This PR adds an ability to estimate per deployment and per allocation memory usage of NLP transformer models. It uses torch.profiler and performs logs the peak memory usage during the inference.

This information is then used in Elasticsearch to provision models with sufficient memory (elastic/elasticsearch#98874).

…model assignment planner (#98874)"

This reverts commit aa2f6e7.

…model assignment planner (#98874)" (#101834)

There were a number of BWC test failures after the PR was merged today. I'll revert it and investigate the failures locally.

Reverts #98874

…signment planner (elastic#98874)

Building upon elastic#98139, this PR extends the model assignment planning algorithms and the linear solver to use the extended memory fields. It also adds unit tests to verify the new behavior.

I needed to adjust the old unit tests since we use the estimateMemoryUsage routine, which would compute 2*memoryBytes + 240 MB as the memory requirement. Previously, in the unit tests, we were simply using memoryBytes field value.

…in the model assignment planner" (#101853)

The original PR #98874 missed the memory overhead adjustment from #86416. As it caused some BWC test failures on the CI, I reverted it in #101834.

This PR reintegrates the functionality and extends the BWC integration test with the memory constant depending on the version of the old cluster.

…model assignment planner"

This reverts commit 31ca2f7.

The functionality of elastic#98874 is being removed from 8.12 because it
means that models which were working successfully on 2GB nodes in
8.11 will no longer fit on 2GB nodes. This will be frustrating for
trial users.

Before 8.13 we need to do a more thorough assessment of which
models will and won't fit on 2GB nodes as a result of better
memory estimation. It may be possible to tweak the memory usage
estimation so that we require more memory than 8.11 but not so
much more that our recommended trial models no longer fit onto
2GB nodes.