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Transfer learning

Configure your production system​

note

If you would like to use the production features of SuperDuperDB, then you should set the relevant connections and configurations in a configuration file. Otherwise you are welcome to use "development" mode to get going with SuperDuperDB quickly.

import os

os.makedirs('.superduperdb', exist_ok=True)
os.environ['SUPERDUPERDB_CONFIG'] = '.superduperdb/config.yaml'
CFG = '''
data_backend: mongodb://127.0.0.1:27017/documents
artifact_store: filesystem://./artifact_store
cluster:
  cdc:
    strategy: null
    uri: ray://127.0.0.1:20000
  compute:
    uri: ray://127.0.0.1:10001
  vector_search:
    backfill_batch_size: 100
    type: in_memory
    uri: http://127.0.0.1:21000
'''        
with open(os.environ['SUPERDUPERDB_CONFIG'], 'w') as f:
    f.write(CFG)

Start your cluster​

note

Starting a SuperDuperDB cluster is useful in production and model development if you want to enable scalable compute, access to the models by multiple users for collaboration, monitoring.

If you don't need this, then it is simpler to start in development mode.

!python -m superduperdb local-cluster up

Connect to SuperDuperDB​

note

Note that this is only relevant if you are running SuperDuperDB in development mode. Otherwise refer to "Configuring your production system".

from superduperdb import superduper

db = superduper('mongodb://localhost:27017/documents')

Get useful sample data​

from superduperdb import dtype

!curl -O https://superduperdb-public-demo.s3.amazonaws.com/text.json
import json

with open('text.json', 'r') as f:
    data = json.load(f)
sample_datapoint = "What is mongodb?"

chunked_model_datatype = dtype('str')

Setup tables or collections​

# Note this is an optional step for MongoDB
# Users can also work directly with `DataType` if they want to add
# custom data
from superduperdb import Schema, DataType
from superduperdb.backends.mongodb import Collection

table_or_collection = Collection('documents')
USE_SCHEMA = False

if USE_SCHEMA and isinstance(datatype, DataType):
    schema = Schema(fields={'x': datatype})
    db.apply(schema)

Insert data​

In order to create data, we need to create a Schema for encoding our special Datatype column(s) in the databackend.

from superduperdb import Document

def do_insert(data):
    schema = None
    
    
    if schema is None and (datatype is None  or isinstance(datatype, str)) :
        data = [Document({'x': x}) for x in data]
        db.execute(table_or_collection.insert_many(data))
    elif schema is None and datatype is not None and isintance():
        data = [Document({'x': datatype(x)}) for x in data]
        db.execute(table_or_collection.insert_many(data))
    else:
        data = [Document({'x': x}) for x in data]
        db.execute(table_or_collection.insert_many(data, schema='my_schema'))        
do_insert(data[:-len(data) // 4])

Compute features​


key = 'txt'

import sentence_transformers
from superduperdb import vector, Listener
from superduperdb.ext.sentence_transformers import SentenceTransformer

superdupermodel = SentenceTransformer(
    identifier="embedding",
    object=sentence_transformers.SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2"),
    datatype=vector(shape=(384,)),
    postprocess=lambda x: x.tolist(),
)

jobs, listener = db.apply(
    Listener(
        model=superdupermodel,
        select=select,
        key=key,
        identifier="features"
    )
)

Choose input key​

The input key to the fine-tuning model is the output of the previous listener:

input_key = listener.outputs

Build and train classifier​

from sklearn.linear_model import LogisticRegression
from superduperdb.ext.sklearn.model import SklearnTrainer, Estimator

# Create a Logistic Regression model
model = LogisticRegression()
model = Estimator(
    object=model,
    identifier='my-model',
    trainer=SklearnTrainer(
        key=(input_key, 'y'),
        select=Collection('clt').find(),
    )
)

The following command adds the model to the system and trains the model in one command.

db.apply(model)