1.53.0

1.53.0

New Features

• ML Jobs: submit_file, submit_directory, and submit_from_stage accept a parallel kwarg (default False).
  With parallel=True, the entrypoint is executed directly on every instance with the job topology injected into the
  environment (SNOWFLAKE_JOB_INDEX, SNOWFLAKE_JOBS_COUNT, MLRS_HEAD_IP, MLRS_RDZV_PORT, MLRS_NODE_IPS).
  Not supported for callable (@remote) payloads.

• ML Jobs: added MLJob.distributed_result(), the result API for distributed (e.g. parallel=True) jobs, which
  have no single head result. On full success it returns a DistributedResult (success, per-instance exit_codes,
  failed_instance, and instance 0's return_value); if any instance failed it raises DistributedJobError, which
  carries that DistributedResult as .result and the earliest-failing instance's reconstructed exception as its
  cause. DistributedResult and DistributedJobError are exported from snowflake.ml.jobs.

• ML Jobs: submit_file, submit_directory, and submit_from_stage accept a preflight kwarg, supported only
  together with parallel=True. It takes the name of a check level, run on every instance before the entrypoint:
  "wiring" checks the rendezvous plus a small collective, and "reference" additionally times a short synthetic
  DDP step and reports its step times from instance 0. If a requested check fails, the job aborts without running
  the entrypoint and the failure surfaces through distributed_result(). A check that does not apply to the job
  ("reference" on a CPU pool or a single instance) is reported as skipped and the job continues. Covers only the
  PyTorch c10d rendezvous backend.

• Feature Store: Iceberg-backed feature views can enable online storage when using the Postgres online store
  (OnlineConfig(store_type=OnlineStoreType.POSTGRES)). Online storage with Iceberg remains unsupported for
  other store types.

Bug Fixes

Behavior Changes

• Feature Store: register_feature_view now records FV_SOURCE_REFS metadata for managed batch
  feature views (derived from the feature_df schema) when the caller does not supply
  source_refs. Streaming and realtime feature views are unaffected.

Deprecations