoid downloading the whole repository.
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### 3. Run a command {#run}
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> #### project.yml
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>
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> ```yaml
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> commands:
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> - name: preprocess
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> help: "Convert the input data to spaCy's format"
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> script:
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> - 'python -m spacy convert assets/train.conllu corpus/'
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> - 'python -m spacy convert assets/eval.conllu corpus/'
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> deps:
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> - 'assets/train.conllu'
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> - 'assets/eval.conllu'
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> outputs:
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> - 'corpus/train.spacy'
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> - 'corpus/eval.spacy'
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> ```
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Commands consist of one or more steps and can be run with
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[`spacy project run`](/api/cli#project-run). The following will run the command
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`preprocess` defined in the `project.yml`:
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```cli
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$ python -m spacy project run preprocess
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```
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Commands can define their expected [dependencies and outputs](#deps-outputs)
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using the `deps` (files the commands require) and `outputs` (files the commands
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create) keys. This allows your project to track changes and determine whether a
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command needs to be re-run. For instance, if your input data changes, you want
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to re-run the `preprocess` command. But if nothing changed, this step can be
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skipped. You can also set `--force` to force re-running a command, or `--dry` to
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perform a "dry run" and see what would happen (without actually running the
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script).
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### 4. Run a workflow {#run-workfow}
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> #### project.yml
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>
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> ```yaml
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> workflows:
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> all:
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> - preprocess
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> - train
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> - package
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> ```
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Workflows are series of commands that are run in order and often depend on each
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other. For instance, to generate a pipeline package, you might start by
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converting your data, then run [`spacy train`](/api/cli#train) to train your
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pipeline on the converted data and if that's successful, run
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[`spacy package`](/api/cli#package) to turn the best trained artifact into an
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installable Python package. The following command runs the workflow named `all`
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defined in the `project.yml`, and executes the commands it specifies, in order:
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```cli
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$ python -m spacy project run all
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```
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Using the expected [dependencies and outputs](#deps-outputs) defined in the
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commands, spaCy can determine whether to re-run a command (if its inputs or
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outputs have changed) or whether to skip it. If you're looking to implement more
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advanced data pipelines and track your changes in Git, check out the
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[Data Version Control (DVC) integration](#dvc). The
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[`spacy project dvc`](/api/cli#project-dvc) command generates a DVC config file
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from a workflow defined in your `project.yml` so you can manage your spaCy
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project as a DVC repo.
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### 5. Optional: Push to remote storage {#push}
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> ```yaml
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> ### project.yml
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> remotes:
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> default: 's3://my-spacy-bucket'
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> local: '/mnt/scratch/cache'
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> ```
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After training a pipeline, you can optionally use the
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[`spacy project push`](/api/cli#project-push) command to upload your outputs to
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a remote storage, using protocols like [S3](https://aws.amazon.com/s3/),
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[Google Cloud Storage](https://cloud.google.com/storage) or SSH. This can help
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you **export** your pipeline packages, **share** work with your team, or **cache
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results** to avoid repeating work.
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```cli
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$ python -m spacy project push
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```
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The `remotes` section in your `project.yml` lets you assign names to the
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different storages. To download a state from a remote storage, you can use the
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[`spacy project pull`](/api/cli#project-pull) command. For more details, see the
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docs on [remote storage](#remote).
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## Project directory and assets {#directory}
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### project.yml {#project-yml}
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The `project.yml` defines the assets a project depends on, like datasets and
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pretrained weights, as well as a series of commands that can be run separately
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or as a workflow – for instance, to preprocess the data, convert it to spaCy's
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format, train a pipeline, evaluate it and export metrics, package it and spin up
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a quick web demo. It looks pretty similar to a config file used to define CI
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pipelines.
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<!-- TODO: update with better (final) example -->
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```yaml
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https://github.com/explosion/projects/tree/v3/tutorials/ner_fashion_brands/project.yml
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```
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| Section | Description |
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| ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| `title` | An optional project title used in `--help` message and [auto-generated docs](#custom-docs). |
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| `description` | An optional project description used in [auto-generated docs](#custom-docs). |
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| `vars` | A dictionary of variables that can be referenced in paths, URLs and scripts, just like [`config.cfg` variables](/usage/training#config-interpolation). For example, `${vars.name}` will use the value of the variable `name`. Variables need to be defined in the section `vars`, but can be a nested dict, so you're able to reference `${vars.model.name}`. |
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| `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. |
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| `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. Instead of `url`, you can also provide a `git` block with the keys `repo`, `branch` and `path`, to download from a Git repo. |
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| `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. |
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| `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. |
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### Data assets {#data-assets}
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Assets are any files that your project might need, like training and development
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corpora or pretrained weights for initializing your model. Assets are defined in
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the `assets` block of your `project.yml` and can be downloaded using the
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[`project assets`](/api/cli#project-assets) command. Defining checksums lets you
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verify that someone else running your project will use the same files you used.
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Asset URLs can be a number of different **protocols**: HTTP, HTTPS, FTP, SSH,
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and even **cloud storage** such as GCS and S3. You can also download assets from
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a **Git repo** instead.
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#### Downloading from a URL or cloud storage {#data-assets-url}
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Under the hood, spaCy uses the
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[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library so you
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can use any protocol it supports. Note that you may need to install extra
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dependencies to use certain protocols.
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> #### project.yml
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>
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> ```yaml
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> assets:
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> # Download from public HTTPS URL
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> - dest: 'assets/training.spacy'
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> url: 'https://example.com/data.spacy'
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> checksum: '63373dd656daa1fd3043ce166a59474c'
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> # Download from Google Cloud Storage bucket
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> - dest: 'assets/development.spacy'
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> url: 'gs://your-bucket/corpora'
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> checksum: '5113dc04e03f079525edd8df3f4f39e3'
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> ```
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| Name | Description |
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| ------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `dest` | The destination path to save the downloaded asset to (relative to the project directory), including the file name. |
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| `url` | The URL to download from, using the respective protocol. |
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| `checksum` | Optional checksum of the file. If provided, it will be used to verify that the file matches and downloads will be skipped if a local file with the same checksum already exists. |
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| `description` | Optional asset description, used in [auto-generated docs](#custom-docs). |
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#### Downloading from a Git repo {#data-assets-git}
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If a `git` block is provided, the asset is downloaded from the given Git
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repository. You can download from any repo that you have access to. Under the
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hood, this uses Git's "sparse checkout" feature, so you're only downloading the
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files you need and not the whole repo.
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> #### project.yml
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>
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> ```yaml
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> assets:
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> - dest: 'assets/training.spacy'
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> git:
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> repo: 'https://github.com/example/repo'
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> branch: 'master'
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> path: 'path/training.spacy'
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> checksum: '63373dd656daa1fd3043ce166a59474c'
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> description: 'The training data (5000 examples)'
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> ```
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| Name | Description |
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| ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `dest` | The destination path to save the downloaded asset to (relative to the project directory), including the file name. |
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| `git` | `repo`: The URL of the repo to download from.<br />`path`: Path of the file or directory to download, relative to the repo root.<br />`branch`: The branch to download from. Defaults to `"master"`. |
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| `checksum` | Optional checksum of the file. If provided, it will be used to verify that the file matches and downloads will be skipped if a local file with the same checksum already exists. |
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| `description` | Optional asset description, used in [auto-generated docs](#custom-docs). |
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#### Working with private assets {#data-asets-private}
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> #### project.yml
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>
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> ```yaml
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> assets:
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> - dest: 'assets/private_training_data.json'
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> checksum: '63373dd656daa1fd3043ce166a59474c'
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> - dest: 'assets/private_vectors.bin'
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> checksum: '5113dc04e03f079525edd8df3f4f39e3'
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> ```
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For many projects, the datasets and weights you're working with might be
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company-internal and not available over the internet. In that case, you can
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specify the destination paths and a checksum, and leave out the URL. When your
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teammates clone and run your project, they can place the files in the respective
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directory themselves. The [`project assets`](/api/cli#project-assets) command
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will alert you about missing files and mismatched checksums, so you can ensure that
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others are running your project with the same data.
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### Dependencies and outputs {#deps-outputs}
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Each command defined in the `project.yml` can optionally define a list of
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dependencies and outputs. These are the files the command requires and creates.
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For example, a command for training a pipeline may depend on a
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[`config.cfg`](/usage/training#config) and the training and evaluation data, and
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it will export a directory `model-best`, which you can then re-use in other
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commands.
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<!-- prettier-ignore -->
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```yaml
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### project.yml
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commands:
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- name: train
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help: 'Train a spaCy pipeline using the specified corpus and config'
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script:
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- 'python -m spacy train ./configs/config.cfg -o training/ --paths.train ./corpus/training.spacy --paths.dev ./corpus/evaluation.spacy'
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deps:
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- 'configs/config.cfg'
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- 'corpus/training.spacy'
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- 'corpus/evaluation.spacy'
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outputs:
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- 'training/model-best'
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```
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> #### Re-running vs. skipping
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>
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> Under the hood, spaCy uses a `project.lock` lockfile that stores the details
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> for each command, as well as its dependencies and outputs and their checksums.
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> It's updated on each run. If any of this information changes, the command will
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> be re-run. Otherwise, it will be skipped.
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If you're running a command and it depends on files that are missing, spaCy will
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show you an error. If a command defines dependencies and outputs that haven't
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changed since the last run, the command will be skipped. This means that you're
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only re-running commands if they need to be re-run. Commands can also set
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`no_skip: true` if they should never be skipped – for example commands that run
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tests. Commands without outputs are also never skipped. To force re-running a
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command or workflow, even if nothing changed, you can set the `--force` flag.
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Note that [`spacy project`](/api/cli#project) doesn't compile any dependency
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graphs based on the dependencies and outputs, and won't re-run previous steps
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automatically. For instance, if you only run the command `train` that depends on
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data created by `preprocess` and those files are missing, spaCy will show an
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error – it won't just re-run `preprocess`. If you're looking for more advanced
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data management, check out the [Data Version Control (DVC) integration](#dvc). If you're planning on integrating your spaCy project with DVC, you
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can also use `outputs_no_cache` instead of `outputs` to define outputs that
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won't be cached or tracked.
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### Files and directory structure {#project-files}
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The `project.yml` can define a list of `directories` that should be created
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within a project – for instance, `assets`, `training`, `corpus` and so on. spaCy
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will make sure that these directories are always available, so your commands can
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write to and read from them. Project directories will also include all files and
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directories copied from the project template with
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[`spacy project clone`](/api/cli#project-clone). Here's an example of a project
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directory:
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> #### project.yml
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>
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> <!-- prettier-ignore -->
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> ```yaml
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> directories: ['assets', 'configs', 'corpus', 'metas', 'metrics', 'notebooks', 'packages', 'scripts', 'training']
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> ```
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```yaml
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### Example project directory
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├── project.yml # the project settings
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├── project.lock # lockfile that tracks inputs/outputs
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├── assets/ # downloaded data assets
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├── configs/ # pipeline config.cfg files used for training
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├── corpus/ # output directory for training corpus
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├── metas/ # pipeline meta.json templates used for packaging
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├── metrics/ # output directory for evaluation metrics
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├── notebooks/ # directory for Jupyter notebooks
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├── packages/ # output directory for pipeline Python packages
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├── scripts/ # directory for scripts, e.g. referenced in commands
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├── training/ # output directory for trained pipelines
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└── ... # any other files, like a requirements.txt etc.
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```
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If you don't want a project to create a directory, you can delete it and remove
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its entry from the `project.yml` – just make sure it's not required by any of
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the commands. [Custom templates](#custom) can use any directories they need –
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the only file that's required for a project is the `project.yml`.
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---
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## Custom scripts and projects {#custom}
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The `project.yml` lets you define any custom commands and run them as part of
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your training, evaluation or deployment workflows. The `script` section defines
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a list of commands that are called in a subprocess, in order. This lets you
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execute other Python scripts or command-line tools. Let's say you've written a
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few integration tests that load the best model produced by the training command
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and check that it works correctly. You can now define a `test` command that
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calls into [`pytest`](https://docs.pytest.org/en/latest/), runs your tests and
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uses [`pytest-html`](https://github.com/pytest-dev/pytest-html) to export a test
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report:
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|
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```yaml
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### project.yml
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commands:
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- name: test
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help: 'Test the trained pipeline'
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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 pipeline) haven'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 `vars` 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 `${vars.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`.
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
### project.yml
|
||
vars:
|
||
batch_size: 128
|
||
|
||
commands:
|
||
- name: evaluate
|
||
script:
|
||
- 'python scripts/custom_evaluation.py ${vars.batch_size} ./training/model-best ./corpus/eval.json'
|
||
deps:
|
||
- 'training/model-best'
|
||
- 'corpus/eval.json'
|
||
```
|
||
|
||
### Documenting your project {#custom-docs}
|
||
|
||
> #### Readme Example
|
||
>
|
||
> For more examples, see the [`projects`](https://github.com/explosion/projects)
|
||
> repo.
|
||
>
|
||
> 
|
||
|
||
When your custom project is ready and you want to share it with others, you can
|
||
use the [`spacy project document`](/api/cli#project-document) command to
|
||
**auto-generate** a pretty, markdown-formatted `README` file based on your
|
||
project's `project.yml`. It will list all commands, workflows and assets defined
|
||
in the project and include details on how to run the project, as well as links
|
||
to the relevant spaCy documentation to make it easy for others to get started
|
||
using your project.
|
||
|
||
```cli
|
||
$ python -m spacy project document --output README.md
|
||
```
|
||
|
||
Under the hood, hidden markers are added to identify where the auto-generated
|
||
content starts and ends. This means that you can add your own custom content
|
||
before or after it and re-running the `project document` command will **only
|
||
update the auto-generated part**. This makes it easy to keep your documentation
|
||
up to date.
|
||
|
||
<Infobox variant="warning">
|
||
|
||
Note that the contents of an existing file will be **replaced** if no existing
|
||
auto-generated docs are found. If you want spaCy to ignore a file and not update
|
||
it, you can add the comment marker `<!-- SPACY PROJECT: IGNORE -->` anywhere in
|
||
your markup.
|
||
|
||
</Infobox>
|
||
|
||
### 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.
|
||
|
||
<Infobox title="Important note about assets" variant="warning">
|
||
|
||
It's typically not a good idea to check large data assets, trained pipelines 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.
|
||
|
||
</Infobox>
|
||
|
||
## Remote Storage {#remote}
|
||
|
||
You can persist your project outputs to a remote storage using the
|
||
[`project push`](/api/cli#project-push) command. This can help you **export**
|
||
your pipeline packages, **share** work with your team, or **cache results** to
|
||
avoid repeating work. The [`project pull`](/api/cli#project-pull) command will
|
||
download any outputs that are in the remote storage and aren't available
|
||
locally.
|
||
|
||
You can list one or more remotes in the `remotes` section of your
|
||
[`project.yml`](#project-yml) by mapping a string name to the URL of the
|
||
storage. Under the hood, spaCy uses the
|
||
[`smart-open`](https://github.com/RaRe-Technologies/smart_open) library to
|
||
communicate with the remote storages, so you can use any protocol that
|
||
`smart-open` supports, including [S3](https://aws.amazon.com/s3/),
|
||
[Google Cloud Storage](https://cloud.google.com/storage), SSH and more, although
|
||
you may need to install extra dependencies to use certain protocols.
|
||
|
||
> #### Example
|
||
>
|
||
> ```cli
|
||
> $ python -m spacy project pull local
|
||
> ```
|
||
|
||
```yaml
|
||
### project.yml
|
||
remotes:
|
||
default: 's3://my-spacy-bucket'
|
||
local: '/mnt/scratch/cache'
|
||
stuff: 'ssh://myserver.example.com/whatever'
|
||
```
|
||
|
||
<Infobox title="How it works" emoji="💡">
|
||
|
||
Inside the remote storage, spaCy uses a clever **directory structure** to avoid
|
||
overwriting files. The top level of the directory structure is a URL-encoded
|
||
version of the output's path. Within this directory are subdirectories named
|
||
according to a hash of the command string and the command's dependencies.
|
||
Finally, within those directories are files, named according to an MD5 hash of
|
||
their contents.
|
||
|
||
<!-- TODO: update with actual real example? -->
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
└── urlencoded_file_path # Path of original file
|
||
├── some_command_hash # Hash of command you ran
|
||
│ ├── some_content_hash # Hash of file content
|
||
│ └── another_content_hash
|
||
└── another_command_hash
|
||
└── third_content_hash
|
||
```
|
||
|
||
</Infobox>
|
||
|
||
For instance, let's say you had the following command in your `project.yml`:
|
||
|
||
```yaml
|
||
### project.yml
|
||
- name: train
|
||
help: 'Train a spaCy pipeline using the specified corpus and config'
|
||
script:
|
||
- 'spacy train ./config.cfg --output training/'
|
||
deps:
|
||
- 'corpus/train'
|
||
- 'corpus/dev'
|
||
- 'config.cfg'
|
||
outputs:
|
||
- 'training/model-best'
|
||
```
|
||
|
||
> #### Example
|
||
>
|
||
> ```
|
||
> └── s3://my-spacy-bucket/training%2Fmodel-best
|
||
> └── 1d8cb33a06cc345ad3761c6050934a1b
|
||
> └── d8e20c3537a084c5c10d95899fe0b1ff
|
||
> ```
|
||
|
||
After you finish training, you run [`project push`](/api/cli#project-push) to
|
||
make sure the `training/model-best` output is saved to remote storage. spaCy
|
||
will then construct a hash from your command script and the listed dependencies,
|
||
`corpus/train`, `corpus/dev` and `config.cfg`, in order to identify the
|
||
execution context of your output. It would then compute an MD5 hash of the
|
||
`training/model-best` directory, and use those three pieces of information to
|
||
construct the storage URL.
|
||
|
||
```cli
|
||
$ python -m spacy project run train
|
||
$ python -m spacy project push
|
||
```
|
||
|
||
If you change the command or one of its dependencies (for instance, by editing
|
||
the [`config.cfg`](/usage/training#config) file to tune the hyperparameters, a
|
||
different creation hash will be calculated, so when you use
|
||
[`project push`](/api/cli#project-push) you won't be overwriting your previous
|
||
file. The system even supports multiple outputs for the same file and the same
|
||
context, which can happen if your training process is not deterministic, or if
|
||
you have dependencies that aren't represented in the command.
|
||
|
||
In summary, the [`spacy project`](/api/cli#project) remote storages are designed
|
||
to make a particular set of trade-offs. Priority is placed on **convenience**,
|
||
**correctness** and **avoiding data loss**. You can use
|
||
[`project push`](/api/cli#project-push) freely, as you'll never overwrite remote
|
||
state, and you don't have to come up with names or version numbers. However,
|
||
it's up to you to manage the size of your remote storage, and to remove files
|
||
that are no longer relevant to you.
|
||
|
||
## Integrations {#integrations}
|
||
|
||
### Data Version Control (DVC) {#dvc} <IntegrationLogo name="dvc" title="DVC" width={70} height="auto" align="right" />
|
||
|
||
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
|
||
```
|
||
|
||
<Infobox title="Important note on privacy" variant="warning">
|
||
|
||
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).
|
||
|
||
</Infobox>
|
||
|
||
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]
|
||
```
|
||
|
||
<Infobox title="Important note for multiple workflows" variant="warning">
|
||
|
||
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.
|
||
|
||
</Infobox>
|
||
|
||
<!-- TODO: <Project id="integrations/dvc">
|
||
|
||
</Project> -->
|
||
|
||
---
|
||
|
||
### Prodigy {#prodigy} <IntegrationLogo name="prodigy" width={100} height="auto" align="right" />
|
||
|
||
<Infobox title="This section is still under construction" emoji="🚧" variant="warning">
|
||
|
||
The Prodigy integration will require a nightly version of Prodigy that supports
|
||
spaCy v3+.
|
||
|
||
</Infobox>
|
||
|
||
[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
|
||
> ```
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
### project.yml
|
||
vars:
|
||
prodigy:
|
||
dataset: 'ner_articles'
|
||
labels: 'PERSON,ORG,PRODUCT'
|
||
model: 'en_core_web_md'
|
||
|
||
commands:
|
||
- name: annotate
|
||
- script:
|
||
- 'python -m prodigy ner.correct ${vars.prodigy.dataset} ./assets/raw_data.jsonl ${vars.prodigy.model} --labels ${vars.prodigy.labels}'
|
||
- 'python -m prodigy data-to-spacy ./corpus/train.json ./corpus/eval.json --ner ${vars.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.
|
||
|
||
<!-- TODO: <Project id="integrations/prodigy">
|
||
|
||
</Project> -->
|
||
|
||
---
|
||
|
||
### Streamlit {#streamlit} <IntegrationLogo name="streamlit" width={150} height="auto" align="right" />
|
||
|
||
[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 pipelines interactively. It includes a
|
||
full embedded visualizer, as well as individual components.
|
||
|
||
<!-- TODO: update once version is stable -->
|
||
|
||
> #### Installation
|
||
>
|
||
> ```bash
|
||
> $ pip install "spacy-streamlit>=1.0.0a0"
|
||
> ```
|
||
|
||
|
||
|
||
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 pipeline you trained, or a selection of pipelines
|
||
so you can compare their results.
|
||
|
||
<Project id="integrations/streamlit">
|
||
|
||
Get started with spaCy and Streamlit using our project template. It includes a
|
||
script to spin up a custom visualizer and commands you can adjust to showcase
|
||
and explore your own custom trained pipelines.
|
||
|
||
</Project>
|
||
|
||
> #### Example usage
|
||
>
|
||
> ```cli
|
||
> $ python -m spacy project run visualize
|
||
> ```
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
### project.yml
|
||
commands:
|
||
- name: visualize
|
||
help: "Visualize the pipeline's output interactively using Streamlit"
|
||
script:
|
||
- 'streamlit run ./scripts/visualize.py ./training/model-best "I like Adidas shoes."'
|
||
deps:
|
||
- "training/model-best"
|
||
```
|
||
|
||
The following script is called from the `project.yml` and takes two positional
|
||
command-line argument: a comma-separated list of paths or packages to load the
|
||
pipelines from and an example text to use as the default text.
|
||
|
||
```python
|
||
https://github.com/explosion/projects/blob/v3/integrations/streamlit/scripts/visualize.py
|
||
```
|
||
|
||
---
|
||
|
||
### FastAPI {#fastapi} <IntegrationLogo name="fastapi" width={100} height="auto" align="right" />
|
||
|
||
[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 pipeline and make it available behind a
|
||
REST API.
|
||
|
||
<Project id="integrations/fastapi">
|
||
|
||
Get started with spaCy and FastAPI using our project template. It includes a
|
||
simple REST API for processing batches of text, and usage examples for how to
|
||
query your API from Python and JavaScript (Vanilla JS and React).
|
||
|
||
</Project>
|
||
|
||
> #### Example usage
|
||
>
|
||
> ```cli
|
||
> $ python -m spacy project run serve
|
||
> ```
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
### project.yml
|
||
- name: "serve"
|
||
help: "Serve the models via a FastAPI REST API using the given host and port"
|
||
script:
|
||
- "uvicorn scripts.main:app --reload --host 127.0.0.1 --port 5000"
|
||
deps:
|
||
- "scripts/main.py"
|
||
no_skip: true
|
||
```
|
||
|
||
The script included in the template shows a simple REST API with a `POST`
|
||
endpoint that accepts batches of texts and returns batches of predictions, e.g.
|
||
named entities found in the documents. Type hints and
|
||
[`pydantic`](https://github.com/samuelcolvin/pydantic) are used to define the
|
||
expected data types.
|
||
|
||
```python
|
||
https://github.com/explosion/projects/blob/v3/integrations/fastapi/scripts/main.py
|
||
```
|
||
|
||
---
|
||
|
||
### Ray {#ray} <IntegrationLogo name="ray" width={100} height="auto" align="right" />
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||
|
||
> #### Installation
|
||
>
|
||
> ```cli
|
||
> $ pip install spacy-ray
|
||
> # Check that the CLI is registered
|
||
> $ python -m spacy ray --help
|
||
> ```
|
||
|
||
[Ray](https://ray.io/) is a fast and simple framework for building and running
|
||
**distributed applications**. You can use Ray for parallel and distributed
|
||
training with spaCy via our lightweight
|
||
[`spacy-ray`](https://github.com/explosion/spacy-ray) extension package. If the
|
||
package is installed in the same environment as spaCy, it will automatically add
|
||
[`spacy ray`](/api/cli#ray) commands to your spaCy CLI. See the usage guide on
|
||
[parallel training](/usage/training#parallel-training) for more details on how
|
||
it works under the hood.
|
||
|
||
You can integrate [`spacy ray train`](/api/cli#ray-train) into your
|
||
`project.yml` just like the regular training command and pass it the config, and
|
||
optional output directory or remote storage URL and config overrides if needed.
|
||
|
||
<!-- prettier-ignore -->
|
||
```yaml
|
||
### project.yml
|
||
commands:
|
||
- name: "ray"
|
||
help: "Train a model via parallel training with Ray"
|
||
script:
|
||
- "python -m spacy ray train configs/config.cfg -o training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy"
|
||
deps:
|
||
- "corpus/train.spacy"
|
||
- "corpus/dev.spacy"
|
||
outputs:
|
||
- "training/model-best"
|
||
```
|
||
|
||
<!-- TODO: <Project id="integrations/ray">
|
||
|
||
</Project> -->
|
||
|
||
---
|
||
|
||
### Weights & Biases {#wandb} <IntegrationLogo name="wandb" width={175} height="auto" align="right" />
|
||
|
||
[Weights & Biases](https://www.wandb.com/) is a popular platform for experiment
|
||
tracking. spaCy integrates with it out-of-the-box via the
|
||
[`WandbLogger`](/api/top-level#WandbLogger), which you can add as the
|
||
`[training.logger]` block of your training [config](/usage/training#config). The
|
||
results of each step are then logged in your project, together with the full
|
||
**training config**. This means that _every_ hyperparameter, registered function
|
||
name and argument will be tracked and you'll be able to see the impact it has on
|
||
your results.
|
||
|
||
> #### Example config
|
||
>
|
||
> ```ini
|
||
> [training.logger]
|
||
> @loggers = "spacy.WandbLogger.v1"
|
||
> project_name = "monitor_spacy_training"
|
||
> remove_config_values = ["paths.train", "paths.dev", "training.dev_corpus.path", "training.train_corpus.path"]
|
||
> ```
|
||
|
||
|
||
|
||
|
||
|
||
<!-- TODO:
|
||
|
||
<Project id="integrations/wandb">
|
||
|
||
Get started with tracking your spaCy training runs in Weights & Biases using our
|
||
project template. It includes a simple config using the `WandbLogger`, as well
|
||
as a custom logger implementation you can adjust for your specific use case.
|
||
|
||
</Project>
|
||
|
||
-->
|