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Update projects docs etc.
This commit is contained in:
Ines Montani
parent
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commit
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@ -297,60 +297,41 @@ will not be available.
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## Train {#train}
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<!-- TODO: document new training -->
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Train a model. Expects data in spaCy's
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[JSON format](/api/data-formats#json-input). On each epoch, a model will be
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saved out to the directory. Accuracy scores and model details will be added to a
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[`meta.json`](/usage/training#models-generating) to allow packaging the model
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using the [`package`](/api/cli#package) command.
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[binary format](/api/data-formats#training) and a
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[config file](/api/data-formats#config) with all settings and hyperparameters.
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Will save out the best model from all epochs, as well as the final model. The
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`--code` argument can be used to provide a Python file that's imported before
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the training process starts. This lets you register
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[custom functions](/usage/training#custom-models) and architectures and refer to
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them in your config, all while still using spaCy's built-in `train` workflow. If
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you need to manage complex multi-step training workflows, check out the new
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[spaCy projects](/usage/projects).
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<Infobox title="New in v3.0" variant="warning">
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As of spaCy v3.0, the `train` command doesn't take a long list of command-line
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arguments anymore and instead expects a single
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[`config.cfg` file](/usage/training#config) containing all settings for the
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pipeline, training process and hyperparameters.
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</Infobox>
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```bash
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$ python -m spacy train [lang] [output_path] [train_path] [dev_path]
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[--base-model] [--pipeline] [--vectors] [--n-iter] [--n-early-stopping]
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[--n-examples] [--use-gpu] [--version] [--meta-path] [--init-tok2vec]
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[--parser-multitasks] [--entity-multitasks] [--gold-preproc] [--noise-level]
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[--orth-variant-level] [--learn-tokens] [--textcat-arch] [--textcat-multilabel]
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[--textcat-positive-label] [--verbose]
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$ python -m spacy train [train_path] [dev_path] [config_path] [--output]
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[--code] [--verbose]
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```
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| Argument | Type | Description |
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| --------------------------------------------------------------- | ------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `lang` | positional | Model language. |
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| `output_path` | positional | Directory to store model in. Will be created if it doesn't exist. |
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| `train_path` | positional | Location of JSON-formatted training data. Can be a file or a directory of files. |
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| `dev_path` | positional | Location of JSON-formatted development data for evaluation. Can be a file or a directory of files. |
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| `--base-model`, `-b` <Tag variant="new">2.1</Tag> | option | Optional name of base model to update. Can be any loadable spaCy model. |
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| `--pipeline`, `-p` <Tag variant="new">2.1</Tag> | option | Comma-separated names of pipeline components to train. Defaults to `'tagger,parser,ner'`. |
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| `--replace-components`, `-R` | flag | Replace components from the base model. |
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| `--vectors`, `-v` | option | Model to load vectors from. |
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| `--n-iter`, `-n` | option | Number of iterations (default: `30`). |
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| `--n-early-stopping`, `-ne` | option | Maximum number of training epochs without dev accuracy improvement. |
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| `--n-examples`, `-ns` | option | Number of examples to use (defaults to `0` for all examples). |
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| `--use-gpu`, `-g` | option | GPU ID or `-1` for CPU only (default: `-1`). |
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| `--version`, `-V` | option | Model version. Will be written out to the model's `meta.json` after training. |
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| `--meta-path`, `-m` <Tag variant="new">2</Tag> | option | Optional path to model [`meta.json`](/usage/training#models-generating). All relevant properties like `lang`, `pipeline` and `spacy_version` will be overwritten. |
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| `--init-tok2vec`, `-t2v` <Tag variant="new">2.1</Tag> | option | Path to pretrained weights for the token-to-vector parts of the models. See `spacy pretrain`. Experimental. |
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| `--parser-multitasks`, `-pt` | option | Side objectives for parser CNN, e.g. `'dep'` or `'dep,tag'` |
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| `--entity-multitasks`, `-et` | option | Side objectives for NER CNN, e.g. `'dep'` or `'dep,tag'` |
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| `--width`, `-cw` <Tag variant="new">2.2.4</Tag> | option | Width of CNN layers of `Tok2Vec` component. |
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| `--conv-depth`, `-cd` <Tag variant="new">2.2.4</Tag> | option | Depth of CNN layers of `Tok2Vec` component. |
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| `--cnn-window`, `-cW` <Tag variant="new">2.2.4</Tag> | option | Window size for CNN layers of `Tok2Vec` component. |
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| `--cnn-pieces`, `-cP` <Tag variant="new">2.2.4</Tag> | option | Maxout size for CNN layers of `Tok2Vec` component. |
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| `--use-chars`, `-chr` <Tag variant="new">2.2.4</Tag> | flag | Whether to use character-based embedding of `Tok2Vec` component. |
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| `--bilstm-depth`, `-lstm` <Tag variant="new">2.2.4</Tag> | option | Depth of BiLSTM layers of `Tok2Vec` component (requires PyTorch). |
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| `--embed-rows`, `-er` <Tag variant="new">2.2.4</Tag> | option | Number of embedding rows of `Tok2Vec` component. |
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| `--noise-level`, `-nl` | option | Float indicating the amount of corruption for data augmentation. |
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| `--orth-variant-level`, `-ovl` <Tag variant="new">2.2</Tag> | option | Float indicating the orthography variation for data augmentation (e.g. `0.3` for making 30% of occurrences of some tokens subject to replacement). |
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| `--gold-preproc`, `-G` | flag | Use gold preprocessing. |
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| `--learn-tokens`, `-T` | flag | Make parser learn gold-standard tokenization by merging ] subtokens. Typically used for languages like Chinese. |
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| `--textcat-multilabel`, `-TML` <Tag variant="new">2.2</Tag> | flag | Text classification classes aren't mutually exclusive (multilabel). |
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| `--textcat-arch`, `-ta` <Tag variant="new">2.2</Tag> | option | Text classification model architecture. Defaults to `"bow"`. |
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| `--textcat-positive-label`, `-tpl` <Tag variant="new">2.2</Tag> | option | Text classification positive label for binary classes with two labels. |
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| `--tag-map-path`, `-tm` <Tag variant="new">2.2.4</Tag> | option | Location of JSON-formatted tag map. |
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| `--verbose`, `-VV` <Tag variant="new">2.0.13</Tag> | flag | Show more detailed messages during training. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| **CREATES** | model, pickle | A spaCy model on each epoch. |
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| Argument | Type | Description |
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| ----------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `train_path` | positional | Location of training data in spaCy's [binary format](/api/data-formats#training). Can be a file or a directory of files. |
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| `dev_path` | positional | Location of development data for evaluation in spaCy's [binary format](/api/data-formats#training). Can be a file or a directory of files. |
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| `config_path` | positional | Path to [training config](/api/data-formats#config) file containing all settings and hyperparameters. |
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| `--output`, `-o` | positional | Directory to store model in. Will be created if it doesn't exist. |
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| `--code`, `-c` | option | Path to Python file with additional code to be imported. Allows [registering custom functions](/usage/training#custom-models) for new architectures. |
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| `--verbose`, `-V` | flag | Show more detailed messages during training. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| **CREATES** | model | The final model and the best model. |
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## Pretrain {#pretrain new="2.1" tag="experimental"}
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@ -507,12 +488,13 @@ so you don't have to run `python setup.py sdist` separately anymore.
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$ python -m spacy package [input_dir] [output_dir] [--meta-path] [--create-meta] [--force]
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```
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```bash
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### Example
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python -m spacy package /input /output
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cd /output/en_model-0.0.0
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pip install dist/en_model-0.0.0.tar.gz
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```
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> #### Example
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>
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> ```bash
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> python -m spacy package /input /output
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> cd /output/en_model-0.0.0
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> pip install dist/en_model-0.0.0.tar.gz
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> ```
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| Argument | Type | Description |
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| ------------------------------------------------ | ---------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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@ -525,14 +507,143 @@ pip install dist/en_model-0.0.0.tar.gz
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| **CREATES** | directory | A Python package containing the spaCy model. |
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## Project {#project}
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## Project {#project new="3"}
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<!-- TODO: document project command and subcommands. We should probably wait and only finalize this once we've finalized the design -->
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The `spacy project` CLI includes subcommands for working with
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[spaCy projects](/usage/projects), end-to-end workflows for building and
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deploying custom spaCy models.
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### project clone {#project-clone}
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Clone a project template from a Git repository. Calls into `git` under the hood
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and uses the sparse checkout feature, so you're only downloading what you need.
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By default, spaCy's
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[project templates repo](https://github.com/explosion/projects) is used, but you
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can provide any other repo (public or private) that you have access to using the
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`--repo` option.
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<!-- TODO: update example once we've decided on repo structure -->
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```bash
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$ python -m spacy project clone [name] [dest] [--repo]
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```
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> #### Example
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>
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> ```bash
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> $ python -m spacy project clone some_example
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> ```
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>
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> Clone from custom repo:
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>
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> ```bash
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> $ python -m spacy project clone template --repo https://github.com/your_org/your_repo
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> ```
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| Argument | Type | Description |
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| -------------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
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| `name` | positional | The name of the template to clone, relative to the repo. Can be a top-level directory or a subdirectory like `dir/template`. |
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| `dest` | positional | Where to clone the project. Defaults to current working directory. |
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| `--repo`, `-r` | option | The repository to clone from. Can be any public or private Git repo you have access to. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| **CREATES** | directory | The cloned [project directory](/usage/projects#project-files). |
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### project assets {#project-assets}
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Fetch project assets like datasets and pretrained weights. Assets are defined in
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the `assets` section of the [`project.yml`](/usage/projects#project-yml). If a
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`checksum` is provided, the file is only downloaded if no local file with the
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same checksum exists and spaCy will show an error if the checksum of the
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downloaded file doesn't match. If assets don't specify a `url` they're
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considered "private" and you have to take care of putting them into the
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destination directory yourself. If a local path is provided, the asset is copied
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into the current project.
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<!-- TODO: update example once we've decided on repo structure -->
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```bash
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$ python -m spacy project assets [project_dir]
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```
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> #### Example
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>
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> ```bash
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> $ python -m spacy project assets
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> ```
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| Argument | Type | Description |
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| -------------- | ---------- | ----------------------------------------------------------------- |
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| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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| **CREATES** | files | Downloaded or copied assets defined in the `project.yml`. |
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### project run {#project-run}
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Run a named command or workflow defined in the
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[`project.yml`](/usage/projects#project-yml). If a workflow name is specified,
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all commands in the workflow are run, in order. If commands define
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[dependencies or outputs](/usage/projects#deps-outputs), they will only be
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re-run if state has changed. For example, if the input dataset changes, a
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preprocessing command that depends on those files will be re-run.
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<!-- TODO: update example once we've decided on repo structure -->
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```bash
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$ python -m spacy project run [subcommand] [project_dir] [--force] [--dry]
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```
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> #### Example
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>
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> ```bash
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> $ python -m spacy project run train
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> ```
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| Argument | Type | Description |
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| --------------- | ---------- | ----------------------------------------------------------------- |
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| `subcommand` | positional | Name of the command or workflow to run. |
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| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
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| `--force`, `-F` | flag | Force re-running steps, even if nothing changed. |
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| `--dry`, `-D` | flag | Perform a dry run and don't execute scripts. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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### project dvc {#project-dvc}
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Auto-generate [Data Version Control](https://dvc.org) (DVC) config file. Calls
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[`dvc run`](https://dvc.org/doc/command-reference/run) with `--no-exec` under
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the hood to generate the `dvc.yaml`. A DVC project can only define one pipeline,
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so you need to specify one workflow defined in the
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[`project.yml`](/usage/projects#project-yml). If no workflow is specified, the
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first defined workflow is used. The DVC config will only be updated if the
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`project.yml` changed. For details, see the
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[DVC integration](/usage/projects#dvc) docs.
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<Infobox variant="warning">
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This command requires DVC to be installed and initialized in the project
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directory, e.g. via [`dvc init`](https://dvc.org/doc/command-reference/init).
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You'll also need to add the assets you want to track with
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[`dvc add`](https://dvc.org/doc/command-reference/add).
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</Infobox>
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```bash
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$ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose]
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```
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> #### Example
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>
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> ```bash
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> git init
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> dvc init
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> python -m spacy project dvc all
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> ```
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| Argument | Type | Description |
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| ----------------- | ---------- | --------------------------------------------------------------------------------- |
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| `project_dir` | positional | Path to project directory. Defaults to current working directory. |
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| `workflow` | positional | Name of workflow defined in `project.yml`. Defaults to first workflow if not set. |
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| `--force`, `-F` | flag | Force-updating config file. |
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| `--verbose`, `-V` | flag | Print more output generated by DVC. |
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| `--help`, `-h` | flag | Show help message and available arguments. |
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|
|
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@ -5,25 +5,29 @@ 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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- ['Integrations', 'integrations']
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---
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> #### Project templates
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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 tasks and models that you can clone and run.
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<!-- TODO: write more about templates in aside -->
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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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training, packaging and serving your custom models. You can start off by cloning
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a pre-defined project template, adjust it to fit your needs, load in your data,
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train a model, export it as a Python package and share the project templates
|
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with your team. Under the hood, project use
|
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[Data Version Control](https://dvc.org) (DVC) to track and version inputs and
|
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outputs, and make sure you're only re-running what's needed. spaCy projects can
|
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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.
|
||||
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.
|
||||
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||||
<!-- TODO: mention integrations -->
|
||||
|
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## Introduction and workflow {#intro}
|
||||
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|
|
@ -37,18 +41,32 @@ mattis pretium.
|
|||
|
||||
</Project>
|
||||
|
||||
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.
|
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|
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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>
|
||||
<Integration title="Prodigy" logo="prodigy" url="#prodigy">Create labelled training data</Integration>
|
||||
<Integration title="Streamlit" logo="streamlit" url="#streamlit">Visualize and demo your models</Integration>
|
||||
<Integration title="FastAPI" logo="fastapi" url="#fastapi">Serve your models and host APIs</Integration>
|
||||
<Integration title="Ray" logo="ray" url="#ray">Distributed and parallel training</Integration>
|
||||
<Integration title="Weights & Biases" logo="wandb" url="#wandb">Track your experiments and results</Integration>
|
||||
</Grid>
|
||||
|
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### 1. Clone a project template {#clone}
|
||||
|
||||
> #### Cloning under the hoodimport { ReactComponent as WandBLogo } from '../images/logos/wandb.svg'
|
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>
|
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> 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.
|
||||
|
||||
> #### 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.
|
||||
|
||||
```bash
|
||||
$ python -m spacy clone some_example_project
|
||||
```
|
||||
|
|
@ -59,46 +77,169 @@ can specify an optional second argument to define the output directory. The
|
|||
to use the spaCy [`projects`](https://github.com/explosion/projects) repo. You
|
||||
can also use any private repo you have access to with Git.
|
||||
|
||||
If you plan on making the project a Git repo, you can set the `--git` flag to
|
||||
set it up automatically _before_ initializing DVC, so DVC can integrate with
|
||||
Git. This means that it will automatically add asset files to a `.gitignore` (so
|
||||
you never check assets into the repo, only the asset meta files).
|
||||
|
||||
### 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. <!-- TODO: ... -->
|
||||
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:
|
||||
|
||||
```bash
|
||||
cd some_example_project
|
||||
python -m spacy project assets
|
||||
```
|
||||
|
||||
### 3. Run the steps {#run-all}
|
||||
### 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`:
|
||||
|
||||
```bash
|
||||
$ python -m spacy project run-all
|
||||
$ python -m spacy project run preprocess
|
||||
```
|
||||
|
||||
### 4. Run single commands {#run}
|
||||
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 run the workflow named `all` defined in the `project.yml`, and
|
||||
execute the commands it specifies, in order:
|
||||
|
||||
```bash
|
||||
$ python -m spacy project run visualize
|
||||
$ 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 config, `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 pipeline – 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.
|
||||
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.
|
||||
|
||||
<!-- TODO: include example etc. -->
|
||||
<!-- TODO: update with better (final) example -->
|
||||
|
||||
```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`. |
|
||||
| `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 commands 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.
|
||||
|
||||
<!-- prettier-ignore -->
|
||||
```yaml
|
||||
### project.yml
|
||||
commands:
|
||||
- name: train
|
||||
help: 'Train a spaCy model using the specified corpus and config'
|
||||
script:
|
||||
- 'python -m spacy train ./corpus/training.spacy ./corpus/evaluation.spacy ./configs/config.cfg -o training/'
|
||||
deps:
|
||||
- 'configs/config.cfg'
|
||||
- 'corpus/training.spacy'
|
||||
- 'corpus/evaluation.spacy'
|
||||
outputs:
|
||||
- 'training/model-best'
|
||||
```
|
||||
|
||||
> #### Re-running vs. skipping
|
||||
>
|
||||
> Under the hood, spaCy uses a `project.lock` lockfile that stores the details
|
||||
> for each command, as well as its dependencies and outputs and their checksums.
|
||||
> It's updated on each run. If any of this information changes, the command will
|
||||
> be re-run. Otherwise, it will be skipped.
|
||||
|
||||
If you're running a command and it depends on files that are missing, spaCy will
|
||||
show you an error. If a command defines dependencies and outputs that haven't
|
||||
changed since the last run, the command will be skipped. This means that you're
|
||||
only re-running commands if they need to be re-run. 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}
|
||||
|
||||
|
|
@ -109,10 +250,9 @@ scripts).
|
|||
|
||||
```yaml
|
||||
### Project directory
|
||||
├── project.yml # the project configuration
|
||||
├── dvc.yaml # auto-generated Data Version Control config
|
||||
├── dvc.lock # auto-generated Data Version control lock file
|
||||
├── assets/ # downloaded data assets and DVC meta files
|
||||
├── project.yml # the project settings
|
||||
├── project.lock # lockfile that tracks inputs/outputs
|
||||
├── assets/ # downloaded data assets
|
||||
├── metrics/ # output directory for evaluation metrics
|
||||
├── training/ # output directory for trained models
|
||||
├── corpus/ # output directory for training corpus
|
||||
|
|
@ -125,13 +265,89 @@ scripts).
|
|||
└── ... # any other files, like a requirements.txt etc.
|
||||
```
|
||||
|
||||
When the project is initialized, spaCy will auto-generate a `dvc.yaml` based on
|
||||
the project config. The file is updated whenever the project config has changed
|
||||
and includes all commands defined in the `run` section of the project config.
|
||||
This allows DVC to track the inputs and outputs and know which steps need to be
|
||||
re-run.
|
||||
---
|
||||
|
||||
#### Why Data Version Control (DVC)?
|
||||
## 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/) and runs your tests:
|
||||
|
||||
> #### 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
|
||||
commands:
|
||||
- name: test
|
||||
help: 'Test the trained model'
|
||||
script:
|
||||
- 'python -m pytest ./scripts/tests'
|
||||
deps:
|
||||
- 'training/model-best'
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
<!-- TODO: add another example -->
|
||||
|
||||
### 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.
|
||||
|
||||
```bash
|
||||
$ python -m spacy project your_project --repo https://github.com/you/repo
|
||||
```
|
||||
|
||||
At a minimum, a valid project template needs to contain a
|
||||
[`project.yml`](#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 models or
|
||||
other artifacts into a Git repo and you should exclude them from your project
|
||||
template. If you want to version your data and models, check out
|
||||
[Data Version Control](#dvc) (DVC), which integrates with spaCy projects.
|
||||
|
||||
</Infobox>
|
||||
|
||||
### 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} <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
|
||||
|
|
@ -140,19 +356,187 @@ steps that depend on each other, like a preprocessing step that generates your
|
|||
training data, you need to make sure the data is always up-to-date, and re-run
|
||||
all steps of your process every time, just to be safe.
|
||||
|
||||
[Data Version Control (DVC)](https://dvc.org) is a standalone open-source tool
|
||||
[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. `spacy project`
|
||||
uses DVC under the hood and you typically don't have to think about it if you
|
||||
don't want to. But if you do want to integrate with DVC more deeply, you can.
|
||||
Each spaCy project is also a regular DVC project.
|
||||
repo, so you'll always be able to reproduce your experiments.
|
||||
|
||||
#### Checking projects into Git
|
||||
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
|
||||
```
|
||||
|
||||
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.
|
||||
|
||||
```bash
|
||||
$ 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>
|
||||
|
||||
<Project id="integrations/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.
|
||||
|
||||
</Project>
|
||||
|
||||
---
|
||||
|
||||
## Custom projects and scripts {#custom}
|
||||
### Prodigy {#prodigy} <IntegrationLogo name="prodigy" width={100} height="auto" align="right" />
|
||||
|
||||
[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
|
||||
|
||||
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.
|
||||
|
||||
<!-- prettier-ignore -->
|
||||
```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.spacy
|
||||
./corpus/eval.spacy --ner {PRODIGY_DATASET}'
|
||||
- deps:
|
||||
- 'assets/raw_data.jsonl'
|
||||
- outputs:
|
||||
- 'corpus/train.spacy'
|
||||
- 'corpus/eval.spacy'
|
||||
```
|
||||
|
||||
<Project id="integrations/prodigy">
|
||||
|
||||
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
|
||||
sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
|
||||
mattis pretium.
|
||||
|
||||
</Project>
|
||||
|
||||
---
|
||||
|
||||
### Streamlit {#streamlit} <IntegrationLogo name="streamlit" width={150} height="auto" align="right" />
|
||||
|
||||
<Grid cols={2} gutterBottom={false}>
|
||||
|
||||
<div>
|
||||
|
||||
[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
|
||||
```
|
||||
|
||||
</div>
|
||||
|
||||
|
||||
|
||||
</Grid>
|
||||
|
||||
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"])
|
||||
```
|
||||
|
||||
<!-- prettier-ignore -->
|
||||
```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'
|
||||
```
|
||||
|
||||
<Project id="integrations/streamlit">
|
||||
|
||||
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
|
||||
sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
|
||||
mattis pretium.
|
||||
|
||||
</Project>
|
||||
|
||||
---
|
||||
|
||||
### FastAPI {#fastapi} <IntegrationLogo name="fastapi" width={100} height="auto" align="right" />
|
||||
|
||||
<!-- TODO: come up with example – there's not much integration needed, but it'd be nice to show an example that addresses some of the main concerns for serving ML (workers etc.) -->
|
||||
|
||||
<Project id="integrations/fastapi">
|
||||
|
||||
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Phasellus interdum
|
||||
sodales lectus, ut sodales orci ullamcorper id. Sed condimentum neque ut erat
|
||||
mattis pretium.
|
||||
|
||||
</Project>
|
||||
|
||||
---
|
||||
|
||||
### Ray {#ray} <IntegrationLogo name="ray" width={100} height="auto" align="right" />
|
||||
|
||||
<!-- TODO: document -->
|
||||
|
||||
---
|
||||
|
||||
### Weights & Biases {#wandb} <IntegrationLogo name="wandb" width={175} height="auto" align="right" />
|
||||
|
||||
<!-- TODO: decide how we want this to work? Just send results plus config from spacy evaluate in a separate command/script? -->
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ teaser: Visualize dependencies and entities in your browser or in a notebook
|
|||
new: 2
|
||||
menu:
|
||||
- ['Dependencies', 'dep']
|
||||
- ['Entities', 'ent']
|
||||
- ['Named Entities', 'ent']
|
||||
- ['Jupyter Notebooks', 'jupyter']
|
||||
- ['Rendering HTML', 'html']
|
||||
- ['Web app usage', 'webapp']
|
||||
|
|
@ -356,6 +356,6 @@ Alternatively, if you're using [Streamlit](https://streamlit.io), check out the
|
|||
helps you integrate spaCy visualizations into your apps. It includes a full
|
||||
embedded visualizer, as well as individual components.
|
||||
|
||||
]
|
||||
|
||||
|
||||
</Grid>
|
||||
|
|
|
|||
|
|
@ -1,29 +1,32 @@
|
|||
import React from 'react'
|
||||
import PropTypes from 'prop-types'
|
||||
import classNames from 'classnames'
|
||||
|
||||
import Link from './link'
|
||||
import { H5 } from './typography'
|
||||
import classes from '../styles/card.module.sass'
|
||||
|
||||
const Card = ({ title, to, image, header, onClick, children }) => (
|
||||
<div className={classes.root}>
|
||||
const Card = ({ title, to, image, header, small, onClick, children }) => (
|
||||
<div className={classNames(classes.root, { [classes.small]: !!small })}>
|
||||
{header && (
|
||||
<Link to={to} onClick={onClick} hidden>
|
||||
{header}
|
||||
</Link>
|
||||
)}
|
||||
<H5>
|
||||
{image && (
|
||||
<div className={classes.image}>
|
||||
<img src={image} width={35} alt="" />
|
||||
</div>
|
||||
)}
|
||||
{title && (
|
||||
<Link to={to} onClick={onClick} hidden>
|
||||
{title}
|
||||
</Link>
|
||||
)}
|
||||
</H5>
|
||||
{(title || image) && (
|
||||
<H5 className={classes.title}>
|
||||
{image && (
|
||||
<div className={classes.image}>
|
||||
<img src={image} width={35} alt="" />
|
||||
</div>
|
||||
)}
|
||||
{title && (
|
||||
<Link to={to} onClick={onClick} hidden>
|
||||
{title}
|
||||
</Link>
|
||||
)}
|
||||
</H5>
|
||||
)}
|
||||
<Link to={to} onClick={onClick} hidden>
|
||||
{children}
|
||||
</Link>
|
||||
|
|
@ -31,10 +34,10 @@ const Card = ({ title, to, image, header, onClick, children }) => (
|
|||
)
|
||||
|
||||
Card.propTypes = {
|
||||
title: PropTypes.string,
|
||||
title: PropTypes.node,
|
||||
header: PropTypes.node,
|
||||
to: PropTypes.string,
|
||||
image: PropTypes.string,
|
||||
card: PropTypes.node,
|
||||
onClick: PropTypes.func,
|
||||
children: PropTypes.node,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -37,7 +37,7 @@ Infobox.defaultProps = {
|
|||
}
|
||||
|
||||
Infobox.propTypes = {
|
||||
title: PropTypes.string,
|
||||
title: PropTypes.node,
|
||||
id: PropTypes.string,
|
||||
variant: PropTypes.oneOf(['default', 'warning', 'danger']),
|
||||
className: PropTypes.string,
|
||||
|
|
|
|||
|
|
@ -29,7 +29,7 @@ function getCellContent(children) {
|
|||
function isDividerRow(children) {
|
||||
if (children.length && children[0].props && children[0].props.name == 'td') {
|
||||
const tdChildren = children[0].props.children
|
||||
if (!Array.isArray(tdChildren) && tdChildren.props) {
|
||||
if (tdChildren && !Array.isArray(tdChildren) && tdChildren.props) {
|
||||
return tdChildren.props.name === 'em'
|
||||
}
|
||||
}
|
||||
|
|
|
|||
5
website/src/images/logos/dvc.svg
Normal file
5
website/src/images/logos/dvc.svg
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="36" height="22" viewBox="0 0 36 22">
|
||||
<path d="M0 16.584V.696A.44.44 0 01.132.372a.413.413 0 01.3-.132h5.856c2.32 0 4.304.82 5.952 2.46 1.648 1.64 2.472 3.612 2.472 5.916 0 2.32-.824 4.304-2.472 5.952-1.648 1.648-3.632 2.472-5.952 2.472H.432a.413.413 0 01-.3-.132.44.44 0 01-.132-.324zm3.744-3.096h2.304c1.344 0 2.46-.468 3.348-1.404.888-.936 1.332-2.092 1.332-3.468 0-1.36-.444-2.508-1.332-3.444-.888-.936-2.004-1.404-3.348-1.404H3.744v9.72z" fill="#13ADC7"></path>
|
||||
<path d="M17.121 21.016L9.633 4.864c-.08-.16-.076-.304.012-.432s.22-.192.396-.192h3.192c.208 0 .344.088.408.264l3.96 8.76h.144l3.96-8.76c.064-.176.2-.264.408-.264h3.192c.176 0 .308.064.396.192.088.128.092.272.012.432l-7.536 16.152c-.096.176-.232.264-.408.264h-.24c-.176 0-.312-.088-.408-.264z" fill="#945DD6"></path>
|
||||
<path d="M23.196 14.784c-1.68-1.68-2.52-3.72-2.52-6.12s.844-4.444 2.532-6.132C24.896.844 26.94 0 29.34 0c2.24 0 4.176.744 5.808 2.232.24.224.248.448.024.672L33.3 4.848c-.208.192-.408.192-.6 0-.912-.816-1.992-1.224-3.24-1.224-1.344 0-2.464.484-3.36 1.452-.896.968-1.344 2.132-1.344 3.492 0 1.344.452 2.492 1.356 3.444.904.952 2.028 1.428 3.372 1.428 1.248 0 2.32-.384 3.216-1.152.224-.192.432-.184.624.024l1.872 1.992c.208.208.2.424-.024.648-1.6 1.552-3.544 2.328-5.832 2.328-2.4 0-4.448-.832-6.144-2.496z" fill="#F46737"></path>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.3 KiB |
14
website/src/images/logos/fastapi.svg
Normal file
14
website/src/images/logos/fastapi.svg
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="1309.1" height="241.8" viewBox="0 0 346.4 64">
|
||||
<g fill="#009688">
|
||||
<path d="M32 0a32 32 0 100 64 32 32 0 000-64zm-1.7 57.6v-20H19.2l16-31.3v20.1h10.7z"/>
|
||||
<g stroke-width="2">
|
||||
<path d="M89.5 59.4V4.2h33.3v6.6H97.3v16.6H120V34H97.3v25.4z" />
|
||||
<path d="M141.8 54h4.7l3.4-.5V41.2q-.8-.4-2.6-.7-1.8-.3-4.3-.3l-3.6.2q-1.9.3-3.5 1-1.5.8-2.5 2.1t-1 3.5q0 4 2.5 5.6 2.6 1.5 7 1.5zm-.6-37q4.5 0 7.5 1.1 3.1 1.1 5 3.3 1.9 2 2.7 5 .7 2.9.7 6.4v25.9l-2.7.5-3.8.4q-2.1.3-4.7.4-2.5.3-5 .3-3.4 0-6.4-.7-3-.7-5-2.3-2.2-1.6-3.4-4.1-1.2-2.6-1.2-6.1 0-3.5 1.3-6 1.5-2.4 3.8-4 2.4-1.4 5.6-2.2 3.2-.7 6.7-.7l2.3.2q1.2 0 2.3.3l1.9.3 1 .3v-2.1q0-1.8-.3-3.6-.4-1.8-1.4-3.2-1-1.4-3-2.2-1.7-.9-4.5-.9-3.7 0-6.5.6-2.7.4-4 1l-1-6.1q1.5-.7 4.9-1.2 3.3-.7 7.2-.7z" />
|
||||
<path d="M179 54q4.6 0 6.7-1.1 2.3-1.2 2.3-3.9t-2.2-4.3q-2.1-1.6-7-3.5l-4.7-2q-2.2-1-3.8-2.3-1.6-1.4-2.5-3.3-1-2-1-4.7 0-5.5 4.1-8.7 4-3.3 11-3.3 1.8 0 3.6.3 1.7.1 3.3.4l2.6.6 1.8.6L192 25q-1.2-.6-3.8-1.2-2.5-.8-6.1-.8-3.1 0-5.4 1.3-2.4 1.2-2.4 3.8 0 1.4.5 2.4.6 1 1.6 2L179 34l3.9 1.5q2.9 1 5.2 2.2 2.3 1 4 2.5 1.6 1.4 2.5 3.5.8 2 .8 5 0 5.7-4.3 8.6-4.2 3-12 3-5.6 0-8.7-1l-4.2-1.3 1.3-6.4 4.1 1.4q2.8 1 7.4 1z" />
|
||||
<path d="M211.8 18h15.7v6.2h-15.7v19.1q0 3.1.5 5.2.5 2 1.4 3.2 1 1 2.4 1.6 1.5.5 3.4.5 3.3 0 5.3-.8t2.9-1l1.4 6q-1.1.6-3.9 1.4-2.8.9-6.4.9-4.2 0-7-1-2.7-1.2-4.4-3.3-1.6-2.2-2.4-5.3-.6-3.2-.6-7.3v-37l7.4-1.3z" />
|
||||
<path d="M274.3 59.4l-2.5-7-2.5-7.1h-25l-5 14.1h-8l5.9-16.2 5.4-14.2q2.7-6.7 5.4-12.7 2.6-6.2 5.5-12.1h7q3 6 5.5 12 2.7 6.1 5.3 12.8l5.5 14.2 6 16.2zM267.1 39L262 25.5q-2.5-6.5-5.2-12.5-2.8 6-5.3 12.5l-5 13.4z" />
|
||||
<path d="M304.9 3.6q11.6 0 17.8 4.5 6.3 4.4 6.3 13.1 0 4.8-1.7 8.2-1.7 3.4-5 5.5-3.2 2.1-7.8 3-4.6 1-10.4 1h-6.2v20.5h-7.7V5q3.3-.8 7.3-1 4-.4 7.4-.4zm.6 6.8q-5 0-7.6.2v21.7h5.9q4 0 7.1-.5 3.2-.5 5.4-1.7 2.2-1.3 3.4-3.5 1.2-2.1 1.2-5.5 0-3.1-1.3-5.2-1.2-2-3.3-3.3-2-1.3-4.9-1.7-2.8-.5-5.9-.5z" />
|
||||
<path d="M338.6 4.2h7.8v55.2h-7.8z" />
|
||||
</g>
|
||||
</g>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.0 KiB |
3
website/src/images/logos/prodigy.svg
Normal file
3
website/src/images/logos/prodigy.svg
Normal file
|
|
@ -0,0 +1,3 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 540 158" width="540" height="158">
|
||||
<path d="M70.6 48.6c7 7.3 10.5 17 10.5 29.2s-3.3 22-10.4 29.2c-7 7.3-16 11-27 11-9.4 0-16.8-2.6-21.7-8v44.7H0V39h20.7v8c4.8-6.3 12.4-9.5 23-9.5 11 0 20 3.7 27 11zM22 76v3.6c0 12 7.2 19.8 18.2 19.8 11.2 0 18.7-8 18.7-21.6S51.3 56.2 40 56.2C29.2 56.2 22 64 22 76zM133.8 59.4c-12.6 0-20.5 7-20.5 17.8v39.3h-22V39h21v8.8c4-6.4 11.3-9.6 21.4-9.6v21.2zM209.5 107c-7.6 7.4-17.5 11.2-29.5 11.2s-22-3.8-29.7-11c-7.6-7.6-11.5-17.3-11.5-29.3 0-12.2 4-22 11.5-29.3 7.8-7.3 17.7-11 29.7-11s22 3.7 29.5 11c7.8 7.3 11.7 17 11.7 29.2 0 11.8-4 21.6-11.7 29zM180 56.3c-5.7 0-10.3 2-13.8 5.8s-5.2 9-5.2 15.7c0 6.7 1.8 12 5.2 15.7 3.4 3.8 8 5.7 13.8 5.7s10.3-1.8 13.8-5.6 5.2-9 5.2-15.7c0-6.8-1.8-12-5.2-15.7-3.5-3.8-8-5.8-13.8-5.8zM313 116.5h-20.5v-8c-4.4 5.6-12.7 9.7-23 9.7-11 0-20-3.8-27-11-7-7.5-10.5-17.2-10.5-29.4s3.5-22 10.3-29.2c7-7.3 16-11 27-11 9.7 0 17 2.6 22 8V0H313v116.5zm-58.8-38.7c0 13.6 7.5 21.4 18.7 21.4 10.8 0 18.2-7.3 18.2-19.8V76c0-12.2-7.3-19.8-18.3-19.8-11.3 0-18.8 8-18.8 21.6zM354 13.6c0 3.6-1.2 6.8-3.8 9.3-5 4.8-13.6 4.8-18.6 0C323.2 15.3 330-.3 341 .3c7.3 0 13 6 13 13.2zm-2 103h-22V39h22v77.5zM425 47v-8h20.6v80.4c0 11.2-3.6 20-10.6 26.8-7 6.7-16.6 10-28.5 10-23.4 0-37-11.4-40-29.8l21.8-.8c1 7.6 7.6 12 17.4 12 11.2 0 18-5.8 18-16.6v-11c-5 5.4-12.4 8-21.8 8-11 0-20-3.7-27-11s-10.4-17-10.4-29.2 3.5-22 10.3-29.2c7-7.3 16-11 27-11 10.6 0 18.3 3 23 9.5zM387 78c0 13.6 7.5 21.6 18.7 21.6 11 0 18.3-7.6 18.3-19.8V76c0-12.2-7.3-19.8-18.3-19.8-11.2 0-18.7 8-18.7 21.6zM488.8 154.8H465l19.8-45L454.5 39h24l18 46.2L514 39h24.3l-49.7 115.8z" fill="#1a1e24"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.6 KiB |
4
website/src/images/logos/ray.svg
Normal file
4
website/src/images/logos/ray.svg
Normal file
|
|
@ -0,0 +1,4 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="945.3" height="350" viewBox="90 90 850 300">
|
||||
<path fill="#231f20" d="M549.5 308.7h18.7L534.6 260c20.7-6.7 32.1-22.7 32.1-46.4 0-30.5-20.6-48-56.4-48h-54.4v143h16.3v-45.1h38c3.3 0 6.6-.2 9.7-.4zm-77.3-61v-66.4h38c26 0 40.8 11 40.8 32.7 0 22.3-14.8 33.7-40.7 33.7zm224.2 24.8l16 36.2H730l-64-143.2h-17L585 308.7h17.4l16-36.2zm-7-15.8h-64.2l32-72.6zm106.6 3l54.4-94.2h-16.8l-45.8 74.7-46.4-74.7h-17l55.4 94.7v48.5H796v-49"/>
|
||||
<path fill="#00adef" d="M204.3 227.2a40.5 40.5 0 0178.7 0H313c1-3.9 2.5-7.6 4.5-11l-53-52.8a40.4 40.4 0 01-49.4-6.2A40.4 40.4 0 01243.7 88a40.5 40.5 0 0134.8 61.4l53 53a40.4 40.4 0 0149.4 6.1 40.5 40.5 0 01-28.7 69.3c-7.6 0-14.7-2.1-20.8-5.8l-53 53a40.4 40.4 0 01-6 49.5 40.5 40.5 0 01-69.3-28.7 40.4 40.4 0 0161.4-34.8l52.9-53c-2-3.3-3.5-7-4.5-10.9H283a40.5 40.5 0 01-78.7 0h-29.9a40.5 40.5 0 110-19.8zM229 360.4a20.7 20.7 0 0035.4-14.7A20.7 20.7 0 00229 331a20.7 20.7 0 000 29.4zm138-108.6a20.7 20.7 0 00-14.8-35.4 20.7 20.7 0 00-14.6 35.4 20.7 20.7 0 0029.3 0zm-108.7-138a20.7 20.7 0 00-35.4 14.7 20.7 20.7 0 0035.4 14.7 20.7 20.7 0 000-29.4zm-138 108.6a20.7 20.7 0 0014.8 35.5 20.7 20.7 0 0014.6-35.5 20.7 20.7 0 00-29.3 0zm108.7 0a20.7 20.7 0 0014.7 35.5 20.7 20.7 0 0014.6-35.5 20.7 20.7 0 00-29.3 0"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.3 KiB |
14
website/src/images/logos/streamlit.svg
Normal file
14
website/src/images/logos/streamlit.svg
Normal file
|
|
@ -0,0 +1,14 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="800" height="200" viewBox="130 250 550 100">
|
||||
<path fill="#262730" d="M300.4 316.8h8c1 8.4 4.3 15.4 18.2 15.4 8.8 0 15.6-5.3 15.6-13.2 0-8-3.6-11.3-16.6-13.5-14.4-2.3-23-6.9-23-19.3 0-10.8 9.2-19.1 22.1-19.1 13.8 0 22.3 6.9 23.6 19.4H341c-1.7-8.9-6.7-12.5-16.2-12.5-9.4 0-14.3 4.8-14.3 11.4 0 6.8 2.5 10.4 16.5 12.5 15.3 2.5 23.4 7.2 23.4 20.5 0 11.8-10.1 20.7-23.7 20.7-18.4 0-25-10.3-26.2-22.3z"/>
|
||||
<path fill="#262730" d="M362.4 325.4v-33h-7.2v-6.8h7.2v-12.2h8v12.2H382v6.8h-11.7v32.3c0 5 2.2 7.4 6.2 7.4 2.5 0 4.4-.4 6.1-1v6.7c-1.6.6-3.5 1.1-6.8 1.1-9 0-13.5-5.5-13.5-13.4z"/>
|
||||
<path fill="#262730" d="M390.3 285.6h8v9.2c3.1-5.8 7.5-9.8 16.5-10v7.5c-10 .5-16.5 4.8-16.5 17.4v28.5h-8v-52.6z"/>
|
||||
<path fill="#262730" d="M454.5 307.4c-.4-11.2-6.1-16-14.4-16-8.1 0-13.7 6-15 16h29.4zm-37.9 5.5V311c0-16.8 9.7-26.4 23.5-26.4 11.6 0 22.6 7 22.6 25.8v3.4h-37.8c.3 11.6 5.8 18.5 16 18.5 7.9 0 12.4-3 13.4-8.9h8c-1.7 10.2-10 15.6-21.4 15.6-14.2 0-24.3-10.5-24.3-26.2z"/>
|
||||
<path fill="#262730" d="M500.8 320.4v-6.8H494c-10.9 0-17.5 2.7-17.5 10.2 0 5.2 2.5 8.9 9.5 8.9 8.5 0 15-4.4 15-12.3zm-32.3 3.4c0-11.8 11.3-16 25.1-16h7.2V303c0-8-3-11.5-10.7-11.5-7 0-10.8 3-11.6 9.2h-8c1.1-11.6 10.3-15.9 20-15.9 9.6 0 18.3 3 18.3 17.2v36.3h-8v-6.4a18 18 0 01-15.6 7.3c-9.3 0-16.7-4.9-16.7-15.3z"/>
|
||||
<path fill="#262730" d="M519.4 285.6h8v7.8c2.2-4.8 7.7-8.7 15-8.7 6.3 0 12 2.7 14.6 9.6 3.5-6.7 11-9.6 17.2-9.6 9 0 16.9 5.5 16.9 20.3v33.2h-8v-33.8c0-9-3.7-12.7-10.6-12.7a13 13 0 00-13.3 13.7v32.8h-8v-33.8c0-9-3.7-12.7-10.6-12.7a13 13 0 00-13.3 13.7v32.8h-8v-52.6z"/>
|
||||
<path fill="#262730" d="M602.1 338.2h8v-72.9h-8v73z"/>
|
||||
<path fill="#262730" d="M623 338.2h8v-52.6h-8v52.6zm-1.4-67.5c0-3 2.4-5.4 5.3-5.4a5.4 5.4 0 010 10.8 5.4 5.4 0 01-5.3-5.4z"/>
|
||||
<path fill="#262730" d="M646.6 325.4v-33h-7.2v-6.8h7.2v-12.2h8v12.2h11.7v6.8h-11.7v32.3c0 5 2.2 7.4 6.2 7.4 2.5 0 4.5-.4 6.2-1v6.7c-1.7.6-3.6 1.1-6.8 1.1-9.2 0-13.6-5.5-13.6-13.4z"/>
|
||||
<path fill="#FFC7C7" d="M276.4 276v.1l-22.2 57.7c-1.5 2.7-3.7 4.4-7 4.4h-84.7c-2 0-4.2-1-5.3-2l116.4-62.5c1.6-1 3.3.6 2.8 2.3z"/>
|
||||
<path fill="#FF8C8C" d="M254.1 333.8c-1.5 2.8-3.7 4.4-7 4.4h-84.6c-3 0-5.8-1.6-7.1-4.4l47-71.3c1.1-1.6 3.5-1.6 4.7 0l47 71.3z"/>
|
||||
<path fill="#FF4B4B" d="M252.4 336.2c-1 1.1-2.9 2-5.2 2h-84.7c-3 0-6-1.5-7.1-4.4L133 276c-.6-1.6 1-3.2 2.7-2.5h.1l116.5 62.6z"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 2.3 KiB |
28
website/src/images/logos/wandb.svg
Normal file
28
website/src/images/logos/wandb.svg
Normal file
|
|
@ -0,0 +1,28 @@
|
|||
<svg xmlns="http://www.w3.org/2000/svg" width="700" height="109" fill="none" viewBox="0 0 700 109">
|
||||
<path fill="#000" d="M181.2 83.4h3.2l12.8-47.1 6.3-1v-3h-17.1v3l6.9 1-9.6 35-11.3-35 7.6-1v-3h-20.5v3l7.5 1L155.5 71l-9.3-34.8 7-.8v-3.1h-20.6v3l6.3.8 13.6 47.3h3.3L168 44l13 39.3z"/>
|
||||
<path fill="#000" d="M219.2 49.4c4.6 0 8.2 3.9 8.2 8.2 0 2.2-.8 4-4.5 4h-13.7c.8-8.8 5.7-12.2 10-12.2zm.4-3.5c-9.4 0-17.4 7.8-17.4 19.3 0 11.6 6.7 18.6 16.9 18.6 6.9 0 11.7-3.4 14.5-8.7l-1.8-1.5C229 76.8 226 79 221.3 79c-7.3 0-12.1-5-12.3-14.2h24.1c.3-1 .6-2.5.6-4.2 0-9.2-6.2-14.6-14-14.6z"/>
|
||||
<path fill="#000" d="M249 37.6c2.7 0 5-2 5-4.8 0-3-2.3-4.8-5-4.8-3 0-5.1 1.8-5.1 4.8 0 2.8 2.1 4.8 5 4.8zm8.5 42.5l-5.3-1-.1-12.1V57l.2-10.3-1.1-.8-11.7 4.7v2.6l6 .7c.2 3 .3 5.4.3 9.5V67l-.1 12-5.7 1.1v2.7h17.5v-2.7z"/>
|
||||
<path fill="#000" d="M278 49c4.8 0 7.5 3.8 7.5 9.1 0 5.6-2.9 9.6-7.7 9.6-4.7 0-7.3-3.8-7.3-9.3 0-5.5 2.8-9.4 7.6-9.4zm-13.7 9.3c0 5.2 2.2 8.7 5.8 10.7-3.7 3.5-5 6-5 8.4 0 3 1.6 4.8 4.9 5.7-5.6 2.8-7.4 5.4-7.4 8.7 0 5.3 4.7 9.1 15.6 9.1 12.4 0 18.7-6.5 18.7-13 0-6.2-3.8-9.8-12.2-9.8h-9.9c-3.5 0-4.3-1.3-4.3-3.4 0-1.7.6-3.1 1.8-4.8 1.7.5 3.6.8 5.6.8 7.9 0 13.7-4.4 13.7-12.4 0-2.7-.6-5-1.7-6.8h7.4v-4.8l-1.1-.8-8.6 3a15 15 0 00-9.6-3c-7.9 0-13.7 4.5-13.7 12.4zm14.2 39.1c-6 0-9.7-2-9.7-6.9 0-2.4.8-4.5 3-6.8l3 .2h8.9c5.4 0 7.5 2.6 7.5 5.9 0 4.2-4.4 7.6-12.7 7.6z"/>
|
||||
<path fill="#000" d="M306.3 67l-.1 12-5.7 1.1v2.7h17.6v-2.7l-5.3-1-.1-12.1V56.3c3.8-3.5 7-5 10.7-5 4.4 0 6.1 2.3 6.1 9.2V67l-.1 12.1-5.5 1v2.7h17.5v-2.7l-5.3-1L336 67v-7c0-10-3.2-14-9.7-14a20 20 0 00-13.8 7V38.3l.3-10.7-1-.6-11.7 3.4V33l6.2.8V67z"/>
|
||||
<path fill="#000" d="M356.7 74.4V50.9h10.2v-4h-10l.3-10.2h-4.5l-2 10-6.7 1v3.2h6.2V75.2c0 5.9 3.3 8.6 8.6 8.6 3.8 0 7-1.4 8.9-4l-1.8-2c-2 1.4-3.4 2-5 2-2.7 0-4.2-1.6-4.2-5.4z"/>
|
||||
<path fill="#000" d="M383 83.8c9.7 0 14.7-5 14.7-10.8 0-4.8-2.9-8.3-10.4-11.1l-2.4-.8c-5-2-7-3.2-7-6.5 0-3.1 2.5-5.2 7.1-5.2 1.8 0 3.6.5 5.2 1.3l1.2 6h4l.3-7.7c-3.3-2-6.5-3-10.6-3-8.5 0-13.2 5-13.2 10.6 0 5.1 3.7 8.2 9.2 10.2l3.5 1.3c4.7 1.7 7 3.3 7 6.6 0 3.4-2.7 5.7-8.5 5.7-2.3 0-4.3-.4-6-1.2l-1.5-6.6h-4.2l.3 8.3c3.7 2 7 3 11.3 3z"/>
|
||||
<path fill="#000" d="M435 49.4c-2.7-3.5-3.3-5.9-3.3-8.6 0-4 3-7 6.8-7 3.7 0 6.6 2.7 6.6 7 0 4.5-3.3 7.8-8 11-.7-.7-1.4-1.5-2-2.4zM469 83l.2-3.4-7.3-1-6.2-6.5a55.5 55.5 0 007.9-17.7l5.7-1v-3H454v3l6 1c-1.4 5.9-3.5 10.8-6.3 15.5-3-3.2-5.7-6-8.7-9.4a335 335 0 01-6-6.7c7.6-4.2 11.1-7.9 11.1-13 0-5.8-4-9.8-11.5-9.8-7 0-12.4 4.1-12.4 10.8 0 4 1.5 7.8 5.2 12.1l.3.4c-7.5 3.8-11.4 8.9-11.4 15.7 0 7.6 6.5 13.7 16 13.7 7.2 0 12-2.8 15.4-6.4l2.6 3c2.5 2.5 4.9 3.4 8.9 3.4 2 0 3.5-.2 5.7-.7zm-27.2-16.8l7.7 8.7c-2.9 2.5-7 4-11.2 4-7.3 0-11.4-5.5-11.4-11a13 13 0 016.6-11.5c2.5 2.9 4.9 5.6 8.3 9.8z"/>
|
||||
<path fill="#000" d="M500.8 59.4c0 6.6 0 13.2-.2 19.5l-7 .8v3.1h20.8c14.6 0 20.2-7 20.2-13.8 0-6.1-4.2-11-14.3-12.5 8.4-1.7 11.8-6.5 11.8-12 0-7-6-12.2-16.5-12.2h-22v3l7 .9c.2 6.5.2 13 .2 19.5v3.7zm11.3-1c10.4 0 15 3.6 15 10.6 0 6.7-4.7 10.3-14.4 10.3h-4.6c-.2-6.4-.2-13-.2-20.9h4.2zm1.2-22.7c7.7 0 11.6 2.5 11.6 9.4S521 55 512 55h-4c0-6 0-12.7.2-19.2h5.2z"/>
|
||||
<path fill="#000" d="M549.4 37.6c2.8 0 5-2 5-4.8 0-3-2.2-4.8-5-4.8s-5 1.8-5 4.8c0 2.8 2.2 4.8 5 4.8zm8.6 42.5l-5.2-1-.2-12.1V57l.2-10.3-1.1-.8-11.6 4.7v2.6l6 .7c.2 3 .2 5.4.2 9.5V67l-.1 12-5.7 1.1v2.7H558v-2.7z"/>
|
||||
<path fill="#000" d="M591.8 83.6c2.7 0 4.8-1 6.6-4l-1.5-1.6c-.8 1-1.7 1.7-3 1.7-1.6 0-2.7-1.2-2.7-4.5V59.1c0-9.5-3.8-13.2-11.7-13.2-7.7 0-13.3 3.7-14.7 9.4.2 2 1.4 3.1 3.5 3.1 2 0 3.3-1.3 4-3.6l1.4-5c1.6-.3 3-.4 4-.4 5 0 7.2 1.8 7.2 9.2v1.9c-3 .6-6.2 1.6-8.6 2.5-10.2 3.7-12.5 7-12.5 11.4 0 6.2 4.6 9.4 10.1 9.4 4.5 0 7-2 11.1-6a6.6 6.6 0 006.8 5.8zm-21.3-10.4c0-2.2.8-5.1 7.4-7.7 1.6-.6 4.3-1.5 7-2.2v11.6c-4.3 3-5.8 4-8.3 4-3.5 0-6-1.7-6-5.7z"/>
|
||||
<path fill="#000" d="M614 83.8c9.7 0 14.7-5 14.7-10.8 0-4.8-2.9-8.3-10.4-11.1l-2.4-.8c-5.1-2-7-3.2-7-6.5 0-3.1 2.5-5.2 7-5.2 2 0 3.6.5 5.3 1.3l1.2 6h4l.3-7.7c-3.3-2-6.5-3-10.6-3-8.5 0-13.2 5-13.2 10.6 0 5.1 3.6 8.2 9.2 10.2l3.4 1.3c4.8 1.7 7 3.3 7 6.6 0 3.4-2.7 5.7-8.4 5.7-2.3 0-4.3-.4-6.1-1.2l-1.5-6.6h-4.1l.3 8.3c3.7 2 7 3 11.3 3z"/>
|
||||
<path fill="#000" d="M652 49.4c4.6 0 8.2 3.9 8.2 8.2 0 2.2-.8 4-4.5 4H642c.8-8.8 5.7-12.2 10-12.2zm.4-3.5c-9.5 0-17.4 7.8-17.4 19.3 0 11.6 6.7 18.6 16.9 18.6 6.8 0 11.7-3.4 14.5-8.7l-1.8-1.5c-2.8 3.2-5.7 5.3-10.5 5.3-7.3 0-12.1-5-12.3-14.2H666c.3-1 .6-2.5.6-4.2 0-9.2-6.2-14.6-14.1-14.6z"/>
|
||||
<path fill="#000" d="M685.3 83.8c9.7 0 14.7-5 14.7-10.8 0-4.8-2.9-8.3-10.4-11.1l-2.4-.8c-5.1-2-7-3.2-7-6.5 0-3.1 2.5-5.2 7.1-5.2 1.8 0 3.6.5 5.2 1.3l1.2 6h4l.3-7.7c-3.3-2-6.5-3-10.6-3-8.5 0-13.2 5-13.2 10.6 0 5.1 3.7 8.2 9.2 10.2l3.4 1.3c4.8 1.7 7 3.3 7 6.6 0 3.4-2.6 5.7-8.4 5.7-2.3 0-4.3-.4-6.1-1.2l-1.4-6.6h-4.2l.3 8.3c3.7 2 7 3 11.3 3z"/>
|
||||
<path fill="#FC3" d="M0 35.1a11.2 11.2 0 1022.5 0 11.2 11.2 0 00-22.5 0z"/>
|
||||
<path fill="#FC3" d="M0 91.3a11.2 11.2 0 1022.5 0 11.2 11.2 0 00-22.5 0z"/>
|
||||
<path fill="#FC3" d="M4.2 63.2a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M4.2 7a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M43.1 73.6a11.2 11.2 0 1022.5 0 11.2 11.2 0 00-22.5 0z"/>
|
||||
<path fill="#FC3" d="M47.3 101.7a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M47.3 45.5a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M47.3 17.4a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M86.2 35.1a11.2 11.2 0 1022.5 0 11.2 11.2 0 00-22.5 0z"/>
|
||||
<path fill="#FC3" d="M90.5 7a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M90.5 63.2a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
<path fill="#FC3" d="M90.5 91.3a7 7 0 1014 0 7 7 0 00-14 0z"/>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 5.4 KiB |
|
|
@ -5,6 +5,15 @@
|
|||
font: var(--font-size-md)/var(--line-height-md) var(--font-primary)
|
||||
margin-bottom: var(--spacing-sm)
|
||||
|
||||
.small
|
||||
padding: 1.5rem
|
||||
font-size: var(--font-size-sm)
|
||||
line-height: var(--line-height-sm)
|
||||
color: var(--color-dark)
|
||||
|
||||
.title
|
||||
margin-bottom: var(--spacing-xs)
|
||||
|
||||
.image
|
||||
$image-size: 35px
|
||||
width: $image-size
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ import { YouTube, SoundCloud, Iframe, Image } from '../components/embed'
|
|||
import Alert from '../components/alert'
|
||||
import Search from '../components/search'
|
||||
import Project from '../widgets/project'
|
||||
import { Integration, IntegrationLogo } from '../widgets/integration'
|
||||
|
||||
const mdxComponents = {
|
||||
a: Link,
|
||||
|
|
@ -75,6 +76,8 @@ const scopeComponents = {
|
|||
Grid,
|
||||
InlineCode,
|
||||
Project,
|
||||
Integration,
|
||||
IntegrationLogo,
|
||||
}
|
||||
|
||||
const AlertSpace = ({ nightly }) => {
|
||||
|
|
|
|||
46
website/src/widgets/integration.js
Normal file
46
website/src/widgets/integration.js
Normal file
|
|
@ -0,0 +1,46 @@
|
|||
import React from 'react'
|
||||
|
||||
import Card from '../components/card'
|
||||
|
||||
import { ReactComponent as DVCLogo } from '../images/logos/dvc.svg'
|
||||
import { ReactComponent as ProdigyLogo } from '../images/logos/prodigy.svg'
|
||||
import { ReactComponent as StreamlitLogo } from '../images/logos/streamlit.svg'
|
||||
import { ReactComponent as FastAPILogo } from '../images/logos/fastapi.svg'
|
||||
import { ReactComponent as WandBLogo } from '../images/logos/wandb.svg'
|
||||
import { ReactComponent as RayLogo } from '../images/logos/ray.svg'
|
||||
|
||||
const LOGOS = {
|
||||
dvc: DVCLogo,
|
||||
prodigy: ProdigyLogo,
|
||||
streamlit: StreamlitLogo,
|
||||
fastapi: FastAPILogo,
|
||||
wandb: WandBLogo,
|
||||
ray: RayLogo,
|
||||
}
|
||||
|
||||
export const IntegrationLogo = ({ name, title, width, height, maxWidth, align, ...props }) => {
|
||||
const Logo = LOGOS[name]
|
||||
if (!Logo) throw new Error(`Unknown logo: ${name}`)
|
||||
const style = { maxWidth, float: align || 'none' }
|
||||
return (
|
||||
<Logo
|
||||
aria-label={title}
|
||||
aria-hidden={title ? undefined : 'true'}
|
||||
width={width}
|
||||
height={height}
|
||||
style={style}
|
||||
{...props}
|
||||
/>
|
||||
)
|
||||
}
|
||||
|
||||
export const Integration = ({ height = 30, url, logo, title, children }) => {
|
||||
const header = logo && (
|
||||
<IntegrationLogo name={logo} title={title} height={height} width="auto" maxWidth="80%" />
|
||||
)
|
||||
return (
|
||||
<Card title={header} to={url} small>
|
||||
{children}
|
||||
</Card>
|
||||
)
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user