pub unsafe trait MTL4MachineLearningCommandEncoder: MTL4CommandEncoder {
// Provided methods
fn setPipelineState(
&self,
pipeline_state: &ProtocolObject<dyn MTL4MachineLearningPipelineState>,
)
where Self: Sized + Message { ... }
fn setArgumentTable(
&self,
argument_table: &ProtocolObject<dyn MTL4ArgumentTable>,
)
where Self: Sized + Message { ... }
fn dispatchNetworkWithIntermediatesHeap(
&self,
heap: &ProtocolObject<dyn MTLHeap>,
)
where Self: Sized + Message { ... }
}MTL4CommandEncoder and MTL4MachineLearningCommandEncoder only.Expand description
Encodes commands for dispatching machine learning networks on Apple silicon.
See also Apple’s documentation
Provided Methods§
Sourcefn setPipelineState(
&self,
pipeline_state: &ProtocolObject<dyn MTL4MachineLearningPipelineState>,
)
Available on crate features MTL4MachineLearningPipeline and MTLAllocation only.
fn setPipelineState( &self, pipeline_state: &ProtocolObject<dyn MTL4MachineLearningPipelineState>, )
MTL4MachineLearningPipeline and MTLAllocation only.Configures the encoder with a machine learning pipeline state instance.
The pipeline state instance affects all subsequent Machine Learning commands.
- Parameters:
- pipelineState: A Machine Learning pipeline state instance.
Sourcefn setArgumentTable(
&self,
argument_table: &ProtocolObject<dyn MTL4ArgumentTable>,
)
Available on crate feature MTL4ArgumentTable only.
fn setArgumentTable( &self, argument_table: &ProtocolObject<dyn MTL4ArgumentTable>, )
MTL4ArgumentTable only.Sets an argument table for the command encoder’s machine learning shader stage.
The argument table provides inputs to all subsequent Machine Learning dispatches.
- Parameters:
- argumentTable: An argument table to set on the command encoder’s Machine Learning stage.
Sourcefn dispatchNetworkWithIntermediatesHeap(
&self,
heap: &ProtocolObject<dyn MTLHeap>,
)
Available on crate features MTLAllocation and MTLHeap only.
fn dispatchNetworkWithIntermediatesHeap( &self, heap: &ProtocolObject<dyn MTLHeap>, )
MTLAllocation and MTLHeap only.Dispatches a machine learning network using the current pipeline state and argument table.
This method takes a parameter consisting of a MTLHeap that Metal can use to allocate intermediate tensors.
You can query the minimum size Metal requires for this heap by calling
MTL4MachineLearningPipelineState/intermediatesHeapSize.
- Parameters:
- heap: a heap that Metal can use to allocate intermediate tensors.
Trait Implementations§
impl<T> ImplementedBy<T> for dyn MTL4MachineLearningCommandEncoder
Source§impl ProtocolType for dyn MTL4MachineLearningCommandEncoder
impl ProtocolType for dyn MTL4MachineLearningCommandEncoder
Dyn Compatibility§
This trait is dyn compatible.
In older versions of Rust, dyn compatibility was called "object safety".