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Model Configurations
Gretel configurations are declarative objects that specify how a model should be created. Configurations can be authored in YAML or JSON.
All Gretel models follow the same high-level configuration file format structure. All configurations include
schema_version
and name
keys, as well as a models
array that is keyed by a [model_id]
. Within the [model_id]
object, all model configurations have a data_source
key.schema_version: "1.0"
name: "my-model"
models:
- [model_id]:
data_source: __tmp__
[model_id]
is replaced with the type of model you wish to train (e.g.synthetics
,gpt_x
,actgan
,timeseries_dgan
,amplify
,transform
,classify
).data_source
must point to a valid and accessible file in CSV, JSON, or JSONL format.- Supported storage formats include S3, GCS, Azure Blog Storage, HDFS, WebHDFS, HTTP, HTTPS, SFTP, or local filesystem.
- Note: Some models have specific data source format requirements
data_source: __tmp__
can be used when the source file is specified elsewhere using:--in_data
parameter via CLI,- parameter via SDK,
- dataset
button
via Console.
Each Gretel model have different additional keys within the
model_id
object and unique configuration parameters specific to that model. For details on the configuration parameters for each model, see the specific model page:
Last modified 3mo ago