Merge remote-tracking branch 'upstream/develop' into whatif/arrow

# Conflicts:
#	spacy/cli/__init__.py
#	spacy/cli/convert.py
#	spacy/cli/debug_data.py
#	spacy/cli/evaluate.py
#	spacy/cli/train.py

requirements.txt

@@ -5,13 +5,13 @@ thinc==8.0.0a9
 blis>=0.4.0,<0.5.0
 ml_datasets>=0.1.1
 murmurhash>=0.28.0,<1.1.0
-wasabi>=0.4.0,<1.1.0
+wasabi>=0.7.0,<1.1.0
-srsly>=2.0.0,<3.0.0
+srsly>=2.1.0,<3.0.0
 catalogue>=0.0.7,<1.1.0
+typer>=0.2.1,<1.0.0
 # Third party dependencies
 numpy>=1.15.0
 requests>=2.13.0,<3.0.0
-plac>=0.9.6,<1.2.0
 tqdm>=4.38.0,<5.0.0
 pydantic>=1.3.0,<2.0.0
 # Official Python utilities

setup.cfg

@@ -44,14 +44,13 @@ install_requires =
     preshed>=3.0.2,<3.1.0
     thinc==8.0.0a9
     blis>=0.4.0,<0.5.0
-    wasabi>=0.4.0,<1.1.0
+    wasabi>=0.7.0,<1.1.0
-    srsly>=2.0.0,<3.0.0
+    srsly>=2.1.0,<3.0.0
     catalogue>=0.0.7,<1.1.0
-    ml_datasets>=0.1.1
+    typer>=0.2.1,<1.0.0
     # Third-party dependencies
     tqdm>=4.38.0,<5.0.0
     numpy>=1.15.0
-    plac>=0.9.6,<1.2.0
     requests>=2.13.0,<3.0.0
     pydantic>=1.3.0,<2.0.0
     # Official Python utilities

spacy/__init__.py

@@ -8,7 +8,7 @@ warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
 from thinc.api import prefer_gpu, require_gpu
 
 from . import pipeline
-from .cli.info import info as cli_info
+from .cli.info import info
 from .glossary import explain
 from .about import __version__
 from .errors import Errors, Warnings
@@ -34,7 +34,3 @@ def load(name, **overrides):
 def blank(name, **kwargs):
     LangClass = util.get_lang_class(name)
     return LangClass(**kwargs)
-
-
-def info(model=None, markdown=False, silent=False):
-    return cli_info(model, markdown, silent)

spacy/__main__.py

@@ -1,31 +1,4 @@
 if __name__ == "__main__":
-    import plac
+    from spacy.cli import setup_cli
-    import sys
-    from wasabi import msg
-    from spacy.cli import download, link, info, package, pretrain, convert
-    from spacy.cli import init_model, profile, evaluate, validate, debug_data
-    from spacy.cli import train_cli
 
-    commands = {
+    setup_cli()
-        "download": download,
-        "link": link,
-        "info": info,
-        "train": train_cli,
-        "pretrain": pretrain,
-        "debug-data": debug_data,
-        "evaluate": evaluate,
-        "convert": convert,
-        "package": package,
-        "init-model": init_model,
-        "profile": profile,
-        "validate": validate,
-    }
-    if len(sys.argv) == 1:
-        msg.info("Available commands", ", ".join(commands), exits=1)
-    command = sys.argv.pop(1)
-    sys.argv[0] = f"spacy {command}"
-    if command in commands:
-        plac.call(commands[command], sys.argv[1:])
-    else:
-        available = f"Available: {', '.join(commands)}"
-        msg.fail(f"Unknown command: {command}", available, exits=1)

spacy/about.py

@@ -5,3 +5,4 @@ __release__ = True
 __download_url__ = "https://github.com/explosion/spacy-models/releases/download"
 __compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
 __shortcuts__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/shortcuts-v2.json"
+__projects__ = "https://github.com/explosion/spacy-boilerplates"

spacy/cli/__init__.py

@@ -1,5 +1,9 @@
 from wasabi import msg
 
+from ._app import app, setup_cli  # noqa: F401
+
+# These are the actual functions, NOT the wrapped CLI commands. The CLI commands
+# are registered automatically and won't have to be imported here.
 from .download import download  # noqa: F401
 from .info import info  # noqa: F401
 from .package import package  # noqa: F401
@@ -13,7 +17,10 @@ from .init_model import init_model  # noqa: F401
 from .validate import validate  # noqa: F401
 
 
+@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)
 def link(*args, **kwargs):
+    """As of spaCy v3.0, model symlinks are deprecated. You can load models
+    using their full names or from a directory path."""
     msg.warn(
         "As of spaCy v3.0, model symlinks are deprecated. You can load models "
         "using their full names or from a directory path."

spacy/cli/_app.py

@@ -0,0 +1,31 @@
+from typing import Optional
+import typer
+from typer.main import get_command
+
+
+COMMAND = "python -m spacy"
+NAME = "spacy"
+HELP = """spaCy Command-line Interface
+
+DOCS: https://spacy.io/api/cli
+"""
+
+
+app = typer.Typer(name=NAME, help=HELP)
+
+
+def Arg(*args, help: Optional[str] = None, **kwargs) -> typer.Argument:
+    """Wrapper for Typer's annotation to keep it short and set defaults."""
+    # Filter out help for now until it's officially supported
+    return typer.Argument(*args, **kwargs)
+
+
+def Opt(*args, **kwargs) -> typer.Option:
+    """Wrapper for Typer's annotation to keep it short and set defaults."""
+    return typer.Option(*args, show_default=True, **kwargs)
+
+
+def setup_cli() -> None:
+    # Ensure that the help messages always display the correct prompt
+    command = get_command(app)
+    command(prog_name=COMMAND)

spacy/cli/convert.py

@@ -1,9 +1,12 @@
+from typing import Optional
+from enum import Enum
 from pathlib import Path
 from wasabi import Printer
 import srsly
 import re
 import sys
 
+from ._app import app, Arg, Opt
 from ..tokens import DocBin
 from ..gold.converters import iob2docs, conll_ner2docs, json2docs
 
@@ -24,36 +27,80 @@ CONVERTERS = {
 
 
 # File types
-FILE_TYPES = ("json", "jsonl", "msg")
+FILE_TYPES_STDOUT = ("json")
-FILE_TYPES_STDOUT = ("json", "jsonl")
 
 
-def convert(
+class FileTypes(str, Enum):
+    json = "json"
+    spacy = "spacy"
+
+
+@app.command("convert")
+def convert_cli(
     # fmt: off
-    input_path: ("Input file or directory", "positional", None, Path),
+    input_path: str = Arg(..., help="Input file or directory", exists=True),
-    output_dir: ("Output directory.", "positional", None, Path),
+    output_dir: Path = Arg("-", help="Output directory. '-' for stdout.", allow_dash=True, exists=True),
-    file_type: (f"Type of data to produce: {FILE_TYPES}", "option", "t", str, FILE_TYPES) = "spacy",
+    file_type: FileTypes = Opt("spacy", "--file-type", "-t", help="Type of data to produce"),
-    n_sents: ("Number of sentences per doc (0 to disable)", "option", "n", int) = 1,
+    n_sents: int = Opt(1, "--n-sents", "-n", help="Number of sentences per doc (0 to disable)"),
-    seg_sents: ("Segment sentences (for -c ner)", "flag", "s") = False,
+    seg_sents: bool = Opt(False, "--seg-sents", "-s", help="Segment sentences (for -c ner)"),
-    model: ("Model for sentence segmentation (for -s)", "option", "b", str) = None,
+    model: Optional[str] = Opt(None, "--model", "-b", help="Model for sentence segmentation (for -s)"),
-    morphology: ("Enable appending morphology to tags", "flag", "m", bool) = False,
+    morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
-    merge_subtokens: ("Merge CoNLL-U subtokens", "flag", "T", bool) = False,
+    merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
-    converter: (f"Converter: {tuple(CONVERTERS.keys())}", "option", "c", str) = "auto",
+    converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
-    ner_map: ("NER tag mapping (as JSON-encoded dict of entity types)", "option", "N", Path) = None,
+    ner_map: Optional[Path] = Opt(None, "--ner-map", "-N", help="NER tag mapping (as JSON-encoded dict of entity types)", exists=True),
-    lang: ("Language (if tokenizer required)", "option", "l", str) = None,
+    lang: Optional[str] = Opt(None, "--lang", "-l", help="Language (if tokenizer required)"),
     # fmt: on
 ):
     """
     Convert files into json or DocBin format for use with train command and other
-    experiment management functions.
+    experiment management functions. If no output_dir is specified, the data
+    is written to stdout, so you can pipe them forward to a JSON file:
+    $ spacy convert some_file.conllu > some_file.json
     """
+    if isinstance(file_type, FileTypes):
+        # We get an instance of the FileTypes from the CLI so we need its string value
+        file_type = file_type.value
     cli_args = locals()
-    no_print = output_dir == "-"
+    silent = output_dir == "-"
     output_dir = Path(output_dir) if output_dir != "-" else "-"
-    msg = Printer(no_print=no_print)
+    msg = Printer(no_print=silent)
     verify_cli_args(msg, **cli_args)
-    converter = _get_converter(msg, converter, input_path)
+    convert(
+        input_path,
+        output_dir,
+        file_type=file_type,
+        n_sents=n_sents,
+        seg_sents=seg_sents,
+        model=model,
+        morphology=morphology,
+        merge_subtokens=merge_subtokens,
+        converter=converter,
+        ner_map=ner_map,
+        lang=lang,
+        silent=silent,
+        msg=msg,
+    )
+
+def convert(
+        input_path: Path,
+        output_dir: Path,
+        *,
+        file_type: str = "json",
+        n_sents: int = 1,
+        seg_sents: bool = False,
+        model: Optional[str] = None,
+        morphology: bool = False,
+        merge_subtokens: bool = False,
+        converter: str = "auto",
+        ner_map: Optional[Path] = None,
+        lang: Optional[str] = None,
+        silent: bool = True,
+        msg: Optional[Path] = None,
+) -> None:
+    if not msg:
+        msg = Printer(no_print=silent)
     ner_map = srsly.read_json(ner_map) if ner_map is not None else None
+
     for input_loc in walk_directory(input_path):
         input_data = input_loc.open("r", encoding="utf-8").read()
         # Use converter function to convert data
@@ -66,25 +113,30 @@ def convert(
             merge_subtokens=merge_subtokens,
             lang=lang,
             model=model,
-            no_print=no_print,
+            no_print=silent,
             ner_map=ner_map,
         )
+    if output_dir != "-":
+        # Export data to a file
         suffix = f".{file_type}"
         subpath = input_loc.relative_to(input_path)
-        output_file = (output_dir / subpath).with_suffix(suffix)
+        output_file = Path(output_dir) / subpath.with_suffix(suffix)
         if not output_file.parent.exists():
             output_file.parent.mkdir(parents=True)
         if file_type == "json":
-            data = docs2json(docs)
             srsly.write_json(output_file, docs2json(docs))
         else:
             data = DocBin(docs=docs).to_bytes()
             with output_file.open("wb") as file_:
                 file_.write(data)
         msg.good(f"Generated output file ({len(docs)} documents): {output_file}")
+    else:
+        # Print to stdout
+        if file_type == "json":
+            srsly.write_json("-", docs)
 
 
-def autodetect_ner_format(input_data):
+def autodetect_ner_format(input_data: str) -> str:
     # guess format from the first 20 lines
     lines = input_data.split("\n")[:20]
     format_guesses = {"ner": 0, "iob": 0}

spacy/cli/debug_data.py

@@ -1,11 +1,14 @@
+from typing import Optional, List, Sequence, Dict, Any, Tuple
 from pathlib import Path
 from collections import Counter
 import sys
 import srsly
 from wasabi import Printer, MESSAGES
 
+from ._app import app, Arg, Opt
 from ..gold import Corpus
 from ..syntax import nonproj
+from ..language import Language
 from ..util import load_model, get_lang_class
 
 
@@ -18,17 +21,18 @@ BLANK_MODEL_MIN_THRESHOLD = 100
 BLANK_MODEL_THRESHOLD = 2000
 
 
-def debug_data(
+@app.command("debug-data")
+def debug_data_cli(
     # fmt: off
-    lang: ("Model language", "positional", None, str),
+    lang: str = Arg(..., help="Model language"),
-    train_path: ("Location of JSON-formatted training data", "positional", None, Path),
+    train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
-    dev_path: ("Location of JSON-formatted development data", "positional", None, Path),
+    dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
-    tag_map_path: ("Location of JSON-formatted tag map", "option", "tm", Path) = None,
+    tag_map_path: Optional[Path] = Opt(None, "--tag-map-path", "-tm", help="Location of JSON-formatted tag map", exists=True, dir_okay=False),
-    base_model: ("Name of model to update (optional)", "option", "b", str) = None,
+    base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Name of model to update (optional)"),
-    pipeline: ("Comma-separated names of pipeline components to train", "option", "p", str) = "tagger,parser,ner",
+    pipeline: str = Opt("tagger,parser,ner", "--pipeline", "-p", help="Comma-separated names of pipeline components to train"),
-    ignore_warnings: ("Ignore warnings, only show stats and errors", "flag", "IW", bool) = False,
+    ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
-    verbose: ("Print additional information and explanations", "flag", "V", bool) = False,
+    verbose: bool = Opt(False, "--verbose", "-V", help="Print additional information and explanations"),
-    no_format: ("Don't pretty-print the results", "flag", "NF", bool) = False,
+    no_format: bool = Opt(False, "--no-format", "-NF", help="Don't pretty-print the results"),
     # fmt: on
 ):
     """
@@ -36,8 +40,36 @@ def debug_data(
     stats, and find problems like invalid entity annotations, cyclic
     dependencies, low data labels and more.
     """
-    msg = Printer(pretty=not no_format, ignore_warnings=ignore_warnings)
+    debug_data(
+        lang,
+        train_path,
+        dev_path,
+        tag_map_path=tag_map_path,
+        base_model=base_model,
+        pipeline=[p.strip() for p in pipeline.split(",")],
+        ignore_warnings=ignore_warnings,
+        verbose=verbose,
+        no_format=no_format,
+        silent=False,
+    )
 
+
+def debug_data(
+    lang: str,
+    train_path: Path,
+    dev_path: Path,
+    *,
+    tag_map_path: Optional[Path] = None,
+    base_model: Optional[str] = None,
+    pipeline: List[str] = ["tagger", "parser", "ner"],
+    ignore_warnings: bool = False,
+    verbose: bool = False,
+    no_format: bool = True,
+    silent: bool = True,
+):
+    msg = Printer(
+        no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
+    )
     # Make sure all files and paths exists if they are needed
     if not train_path.exists():
         msg.fail("Training data not found", train_path, exits=1)
@@ -49,7 +81,6 @@ def debug_data(
         tag_map = srsly.read_json(tag_map_path)
 
     # Initialize the model and pipeline
-    pipeline = [p.strip() for p in pipeline.split(",")]
     if base_model:
         nlp = load_model(base_model)
     else:
@@ -446,7 +477,7 @@ def debug_data(
         sys.exit(1)
 
 
-def _load_file(file_path, msg):
+def _load_file(file_path: Path, msg: Printer) -> None:
     file_name = file_path.parts[-1]
     if file_path.suffix == ".json":
         with msg.loading(f"Loading {file_name}..."):
@@ -465,7 +496,9 @@ def _load_file(file_path, msg):
     )
 
 
-def _compile_gold(examples, pipeline, nlp):
+def _compile_gold(
+    examples: Sequence[Example], pipeline: List[str], nlp: Language
+) -> Dict[str, Any]:
     data = {
         "ner": Counter(),
         "cats": Counter(),
@@ -537,13 +570,13 @@ def _compile_gold(examples, pipeline, nlp):
     return data
 
 
-def _format_labels(labels, counts=False):
+def _format_labels(labels: List[Tuple[str, int]], counts: bool = False) -> str:
     if counts:
         return ", ".join([f"'{l}' ({c})" for l, c in labels])
     return ", ".join([f"'{l}'" for l in labels])
 
 
-def _get_examples_without_label(data, label):
+def _get_examples_without_label(data: Sequence[Example], label: str) -> int:
     count = 0
     for eg in data:
         labels = [
@@ -556,7 +589,7 @@ def _get_examples_without_label(data, label):
     return count
 
 
-def _get_labels_from_model(nlp, pipe_name):
+def _get_labels_from_model(nlp: Language, pipe_name: str) -> Sequence[str]:
     if pipe_name not in nlp.pipe_names:
         return set()
     pipe = nlp.get_pipe(pipe_name)

spacy/cli/download.py

@@ -1,23 +1,36 @@
+from typing import Optional, Sequence, Union
 import requests
-import os
-import subprocess
 import sys
 from wasabi import msg
+import typer
 
+from ._app import app, Arg, Opt
 from .. import about
-from ..util import is_package, get_base_version
+from ..util import is_package, get_base_version, run_command
 
 
-def download(
+@app.command(
-    model: ("Model to download (shortcut or name)", "positional", None, str),
+    "download",
-    direct: ("Force direct download of name + version", "flag", "d", bool) = False,
+    context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
-    *pip_args: ("Additional arguments to be passed to `pip install` on model install"),
+)
+def download_cli(
+    # fmt: off
+    ctx: typer.Context,
+    model: str = Arg(..., help="Model to download (shortcut or name)"),
+    direct: bool = Opt(False, "--direct", "-d", "-D", help="Force direct download of name + version"),
+    # fmt: on
 ):
     """
     Download compatible model from default download path using pip. If --direct
     flag is set, the command expects the full model name with version.
-    For direct downloads, the compatibility check will be skipped.
+    For direct downloads, the compatibility check will be skipped. All
+    additional arguments provided to this command will be passed to `pip install`
+    on model installation.
     """
+    download(model, direct, *ctx.args)
+
+
+def download(model: str, direct: bool = False, *pip_args) -> None:
     if not is_package("spacy") and "--no-deps" not in pip_args:
         msg.warn(
             "Skipping model package dependencies and setting `--no-deps`. "
@@ -33,22 +46,20 @@ def download(
         components = model.split("-")
         model_name = "".join(components[:-1])
         version = components[-1]
-        dl = download_model(dl_tpl.format(m=model_name, v=version), pip_args)
+        download_model(dl_tpl.format(m=model_name, v=version), pip_args)
     else:
         shortcuts = get_json(about.__shortcuts__, "available shortcuts")
         model_name = shortcuts.get(model, model)
         compatibility = get_compatibility()
         version = get_version(model_name, compatibility)
-        dl = download_model(dl_tpl.format(m=model_name, v=version), pip_args)
+        download_model(dl_tpl.format(m=model_name, v=version), pip_args)
-        if dl != 0:  # if download subprocess doesn't return 0, exit
+    msg.good(
-            sys.exit(dl)
+        "Download and installation successful",
-        msg.good(
+        f"You can now load the model via spacy.load('{model_name}')",
-            "Download and installation successful",
+    )
-            f"You can now load the model via spacy.load('{model_name}')",
-        )
 
 
-def get_json(url, desc):
+def get_json(url: str, desc: str) -> Union[dict, list]:
     r = requests.get(url)
     if r.status_code != 200:
         msg.fail(
@@ -62,7 +73,7 @@ def get_json(url, desc):
     return r.json()
 
 
-def get_compatibility():
+def get_compatibility() -> dict:
     version = get_base_version(about.__version__)
     comp_table = get_json(about.__compatibility__, "compatibility table")
     comp = comp_table["spacy"]
@@ -71,7 +82,7 @@ def get_compatibility():
     return comp[version]
 
 
-def get_version(model, comp):
+def get_version(model: str, comp: dict) -> str:
     model = get_base_version(model)
     if model not in comp:
         msg.fail(
@@ -81,10 +92,12 @@ def get_version(model, comp):
     return comp[model][0]
 
 
-def download_model(filename, user_pip_args=None):
+def download_model(
+    filename: str, user_pip_args: Optional[Sequence[str]] = None
+) -> None:
     download_url = about.__download_url__ + "/" + filename
     pip_args = ["--no-cache-dir"]
     if user_pip_args:
         pip_args.extend(user_pip_args)
     cmd = [sys.executable, "-m", "pip", "install"] + pip_args + [download_url]
-    return subprocess.call(cmd, env=os.environ.copy())
+    run_command(cmd)

spacy/cli/evaluate.py

@@ -1,26 +1,56 @@
+from typing import Optional, List
 from timeit import default_timer as timer
-from wasabi import msg
+from wasabi import Printer
+from pathlib import Path
 
 from ..gold import Corpus
+from ..tokens import Doc
+from ._app import app, Arg, Opt
+from ..scorer import Scorer
 from .. import util
 from .. import displacy
 
 
-def evaluate(
+@app.command("evaluate")
+def evaluate_cli(
     # fmt: off
-    model: ("Model name or path", "positional", None, str),
+    model: str = Arg(..., help="Model name or path"),
-    data_path: ("Location of JSON-formatted evaluation data", "positional", None, str),
+    data_path: Path = Arg(..., help="Location of JSON-formatted evaluation data", exists=True),
-    gpu_id: ("Use GPU", "option", "g", int) = -1,
+    gpu_id: int = Opt(-1, "--gpu-id", "-g", help="Use GPU"),
-    gold_preproc: ("Use gold preprocessing", "flag", "G", bool) = False,
+    gold_preproc: bool = Opt(False, "--gold-preproc", "-G", help="Use gold preprocessing"),
-    displacy_path: ("Directory to output rendered parses as HTML", "option", "dp", str) = None,
+    displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False),
-    displacy_limit: ("Limit of parses to render as HTML", "option", "dl", int) = 25,
+    displacy_limit: int = Opt(25, "--displacy-limit", "-dl", help="Limit of parses to render as HTML"),
-    return_scores: ("Return dict containing model scores", "flag", "R", bool) = False,
+    return_scores: bool = Opt(False, "--return-scores", "-R", help="Return dict containing model scores"),
-    # fmt: on
+
+        # fmt: on
 ):
     """
     Evaluate a model. To render a sample of parses in a HTML file, set an
     output directory as the displacy_path argument.
     """
+    evaluate(
+        model,
+        data_path,
+        gpu_id=gpu_id,
+        gold_preproc=gold_preproc,
+        displacy_path=displacy_path,
+        displacy_limit=displacy_limit,
+        silent=False,
+        return_scores=return_scores,
+    )
+
+
+def evaluate(
+    model: str,
+    data_path: Path,
+    gpu_id: int = -1,
+    gold_preproc: bool = False,
+    displacy_path: Optional[Path] = None,
+    displacy_limit: int = 25,
+    silent: bool = True,
+    return_scores: bool = False,
+) -> Scorer:
+    msg = Printer(no_print=silent, pretty=not silent)
     util.fix_random_seed()
     if gpu_id >= 0:
         util.use_gpu(gpu_id)
@@ -79,7 +109,14 @@ def evaluate(
         return scorer.scores
 
 
-def render_parses(docs, output_path, model_name="", limit=250, deps=True, ents=True):
+def render_parses(
+    docs: List[Doc],
+    output_path: Path,
+    model_name: str = "",
+    limit: int = 250,
+    deps: bool = True,
+    ents: bool = True,
+):
     docs[0].user_data["title"] = model_name
     if ents:
         html = displacy.render(docs[:limit], style="ent", page=True)

spacy/cli/info.py

@@ -1,77 +1,109 @@
+from typing import Optional, Dict, Any, Union
 import platform
 from pathlib import Path
-from wasabi import msg
+from wasabi import Printer
 import srsly
 
-from .validate import get_model_pkgs
+from ._app import app, Arg, Opt
 from .. import util
 from .. import about
 
 
-def info(
+@app.command("info")
-    model: ("Optional model name", "positional", None, str) = None,
+def info_cli(
-    markdown: ("Generate Markdown for GitHub issues", "flag", "md", str) = False,
+    # fmt: off
-    silent: ("Don't print anything (just return)", "flag", "s") = False,
+    model: Optional[str] = Arg(None, help="Optional model name"),
+    markdown: bool = Opt(False, "--markdown", "-md", help="Generate Markdown for GitHub issues"),
+    silent: bool = Opt(False, "--silent", "-s", "-S", help="Don't print anything (just return)"),
+    # fmt: on
 ):
     """
     Print info about spaCy installation. If a model is speficied as an argument,
     print model information. Flag --markdown prints details in Markdown for easy
     copy-pasting to GitHub issues.
     """
+    info(model, markdown=markdown, silent=silent)
+
+
+def info(
+    model: Optional[str] = None, *, markdown: bool = False, silent: bool = True
+) -> Union[str, dict]:
+    msg = Printer(no_print=silent, pretty=not silent)
     if model:
-        if util.is_package(model):
+        title = f"Info about model '{model}'"
-            model_path = util.get_package_path(model)
+        data = info_model(model, silent=silent)
-        else:
+    else:
-            model_path = model
+        title = "Info about spaCy"
-        meta_path = model_path / "meta.json"
+        data = info_spacy()
-        if not meta_path.is_file():
+    raw_data = {k.lower().replace(" ", "_"): v for k, v in data.items()}
-            msg.fail("Can't find model meta.json", meta_path, exits=1)
+    if "Models" in data and isinstance(data["Models"], dict):
-        meta = srsly.read_json(meta_path)
+        data["Models"] = ", ".join(f"{n} ({v})" for n, v in data["Models"].items())
-        if model_path.resolve() != model_path:
+    markdown_data = get_markdown(data, title=title)
-            meta["link"] = str(model_path)
+    if markdown:
-            meta["source"] = str(model_path.resolve())
-        else:
-            meta["source"] = str(model_path)
         if not silent:
-            title = f"Info about model '{model}'"
+            print(markdown_data)
-            model_meta = {
+        return markdown_data
-                k: v for k, v in meta.items() if k not in ("accuracy", "speed")
+    if not silent:
-            }
+        table_data = dict(data)
-            if markdown:
+        msg.table(table_data, title=title)
-                print_markdown(model_meta, title=title)
+    return raw_data
-            else:
+
-                msg.table(model_meta, title=title)
+
-        return meta
+def info_spacy() -> Dict[str, any]:
-    all_models, _ = get_model_pkgs()
+    """Generate info about the current spaCy intallation.
-    data = {
+
+    RETURNS (dict): The spaCy info.
+    """
+    all_models = {}
+    for pkg_name in util.get_installed_models():
+        package = pkg_name.replace("-", "_")
+        all_models[package] = util.get_package_version(pkg_name)
+    return {
         "spaCy version": about.__version__,
         "Location": str(Path(__file__).parent.parent),
         "Platform": platform.platform(),
         "Python version": platform.python_version(),
-        "Models": ", ".join(
+        "Models": all_models,
-            f"{m['name']} ({m['version']})" for m in all_models.values()
-        ),
     }
-    if not silent:
-        title = "Info about spaCy"
-        if markdown:
-            print_markdown(data, title=title)
-        else:
-            msg.table(data, title=title)
-    return data
 
 
-def print_markdown(data, title=None):
+def info_model(model: str, *, silent: bool = True) -> Dict[str, Any]:
-    """Print data in GitHub-flavoured Markdown format for issues etc.
+    """Generate info about a specific model.
+
+    model (str): Model name of path.
+    silent (bool): Don't print anything, just return.
+    RETURNS (dict): The model meta.
+    """
+    msg = Printer(no_print=silent, pretty=not silent)
+    if util.is_package(model):
+        model_path = util.get_package_path(model)
+    else:
+        model_path = model
+    meta_path = model_path / "meta.json"
+    if not meta_path.is_file():
+        msg.fail("Can't find model meta.json", meta_path, exits=1)
+    meta = srsly.read_json(meta_path)
+    if model_path.resolve() != model_path:
+        meta["link"] = str(model_path)
+        meta["source"] = str(model_path.resolve())
+    else:
+        meta["source"] = str(model_path)
+    return {k: v for k, v in meta.items() if k not in ("accuracy", "speed")}
+
+
+def get_markdown(data: Dict[str, Any], title: Optional[str] = None) -> str:
+    """Get data in GitHub-flavoured Markdown format for issues etc.
 
     data (dict or list of tuples): Label/value pairs.
     title (str / None): Title, will be rendered as headline 2.
+    RETURNS (str): The Markdown string.
     """
     markdown = []
     for key, value in data.items():
         if isinstance(value, str) and Path(value).exists():
             continue
         markdown.append(f"* **{key}:** {value}")
+    result = "\n{}\n".format("\n".join(markdown))
     if title:
-        print(f"\n## {title}")
+        result = f"\n## {title}\n{result}"
-    print("\n{}\n".format("\n".join(markdown)))
+    return result

spacy/cli/init_model.py

@@ -1,3 +1,4 @@
+from typing import Optional, List, Dict, Any, Union, IO
 import math
 from tqdm import tqdm
 import numpy
@@ -9,10 +10,12 @@ import gzip
 import zipfile
 import srsly
 import warnings
-from wasabi import msg
+from wasabi import Printer
 
+from ._app import app, Arg, Opt
 from ..vectors import Vectors
 from ..errors import Errors, Warnings
+from ..language import Language
 from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
 from ..lookups import Lookups
 
@@ -25,20 +28,21 @@ except ImportError:
 DEFAULT_OOV_PROB = -20
 
 
-def init_model(
+@app.command("init-model")
+def init_model_cli(
     # fmt: off
-    lang: ("Model language", "positional", None, str),
+    lang: str = Arg(..., help="Model language"),
-    output_dir: ("Model output directory", "positional", None, Path),
+    output_dir: Path = Arg(..., help="Model output directory"),
-    freqs_loc: ("Location of words frequencies file", "option", "f", Path) = None,
+    freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
-    clusters_loc: ("Optional location of brown clusters data", "option", "c", str) = None,
+    clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
-    jsonl_loc: ("Location of JSONL-formatted attributes file", "option", "j", Path) = None,
+    jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
-    vectors_loc: ("Optional vectors file in Word2Vec format", "option", "v", str) = None,
+    vectors_loc: Optional[Path] = Opt(None, "--vectors-loc", "-v", help="Optional vectors file in Word2Vec format", exists=True),
-    prune_vectors: ("Optional number of vectors to prune to", "option", "V", int) = -1,
+    prune_vectors: int = Opt(-1 , "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
-    truncate_vectors: ("Optional number of vectors to truncate to when reading in vectors file", "option", "t", int) = 0,
+    truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
-    vectors_name: ("Optional name for the word vectors, e.g. en_core_web_lg.vectors", "option", "vn", str) = None,
+    vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
-    model_name: ("Optional name for the model meta", "option", "mn", str) = None,
+    model_name: Optional[str] = Opt(None, "--model-name", "-mn", help="Optional name for the model meta"),
-    omit_extra_lookups: ("Don't include extra lookups in model", "flag", "OEL", bool) = False,
+    omit_extra_lookups: bool = Opt(False, "--omit-extra-lookups", "-OEL", help="Don't include extra lookups in model"),
-    base_model: ("Base model (for languages with custom tokenizers)", "option", "b", str) = None
+    base_model: Optional[str] = Opt(None, "--base-model", "-b", help="Base model (for languages with custom tokenizers)")
     # fmt: on
 ):
     """
@@ -46,6 +50,38 @@ def init_model(
     and word vectors. If vectors are provided in Word2Vec format, they can
     be either a .txt or zipped as a .zip or .tar.gz.
     """
+    init_model(
+        lang,
+        output_dir,
+        freqs_loc=freqs_loc,
+        clusters_loc=clusters_loc,
+        jsonl_loc=jsonl_loc,
+        prune_vectors=prune_vectors,
+        truncate_vectors=truncate_vectors,
+        vectors_name=vectors_name,
+        model_name=model_name,
+        omit_extra_lookups=omit_extra_lookups,
+        base_model=base_model,
+        silent=False,
+    )
+
+
+def init_model(
+    lang: str,
+    output_dir: Path,
+    freqs_loc: Optional[Path] = None,
+    clusters_loc: Optional[Path] = None,
+    jsonl_loc: Optional[Path] = None,
+    vectors_loc: Optional[Path] = None,
+    prune_vectors: int = -1,
+    truncate_vectors: int = 0,
+    vectors_name: Optional[str] = None,
+    model_name: Optional[str] = None,
+    omit_extra_lookups: bool = False,
+    base_model: Optional[str] = None,
+    silent: bool = True,
+) -> Language:
+    msg = Printer(no_print=silent, pretty=not silent)
     if jsonl_loc is not None:
         if freqs_loc is not None or clusters_loc is not None:
             settings = ["-j"]
@@ -68,7 +104,7 @@ def init_model(
         freqs_loc = ensure_path(freqs_loc)
         if freqs_loc is not None and not freqs_loc.exists():
             msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
-        lex_attrs = read_attrs_from_deprecated(freqs_loc, clusters_loc)
+        lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)
 
     with msg.loading("Creating model..."):
         nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
@@ -83,7 +119,9 @@ def init_model(
 
     msg.good("Successfully created model")
     if vectors_loc is not None:
-        add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name)
+        add_vectors(
+            msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
+        )
     vec_added = len(nlp.vocab.vectors)
     lex_added = len(nlp.vocab)
     msg.good(
@@ -95,7 +133,7 @@ def init_model(
     return nlp
 
 
-def open_file(loc):
+def open_file(loc: Union[str, Path]) -> IO:
     """Handle .gz, .tar.gz or unzipped files"""
     loc = ensure_path(loc)
     if tarfile.is_tarfile(str(loc)):
@@ -111,7 +149,9 @@ def open_file(loc):
         return loc.open("r", encoding="utf8")
 
 
-def read_attrs_from_deprecated(freqs_loc, clusters_loc):
+def read_attrs_from_deprecated(
+    msg: Printer, freqs_loc: Optional[Path], clusters_loc: Optional[Path]
+) -> List[Dict[str, Any]]:
     if freqs_loc is not None:
         with msg.loading("Counting frequencies..."):
             probs, _ = read_freqs(freqs_loc)
@@ -139,7 +179,12 @@ def read_attrs_from_deprecated(freqs_loc, clusters_loc):
     return lex_attrs
 
 
-def create_model(lang, lex_attrs, name=None, base_model=None):
+def create_model(
+    lang: str,
+    lex_attrs: List[Dict[str, Any]],
+    name: Optional[str] = None,
+    base_model: Optional[Union[str, Path]] = None,
+) -> Language:
     if base_model:
         nlp = load_model(base_model)
         # keep the tokenizer but remove any existing pipeline components due to
@@ -166,7 +211,14 @@ def create_model(lang, lex_attrs, name=None, base_model=None):
     return nlp
 
 
-def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
+def add_vectors(
+    msg: Printer,
+    nlp: Language,
+    vectors_loc: Optional[Path],
+    truncate_vectors: int,
+    prune_vectors: int,
+    name: Optional[str] = None,
+) -> None:
     vectors_loc = ensure_path(vectors_loc)
     if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
         nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
@@ -176,7 +228,7 @@ def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
     else:
         if vectors_loc:
             with msg.loading(f"Reading vectors from {vectors_loc}"):
-                vectors_data, vector_keys = read_vectors(vectors_loc)
+                vectors_data, vector_keys = read_vectors(msg, vectors_loc)
             msg.good(f"Loaded vectors from {vectors_loc}")
         else:
             vectors_data, vector_keys = (None, None)
@@ -195,7 +247,7 @@ def add_vectors(nlp, vectors_loc, truncate_vectors, prune_vectors, name=None):
         nlp.vocab.prune_vectors(prune_vectors)
 
 
-def read_vectors(vectors_loc, truncate_vectors=0):
+def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int = 0):
     f = open_file(vectors_loc)
     shape = tuple(int(size) for size in next(f).split())
     if truncate_vectors >= 1:
@@ -215,7 +267,9 @@ def read_vectors(vectors_loc, truncate_vectors=0):
     return vectors_data, vectors_keys
 
 
-def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
+def read_freqs(
+    freqs_loc: Path, max_length: int = 100, min_doc_freq: int = 5, min_freq: int = 50
+):
     counts = PreshCounter()
     total = 0
     with freqs_loc.open() as f:
@@ -244,7 +298,7 @@ def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
     return probs, oov_prob
 
 
-def read_clusters(clusters_loc):
+def read_clusters(clusters_loc: Path) -> dict:
     clusters = {}
     if ftfy is None:
         warnings.warn(Warnings.W004)

spacy/cli/package.py

@@ -1,19 +1,24 @@
+from typing import Optional, Union, Any, Dict
 import shutil
 from pathlib import Path
-from wasabi import msg, get_raw_input
+from wasabi import Printer, get_raw_input
 import srsly
+import sys
 
+from ._app import app, Arg, Opt
+from ..schemas import validate, ModelMetaSchema
 from .. import util
 from .. import about
 
 
-def package(
+@app.command("package")
+def package_cli(
     # fmt: off
-    input_dir: ("Directory with model data", "positional", None, str),
+    input_dir: Path = Arg(..., help="Directory with model data", exists=True, file_okay=False),
-    output_dir: ("Output parent directory", "positional", None, str),
+    output_dir: Path = Arg(..., help="Output parent directory", exists=True, file_okay=False),
-    meta_path: ("Path to meta.json", "option", "m", str) = None,
+    meta_path: Optional[Path] = Opt(None, "--meta-path", "-m", help="Path to meta.json", exists=True, dir_okay=False),
-    create_meta: ("Create meta.json, even if one exists", "flag", "c", bool) = False,
+    create_meta: bool = Opt(False, "--create-meta", "-c", "-C", help="Create meta.json, even if one exists"),
-    force: ("Force overwriting existing model in output directory", "flag", "f", bool) = False,
+    force: bool = Opt(False, "--force", "-f", "-F", help="Force overwriting existing model in output directory"),
     # fmt: on
 ):
     """
@@ -23,6 +28,25 @@ def package(
     set and a meta.json already exists in the output directory, the existing
     values will be used as the defaults in the command-line prompt.
     """
+    package(
+        input_dir,
+        output_dir,
+        meta_path=meta_path,
+        create_meta=create_meta,
+        force=force,
+        silent=False,
+    )
+
+
+def package(
+    input_dir: Path,
+    output_dir: Path,
+    meta_path: Optional[Path] = None,
+    create_meta: bool = False,
+    force: bool = False,
+    silent: bool = True,
+) -> None:
+    msg = Printer(no_print=silent, pretty=not silent)
     input_path = util.ensure_path(input_dir)
     output_path = util.ensure_path(output_dir)
     meta_path = util.ensure_path(meta_path)
@@ -33,23 +57,20 @@ def package(
     if meta_path and not meta_path.exists():
         msg.fail("Can't find model meta.json", meta_path, exits=1)
 
-    meta_path = meta_path or input_path / "meta.json"
+    meta_path = meta_path or input_dir / "meta.json"
-    if meta_path.is_file():
+    if not meta_path.exists() or not meta_path.is_file():
-        meta = srsly.read_json(meta_path)
+        msg.fail("Can't load model meta.json", meta_path, exits=1)
-        if not create_meta:  # only print if user doesn't want to overwrite
+    meta = srsly.read_json(meta_path)
-            msg.good("Loaded meta.json from file", meta_path)
+    if not create_meta:  # only print if user doesn't want to overwrite
-        else:
+        msg.good("Loaded meta.json from file", meta_path)
-            meta = generate_meta(input_dir, meta, msg)
+    else:
-    for key in ("lang", "name", "version"):
+        meta = generate_meta(input_dir, meta, msg)
-        if key not in meta or meta[key] == "":
+    errors = validate(ModelMetaSchema, meta)
-            msg.fail(
+    if errors:
-                f"No '{key}' setting found in meta.json",
+        msg.fail("Invalid model meta.json", "\n".join(errors), exits=1)
-                "This setting is required to build your package.",
-                exits=1,
-            )
     model_name = meta["lang"] + "_" + meta["name"]
     model_name_v = model_name + "-" + meta["version"]
-    main_path = output_path / model_name_v
+    main_path = output_dir / model_name_v
     package_path = main_path / model_name
 
     if package_path.exists():
@@ -63,21 +84,26 @@ def package(
                 exits=1,
             )
     Path.mkdir(package_path, parents=True)
-    shutil.copytree(str(input_path), str(package_path / model_name_v))
+    shutil.copytree(str(input_dir), str(package_path / model_name_v))
     create_file(main_path / "meta.json", srsly.json_dumps(meta, indent=2))
     create_file(main_path / "setup.py", TEMPLATE_SETUP)
     create_file(main_path / "MANIFEST.in", TEMPLATE_MANIFEST)
     create_file(package_path / "__init__.py", TEMPLATE_INIT)
     msg.good(f"Successfully created package '{model_name_v}'", main_path)
-    msg.text("To build the package, run `python setup.py sdist` in this directory.")
+    with util.working_dir(main_path):
+        util.run_command([sys.executable, "setup.py", "sdist"])
+    zip_file = main_path / "dist" / f"{model_name_v}.tar.gz"
+    msg.good(f"Successfully created zipped Python package", zip_file)
 
 
-def create_file(file_path, contents):
+def create_file(file_path: Path, contents: str) -> None:
     file_path.touch()
     file_path.open("w", encoding="utf-8").write(contents)
 
 
-def generate_meta(model_path, existing_meta, msg):
+def generate_meta(
+    model_path: Union[str, Path], existing_meta: Dict[str, Any], msg: Printer
+) -> Dict[str, Any]:
     meta = existing_meta or {}
     settings = [
         ("lang", "Model language", meta.get("lang", "en")),

spacy/cli/pretrain.py

@@ -1,14 +1,15 @@
+from typing import Optional
 import random
 import numpy
 import time
 import re
 from collections import Counter
-import plac
 from pathlib import Path
 from thinc.api import Linear, Maxout, chain, list2array, use_pytorch_for_gpu_memory
 from wasabi import msg
 import srsly
 
+from ._app import app, Arg, Opt
 from ..errors import Errors
 from ..ml.models.multi_task import build_masked_language_model
 from ..tokens import Doc
@@ -17,25 +18,17 @@ from .. import util
 from ..gold import Example
 
 
-@plac.annotations(
+@app.command("pretrain")
+def pretrain_cli(
     # fmt: off
-    texts_loc=("Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", "positional", None, str),
+    texts_loc: Path = Arg(..., help="Path to JSONL file with raw texts to learn from, with text provided as the key 'text' or tokens as the key 'tokens'", exists=True),
-    vectors_model=("Name or path to spaCy model with vectors to learn from", "positional", None, str),
+    vectors_model: str = Arg(..., help="Name or path to spaCy model with vectors to learn from"),
-    output_dir=("Directory to write models to on each epoch", "positional", None, Path),
+    output_dir: Path = Arg(..., help="Directory to write models to on each epoch"),
-    config_path=("Path to config file", "positional", None, Path),
+    config_path: Path = Arg(..., help="Path to config file", exists=True, dir_okay=False),
-    use_gpu=("Use GPU", "option", "g", int),
+    use_gpu: int = Opt(-1, "--use-gpu", "-g", help="Use GPU"),
-    resume_path=("Path to pretrained weights from which to resume pretraining", "option", "r", Path),
+    resume_path: Optional[Path] = Opt(None, "--resume-path", "-r", help="Path to pretrained weights from which to resume pretraining"),
-    epoch_resume=("The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files.", "option", "er", int),
+    epoch_resume: Optional[int] = Opt(None, "--epoch-resume", "-er", help="The epoch to resume counting from when using '--resume_path'. Prevents unintended overwriting of existing weight files."),
     # fmt: on
-)
-def pretrain(
-    texts_loc,
-    vectors_model,
-    config_path,
-    output_dir,
-    use_gpu=-1,
-    resume_path=None,
-    epoch_resume=None,
 ):
     """
     Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
@@ -52,6 +45,26 @@ def pretrain(
     all settings are the same between pretraining and training. Ideally,
     this is done by using the same config file for both commands.
     """
+    pretrain(
+        texts_loc,
+        vectors_model,
+        output_dir,
+        config_path,
+        use_gpu=use_gpu,
+        resume_path=resume_path,
+        epoch_resume=epoch_resume,
+    )
+
+
+def pretrain(
+    texts_loc: Path,
+    vectors_model: str,
+    output_dir: Path,
+    config_path: Path,
+    use_gpu: int = -1,
+    resume_path: Optional[Path] = None,
+    epoch_resume: Optional[int] = None,
+):
     if not config_path or not config_path.exists():
         msg.fail("Config file not found", config_path, exits=1)
 

spacy/cli/profile.py

@@ -1,3 +1,4 @@
+from typing import Optional, Sequence, Union, Iterator
 import tqdm
 from pathlib import Path
 import srsly
@@ -5,17 +6,19 @@ import cProfile
 import pstats
 import sys
 import itertools
-import ml_datasets
+from wasabi import msg, Printer
-from wasabi import msg
 
+from ._app import app, Arg, Opt
+from ..language import Language
 from ..util import load_model
 
 
-def profile(
+@app.command("profile")
+def profile_cli(
     # fmt: off
-    model: ("Model to load", "positional", None, str),
+    model: str = Arg(..., help="Model to load"),
-    inputs: ("Location of input file. '-' for stdin.", "positional", None, str) = None,
+    inputs: Optional[Path] = Arg(None, help="Location of input file. '-' for stdin.", exists=True, allow_dash=True),
-    n_texts: ("Maximum number of texts to use if available", "option", "n", int) = 10000,
+    n_texts: int = Opt(10000, "--n-texts", "-n", help="Maximum number of texts to use if available"),
     # fmt: on
 ):
     """
@@ -24,6 +27,18 @@ def profile(
     It can either be provided as a JSONL file, or be read from sys.sytdin.
     If no input file is specified, the IMDB dataset is loaded via Thinc.
     """
+    profile(model, inputs=inputs, n_texts=n_texts)
+
+
+def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) -> None:
+    try:
+        import ml_datasets
+    except ImportError:
+        msg.fail(
+            "This command requires the ml_datasets library to be installed:"
+            "pip install ml_datasets",
+            exits=1,
+        )
     if inputs is not None:
         inputs = _read_inputs(inputs, msg)
     if inputs is None:
@@ -43,12 +58,12 @@ def profile(
     s.strip_dirs().sort_stats("time").print_stats()
 
 
-def parse_texts(nlp, texts):
+def parse_texts(nlp: Language, texts: Sequence[str]) -> None:
     for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16):
         pass
 
 
-def _read_inputs(loc, msg):
+def _read_inputs(loc: Union[Path, str], msg: Printer) -> Iterator[str]:
     if loc == "-":
         msg.info("Reading input from sys.stdin")
         file_ = sys.stdin

spacy/cli/train.py

@@ -1,10 +1,10 @@
-from typing import Optional, Dict, List, Union, Sequence
+from typing import Optional, Dict
 from timeit import default_timer as timer
 
 import plac
 import srsly
-from pydantic import BaseModel, FilePath
 import tqdm
+from pydantic import BaseModel, FilePath
 from pathlib import Path
 from wasabi import msg
 import thinc
@@ -12,6 +12,7 @@ import thinc.schedules
 from thinc.api import Model, use_pytorch_for_gpu_memory
 import random
 
+from ._app import app, Arg, Opt
 from ..gold import Corpus
 from ..lookups import Lookups
 from .. import util
@@ -20,6 +21,9 @@ from ..errors import Errors
 # Don't remove - required to load the built-in architectures
 from ..ml import models  # noqa: F401
 
+# from ..schemas import ConfigSchema  # TODO: include?
+
+
 registry = util.registry
 
 CONFIG_STR = """
@@ -116,35 +120,21 @@ class ConfigSchema(BaseModel):
         extra = "allow"
 
 
-@plac.annotations(
+@app.command("train")
-    # fmt: off
-    train_path=("Location of JSON-formatted training data", "positional", None, Path),
-    dev_path=("Location of JSON-formatted development data", "positional", None, Path),
-    config_path=("Path to config file", "positional", None, Path),
-    output_path=("Output directory to store model in", "option", "o", Path),
-    code_path=("Path to Python file with additional code (registered functions) to be imported", "option", "c", Path),
-    init_tok2vec=(
-    "Path to pretrained weights for the tok2vec components. See 'spacy pretrain'. Experimental.", "option", "t2v",
-    Path),
-    raw_text=("Path to jsonl file with unlabelled text documents.", "option", "rt", Path),
-    verbose=("Display more information for debugging purposes", "flag", "VV", bool),
-    use_gpu=("Use GPU", "option", "g", int),
-    tag_map_path=("Location of JSON-formatted tag map", "option", "tm", Path),
-    omit_extra_lookups=("Don't include extra lookups in model", "flag", "OEL", bool),
-    # fmt: on
-)
 def train_cli(
-    train_path,
+    # fmt: off
-    dev_path,
+    train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
-    config_path,
+    dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
-    output_path=None,
+    config_path: Path = Arg(..., help="Path to config file", exists=True),
-    code_path=None,
+    output_path: Optional[Path] = Opt(None, "--output-path", "-o", help="Output directory to store model in"),
-    init_tok2vec=None,
+    code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
-    raw_text=None,
+    init_tok2vec: Optional[Path] = Opt(None, "--init-tok2vec", "-t2v", help="Path to pretrained weights for the tok2vec components. See 'spacy pretrain'. Experimental."),
-    verbose=False,
+    raw_text: Optional[Path] = Opt(None, "--raw-text", "-rt", help="Path to jsonl file with unlabelled text documents."),
-    use_gpu=-1,
+    verbose: bool = Opt(False, "--verbose", "-VV", help="Display more information for debugging purposes"),
-    tag_map_path=None,
+    use_gpu: int = Opt(-1, "--use-gpu", "-g", help="Use GPU"),
-    omit_extra_lookups=False,
+    tag_map_path: Optional[Path] = Opt(None, "--tag-map-path", "-tm", help="Location of JSON-formatted tag map"),
+    omit_extra_lookups: bool = Opt(False, "--omit-extra-lookups", "-OEL", help="Don't include extra lookups in model"),
+    # fmt: on
 ):
     """
     Train or update a spaCy model. Requires data to be formatted in spaCy's
@@ -183,14 +173,14 @@ def train_cli(
 
 
 def train(
-    config_path,
+    config_path: Path,
-    data_paths,
+    data_paths: Dict[str, Path],
-    raw_text=None,
+    raw_text: Optional[Path] = None,
-    output_path=None,
+    output_path: Optional[Path] = None,
-    tag_map=None,
+    tag_map: Optional[Path] = None,
-    weights_data=None,
+    weights_data: Optional[bytes] = None,
-    omit_extra_lookups=False,
+    omit_extra_lookups: bool = False,
-):
+) -> None:
     msg.info(f"Loading config from: {config_path}")
     # Read the config first without creating objects, to get to the original nlp_config
     config = util.load_config(config_path, create_objects=False)

spacy/cli/validate.py

@@ -1,18 +1,25 @@
+from typing import Tuple
 from pathlib import Path
 import sys
 import requests
-from wasabi import msg
+from wasabi import msg, Printer
 
+from ._app import app
 from .. import about
 from ..util import get_package_version, get_installed_models, get_base_version
 from ..util import get_package_path, get_model_meta, is_compatible_version
 
 
-def validate():
+@app.command("validate")
+def validate_cli():
     """
     Validate that the currently installed version of spaCy is compatible
     with the installed models. Should be run after `pip install -U spacy`.
     """
+    validate()
+
+
+def validate() -> None:
     model_pkgs, compat = get_model_pkgs()
     spacy_version = get_base_version(about.__version__)
     current_compat = compat.get(spacy_version, {})
@@ -55,7 +62,8 @@ def validate():
         sys.exit(1)
 
 
-def get_model_pkgs():
+def get_model_pkgs(silent: bool = False) -> Tuple[dict, dict]:
+    msg = Printer(no_print=silent, pretty=not silent)
     with msg.loading("Loading compatibility table..."):
         r = requests.get(about.__compatibility__)
         if r.status_code != 200:
@@ -93,7 +101,7 @@ def get_model_pkgs():
     return pkgs, compat
 
 
-def reformat_version(version):
+def reformat_version(version: str) -> str:
     """Hack to reformat old versions ending on '-alpha' to match pip format."""
     if version.endswith("-alpha"):
         return version.replace("-alpha", "a0")

spacy/schemas.py

@@ -1,8 +1,9 @@
-from typing import Dict, List, Union, Optional
+from typing import Dict, List, Union, Optional, Sequence, Any
 from enum import Enum
 from pydantic import BaseModel, Field, ValidationError, validator
-from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
+from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool, FilePath
 from collections import defaultdict
+from thinc.api import Model
 
 from .attrs import NAMES
 
@@ -163,24 +164,48 @@ class ModelMetaSchema(BaseModel):
     email: Optional[StrictStr] = Field(None, title="Model author email")
     url: Optional[StrictStr] = Field(None, title="Model author URL")
     sources: Optional[Union[List[StrictStr], Dict[str, str]]] = Field(None, title="Training data sources")
-    vectors: Optional[Dict[str, int]] = Field(None, title="Included word vectors")
+    vectors: Optional[Dict[str, Any]] = Field(None, title="Included word vectors")
     accuracy: Optional[Dict[str, Union[float, int]]] = Field(None, title="Accuracy numbers")
     speed: Optional[Dict[str, Union[float, int]]] = Field(None, title="Speed evaluation numbers")
     # fmt: on
 
 
-# Training data object in "simple training style"
+# JSON training format
 
 
-class SimpleTrainingSchema(BaseModel):
+class PipelineComponent(BaseModel):
-    # TODO: write
+    factory: str
+    model: Model
 
     class Config:
-        title = "Schema for training data dict in passed to nlp.update"
+        arbitrary_types_allowed = True
-        extra = "forbid"
 
 
-# JSON training format
+class ConfigSchema(BaseModel):
+    optimizer: Optional["Optimizer"]
+
+    class training(BaseModel):
+        patience: int = 10
+        eval_frequency: int = 100
+        dropout: float = 0.2
+        init_tok2vec: Optional[FilePath] = None
+        max_epochs: int = 100
+        orth_variant_level: float = 0.0
+        gold_preproc: bool = False
+        max_length: int = 0
+        use_gpu: int = 0
+        scores: List[str] = ["ents_p", "ents_r", "ents_f"]
+        score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
+        limit: int = 0
+        batch_size: Union[Sequence[int], int]
+
+    class nlp(BaseModel):
+        lang: str
+        vectors: Optional[str]
+        pipeline: Optional[Dict[str, PipelineComponent]]
+
+    class Config:
+        extra = "allow"
 
 
 class TrainingSchema(BaseModel):
@@ -189,3 +214,34 @@ class TrainingSchema(BaseModel):
     class Config:
         title = "Schema for training data in spaCy's JSON format"
         extra = "forbid"
+
+
+# Project config Schema
+
+
+class ProjectConfigAsset(BaseModel):
+    dest: StrictStr = Field(..., title="Destination of downloaded asset")
+    url: StrictStr = Field(..., title="URL of asset")
+
+
+class ProjectConfigCommand(BaseModel):
+    # fmt: off
+    name: StrictStr = Field(..., title="Name of command")
+    help: Optional[StrictStr] = Field(None, title="Command description")
+    script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
+    dvc_deps: List[StrictStr] = Field([], title="Data Version Control dependencies")
+    dvc_outputs: List[StrictStr] = Field([], title="Data Version Control outputs")
+    dvc_outputs_no_cache: List[StrictStr] = Field([], title="Data Version Control outputs (no cache)")
+    # fmt: on
+
+
+class ProjectConfigSchema(BaseModel):
+    # fmt: off
+    variables: Dict[StrictStr, Union[str, int, float, bool]] = Field({}, title="Optional variables to substitute in commands")
+    assets: List[ProjectConfigAsset] = Field([], title="Data assets")
+    run: List[StrictStr] = Field([], title="Names of project commands to execute, in order")
+    commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
+    # fmt: on
+
+    class Config:
+        title = "Schema for project configuration file"

spacy/util.py

@@ -1,10 +1,10 @@
+from typing import List, Union
 import os
 import importlib
 import importlib.util
 import re
 from pathlib import Path
 import random
-from typing import List
 import thinc
 from thinc.api import NumpyOps, get_current_ops, Adam, require_gpu, Config
 import functools
@@ -17,6 +17,8 @@ import sys
 import warnings
 from packaging.specifiers import SpecifierSet, InvalidSpecifier
 from packaging.version import Version, InvalidVersion
+import subprocess
+from contextlib import contextmanager
 
 
 try:
@@ -429,6 +431,30 @@ def get_package_path(name):
     return Path(pkg.__file__).parent
 
 
+def run_command(command: List[str]) -> None:
+    """Run a command on the command line as a subprocess.
+
+    command (list): The split command.
+    """
+    status = subprocess.call(command, env=os.environ.copy())
+    if status != 0:
+        sys.exit(status)
+
+
+@contextmanager
+def working_dir(path: Union[str, Path]) -> None:
+    """Change current working directory and returns to previous on exit.
+
+    path (str / Path): The directory to navigate to.
+    """
+    prev_cwd = Path.cwd()
+    os.chdir(str(path))
+    try:
+        yield
+    finally:
+        os.chdir(prev_cwd)
+
+
 def is_in_jupyter():
     """Check if user is running spaCy from a Jupyter notebook by detecting the
     IPython kernel. Mainly used for the displaCy visualizer.