When this type of benchmarks is triggered, only the tests that
benefits from GPU acceleration are run on a specific AWS EC2
instance. Note that this instance (p3.2xlarge) is not a bare metal
one, so performance may variate due to hypervisor controlling the
machine.
This CI "feature" is meant to circumvent the 6 hours hard-limit
for a job in GitHub Action.
The benchmark is done using a matrix which is handled by Slab.
Here's the workflow:
1. ML benchmarks are started in a fire and forget fashion via
start_ml_benchmarks.yml
2. Slab will read ci/slab.toml to get the AWS EC2 configuration
and the matrix parameters
3. Slab will launch at most max_parallel_jobs EC2 instances in
parallel
4. Each job will trigger ml_benchmark_subset.yml which will run
only one of the generated YAML file via make generate-mlbench,
based on the value of the matrix item they were given.
5. As soon as a job is completed, the next one in the matrix
will start promptly.
This is done until all the matrix items are exhausted.
Bench just one compilation option for automatic benchmarks. Only 'loop'
option is tested to take advantage of hardware with a lot of available
CPUs. Running benchmarks with 'default' option is suboptimal for this
kind of hardware since it uses only one CPU.
This also remove time consuming MNIST test, as it should be in ML benchmarks.
Moreover Makefile is fixed to use provided Python executable instead of
relying on system one to generate MLIR Yaml files.
This patch adds support for scalar results to the client/server
protocol and tests. In addition to `TensorData`, a new type
`ScalarData` is added. Previous representations of scalar values using
one-dimensional `TensorData` instances have been replaced with proper
instantiations of `ScalarData`.
The generic use of `TensorData` for scalar and tensor values has been
replaced with uses of a new variant `ScalarOrTensorData`, which can
either hold an instance of `TensorData` or `ScalarData`.
