Neuron Compiler FAQs¶
The one-time compilation step from the standard framework-level model to NEFF binary may be performed on any EC2 instance or even on-premises.
We recommend using a high-performance compute server of choice (C5 or z1d instance types), for the fastest compile times and ease of use with a prebuilt DLAMI. Developers can also install Neuron in their own environments; this approach may work well for example when building a large fleet for inference, allowing the model creation, training and compilation to be done in the training fleet, with the NEFF files being distributed by a configuration management application to the inference fleet.
Developers who want to train their models in FP32 for best accuracy can compile and deploy them with Neuron. Since Inferentia chips support FP16, BFloat16 mixed-precision data-types and INT8 the trained graph needs to be converted to one of these data types for execution on Inferentia. Neuron can compile and execute FP32 neural nets by automatically converting them to BFloat16. Given an input using FP32, the compiler output will ensure that the executed graph can accept input inference requests in FP32. Since BFloat16 has the same dynamic range as FP32, most models will have no accuracy degragation, and will benefit from the fast BF16 execution. Also see Neuron Data-Types.
The default optimization level is –O2. The compiler compiles the input graph
for a single NeuronCore by default. Using the The
neuroncore-pipeline-cores” option directs the compiler to
partition so as to run on a specified number of NeuronCores. This number can
be less than the total available NeuronCores on an instance.
See Application Note: Performance Tuning for
You can also use the “neuron-cc list-operators” command on the cli to list the operators. See neuron-cc list-operators
If your model contains operators missing from the above list, and you can’t reach your performance goals, please post a message on the Neuron developer forum or open a github issue to let us know.
Models with control-flow and dynamic shapes are not supported. You will need to partition the model using the framework prior to compilation. See the Neuron Compiler.
The compiler and runtime are committed to maintaining compatibility for major version releases with each other. The versioning is defined as major.minor, with compatibility for all versions with the same major number. If the versions mismatch, an error notification is logged and the load will fail. This will then require the model to be recompiled.
generated it?** We will bring a utility out to help with this soon.