When starting a trained model deployment the user can tweak performance
by setting the `model_threads` and `inference_threads` parameters.
These parameters are hard to understand and cause confusion.
This commit renames these as well as the fields where their values are
reported in the stats API.
- `model_threads` => `number_of_allocations`
- `inference_threads` => `threads_per_allocation`
Now the terminology is as follows.
A model deployment starts with a requested `number_of_allocations`.
Each allocation means the model gets another thread for executing
parallel inference requests. Thus, more allocations should increase
throughput. In its turn, each allocation is may be using a number
of threads to parallelize each individual inference request.
This is the `threads_per_allocation` setting and increases inference
speed (which might also result in improved throughput).
This commit adds a new `_ml/trained_models/{model_id}/_infer` API. This api works for both native NLP models and supervised models trained via Data Frame analytics.
The format of the API is the same as the old `_ml/trained_models/{model_id}/deployment/_infer`. Taking a `docs` and an `inference_config` parameter.
This PR also deprecates the old experimental `_ml/trained_models/{model_id}/deployment/_infer` API.
The biggest difference is that the response now nests all results under an "inference_results" object.
closes: https://github.com/elastic/elasticsearch/issues/86032
This renames the internal concept of a trained model allocation into an assignment.
Now models are assigned to a node and routes created for inference. Not "allocated".
This is an internal rename only. The user facing concepts of trained models and deployments are untouched.
This reverts commit 4eaedb265d.
On further investigation of how to improve allocation of trained models,
we concluded that being able to set `inference_threads` in combination with
`model_threads` is fundamental for scalability.
Starting a trained model deployment the user may set values for `inference_threads`
of `model_threads`. The first improves latency whereas the latter improves throughput.
It is easier to reason on how a model allocation uses resources if we ensure only
one of those two may be greater than one. In addition, it allows us to distribute
the cores of the ML nodes in the cluster across the model allocations in the future.
This commit adds a validation that prevents both `inference_threads` and `model_threads`
to be greater than one.
Lisa Cawley
Dimitris Athanasiou
Benjamin Trent
David Kyle