MOSTLY AI Cheat Sheet

Generators

# shorthand syntax to train a new single-table generator
g = mostly.train(name: str, data: pd.DataFrame)

# train a new generator
g = mostly.train(config: dict | GeneratorConfig, start: bool, wait: bool)

# configure a new generator and launch separately
g = mostly.generators.create(config: dict | GeneratorConfig)
g.training.start()
g.training.wait()

# iterate over all your available generators
for g in mostly.generators.list():
    print(g.id, g.name)

# fetch a generator by id
g = mostly.generators.get(id: str)

# fetch a generator's configuration
config = g.config()

# open a generator in a new browser tab
g.open()

# update a generator
g.update(name: str, ...)

# delete a generator
g.delete()

# clone a generator
# 1. re-use configs, and start training from scratch
g1 = g.clone(training_status="NEW")
# 2. or re-use configs, and weights to continue training
g2 = g.clone(training_status="CONTINUE")

Synthetic Datasets

# shorthand syntax for generating a new synthetic dataset
sd = mostly.generate(g, size: int)

# shorthand syntax for conditionally generating a new synthetic dataset
sd = mostly.generate(g, seed: pd.DataFrame)

# generate a new synthetic dataset
sd = mostly.generate(g, config: dict | SyntheticDatasetConfig, start: bool, wait: bool)

# configure a new synthetic dataset and launch separately
sd = mostly.synthetic_datasets.create(config: dict | SyntheticDatasetConfig)
sd.generation.start()
sd.generation.wait()

# iterate over all your available synthetic datasets
for sd in mostly.synthetic_datasets.list():
    print(sd.id, sd.name)

# fetch a synthetic dataset by id
sd = mostly.synthetic_datasets.get(id: str)

# fetch a synthetic dataset's configuration
config = sd.config()

# open a synthetic dataset in a new browser tab
sd.open()

# download a synthetic dataset
sd.download(file: str, format: str)

# fetch the synthetic dataset's data
df = sd.data()

# update a synthetic dataset
sd.update(name: str, ...)

# delete a synthetic dataset
sd.delete()

Synthetic probes allow to instantly generate synthetic samples on demand, without storing these on the platform. This feature depends on the availability of Live Probing on the platform. The syntax is similar to generating a synthetic dataset, with the notable difference that its return value is already the synthetic data as pandas DataFrame(s).

# shorthand syntax for probing for synthetic samples
df = mostly.probe(g, size: int)

# shorthand syntax for conditionally probing for synthetic samples
df = mostly.probe(g, seed: pd.DataFrame)

# probe for synthetic samples
df = mostly.probe(g, config: dict | SyntheticDatasetConfig)

Connectors

Connectors can be used both as a source of original data for training a generator, as well as a destination for delivering the generated synthetic data samples to. Please see our Platform Documentation, respectively our Open API specificiations for the full list of available connectors, and their corresponding configuration parameters.

# create a new connector
c = mostly.connect(config: dict | ConnectorConfig)

# fetch a connector by id
c = mostly.connectors.get(id: str)

# list all locations of a connector
ls = c.locations(prefix: str)

# iterate over all your available connectors
for c in mostly.connectors.list():
    print(c.id, c.name)

# update a connector
c.update(name: str, ...)

# fetch a connector's configuration
config = c.config()

# open a connector in a new browser tab
c.open()

# delete a connector
c.delete()

In order to use a connector as a source, pass its ID as a parameter to the SourceTableConfig:

c = mostly.connect(config={
    "name": "My S3 Source Storage",
    "type": "S3_STORAGE",
    "access_type": "SOURCE",
    "config": {
        "access_key": "INSERT_YOUR_ACCESS_KEY",
    },
    "secrets": {
        "secret_key": "INSERT_YOUR_SECRET_KEY",
    }
})
g = mostly.train(config={
    "name": "US Census Income",
    "tables": [{
        "name": "census",
        "source_connector_id": c.id,
        "location": "s3://bucket/path_to_original"
    }]
})

In order to use a connector as a destination, pass its ID as a parameter to the SourceTableConfig:

c = mostly.connect(config={
    "name": "My S3 Destination Storage",
    "type": "S3_STORAGE",
    "access_type": "DESTINATION",
    "config": {
        "access_key": "INSERT_YOUR_ACCESS_KEY",
    },
    "secrets": {
        "secret_key": "INSERT_YOUR_SECRET_KEY",
    }
})
sd = mostly.generate(g, config={
    "name": "US Census Income",
    "delivery": {
        "destination_connector_id": c.id,
        "location": "s3://bucket/path_to_synthetic"
    }
})

Miscellaneous

# fetch info on your user account
mostly.me()

# fetch info about the platform
mostly.about()

# list all available TABULAR models
mostly.models(model_type='TABULAR')

# list all available LANGUAGE models
mostly.models(model_type='LANGUAGE')

# list all available computes
mostly.computes()