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@ -90,7 +90,7 @@ can also use any private repo you have access to with Git.
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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/developments.spacy'
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> path: 'path/development.spacy'
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> checksum: '5113dc04e03f079525edd8df3f4f39e3'
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> ```
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@ -910,9 +910,6 @@ https://github.com/explosion/projects/blob/v3/integrations/fastapi/scripts/main.
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### Ray {#ray} <IntegrationLogo name="ray" width={100} height="auto" align="right" />
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<Infobox title="This section is still under construction" emoji="🚧" variant="warning">
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</Infobox>
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> #### Installation
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>
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> ```cli
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@ -926,21 +923,27 @@ https://github.com/explosion/projects/blob/v3/integrations/fastapi/scripts/main.
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training with spaCy via our lightweight
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[`spacy-ray`](https://github.com/explosion/spacy-ray) extension package. If the
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package is installed in the same environment as spaCy, it will automatically add
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[`spacy ray`](/api/cli#ray) commands to your spaCy CLI.
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[`spacy ray`](/api/cli#ray) commands to your spaCy CLI. See the usage guide on
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[parallel training](/usage/training#parallel-training) for more details on how
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it works under the hood.
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You can integrate [`spacy ray train`](/api/cli#ray-train) into your
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`project.yml` just like the regular training command:
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`project.yml` just like the regular training command and pass it the config, and
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optional output directory or remote storage URL and config overrides if needed.
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<!-- prettier-ignore -->
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```yaml
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### project.yml
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- name: "ray"
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commands:
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- name: "ray"
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help: "Train a model via parallel training with Ray"
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script:
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- "python -m spacy ray train configs/config.cfg --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy"
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- "python -m spacy ray train configs/config.cfg -o training/ --paths.train corpus/train.spacy --paths.dev corpus/dev.spacy"
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deps:
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- "corpus/train.spacy"
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- "corpus/dev.spacy"
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outputs:
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- "training/model-best"
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```
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<!-- TODO: <Project id="integrations/ray">
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@ -92,6 +92,26 @@ spaCy's binary `.spacy` format. You can either include the data paths in the
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$ python -m spacy train config.cfg --output ./output --paths.train ./train.spacy --paths.dev ./dev.spacy
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```
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<Accordion title="How are the config recommendations generated?" id="quickstart-source">
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The recommended config settings generated by the quickstart widget and the
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[`init config`](/api/cli#init-config) command are based on some general **best
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practices** and things we've found to work well in our experiments. The goal is
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to provide you with the most **useful defaults**.
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Under the hood, the
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[`quickstart_training.jinja`](%%GITHUB_SPACY/spacy/cli/templates/quickstart_training.jinja)
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template defines the different combinations – for example, which parameters to
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change if the pipeline should optimize for efficiency vs. accuracy. The file
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[`quickstart_training_recommendations.yml`](%%GITHUB_SPACY/spacy/cli/templates/quickstart_training_recommendations.yml)
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collects the recommended settings and available resources for each language
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including the different transformer weights. For some languages, we include
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different transformer recommendations, depending on whether you want the model
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to be more efficient or more accurate. The recommendations will be **evolving**
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as we run more experiments.
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</Accordion>
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## Training config {#config}
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Training config files include all **settings and hyperparameters** for training
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@ -8,9 +8,9 @@ import socialImageApi from '../images/social_api.jpg'
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import socialImageUniverse from '../images/social_universe.jpg'
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import socialImageNightly from '../images/social_nightly.jpg'
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function getPageTitle(title, sitename, slogan, sectionTitle) {
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function getPageTitle(title, sitename, slogan, sectionTitle, nightly) {
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if (sectionTitle && title) {
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return `${title} · ${sitename} ${sectionTitle}`
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return `${title} · ${sitename} ${sectionTitle}${nightly ? ' (nightly)' : ''}`
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}
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if (title) {
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return `${title} · ${sitename}`
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@ -44,7 +44,8 @@ export default function SEO({
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title,
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siteMetadata.title,
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siteMetadata.slogan,
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sectionTitle
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sectionTitle,
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nightly
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)
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const socialImage = siteMetadata.siteUrl + getImage(section, nightly)
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const meta = [
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