Models
Models are an artifact of calls to pgml.train()
. See Training Overview for ways to create new models.
Schema
CREATE TABLE IF NOT EXISTS pgml.models(
id BIGSERIAL PRIMARY KEY,
project_id BIGINT NOT NULL,
snapshot_id BIGINT NOT NULL,
num_features INT NOT NULL,
algorithm TEXT NOT NULL,
runtime pgml.runtime DEFAULT 'python'::pgml.runtime,
hyperparams JSONB NOT NULL,
status TEXT NOT NULL,
metrics JSONB,
search TEXT,
search_params JSONB NOT NULL,
search_args JSONB NOT NULL,
created_at TIMESTAMP WITHOUT TIME ZONE NOT NULL DEFAULT clock_timestamp(),
updated_at TIMESTAMP WITHOUT TIME ZONE NOT NULL DEFAULT clock_timestamp(),
CONSTRAINT project_id_fk FOREIGN KEY(project_id) REFERENCES pgml.projects(id) ON DELETE CASCADE,
CONSTRAINT snapshot_id_fk FOREIGN KEY(snapshot_id) REFERENCES pgml.snapshots(id) ON DELETE SET NULL
);
CREATE TABLE IF NOT EXISTS pgml.files(
id BIGSERIAL PRIMARY KEY,
model_id BIGINT NOT NULL,
path TEXT NOT NULL,
part INTEGER NOT NULL,
created_at TIMESTAMP WITHOUT TIME ZONE NOT NULL DEFAULT clock_timestamp(),
updated_at TIMESTAMP WITHOUT TIME ZONE NOT NULL DEFAULT clock_timestamp(),
data BYTEA NOT NULL,
CONSTRAINT model_id_fk FOREIGN KEY(model_id) REFERENCES pgml.models(id) ON DELETE CASCADE
);
Files
Models are partitioned into parts and stored in the pgml.files
table. Most models are relatively small (just a few megabytes), but some neural networks can grow to gigabytes in size, and would therefore exceed the maximum possible size of a column Postgres.
Partitioning fixes that limitation and allows us to store models up to 32TB in size (or larger, if we employ table partitioning).
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