Submodule bindings for vllm.multimodal.
Version
- Requested: 0.14.0
- Observed at generation: 0.14.0
Runtime Options
All functions accept a __runtime__ option for controlling execution behavior:
Vllm.Multimodal.some_function(args, __runtime__: [timeout: 120_000])Supported runtime options
:timeout- Call timeout in milliseconds (default: 120,000ms / 2 minutes):timeout_profile- Use a named profile (:default,:ml_inference,:batch_job,:streaming):stream_timeout- Timeout for streaming operations (default: 1,800,000ms / 30 minutes):session_id- Override the session ID for this call:pool_name- Target a specific Snakepit pool (multi-pool setups):affinity- Override session affinity (:hint,:strict_queue,:strict_fail_fast)
Timeout Profiles
:default- 2 minute timeout for regular calls:ml_inference- 10 minute timeout for ML/LLM workloads:batch_job- Unlimited timeout for long-running jobs:streaming- 2 minute timeout, 30 minute stream_timeout
Example with timeout override
# For a long-running ML inference call
Vllm.Multimodal.predict(data, __runtime__: [timeout_profile: :ml_inference])
# Or explicit timeout
Vllm.Multimodal.predict(data, __runtime__: [timeout: 600_000])
# Route to a pool and enforce strict affinity
Vllm.Multimodal.predict(data, __runtime__: [pool_name: :strict_pool, affinity: :strict_queue])See SnakeBridge.Defaults for global timeout configuration.
Summary
Functions
Python module attribute vllm.multimodal.__all__.
dict() -> new empty dictionary
Python binding for vllm.multimodal.ModalityData.
A Mapping is a generic container for associating key/value
A Mapping is a generic container for associating key/value
A Mapping is a generic container for associating key/value
Python module attribute vllm.multimodal.MULTIMODAL_REGISTRY.
Python binding for vllm.multimodal.NestedTensors.
Functions
@spec __all__() :: {:ok, [term()]} | {:error, Snakepit.Error.t()}
Python module attribute vllm.multimodal.__all__.
Returns
list(term())
@spec batched_tensor_inputs(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
dict() -> new empty dictionary
dict(mapping) -> new dictionary initialized from a mapping object's (key, value) pairs dict(iterable) -> new dictionary initialized as if via: d = {} for k, v in iterable:
d[k] = vdict(**kwargs) -> new dictionary initialized with the name=value pairs in the keyword argument list. For example: dict(one=1, two=2)
Parameters
args(term())kwargs(term())
Returns
term()
@spec modality_data(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
Python binding for vllm.multimodal.ModalityData.
Parameters
args(term())kwargs(term())
Returns
term()
@spec multi_modal_data_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
A Mapping is a generic container for associating key/value
pairs.
This class provides concrete generic implementations of all methods except for getitem, iter, and len.
Parameters
args(term())kwargs(term())
Returns
term()
@spec multi_modal_placeholder_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
A Mapping is a generic container for associating key/value
pairs.
This class provides concrete generic implementations of all methods except for getitem, iter, and len.
Parameters
args(term())kwargs(term())
Returns
term()
@spec multi_modal_uuid_dict(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
A Mapping is a generic container for associating key/value
pairs.
This class provides concrete generic implementations of all methods except for getitem, iter, and len.
Parameters
args(term())kwargs(term())
Returns
term()
@spec multimodal_registry() :: {:ok, term()} | {:error, Snakepit.Error.t()}
Python module attribute vllm.multimodal.MULTIMODAL_REGISTRY.
Returns
term()
@spec nested_tensors(keyword()) :: {:ok, term()} | {:error, Snakepit.Error.t()}
Python binding for vllm.multimodal.NestedTensors.
Parameters
args(term())kwargs(term())
Returns
term()