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
namekeys, as well as a
modelsarray that is keyed by a
[model_id]. Within the
[model_id]object, all model configurations have a
[model_id]is replaced with the type of model you wish to train (e.g.
data_sourcemust 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_dataparameter via CLI,
- parameter via SDK,
Each Gretel model have different additional keys within the
model_idobject and unique configuration parameters specific to that model. For details on the configuration parameters for each model, see the specific model page: