Currently there is no way to remove user-added annotations when a job is deleted or reset.
This change adds an option - delete_user_annotations - to both the delete and reset job APIs.
The default value is false, to keep the behaviour of these calls as it is currently.
This adds model_alias support for native pytorch models.
Model aliases can be used in `_infer` or within the inference processor. This way the alias can be atomically changed without down time to another deployed model.
Restrictions:
- Model alias changes need to be done between two models of the same kind (e.g. pytorch -> pytorch)
- Model alias change is not allowed between a model that is deployed to a model that is not
- Model alias change is not allowed between a model that deployed AND allocated to a model that is deployed but NOT allocated (not assigned to any nodes).
- A deployment cannot be stopped (without supplying the `force` parameter) when the model has a model alias that is used by a pipeline.
closes: https://github.com/elastic/elasticsearch/issues/90960
This adds a new parameter to the start trained model deployment API,
namely `priority`. The available settings are `normal` and `low`.
For normal priority deployments the allocations get distributed so that
node processors are never oversubscribed.
Low priority deployments allow users to test model functionality even if there
are no node processors available. They are limited to 1 allocation with a single thread.
In addition, the process is executed in low priority which limits the amount of
CPU that can be used when the CPU is under pressure. The intention of this is to
limit the impact of low priority deployments on normal priority deployments.
When we rebalance model assignments we now:
1. compute a plan just for normal priority deployments
2. fix the resources used by normal deployments
3. compute a plan just for low priority deployments
4. merge the two plans
Closes#91024
This PR surfaces new information about the impact of the factors on the initial anomaly score in the anomaly record:
- single bucket impact is determined by the deviation between actual and typical in the current bucket
- multi-bucket impact is determined by the deviation between actual and typical in the past 12 buckets
- anomaly characteristics are statistical properties of the current anomaly compared to the historical observations
- high variance penalty is the reduction of anomaly score in the buckets with large confidence intervals.
- incomplete bucket penalty is the reduction of anomaly score in the buckets with fewer samples than historically expected.
Additionally, we compute lower- and upper-confidence bounds and the typical value for the anomaly records. This improves the explainability of the cases where the model plot is not activated with only a slight overhead in performance (1-2%).
This commit adds a new API that users can use calling:
```
POST _ml/trained_models/{model_id}/deployment/_update
{
"number_of_allocations": 4
}
```
This allows a user to update the number of allocations for a deployment
that is `started`.
If the allocations are increased we rebalance and let the assignment
planner find how to allocate the additional allocations.
If the allocations are decreased we cannot use the assignment planner.
Instead, we implement the reduction in a new class `AllocationReducer`
that tries to reduce the allocations so that:
1. availability zone balance is maintained
2. assignments that can be completely stopped are preferred to release memory
Categorization of strings which break down to a huge number of tokens can cause the C++ backend process to choke - see elastic/ml-cpp#2403.
This PR adds a limit filter to the default categorization analyzer which caps the number of tokens passed to the backend at 100.
Unfortunately this isn't a complete panacea to all the issues surrounding categorization of many tokened / large messages as verification checks on the frontend can also fail due to calls to the datafeed _preview API returning an excessive amount of data.
When starting a trained model deployment, a queue is created.
If the queue_capacity is too large, it can lead to OOM and a node
crash.
This commit adds validation that the queue_capacity cannot be more
than 1M.
Closes#89555
This adds a new `_ml/trained_models/<model_id>/deployment/cache/_clear` API. This will clear the inference cache on every node where the model is allocated.
Introduced in: #88439
* [ML] add text_similarity nlp task documentation
* Apply suggestions from code review
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
* Update docs/reference/ml/trained-models/apis/infer-trained-model.asciidoc
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
* Apply suggestions from code review
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
* Update docs/reference/ml/ml-shared.asciidoc
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
Co-authored-by: István Zoltán Szabó <istvan.szabo@elastic.co>
The inference node stats for deployed PyTorch inference
models now contain two new fields: `inference_cache_hit_count`
and `inference_cache_hit_count_last_minute`.
These indicate how many inferences on that node were served
from the C++-side response cache that was added in
https://github.com/elastic/ml-cpp/pull/2305. Cache hits
occur when exactly the same inference request is sent to the
same node more than once.
The `average_inference_time_ms` and
`average_inference_time_ms_last_minute` fields now refer to
the time taken to do the cache lookup, plus, if necessary,
the time to do the inference. We would expect average inference
time to be vastly reduced in situations where the cache hit
rate is high.
With: https://github.com/elastic/ml-cpp/pull/2305 we now support caching pytorch inference responses per node per model.
By default, the cache will be the same size has the model on disk size. This is because our current best estimate for memory used (for deploying) is 2*model_size + constant_overhead.
This is due to the model having to be loaded in memory twice when serializing to the native process.
But, once the model is in memory and accepting requests, its actual memory usage is reduced vs. what we have "reserved" for it within the node.
Consequently, having a cache layer that takes advantage of that unused (but reserved) memory is effectively free. When used in production, especially in search scenarios, caching inference results is critical for decreasing latency.
As the number of cores in CPUs is typically a power of 2,
this commit adds a validation that trained model deployments
start with `threads_per_allocation` set to be a power of 2.
When we look for how we distribute the allocations across the
cluster, this prevents situations where we have a lot of wasted
CPU cores.
In addition, we add a max value limit of `32`.
n larger clusters with complicated datafeed requirements, being able to preview only a specific window of time is important. Previously, datafeed previews would always start at 0 (or from the beginning of the data). This causes issues if the index pattern contains indices on slower hardware, but when the datafeed is actually started, the "start" time is set to more recent data (and thus on faster hardware).
Additionally, when _preview is unbounded (as before), it attempts to only preview indices that are NOT frozen or cold. This is done through a query against the _tier field. Meaning, it only effects newer indices that actually have that field set.
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.
Lisa Cawley
István Zoltán Szabó
Ed Savage
David Kyle
Benjamin Trent
Dimitris Athanasiou
Valeriy Khakhutskyy
David Roberts