1.43.0
New Features
- Registry:
inference_engine_options["engine"] now accepts case-insensitive strings in addition to
InferenceEngine enum members. Supported values are "vllm" and "python_generic" (for example,
"vLLM", "VLLM", and "PYTHON_GENERIC" are all accepted). This applies to ModelVersion.create_service()
and ModelVersion.run_batch().
from snowflake.ml.registry import Registry
registry = Registry(session)
mv = registry.get_model("my_model").version("v1")
mv.create_service(
service_name="my_vllm_service",
service_compute_pool="GPU_COMPUTE_POOL",
gpu_requests="1",
inference_engine_options={
"engine": "vLLM",
"engine_args_override": ["--max-model-len=4096"],
},
)
job = mv.run_batch(
compute_pool="GPU_COMPUTE_POOL",
X=input_df,
output_spec=output_spec,
inference_engine_options={
"engine": "python_generic",
},
)Bug Fixes
- Registry: Fixed
explain() failing with type mismatch errors when passing a Snowpark DataFrame.
Snowpark DataFrame columns are now explicitly cast to match model signature types before SQL
generation. Table functions (used by explain) enforce stricter type coercion than scalar functions
(used by predict), so columns that were implicitly coerced for predict would be rejected by
explain.
Behavior Changes
- Registry:
SentenceTransformers models no longer hard-code a default inference batch size of 32.
When batch_size is not specified at log_model time and is not overridden at inference, the
sentence-transformers library default is used. To pin a specific size, pass batch_size via
log_model(..., options={"batch_size": N}) or as an inference-time parameter. All
SentenceTransformers models logged with this release require a client/serving runtime on version
1.43.0 or later (regardless of whether batch_size was specified).
Deprecations
