def to_disk(self, path, exclude=tuple(), disable=None):
"""Save the current state to a directory. If a model is loaded, this
will include the model.
path (unicode or Path): Path to a directory, which will be created if
it doesn't exist.
exclude (list): Names of components or serialization fields to exclude.
DOCS: https://spacy.io/api/language#to_disk
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
path = util.ensure_path(path)
serializers = OrderedDict()
serializers["tokenizer"] = lambda p: self.tokenizer.to_disk(p, exclude=["vocab"])
serializers["meta.json"] = lambda p: p.open("w").write(srsly.json_dumps(self.meta))
for name, proc in self.pipeline:
if not hasattr(proc, "name"):
continue
if name in exclude:
continue
if not hasattr(proc, "to_disk"):
continue
serializers[name] = lambda p, proc=proc: proc.to_disk(p, exclude=["vocab"])
serializers["vocab"] = lambda p: self.vocab.to_disk(p)
util.to_disk(path, serializers, exclude)
def package(input_dir, output_dir, meta_path=None, create_meta=False, force=False):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over. If --create-meta is
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.
"""
msg = Printer()
input_path = util.ensure_path(input_dir)
output_path = util.ensure_path(output_dir)
meta_path = util.ensure_path(meta_path)
if not input_path or not input_path.exists():
msg.fail("Can't locate model data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
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"
if meta_path.is_file():
meta = srsly.read_json(meta_path)
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(input_dir, meta, msg)
for key in ("lang", "name", "version"):
if key not in meta or meta[key] == "":
msg.fail(
"No '{}' setting found in meta.json".format(key),
"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
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(path2str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing "
"directories.".format(path=path2str(package_path)),
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(path2str(input_path), path2str(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("Successfully created package '{}'".format(model_name_v), main_path)
msg.text("To build the package, run `python setup.py sdist` in this directory.")
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
def callback():
code = request.args.get("code")
token_endpoint = google_config["token_endpoint"]
token_url, headers, body = client.prepare_token_request(
token_endpoint,
authorization_response=request.url,
redirect_url=request.base_url,
code=code
)
token_response = requests.post(token_url, headers=headers, data=body, auth=(CLIENT_ID, CLIENT_SECRET))
client.parse_request_body_response(json_dumps(token_response.json()))
# Now get the user email
userinfo_endpoint = google_config["userinfo_endpoint"]
uri, headers, body = client.add_token(userinfo_endpoint)
userinfo_response = requests.get(uri, headers=headers, data=body)
print(userinfo_response.json())
def to_bytes(self, *, exclude=tuple()):
"""Serialize the pipe to a bytestring.
exclude (Iterable[str]): String names of serialization fields to exclude.
RETURNS (bytes): The serialized object.
DOCS: https://spacy.io/api/entitylinker#to_bytes
"""
self._validate_serialization_attrs()
serialize = {}
if hasattr(self, "cfg") and self.cfg is not None:
serialize["cfg"] = lambda: srsly.json_dumps(self.cfg)
serialize["vocab"] = lambda: self.vocab.to_bytes(exclude=exclude)
serialize["kb"] = self.kb.to_bytes
serialize["model"] = self.model.to_bytes
return util.to_bytes(serialize, exclude)
def to_str(self) -> str:
"""Write the config to a string."""
flattened = get_configparser()
queue: List[Tuple[tuple, "Config"]] = [(tuple(), self)]
for path, node in queue:
for key, value in node.items():
if hasattr(value, "items"):
queue.append((path + (key, ), value))
else:
assert path
section_name = ".".join(path)
if not flattened.has_section(section_name):
flattened.add_section(section_name)
flattened.set(section_name, key, srsly.json_dumps(value))
string_io = io.StringIO()
flattened.write(string_io)
return string_io.getvalue().strip()
def get_ner_stats(data: List[Example],
serialize: bool = False,
return_examples: bool = False) -> Union[NERStats, str, None]:
"""Compute statistics for NER data
Args:
data (List[Example]): Data as a List of examples
serialize (bool, optional): Serialize to a JSON string for printing.
return_examples (bool, optional): Whether to return examples per type
Returns:
Union[NERStats, str, None]:
List of examples or string if serialize and no_print are both True
"""
annotations_per_type: DefaultDict[str, Any] = defaultdict(int)
examples: DefaultDict[str, Any] = defaultdict(list)
n_examples_no_entities = 0
for e in data:
if not e.spans:
n_examples_no_entities += 1
examples[NONE].append(e)
else:
for s in e.spans:
annotations_per_type[s.label] += 1
examples[s.label].append(e)
sorted_anns_by_count = {
a[0]: a[1]
for a in sorted(
annotations_per_type.items(), key=lambda x: x[1], reverse=True)
}
stats = NERStats(
n_examples=len(data),
n_examples_no_entities=n_examples_no_entities,
n_annotations=sum(annotations_per_type.values()),
n_annotations_per_type=sorted_anns_by_count,
)
if return_examples:
stats.examples_with_type = examples
if serialize:
return srsly.json_dumps(stats.dict(), indent=4)
else:
return stats
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / ("model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.tok2vec.to_bytes())
with (output_dir / ("full_model%d%s.bin" % (epoch, is_temp_str))).open(
"wb"
) as file_:
file_.write(model.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
def try_dump_json(value: Any,
data: Union[Dict[str, dict], Config, str] = "") -> str:
"""Dump a config value as JSON and output user-friendly error if it fails."""
# Special case if we have a variable: it's already a string so don't dump
# to preserve ${x:y} vs. "${x:y}"
if isinstance(value, str) and VARIABLE_RE.search(value):
return value
if isinstance(value, str) and value.replace(".", "", 1).isdigit():
# Work around values that are strings but numbers
value = f'"{value}"'
try:
return srsly.json_dumps(value)
except Exception as e:
err_msg = (
f"Couldn't serialize config value of type {type(value)}: {e}. Make "
f"sure all values in your config are JSON-serializable. If you want "
f"to include Python objects, use a registered function that returns "
f"the object instead.")
raise ConfigValidationError(data, [], message=err_msg) from e
def to_str(self) -> str:
"""Write the config to a string."""
flattened = get_configparser()
queue: List[Tuple[tuple, "Config"]] = [(tuple(), self)]
for path, node in queue:
section_name = ".".join(path)
if path and path[-1] != "*" and not flattened.has_section(
section_name):
# Always create sections for non-'*' sections, not only if
# they have leaf entries, as we don't want to expand
# blocks that are undefined
flattened.add_section(section_name)
for key, value in node.items():
if hasattr(value, "items"):
queue.append((path + (key, ), value))
else:
flattened.set(section_name, key, srsly.json_dumps(value))
string_io = io.StringIO()
flattened.write(string_io)
return string_io.getvalue().strip()
def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
"""Serialize the current state to a binary string.
exclude (list): Names of components or serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Language` object.
DOCS: https://spacy.io/api/language#to_bytes
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
serializers = OrderedDict()
serializers["vocab"] = lambda: self.vocab.to_bytes()
serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"])
serializers["meta.json"] = lambda: srsly.json_dumps(self.meta)
for name, proc in self.pipeline:
if name in exclude:
continue
if not hasattr(proc, "to_bytes"):
continue
serializers[name] = lambda proc=proc: proc.to_bytes(exclude=["vocab"])
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
return util.to_bytes(serializers, exclude)
def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
"""Serialize the current state to a binary string.
exclude (list): Names of components or serialization fields to exclude.
RETURNS (bytes): The serialized form of the `Language` object.
DOCS: https://spacy.io/api/language#to_bytes
"""
if disable is not None:
deprecation_warning(Warnings.W014)
exclude = disable
serializers = OrderedDict()
serializers["vocab"] = lambda: self.vocab.to_bytes()
serializers["tokenizer"] = lambda: self.tokenizer.to_bytes(exclude=["vocab"])
serializers["meta.json"] = lambda: srsly.json_dumps(self.meta)
for name, proc in self.pipeline:
if name in exclude:
continue
if not hasattr(proc, "to_bytes"):
continue
serializers[name] = lambda proc=proc: proc.to_bytes(exclude=["vocab"])
exclude = util.get_serialization_exclude(serializers, exclude, kwargs)
return util.to_bytes(serializers, exclude)
def dataset_stats(data: List[Dict[str, object]], serialize=False):
labels = defaultdict(int)
examples = defaultdict(list)
n_examples_no_entities = 0
for e in data:
if not e['spans']:
n_examples_no_entities += 1
examples['NONE'].append(e)
else:
for s in e['spans']:
label = s['label']
labels[label] += 1
examples[label].append(e)
res = {
'n_examples': len(data),
'n_examples_no_entities': n_examples_no_entities,
'ents_per_type': labels
}
if serialize:
return srsly.json_dumps(res, indent=4)
else:
res['examples_with_type'] = examples
return res
def package(input_dir,
output_dir,
meta_path=None,
create_meta=False,
force=False):
"""
Generate Python package for model data, including meta and required
installation files. A new directory will be created in the specified
output directory, and model data will be copied over. If --create-meta is
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.
"""
msg = Printer()
input_path = util.ensure_path(input_dir)
output_path = util.ensure_path(output_dir)
meta_path = util.ensure_path(meta_path)
if not input_path or not input_path.exists():
msg.fail("Can't locate model data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
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"
if meta_path.is_file():
meta = srsly.read_json(meta_path)
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(input_dir, meta, msg)
for key in ("lang", "name", "version"):
if key not in meta or meta[key] == "":
msg.fail(
"No '{}' setting found in meta.json".format(key),
"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
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(path2str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing "
"directories.".format(path=path2str(package_path)),
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(path2str(input_path),
path2str(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("Successfully created package '{}'".format(model_name_v),
main_path)
msg.text(
"To build the package, run `python setup.py sdist` in this directory.")
def to_bytes(self, exclude=tuple(), disable=None, **kwargs):
return srsly.msgpack_dumps({"dummy": srsly.json_dumps(None)})
def create_wikigraph(
output_path: Path,
wiki="en",
version="latest",
dumps_path: Path = None,
max_workers: int = None,
silent: bool = None,
force: bool = None,
):
"""
Create a `WikiGraph` from a specific dump.
It can then be used by directly loading it, or
it can be packaged with the `package-wikigraph` command.
Parameters
----------
output_path : Path
Path in which to store the `WikiGraph`.
wiki : str, optional
Wikipedia dump type to use, by default "en".
version : str, optional
Wikipedia dump version to use, by default "latest".
dumps_path : Path, optional
Path in which to find previously downloaded dumps,
or where to save dumps downloaded in this call, by default None.
max_workers : int, optional
Maximum number of processes to use, by default None.
silent : bool, optional
Do not print anything in stout, by default None.
force : bool, optional
Overwrite content in output_path, if any, by default None.
"""
if not output_path.exists():
output_path.mkdir()
msg.good(f"Created output directory: {output_path}")
graph_name = f"{wiki}wiki_core"
graph_path = output_path.joinpath(graph_name)
if not force and graph_path.exists():
msg.fail(
f"Output path already contains {graph_name} directory",
"Use --force to overwrite it",
exits=1,
)
kwargs = {
"dumps_path": dumps_path,
"max_workers": max_workers,
"wiki": wiki,
"version": version,
"verbose": not silent,
}
wg = WikiGraph.build(**kwargs)
if not graph_path.exists():
graph_path.mkdir()
graph_format = "picklez"
with msg.loading("dump to disk..."):
wg.dump(graph_path, graph_format=graph_format)
meta = get_meta()
meta["name"] = graph_name
meta["version"] = wg.version
meta["graph_format"] = graph_format
meta["spikex_version"] = f">={spikex_version}"
meta["fullname"] = f"{graph_name}-{spikex_version}"
meta["sources"].append("Wikipedia")
meta_path = graph_path.joinpath("meta.json")
meta_path.write_text(json_dumps(meta, indent=2))
msg.good(f"Successfully created {graph_name}.")
def pretrain(
texts_loc,
vectors_model,
output_dir,
width=96,
depth=4,
embed_rows=2000,
loss_func="cosine",
use_vectors=False,
dropout=0.2,
nr_iter=1000,
batch_size=3000,
max_length=500,
min_length=5,
seed=0,
):
"""
Pre-train the 'token-to-vector' (tok2vec) layer of pipeline components,
using an approximate language-modelling objective. Specifically, we load
pre-trained vectors, and train a component like a CNN, BiLSTM, etc to predict
vectors which match the pre-trained ones. The weights are saved to a directory
after each epoch. You can then pass a path to one of these pre-trained weights
files to the 'spacy train' command.
This technique may be especially helpful if you have little labelled data.
However, it's still quite experimental, so your mileage may vary.
To load the weights back in during 'spacy train', you need to ensure
all settings are the same between pretraining and training. The API and
errors around this need some improvement.
"""
config = dict(locals())
msg = Printer()
util.fix_random_seed(seed)
has_gpu = prefer_gpu()
msg.info("Using GPU" if has_gpu else "Not using GPU")
output_dir = Path(output_dir)
if not output_dir.exists():
output_dir.mkdir()
msg.good("Created output directory")
srsly.write_json(output_dir / "config.json", config)
msg.good("Saved settings to config.json")
# Load texts from file or stdin
if texts_loc != "-": # reading from a file
texts_loc = Path(texts_loc)
if not texts_loc.exists():
msg.fail("Input text file doesn't exist", texts_loc, exits=1)
with msg.loading("Loading input texts..."):
texts = list(srsly.read_jsonl(texts_loc))
msg.good("Loaded input texts")
random.shuffle(texts)
else: # reading from stdin
msg.text("Reading input text from stdin...")
texts = srsly.read_jsonl("-")
with msg.loading("Loading model '{}'...".format(vectors_model)):
nlp = util.load_model(vectors_model)
msg.good("Loaded model '{}'".format(vectors_model))
pretrained_vectors = None if not use_vectors else nlp.vocab.vectors.name
model = create_pretraining_model(
nlp,
Tok2Vec(
width,
embed_rows,
conv_depth=depth,
pretrained_vectors=pretrained_vectors,
bilstm_depth=0, # Requires PyTorch. Experimental.
cnn_maxout_pieces=3, # You can try setting this higher
subword_features=True, # Set to False for Chinese etc
),
)
optimizer = create_default_optimizer(model.ops)
tracker = ProgressTracker(frequency=10000)
msg.divider("Pre-training tok2vec layer")
row_settings = {
"widths": (3, 10, 10, 6, 4),
"aligns": ("r", "r", "r", "r", "r")
}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
for epoch in range(nr_iter):
for batch in util.minibatch_by_words(((text, None) for text in texts),
size=batch_size):
docs = make_docs(
nlp,
[text for (text, _) in batch],
max_length=max_length,
min_length=min_length,
)
loss = make_update(model,
docs,
optimizer,
objective=loss_func,
drop=dropout)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if texts_loc == "-" and tracker.words_per_epoch[epoch] >= 10**7:
break
with model.use_params(optimizer.averages):
with (output_dir / ("model%d.bin" % epoch)).open("wb") as file_:
file_.write(model.tok2vec.to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
tracker.epoch_loss = 0.0
if texts_loc != "-":
# Reshuffle the texts if texts were loaded from a file
random.shuffle(texts)
def main(path):
reddit = Reddit(path)
for comment in reddit:
print(srsly.json_dumps(comment))
def get_emails():
result = schema.execute(default_query)
return srsly.json_dumps(result.data)
def to_bytes(self, **kwargs) -> bytes:
serializers = {"cfg": lambda: srsly.json_dumps(self._get_config())}
return util.to_bytes(serializers, [])
def main(path):
reddit = Reddit(path)
for comment in reddit:
print(srsly.json_dumps(comment))
def package(
input_dir: Path,
output_dir: Path,
meta_path: Optional[Path] = None,
code_paths: List[Path] = [],
name: Optional[str] = None,
version: Optional[str] = None,
create_meta: bool = False,
create_sdist: bool = True,
create_wheel: 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)
if create_wheel and not has_wheel():
err = "Generating a binary .whl file requires wheel to be installed"
msg.fail(err, "pip install wheel", exits=1)
if not input_path or not input_path.exists():
msg.fail("Can't locate pipeline data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
if create_sdist or create_wheel:
opts = ["sdist" if create_sdist else "", "wheel" if create_wheel else ""]
msg.info(f"Building package artifacts: {', '.join(opt for opt in opts if opt)}")
for code_path in code_paths:
if not code_path.exists():
msg.fail("Can't find code file", code_path, exits=1)
# Import the code here so it's available when model is loaded (via
# get_meta helper). Also verifies that everything works
util.import_file(code_path.stem, code_path)
if code_paths:
msg.good(f"Including {len(code_paths)} Python module(s) with custom code")
if meta_path and not meta_path.exists():
msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
meta_path = meta_path or input_dir / "meta.json"
if not meta_path.exists() or not meta_path.is_file():
msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
meta = get_meta(input_dir, meta)
if meta["requirements"]:
msg.good(
f"Including {len(meta['requirements'])} package requirement(s) from "
f"meta and config",
", ".join(meta["requirements"]),
)
if name is not None:
if not name.isidentifier():
msg.fail(
f"Model name ('{name}') is not a valid module name. "
"This is required so it can be imported as a module.",
"We recommend names that use ASCII A-Z, a-z, _ (underscore), "
"and 0-9. "
"For specific details see: https://docs.python.org/3/reference/lexical_analysis.html#identifiers",
exits=1,
)
if not _is_permitted_package_name(name):
msg.fail(
f"Model name ('{name}') is not a permitted package name. "
"This is required to correctly load the model with spacy.load.",
"We recommend names that use ASCII A-Z, a-z, _ (underscore), "
"and 0-9. "
"For specific details see: https://www.python.org/dev/peps/pep-0426/#name",
exits=1,
)
meta["name"] = name
if version is not None:
meta["version"] = version
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(meta, msg)
errors = validate(ModelMetaSchema, meta)
if errors:
msg.fail("Invalid pipeline meta.json")
print("\n".join(errors))
sys.exit(1)
model_name = meta["name"]
if not model_name.startswith(meta["lang"] + "_"):
model_name = f"{meta['lang']}_{model_name}"
model_name_v = model_name + "-" + meta["version"]
main_path = output_dir / model_name_v
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing directories.",
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(str(input_dir), str(package_path / model_name_v))
for file_name in FILENAMES_DOCS:
file_path = package_path / model_name_v / file_name
if file_path.exists():
shutil.copy(str(file_path), str(main_path))
readme_path = main_path / "README.md"
if not readme_path.exists():
readme = generate_readme(meta)
create_file(readme_path, readme)
create_file(package_path / model_name_v / "README.md", readme)
msg.good("Generated README.md from meta.json")
else:
msg.info("Using existing README.md from pipeline directory")
imports = []
for code_path in code_paths:
imports.append(code_path.stem)
shutil.copy(str(code_path), str(package_path))
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)
init_py = TEMPLATE_INIT.format(
imports="\n".join(f"from . import {m}" for m in imports)
)
create_file(package_path / "__init__.py", init_py)
msg.good(f"Successfully created package directory '{model_name_v}'", main_path)
if create_sdist:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "sdist"], capture=False)
zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
msg.good(f"Successfully created zipped Python package", zip_file)
if create_wheel:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "bdist_wheel"], capture=False)
wheel_name_squashed = re.sub("_+", "_", model_name_v)
wheel = main_path / "dist" / f"{wheel_name_squashed}{WHEEL_SUFFIX}"
msg.good(f"Successfully created binary wheel", wheel)
if "__" in model_name:
msg.warn(
f"Model name ('{model_name}') contains a run of underscores. "
"Runs of underscores are not significant in installed package names.",
)
def json_dumps(data, indent=0, sort_keys=False):
return srsly.json_dumps(data, indent, sort_keys)
def package(
input_dir: Path,
output_dir: Path,
meta_path: Optional[Path] = None,
code_paths: List[Path] = [],
name: Optional[str] = None,
version: Optional[str] = None,
create_meta: bool = False,
create_sdist: bool = True,
create_wheel: 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)
if create_wheel and not has_wheel():
err = "Generating a binary .whl file requires wheel to be installed"
msg.fail(err, "pip install wheel", exits=1)
if not input_path or not input_path.exists():
msg.fail("Can't locate pipeline data", input_path, exits=1)
if not output_path or not output_path.exists():
msg.fail("Output directory not found", output_path, exits=1)
if create_sdist or create_wheel:
opts = [
"sdist" if create_sdist else "", "wheel" if create_wheel else ""
]
msg.info(
f"Building package artifacts: {', '.join(opt for opt in opts if opt)}"
)
for code_path in code_paths:
if not code_path.exists():
msg.fail("Can't find code file", code_path, exits=1)
# Import the code here so it's available when model is loaded (via
# get_meta helper). Also verifies that everything works
util.import_file(code_path.stem, code_path)
if code_paths:
msg.good(
f"Including {len(code_paths)} Python module(s) with custom code")
if meta_path and not meta_path.exists():
msg.fail("Can't find pipeline meta.json", meta_path, exits=1)
meta_path = meta_path or input_dir / "meta.json"
if not meta_path.exists() or not meta_path.is_file():
msg.fail("Can't load pipeline meta.json", meta_path, exits=1)
meta = srsly.read_json(meta_path)
meta = get_meta(input_dir, meta)
if name is not None:
meta["name"] = name
if version is not None:
meta["version"] = version
if not create_meta: # only print if user doesn't want to overwrite
msg.good("Loaded meta.json from file", meta_path)
else:
meta = generate_meta(meta, msg)
errors = validate(ModelMetaSchema, meta)
if errors:
msg.fail("Invalid pipeline meta.json")
print("\n".join(errors))
sys.exit(1)
model_name = meta["lang"] + "_" + meta["name"]
model_name_v = model_name + "-" + meta["version"]
main_path = output_dir / model_name_v
package_path = main_path / model_name
if package_path.exists():
if force:
shutil.rmtree(str(package_path))
else:
msg.fail(
"Package directory already exists",
"Please delete the directory and try again, or use the "
"`--force` flag to overwrite existing directories.",
exits=1,
)
Path.mkdir(package_path, parents=True)
shutil.copytree(str(input_dir), str(package_path / model_name_v))
license_path = package_path / model_name_v / "LICENSE"
if license_path.exists():
shutil.move(str(license_path), str(main_path))
imports = []
for code_path in code_paths:
imports.append(code_path.stem)
shutil.copy(str(code_path), str(package_path))
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)
init_py = TEMPLATE_INIT.format(imports="\n".join(f"from . import {m}"
for m in imports))
create_file(package_path / "__init__.py", init_py)
msg.good(f"Successfully created package '{model_name_v}'", main_path)
if create_sdist:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "sdist"],
capture=False)
zip_file = main_path / "dist" / f"{model_name_v}{SDIST_SUFFIX}"
msg.good(f"Successfully created zipped Python package", zip_file)
if create_wheel:
with util.working_dir(main_path):
util.run_command([sys.executable, "setup.py", "bdist_wheel"],
capture=False)
wheel = main_path / "dist" / f"{model_name_v}{WHEEL_SUFFIX}"
msg.good(f"Successfully created binary wheel", wheel)
def dqn_training(num_episodes, max_steps=500, display_action=False):
"""
num_episodes: int
number of episodes
visualize_plt: bool
if true, display the cartpole action in the notebook
if false (default), display the episodes x durations graph
"""
score_history = scoreAverage(report_mean_score_over_n)
for i_episode in range(num_episodes):
# Initialize the environment and state
env.reset()
last_screen = get_screen()
current_screen = get_screen()
state = current_screen - last_screen
# state = get_screen().to(device)
total_reward = 0
episode_start_time = datetime.now()
statememory = []
for t in count():
# initialise state memory
# Select and perform an action
# action = select_action(state, SELECT_ACTION_BIAS_LIST)
action = select_action(state, SELECT_ACTION_BIAS_LIST)
# "action", action)
if display_action:
print("action: ", action.squeeze())
_, reward, done, info = env.step(action)
# ('action', 'reward', 'info', 'done')
this_state = {
"action": action.data[0].item(),
"reward": reward[1],
"scaled_reward": reward[0],
"info": info,
"done": done
}
statememory.append(this_state)
total_reward += reward[1]
reward = torch.tensor([reward[0]], device=device)
# Observe new state
last_screen = current_screen
current_screen = get_screen().to(device)
if not done:
next_state = current_screen
next_state = next_state
else:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
# Perform one step of the optimization (on the target network)
optimize_model()
if done or t > max_steps:
episode_end_time = datetime.now()
episode_time = (episode_end_time -
episode_start_time).total_seconds()
score_history.push(total_reward)
# floyd metrics
print(
f'{{"metric": "score", "value": {total_reward}, "epoch": {i_episode+1}}}'
)
print(
f'{{"metric": "rolling mean score", "value": {score_history.mean()}, "epoch": {i_episode+1}}}'
)
print(
f'{{"metric": "steps this episode", "value": {t}, "epoch": {i_episode+1}}}'
)
print(
f'{{"metric": "episode duration", "value": {episode_time}, "epoch": {i_episode+1}}}'
)
print(
f'{{"metric": "steps per second", "value": {float(t) / float(episode_time)}, "epoch": {i_episode+1}}}'
)
# paperspace
# {"chart": "<identifier>", "y": <value>, "x": <value>}
print(
f'{{"chart": "score", "y": {total_reward}, "x": {i_episode+1}}}'
)
print(
f'{{"chart": "rolling_mean_score", "y": {score_history.mean()}, "x": {i_episode+1}}}'
)
print(
f'{{"chart": "steps_this_episode", "y": {t}, "x": {i_episode+1}}}'
)
print(
f'{{"chart": "episode_duration", "y": {episode_time}, "x": {i_episode+1}}}'
)
print(
f'{{"chart": "steps_per_second", "y": {float(t) / float(episode_time)}, "x": {i_episode+1}}}'
)
filename = os.path.join(
STATE_DIR,
f'gamedata-{GAME_NAME}-{LEVEL}-{(i_episode+1):06}.json')
print(f"Writing game history to '{filename}'")
with open(filename, "w") as f:
f.write(json_dumps(statememory))
f.close()
break
# Update the target network
if i_episode % TARGET_UPDATE == 0:
target_net.load_state_dict(policy_net.state_dict())
print('Completed training')
# env.render(close=True)
env.close()
def to_bytes(self, **_kwargs):
serializers = OrderedDict(
(("cfg", lambda: srsly.json_dumps(self._get_config())), ))
return util.to_bytes(serializers, [])
def to_bytes(self, **kwargs):
serializers = {
"cfg": lambda: srsly.json_dumps(self.cfg),
}
return util.to_bytes(serializers, [])