code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def predict(self, X):
"""Predict on a single input.
:param X: The input to predict on.
"""
if isinstance(X, numpy.ndarray):
X = X[None, :]
if self.preprocess is not None:
X = self.preprocess(X)
X = self.object.predict(X, **self.predict_kwargs)[0]
... | Predict on a single input.
:param X: The input to predict on.
| predict | python | superduper-io/superduper | plugins/sklearn/superduper_sklearn/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/sklearn/superduper_sklearn/model.py | Apache-2.0 |
def watch_token_file(databackend):
"""Watch the Snowflake token file for changes.
:param databackend: The data backend instance to reconnect.
This function sets up a file system observer that watches The
Snowflake token file for changes. When the token file is modified,
it will trigger a reconnecti... | Watch the Snowflake token file for changes.
:param databackend: The data backend instance to reconnect.
This function sets up a file system observer that watches The
Snowflake token file for changes. When the token file is modified,
it will trigger a reconnection of the data backend.
| watch_token_file | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/connect.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/connect.py | Apache-2.0 |
def connect(uri):
"""Connect to Snowflake using the Snowpark session.
:param uri: The URI of the Snowflake connection.
If the URI is 'snowflake://', the connection parameters are read
from environment variables.
- SNOWFLAKE_HOST: The Snowflake host.
- SNOWFLAKE_PORT: The Snowflake port.
-... | Connect to Snowflake using the Snowpark session.
:param uri: The URI of the Snowflake connection.
If the URI is 'snowflake://', the connection parameters are read
from environment variables.
- SNOWFLAKE_HOST: The Snowflake host.
- SNOWFLAKE_PORT: The Snowflake port.
- SNOWFLAKE_ACCOUNT: The S... | connect | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/connect.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/connect.py | Apache-2.0 |
def create_tables_and_schemas(self, events: t.List[CreateTable]):
"""Create tables and schemas in the data-backend.
:param events: The events to create.
"""
if not events:
return
tables = set(self.list_tables())
events = [e for e in events if e.identifier not... | Create tables and schemas in the data-backend.
:param events: The events to create.
| create_tables_and_schemas | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def create_table_and_schema(self, identifier: str, schema: Schema, primary_id: str):
"""Create a schema in the data-backend.
:param identifier: The identifier of the schema.
:param schema: The schema to create.
:param primary_id: The primary id of the schema.
"""
if iden... | Create a schema in the data-backend.
:param identifier: The identifier of the schema.
:param schema: The schema to create.
:param primary_id: The primary id of the schema.
| create_table_and_schema | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def _merge_schemas(self, tables: str):
"""Merge schemas.
:param tables: The tables to merge.
"""
fields = {}
for tab in tables:
tab = self.get_table(tab)
fields.update(
{
f.name.removeprefix('"').removesuffix('"'): f.da... | Merge schemas.
:param tables: The tables to merge.
| _merge_schemas | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def drop(self, force: bool = False):
"""Drop the databackend.
:param force: If ``True``, don't ask for confirmation.
"""
if not force and not click.confirm(
"Are you sure you want to drop the database?", default=False
):
return
for table in self.l... | Drop the databackend.
:param force: If ``True``, don't ask for confirmation.
| drop | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def list_tables(self):
"""List all tables or collections in the database."""
results_tables = self.session.sql("SHOW TABLES").collect()
results_views = self.session.sql("SHOW VIEWS").collect()
return [r.name for r in results_tables] + [r.name for r in results_views] | List all tables or collections in the database. | list_tables | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def insert(self, table_name, raw_documents, primary_id: str | None = None):
"""Insert data into the database.
:param table: The table to insert into.
:param raw_documents: The (encoded) documents to insert.
"""
if primary_id is None:
primary_id = self.db.load('Table'... | Insert data into the database.
:param table: The table to insert into.
:param raw_documents: The (encoded) documents to insert.
| insert | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def replace(self, table: str, condition: t.Dict, r: t.Dict) -> t.List[str]:
"""Replace data.
:param table: The table to insert into.
:param condition: The condition to update.
:param r: The document to replace.
"""
t = self.get_table(table)
cond = None
fo... | Replace data.
:param table: The table to insert into.
:param condition: The condition to update.
:param r: The document to replace.
| replace | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def delete(self, table: str, condition: t.Dict):
"""Update data in the database.
:param table: The table to update.
:param condition: The condition to update.
"""
terms = []
for k, v in condition.items():
if isinstance(v, str):
v = f"'{v}'"
... | Update data in the database.
:param table: The table to update.
:param condition: The condition to update.
| delete | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def missing_outputs(self, query, predict_id):
"""Get missing outputs.
:param query: The query to get the missing outputs of.
:param predict_id: The identifier of the output destination.
"""
pid = self.primary_id(query.table)
df = map_superduper_query_to_snowpark_query(se... | Get missing outputs.
:param query: The query to get the missing outputs of.
:param predict_id: The identifier of the output destination.
| missing_outputs | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def select(self, query: Query, primary_id: str | None = None) -> t.List[t.Dict]:
"""Select data from the database.
:param query: The query to perform.
"""
q = map_superduper_query_to_snowpark_query(
self.session,
query,
primary_id or self.primary_id(q... | Select data from the database.
:param query: The query to perform.
| select | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def execute_native(self, query: str):
"""Execute a native query.
:param query: The query to execute.
"""
results = self._run_query(query)
out = []
for r in results:
out.append(r.as_dict())
return out | Execute a native query.
:param query: The query to execute.
| execute_native | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/data_backend.py | Apache-2.0 |
def map_superduper_query_to_snowpark_query(session, query, primary_id: str = 'id'):
"""Map a SuperDuper query to a Snowpark query.
:param session: The Snowpark session.
:param query: The SuperDuper query.
:param primary_id: The primary ID column.
"""
q = session.table(f'"{query.table}"')
i... | Map a SuperDuper query to a Snowpark query.
:param session: The Snowpark session.
:param query: The SuperDuper query.
:param primary_id: The primary ID column.
| map_superduper_query_to_snowpark_query | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/query.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/query.py | Apache-2.0 |
def superduper_to_snowflake_schema(schema: Schema, primary_id: str):
"""Convert a SuperDuper schema to a Snowflake schema.
:param schema: The SuperDuper schema.
:param primary_id: The primary ID column.
"""
snowflake_schema = []
snowflake_schema.append(f'"{primary_id}" VARCHAR PRIMARY KEY')
... | Convert a SuperDuper schema to a Snowflake schema.
:param schema: The SuperDuper schema.
:param primary_id: The primary ID column.
| superduper_to_snowflake_schema | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/schema.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/schema.py | Apache-2.0 |
def build_secret_status_report(db) -> SecretStatusReport:
"""Check if secrets are updated in Snowflake and return structured status.
:param db: The database connection object.
:return: SecretStatusReport with status for each secret
"""
result = db.databackend.execute_native("CALL v1.wrapper('SHOW S... | Check if secrets are updated in Snowflake and return structured status.
:param db: The database connection object.
:return: SecretStatusReport with status for each secret
| build_secret_status_report | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/secrets.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/secrets.py | Apache-2.0 |
def raise_if_secrets_pending(report: SecretStatusReport):
"""Check if any secrets are pending and raise exception if so.
:param report: SecretStatusReport to check
:raises UpdatingSecretException: If any secrets are still updating
"""
pending_secrets = [
secret for secret in report.secrets ... | Check if any secrets are pending and raise exception if so.
:param report: SecretStatusReport to check
:raises UpdatingSecretException: If any secrets are still updating
| raise_if_secrets_pending | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/secrets.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/secrets.py | Apache-2.0 |
def add(self, items: t.Sequence[VectorItem], cache: bool = False) -> None:
"""
Add items to the index.
:param items: t.Sequence of VectorItems
"""
# NOTE: Since we will be doing vector search on tables directly
# seperate vector search is not required. |
Add items to the index.
:param items: t.Sequence of VectorItems
| add | python | superduper-io/superduper | plugins/snowflake/superduper_snowflake/vector_search.py | https://github.com/superduper-io/superduper/blob/master/plugins/snowflake/superduper_snowflake/vector_search.py | Apache-2.0 |
def __init__(self, uri: str, flavour: t.Optional[str] = None):
"""Initialize the thread-local connection manager.
:param uri: URI to the database.
:param flavour: Flavour of the database.
"""
self.uri = uri
self.flavour = flavour
self.local = threading.local()
... | Initialize the thread-local connection manager.
:param uri: URI to the database.
:param flavour: Flavour of the database.
| __init__ | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def _create_connection(self):
"""Create a new connection specifically for this thread."""
name = self.uri.split("//")[0]
in_memory = False
ibis_conn = ibis.connect(self.uri)
return ibis_conn, name, in_memory | Create a new connection specifically for this thread. | _create_connection | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def get_connection(self):
"""Get a connection for the current thread, creating it if it doesn't exist."""
if not hasattr(self.local, "connection"):
with self.lock: # Lock only during connection creation
self.local.connection, self.local.name, self.local.in_memory = (
... | Get a connection for the current thread, creating it if it doesn't exist. | get_connection | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def vector_impl(self):
"""Get the vector implementation based on the URI."""
if self.uri.startswith("snowflake"):
return NativeVector
return Array | Get the vector implementation based on the URI. | vector_impl | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def url(self):
"""Get the URL of the database."""
with self.connection_manager.get_connection() as conn:
return conn.con.url + self.name | Get the URL of the database. | url | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def check_output_dest(self, predict_id) -> bool:
"""Check if the output destination exists.
:param predict_id: The identifier of the prediction.
"""
with self.connection_manager.get_connection() as conn:
try:
conn.table(f"{CFG.output_prefix}{predict_id}")
... | Check if the output destination exists.
:param predict_id: The identifier of the prediction.
| check_output_dest | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def create_table_and_schema(self, identifier: str, schema: Schema, primary_id: str):
"""Create a schema in the data-backend.
:param identifier: The identifier of the table.
:param mapping: The mapping of the schema.
"""
with self.connection_manager.get_connection() as conn:
... | Create a schema in the data-backend.
:param identifier: The identifier of the table.
:param mapping: The mapping of the schema.
| create_table_and_schema | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def drop(self, force: bool = False):
"""Drop tables or collections in the database.
:param force: Whether to force the drop.
"""
if not force and not click.confirm("Are you sure you want to drop all tables?"):
logging.info("Aborting drop tables")
return
... | Drop tables or collections in the database.
:param force: Whether to force the drop.
| drop | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def get_table(self, identifier):
"""Get a table or collection from the database.
:param identifier: The identifier of the table or collection.
"""
with self.connection_manager.get_connection() as conn:
try:
return conn.table(identifier)
except ibi... | Get a table or collection from the database.
:param identifier: The identifier of the table or collection.
| get_table | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def missing_outputs(self, query, predict_id: str) -> t.List[str]:
"""Get missing outputs from the database."""
with self.connection_manager.get_connection() as conn:
pid = self.primary_id(query.table)
query = self._build_native_query(conn, query)
output_table = conn.t... | Get missing outputs from the database. | missing_outputs | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def json_native(self):
"""Check if the database supports JSON natively."""
if self.uri.startswith("postgres"):
return True
return False | Check if the database supports JSON natively. | json_native | python | superduper-io/superduper | plugins/sql/superduper_sql/data_backend.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/data_backend.py | Apache-2.0 |
def convert_data_format(self, data):
"""Convert byte data to base64 format for storage in the database.
:param data: The data to convert.
"""
if isinstance(data, bytes):
return BASE64_PREFIX + base64.b64encode(data).decode("utf-8")
else:
return data | Convert byte data to base64 format for storage in the database.
:param data: The data to convert.
| convert_data_format | python | superduper-io/superduper | plugins/sql/superduper_sql/db_helper.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/db_helper.py | Apache-2.0 |
def recover_data_format(self, data):
"""Recover byte data from base64 format stored in the database.
:param data: The data to recover.
"""
if isinstance(data, str) and data.startswith(BASE64_PREFIX):
return base64.b64decode(data[len(BASE64_PREFIX) :])
else:
... | Recover byte data from base64 format stored in the database.
:param data: The data to recover.
| recover_data_format | python | superduper-io/superduper | plugins/sql/superduper_sql/db_helper.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/db_helper.py | Apache-2.0 |
def process_schema_types(self, schema_mapping):
"""Convert bytes to string in the schema.
:param schema_mapping: The schema mapping to convert.
"""
for key, value in schema_mapping.items():
if value == "Bytes":
schema_mapping[key] = "String"
return sc... | Convert bytes to string in the schema.
:param schema_mapping: The schema mapping to convert.
| process_schema_types | python | superduper-io/superduper | plugins/sql/superduper_sql/db_helper.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/db_helper.py | Apache-2.0 |
def process_before_insert(self, table_name, datas, conn):
"""Convert byte data to base64 format for storage in the database.
:param table_name: The name of the table.
:param datas: The data to insert.
"""
datas = pd.DataFrame(datas)
# change the order of the columns sinc... | Convert byte data to base64 format for storage in the database.
:param table_name: The name of the table.
:param datas: The data to insert.
| process_before_insert | python | superduper-io/superduper | plugins/sql/superduper_sql/db_helper.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/db_helper.py | Apache-2.0 |
def get_db_helper(dialect) -> DBHelper:
"""Get the insert processor for the given dialect.
:param dialect: The dialect of the database.
"""
for helper in DBHelper.__subclasses__():
if helper.match_dialect == dialect:
return helper(dialect)
return DBHelper(dialect) | Get the insert processor for the given dialect.
:param dialect: The dialect of the database.
| get_db_helper | python | superduper-io/superduper | plugins/sql/superduper_sql/db_helper.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/db_helper.py | Apache-2.0 |
def convert_schema_to_fields(schema: Schema, json_native: bool) -> dict:
"""Return the raw fields.
Get a dictionary of fields as keys and datatypes as values.
This is used to create ibis tables.
:param schema: The schema to convert
"""
fields = {}
for k, v in schema.fields.items():
... | Return the raw fields.
Get a dictionary of fields as keys and datatypes as values.
This is used to create ibis tables.
:param schema: The schema to convert
| convert_schema_to_fields | python | superduper-io/superduper | plugins/sql/superduper_sql/utils.py | https://github.com/superduper-io/superduper/blob/master/plugins/sql/superduper_sql/utils.py | Apache-2.0 |
def torchmodel(class_obj):
"""A decorator to convert a `torch.nn.Module` into a `TorchModel`.
Decorate a `torch.nn.Module` so that when it is invoked,
the result is a `TorchModel`.
:param class_obj: Class to decorate
"""
def factory(
identifier: str,
*args,
preprocess:... | A decorator to convert a `torch.nn.Module` into a `TorchModel`.
Decorate a `torch.nn.Module` so that when it is invoked,
the result is a `TorchModel`.
:param class_obj: Class to decorate
| torchmodel | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def get_merkle_tree(self, breaks):
"""Get the merkle tree of the model."""
t = super().get_merkle_tree(breaks)
self.setup()
w = next(iter(self.object.state_dict().values()))
model_h = hash_item(w.tolist())
t['object'] = model_h
return t | Get the merkle tree of the model. | get_merkle_tree | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def train_forward_signature(self):
"""Infer signature of train forward pass."""
if (
self._train_forward_signature is None
and self.forward_method != self.train_forward_method
):
self._train_forward_signature = self._infer_signature(
getattr(se... | Infer signature of train forward pass. | train_forward_signature | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def saving(self):
"""Context manager for saving the model.
This context manager ensures that the model is in evaluation mode
"""
was_training = self.object.training
try:
self.object.eval()
yield
finally:
if was_training:
... | Context manager for saving the model.
This context manager ensures that the model is in evaluation mode
| saving | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def predict(self, *args, **kwargs):
"""Predict on a single input.
:param args: Input arguments
:param kwargs: Input keyword arguments
"""
if self.signature == 'singleton':
item = args[0]
elif self.signature == '*args':
item = args
elif sel... | Predict on a single input.
:param args: Input arguments
:param kwargs: Input keyword arguments
| predict | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def predict_batches(self, dataset: t.Union[t.List, QueryDataset]) -> t.List:
"""Predict on a dataset.
:param dataset: Dataset
"""
with torch.no_grad(), eval(self.object):
inputs = BasicDataset(
items=dataset,
transform=self.preprocess,
... | Predict on a dataset.
:param dataset: Dataset
| predict_batches | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def train_forward(self, X, y=None):
"""The forward method for training.
:param X: Input
:param y: Target
"""
X = X.to(self.device)
if y is not None:
y = y.to(self.device)
method = getattr(self.object, self.train_forward_method)
if hasattr(sel... | The forward method for training.
:param X: Input
:param y: Target
| train_forward | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def unpack_batch(args):
"""Unpack a batch into lines of tensor output.
:param args: a batch of model outputs
>>> unpack_batch(torch.randn(1, 10))[0].shape
torch.Size([10])
>>> out = unpack_batch([torch.randn(2, 10), torch.randn(2, 3, 5)])
>>> type(out)
<class 'list'>
>>> len(out)
2... | Unpack a batch into lines of tensor output.
:param args: a batch of model outputs
>>> unpack_batch(torch.randn(1, 10))[0].shape
torch.Size([10])
>>> out = unpack_batch([torch.randn(2, 10), torch.randn(2, 3, 5)])
>>> type(out)
<class 'list'>
>>> len(out)
2
>>> out = unpack_batch({'a... | unpack_batch | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def create_batch(args):
"""Create a singleton batch in a manner similar to the PyTorch dataloader.
:param args: single data point for batching
>>> create_batch(3.).shape
torch.Size([1])
>>> x, y = create_batch([torch.randn(5), torch.randn(3, 7)])
>>> x.shape
torch.Size([1, 5])
>>> y.sh... | Create a singleton batch in a manner similar to the PyTorch dataloader.
:param args: single data point for batching
>>> create_batch(3.).shape
torch.Size([1])
>>> x, y = create_batch([torch.randn(5), torch.randn(3, 7)])
>>> x.shape
torch.Size([1, 5])
>>> y.shape
torch.Size([1, 3, 7])
... | create_batch | python | superduper-io/superduper | plugins/torch/superduper_torch/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/model.py | Apache-2.0 |
def get_optimizers(self, model):
"""Get the optimizers for the model.
:param model: Model
"""
cls_ = getattr(torch.optim, self.optimizer_cls)
optimizer = cls_(model.parameters(), **self.optimizer_kwargs)
if self.optimizer_state is not None:
self.optimizer.loa... | Get the optimizers for the model.
:param model: Model
| get_optimizers | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def take_step(self, model, batch, optimizers):
"""Take a step in the optimization.
:param model: Model
:param batch: Batch of data
:param optimizers: Optimizers
"""
if self.signature == '*args':
outputs = model.train_forward(*batch)
elif self.signatur... | Take a step in the optimization.
:param model: Model
:param batch: Batch of data
:param optimizers: Optimizers
| take_step | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def compute_validation_objective(self, model, valid_dataloader):
"""Compute the validation objective.
:param model: Model
:param valid_dataloader: Validation dataloader to use
"""
objective_values = []
with model.evaluating(), torch.no_grad():
for batch in va... | Compute the validation objective.
:param model: Model
:param valid_dataloader: Validation dataloader to use
| compute_validation_objective | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def append_metrics(self, d: t.Dict[str, float]) -> None:
"""Append metrics to the metric_values dict.
:param d: Metrics to append
"""
if self.metric_values is not None:
for k, v in d.items():
self.metric_values.setdefault(k, []).append(v) | Append metrics to the metric_values dict.
:param d: Metrics to append
| append_metrics | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def stopping_criterion(self, iteration):
"""Check if the training should stop.
:param iteration: Current iteration
"""
max_iterations = self.max_iterations
no_improve_then_stop = self.no_improve_then_stop
if isinstance(max_iterations, int) and iteration >= max_iterations... | Check if the training should stop.
:param iteration: Current iteration
| stopping_criterion | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def saving_criterion(self):
"""Check if the model should be saved."""
if self.listen == 'objective':
to_listen = [-x for x in self.metric_values['objective']]
else:
to_listen = self.metric_values[self.listen]
if all([to_listen[-1] >= x for x in to_listen[:-1]]):
... | Check if the model should be saved. | saving_criterion | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def log(self, **kwargs):
"""Log the training progress.
:param kwargs: Key-value pairs to log
"""
out = ''
for k, v in kwargs.items():
if isinstance(v, dict):
for kk, vv in v.items():
out += f'{k}/{kk}: {vv}; '
else:
... | Log the training progress.
:param kwargs: Key-value pairs to log
| log | python | superduper-io/superduper | plugins/torch/superduper_torch/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/training.py | Apache-2.0 |
def device_of(module: Module) -> t.Union[_device, str]:
"""
Get device of a model.
:param module: PyTorch model
"""
try:
return next(iter(module.state_dict().values())).device
except StopIteration:
return 'cpu' |
Get device of a model.
:param module: PyTorch model
| device_of | python | superduper-io/superduper | plugins/torch/superduper_torch/utils.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/utils.py | Apache-2.0 |
def eval(module: Module) -> t.Iterator[None]:
"""
Temporarily set a module to evaluation mode.
:param module: PyTorch module
"""
was_training = module.training
try:
module.eval()
yield
finally:
if was_training:
module.train() |
Temporarily set a module to evaluation mode.
:param module: PyTorch module
| eval | python | superduper-io/superduper | plugins/torch/superduper_torch/utils.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/utils.py | Apache-2.0 |
def set_device(module: Module, device: _device):
"""
Temporarily set a device of a module.
:param module: PyTorch module
:param device: Device to set
"""
device_before = device_of(module)
try:
module.to(device)
yield
finally:
module.to(device_before) |
Temporarily set a device of a module.
:param module: PyTorch module
:param device: Device to set
| set_device | python | superduper-io/superduper | plugins/torch/superduper_torch/utils.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/utils.py | Apache-2.0 |
def to_device(
item: t.Any, # lists or dicts of Tensors
device: t.Union[str, _device],
) -> t.Any:
"""
Send tensor leaves of nested list/ dictionaries/ tensors to device.
:param item: torch.Tensor instance
:param device: device to which one would like to send
"""
if isinstance(item, tu... |
Send tensor leaves of nested list/ dictionaries/ tensors to device.
:param item: torch.Tensor instance
:param device: device to which one would like to send
| to_device | python | superduper-io/superduper | plugins/torch/superduper_torch/utils.py | https://github.com/superduper-io/superduper/blob/master/plugins/torch/superduper_torch/utils.py | Apache-2.0 |
def fit(
self,
model: 'TextClassificationPipeline',
db: Datalayer,
train_dataset: QueryDataset,
valid_dataset: QueryDataset,
):
"""Fit the model.
:param model: model
:param db: Datalayer instance
:param train_dataset: training dataset
... | Fit the model.
:param model: model
:param db: Datalayer instance
:param train_dataset: training dataset
:param valid_dataset: validation dataset
| fit | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def from_pretrained(
cls,
model_name_or_path,
identifier="",
prompt_template="{input}",
prompt_func=None,
predict_kwargs=None,
**kwargs,
):
"""A new function to create a LLM model from from_pretrained function.
Allow the user to directly repla... | A new function to create a LLM model from from_pretrained function.
Allow the user to directly replace:
`AutoModelForCausalLM.from_pretrained` -> `LLM.from_pretrained`
:param model_name_or_path: model name or path
:param identifier: model identifier
:param prompt_template: prom... | from_pretrained | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def init_pipeline(
self, adapter_id: t.Optional[str] = None, load_adapter_directly: bool = False
):
"""Initialize pipeline.
:param adapter_id: adapter id
:param load_adapter_directly: load adapter directly
"""
# Do not update model state here
model_kwargs = s... | Initialize pipeline.
:param adapter_id: adapter id
:param load_adapter_directly: load adapter directly
| init_pipeline | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def setup(self, db=None):
"""Initialize the model.
If adapter_id is provided, will load the adapter to the model.
"""
super().setup()
real_adapter_id = None
if self.adapter_id is not None:
if isinstance(self.adapter_id, Checkpoint):
real_adapt... | Initialize the model.
If adapter_id is provided, will load the adapter to the model.
| setup | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def predict(self, X, **kwargs):
"""Generate text from a single prompt.
:param X: a prompt
:param kwargs: additional keyword arguments
"""
X = self._process_inputs(X, **kwargs)
kwargs.pop("context", None)
results = self._batch_generate([X], **kwargs)
retur... | Generate text from a single prompt.
:param X: a prompt
:param kwargs: additional keyword arguments
| predict | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def predict_batches(
self, dataset: t.Union[t.List, QueryDataset], **kwargs
) -> t.List:
"""Generate text from a list of prompts.
:param dataset: a list of prompts
:param kwargs: additional keyword arguments
"""
dataset = [
self._process_inputs(dataset[i]... | Generate text from a list of prompts.
:param dataset: a list of prompts
:param kwargs: additional keyword arguments
| predict_batches | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def _batch_generate(self, prompts: t.List[str], **kwargs) -> t.List[str]:
"""Generate text.
Can overwrite this method to support more inference methods.
"""
kwargs = {**self.predict_kwargs, **kwargs.copy()}
# Set default values, if not will cause bad output
outputs = se... | Generate text.
Can overwrite this method to support more inference methods.
| _batch_generate | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def add_adapter(self, model_id, adapter_name: str):
"""Add adapter to the model.
:param model_id: model id
:param adapter_name: adapter name
"""
# TODO: Support lora checkpoint from s3
try:
from peft import PeftModel
except Exception as e:
... | Add adapter to the model.
:param model_id: model id
:param adapter_name: adapter name
| add_adapter | python | superduper-io/superduper | plugins/transformers/superduper_transformers/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/model.py | Apache-2.0 |
def on_save(self, args, state, control, **kwargs):
"""Event called after a checkpoint save.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other key... | Event called after a checkpoint save.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other keyword arguments from transformers.
| on_save | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def on_evaluate(self, args, state, control, **kwargs):
"""Event called after an evaluation.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other key... | Event called after an evaluation.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other keyword arguments from transformers.
| on_evaluate | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def on_train_end(self, args, state, control, **kwargs):
"""Event called after training ends.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other ke... | Event called after training ends.
:param args: The training arguments from transformers.
:param state: The training state from transformers.
:param control: The training control from transformers.
:param kwargs: Other keyword arguments from transformers.
| on_train_end | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def check_init(self):
"""Check the initialization of the callback."""
# Only check this in the world_rank 0 process
# Rebuild datalayer for the new process
if self.db is None:
self.db = build_datalayer(self.cfg)
self.llm = self.db.load("model", self.identifier)
... | Check the initialization of the callback. | check_init | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def get_compute_metrics(metrics):
"""Get the compute metrics function.
:param metrics: List of callable metric functions.
Each function should take logits and labels as input
and return a metric value.
"""
if not metrics:
retu... | Get the compute metrics function.
:param metrics: List of callable metric functions.
Each function should take logits and labels as input
and return a metric value.
| get_compute_metrics | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def fit(
self,
model: 'LLM',
db: Datalayer,
train_dataset: t.Union[QueryDataset, NativeDataset],
valid_dataset: t.Union[QueryDataset, NativeDataset],
):
"""Fit the model on the training dataset.
:param model: The model to fit.
:param db: The datalayer... | Fit the model on the training dataset.
:param model: The model to fit.
:param db: The datalayer to use.
:param train_dataset: The training dataset to use.
:param valid_dataset: The validation dataset to use.
| fit | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def tokenize(tokenizer, example, X, y):
"""Function to tokenize the example.
:param tokenizer: The tokenizer to use.
:param example: The example to tokenize.
:param X: The input key.
:param y: The output key.
"""
prompt = example[X]
prompt = prompt + tokenizer.eos_token
result = to... | Function to tokenize the example.
:param tokenizer: The tokenizer to use.
:param example: The example to tokenize.
:param X: The input key.
:param y: The output key.
| tokenize | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def train(
training_args: LLMTrainer,
train_dataset: NativeDataset,
eval_datasets: t.Union[NativeDataset, t.Dict[str, NativeDataset]],
model_kwargs: dict,
tokenizer_kwargs: dict,
db: t.Optional["Datalayer"] = None,
llm: t.Optional["LLM"] = None,
ray_configs: t.Optional[dict] = None,
... | Train LLM model on specified dataset.
The training process can be run on these following modes:
- Local node without ray, but only support single GPU
- Local node with ray, support multi-nodes and multi-GPUs
- Remote node with ray, support multi-nodes and multi-GPUs
If run locally, will use train_... | train | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def handle_ray_results(db, llm, results):
"""Handle the ray results.
Will save the checkpoint to db if db and llm provided.
:param db: datalayer, used for saving the checkpoint
:param llm: llm model, used for saving the checkpoint
:param results: the ray training results, contains the checkpoint
... | Handle the ray results.
Will save the checkpoint to db if db and llm provided.
:param db: datalayer, used for saving the checkpoint
:param llm: llm model, used for saving the checkpoint
:param results: the ray training results, contains the checkpoint
| handle_ray_results | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def train_func(
training_args: LLMTrainer,
train_dataset: "Dataset",
eval_datasets: t.Union["Dataset", t.Dict[str, "Dataset"]],
model_kwargs: dict,
tokenizer_kwargs: dict,
trainer_prepare_func: t.Optional[t.Callable] = None,
callbacks=None,
**kwargs,
):
"""Base training function for ... | Base training function for LLM model.
:param training_args: training Arguments, see LLMTrainingArguments
:param train_dataset: training dataset,
can be huggingface datasets.Dataset or ray.data.Dataset
:param eval_datasets: evaluation dataset, can be a dict of datasets
:param model_kwargs: model... | train_func | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def ray_train(
training_args: LLMTrainer,
train_dataset,
eval_datasets,
ray_configs: t.Optional[t.Dict[str, t.Any]] = None,
**kwargs,
):
"""Ray training function for LLM model.
The ray train function will handle the following logic:
- Prepare the datasets for ray
- Build the trainin... | Ray training function for LLM model.
The ray train function will handle the following logic:
- Prepare the datasets for ray
- Build the training_loop_func for ray
- Connect to ray cluster
- Make some modifications to be compatible with ray finetune llm
:param training_args: training Arguments,... | ray_train | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def prepare_lora_training(model, config: LLMTrainer):
"""Prepare LoRA training for the model.
Get the LoRA target modules and convert the model to peft model.
:param model: The model to prepare for LoRA training.
:param config: The configuration to use.
"""
try:
from peft import LoraCo... | Prepare LoRA training for the model.
Get the LoRA target modules and convert the model to peft model.
:param model: The model to prepare for LoRA training.
:param config: The configuration to use.
| prepare_lora_training | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def create_quantization_config(config: LLMTrainer):
"""Create quantization config for LLM training.
:param config: The configuration to use.
"""
compute_dtype = (
torch.float16
if config.fp16
else (torch.bfloat16 if config.bf16 else torch.float32)
)
if config.bits is not... | Create quantization config for LLM training.
:param config: The configuration to use.
| create_quantization_config | python | superduper-io/superduper | plugins/transformers/superduper_transformers/training.py | https://github.com/superduper-io/superduper/blob/master/plugins/transformers/superduper_transformers/training.py | Apache-2.0 |
def predict(
self,
messages: list["ChatCompletionMessageParam"],
**kwargs,
) -> t.Any:
"""Chat with the model.
:param messages: List of messages to chat with the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for mor... | Chat with the model.
:param messages: List of messages to chat with the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for more details
| predict | python | superduper-io/superduper | plugins/vllm/superduper_vllm/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/vllm/superduper_vllm/model.py | Apache-2.0 |
async def async_predict(
self,
messages: list["ChatCompletionMessageParam"],
*args,
**kwargs,
):
"""Chat with the model asynchronously.
:param messages: List of messages to chat with the model
:param kwargs: Additional keyword arguments,
... | Chat with the model asynchronously.
:param messages: List of messages to chat with the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for more details
| async_predict | python | superduper-io/superduper | plugins/vllm/superduper_vllm/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/vllm/superduper_vllm/model.py | Apache-2.0 |
def predict(
self,
prompt: str,
**kwargs,
) -> t.Any:
"""Generate completion for the given prompt.
:param prompt: Prompt to generate completion for the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for more details
... | Generate completion for the given prompt.
:param prompt: Prompt to generate completion for the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for more details
| predict | python | superduper-io/superduper | plugins/vllm/superduper_vllm/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/vllm/superduper_vllm/model.py | Apache-2.0 |
async def async_predict(
self,
prompt: str,
**kwargs,
):
"""Generate completion for the given prompt asynchronously.
:param prompt: Prompt to generate completion for the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams... | Generate completion for the given prompt asynchronously.
:param prompt: Prompt to generate completion for the model
:param kwargs: Additional keyword arguments,
see vllm.SamplingParams for more details
| async_predict | python | superduper-io/superduper | plugins/vllm/superduper_vllm/model.py | https://github.com/superduper-io/superduper/blob/master/plugins/vllm/superduper_vllm/model.py | Apache-2.0 |
def initialize_with_components(self):
"""Initialize the backend with components.
This method is executed when a cluster is initialized.
"""
for info in self.db.show():
obj = self.db.load(info['component'], info['identifier'])
if isreallyinstance(obj, self.cls):
... | Initialize the backend with components.
This method is executed when a cluster is initialized.
| initialize_with_components | python | superduper-io/superduper | superduper/backends/base/backends.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/backends.py | Apache-2.0 |
def put_component(self, component: str, uuid: str, **kwargs):
"""Put a component to the backend.
:param component: Component to put.
:param uuid: UUID of the component.
:param kwargs: Additional arguments.
"""
object = self.db.load(component=component, uuid=uuid)
... | Put a component to the backend.
:param component: Component to put.
:param uuid: UUID of the component.
:param kwargs: Additional arguments.
| put_component | python | superduper-io/superduper | superduper/backends/base/backends.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/backends.py | Apache-2.0 |
def drop_component(self, component: str, identifier: str):
"""Drop the component from backend.
:param component: Component name.
:param identifier: Component identifier.
"""
uuids = self.component_uuid_mapping[(component, identifier)]
tool_ids = []
for uuid in uu... | Drop the component from backend.
:param component: Component name.
:param identifier: Component identifier.
| drop_component | python | superduper-io/superduper | superduper/backends/base/backends.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/backends.py | Apache-2.0 |
def drop(self, component: t.Optional['Component'] = None):
"""Drop the backend.
:param component: Component to Drop.
""" | Drop the backend.
:param component: Component to Drop.
| drop | python | superduper-io/superduper | superduper/backends/base/backends.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/backends.py | Apache-2.0 |
def put_component(self, component: str, uuid: str):
"""Add a component to the deployment.
:param component: ``Component`` to put.
:param uuid: UUID of the component.
""" | Add a component to the deployment.
:param component: ``Component`` to put.
:param uuid: UUID of the component.
| put_component | python | superduper-io/superduper | superduper/backends/base/backends.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/backends.py | Apache-2.0 |
def drop(self, force: bool = False):
"""Drop all of the backends.
:param force: Skip confirmation.
"""
if not force and not click.confirm(
"Are you sure you want to drop the cluster?"
):
return
self.compute.drop()
self.scheduler.drop()
... | Drop all of the backends.
:param force: Skip confirmation.
| drop | python | superduper-io/superduper | superduper/backends/base/cluster.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/cluster.py | Apache-2.0 |
def db(self, value):
"""Set the ``db``.
:param value: ``Datalayer`` instance.
"""
self._db = value
self.scheduler.db = value
self.vector_search.db = value
self.crontab.db = value
if self.compute is not None:
self.compute.db = value
sel... | Set the ``db``.
:param value: ``Datalayer`` instance.
| db | python | superduper-io/superduper | superduper/backends/base/cluster.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/cluster.py | Apache-2.0 |
def put_component(self, component: str, uuid: str):
"""Create handler on component declare.
:param component: Component to put.
:param uuid: UUID of the component.
""" | Create handler on component declare.
:param component: Component to put.
:param uuid: UUID of the component.
| put_component | python | superduper-io/superduper | superduper/backends/base/compute.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/compute.py | Apache-2.0 |
def drop_component(self, component: str, identifier: str):
"""Drop the component from compute.
:param component: Component name.
:param identifier: Component identifier.
""" | Drop the component from compute.
:param component: Component name.
:param identifier: Component identifier.
| drop_component | python | superduper-io/superduper | superduper/backends/base/compute.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/compute.py | Apache-2.0 |
def drop_table(self, table: str):
"""Drop data from table.
:param table: The table to drop.
""" | Drop data from table.
:param table: The table to drop.
| drop_table | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def create_tables_and_schemas(self, events: t.List['CreateTable']):
"""Create a schema in the data-backend.
:param events: List of `CreateTable` events.
"""
from superduper.base.schema import Schema
for event in events:
self.create_table_and_schema(
... | Create a schema in the data-backend.
:param events: List of `CreateTable` events.
| create_tables_and_schemas | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def insert(self, table: str, documents: t.Sequence[t.Dict]) -> t.List[str]:
"""Insert data into the database.
:param table: The table to insert into.
:param documents: The documents to insert.
""" | Insert data into the database.
:param table: The table to insert into.
:param documents: The documents to insert.
| insert | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def do_replace(self, table: str, condition: t.Dict, r: t.Dict):
"""Replace data in the database.
This method is a wrapper around the `replace` method to ensure
that the datatype is set to `None` by default.
:param table: The table to insert into.
:param condition: The condition... | Replace data in the database.
This method is a wrapper around the `replace` method to ensure
that the datatype is set to `None` by default.
:param table: The table to insert into.
:param condition: The condition to update.
:param r: The document to replace.
| do_replace | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def do_update(
self,
table: str,
condition: t.Dict,
key: str,
value: t.Any,
datatype: BaseDataType | None = None,
):
"""Update data in the database.
This method is a wrapper around the `update` method to ensure
that the datatype is set to `Non... | Update data in the database.
This method is a wrapper around the `update` method to ensure
that the datatype is set to `None` by default.
:param table: The table to update.
:param condition: The condition to update.
:param key: The key to update.
:param value: The value... | do_update | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def update(self, table: str, condition: t.Dict, key: str, value: t.Any):
"""Update data in the database.
:param table: The table to update.
:param condition: The condition to update.
:param key: The key to update.
:param value: The value to update.
""" | Update data in the database.
:param table: The table to update.
:param condition: The condition to update.
:param key: The key to update.
:param value: The value to update.
| update | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def missing_outputs(self, query: Query, predict_id: str) -> t.List[str]:
"""Get missing outputs from an outputs query.
This method will be used to perform an anti-join between
the input and the outputs table, and return the missing ids.
:param query: The query to perform.
:para... | Get missing outputs from an outputs query.
This method will be used to perform an anti-join between
the input and the outputs table, and return the missing ids.
:param query: The query to perform.
:param predict_id: The predict id.
| missing_outputs | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def get(self, query: Query, raw: bool = False):
"""Get a single result from a query.
:param query: The query to perform.
:param raw: If ``True``, return raw results.
"""
assert query.type == 'select'
if query.decomposition.pre_like:
return list(self.pre_like... | Get a single result from a query.
:param query: The query to perform.
:param raw: If ``True``, return raw results.
| get | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def execute(self, query: Query, raw: bool = False):
"""Execute a query.
:param query: The query to execute.
:param raw: If ``True``, return raw results.
"""
query = query if '.outputs' not in str(query) else query.complete_uuids(self.db)
schema = self.get_schema(query)
... | Execute a query.
:param query: The query to execute.
:param raw: If ``True``, return raw results.
| execute | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
def get_schema(self, query) -> 'Schema':
"""Get the schema of a query.
:param query: The query to get the schema of.
"""
base_schema = self.db.metadata.get_schema(query.table)
if query.decomposition.outputs:
for predict_id in query.decomposition.outputs.args:
... | Get the schema of a query.
:param query: The query to get the schema of.
| get_schema | python | superduper-io/superduper | superduper/backends/base/data_backend.py | https://github.com/superduper-io/superduper/blob/master/superduper/backends/base/data_backend.py | Apache-2.0 |
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