Adds an API that can be used to find out how much memory ML
is permitted to use and is currently using on each node, both
within the JVM heap, and natively, outside of the JVM.
This commit adds `search_interval` to the datafeed stats API
`running_state` object. When the datafeed is running, it reports
the last search interval that was searched. It is useful to
understand the point in time where the datafeed is currently
searching.
Closes#82405
Emit deprecation warning when creating new jobs with bucket spans that
aren't an integral divisor or multiple of a day.
Relates #81645
Co-authored-by: lcawl <lcawley@elastic.co>
This commit adds support for MPNet based models.
MPNet models differ from BERT style models in that:
- Special tokens are different
- Input to the model doesn't require token positions.
To configure an MPNet tokenizer for your pytorch MPNet based model:
```
"tokenization": {
"mpnet": {...}
}
```
The options provided to `mpnet` are the same as the previously supported `bert` configuration.
This improves reporting of trained model size in the response of the stats API.
In particular, it removes the `model_size_bytes` from the `deployment_stats` section and
replaces it with a top-level `model_size_stats` object that contains:
- `model_size_bytes`: the actual model size
- `required_native_memory_bytes`: the amount of memory required to load a model
In addition, these are now reported for PyTorch models regardless of their deployment state.
For new jobs, when the analysis config field model_prune_window is not set, use a default value of 30 days or 20 times the bucket span, whichever is greater.
Co-authored-by: David Roberts <dave.roberts@elastic.co>
Co-authored-by: Lisa Cawley <lcawley@elastic.co>
Previously the ML model snapshot upgrade endpoint did not
provide a way to reliably monitor progress. This could lead
to the upgrade assistant UI thinking that a model snapshot
upgrade had finished when it actually hadn't.
This change adds a new "stats" API that allows external
interested parties to find out the status of each model
snapshot upgrade and which node (if any) each is running on.
Fixes#81519
Adds a `force` parameter to the delete trained models API
which when set to `true` allows deletion of a model that
is referenced by ingest pipelines or has a started deployment.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
This deprecates estimated_heap_memory_usage_bytes on model put and replaces it with model_size_bytes.
On GET, only model_size_bytes is returned unless v7 rest-api compatibility is requested.
For the ml/info API, only model_size_bytes is returned
A forward-port of: #80545
If the xpack.ml.use_auto_machine_memory_percent setting is true,
and xpack.ml.max_model_memory_limit is not set then
xpack.ml.max_model_memory_limit is now considered to be set to
the largest size that could be assigned in the cluster.
This functionality will be crucial for Cloud once the Elasticsearch
startup code is setting the Elasticsearch JVM heap size. Then the
Cloud code will no longer be able to accurately set
xpack.ml.max_model_memory_limit, so will not set it at all.
Instead the Cloud code will just set
xpack.ml.use_auto_machine_memory_percent and the ML code will
calculate the appropriate maximum model_memory_limit that should
be permitted.
This commit adds a new field deployment_stats that is optionally set for models that are deployed.
If a model does not have a deployment, it will be null.
Also, removes the get deployment stats API and makes the deployment stats action internal only.
When a deployment is started, we do not validate that the definition
documents are all present and not truncated. This commit adds a
validation on _start that prevents a bad state from occurring where the
deployment starts, but the model is incorrectly defined, or some unknown
error occurs to late in the deployment process.
Implements a `force` parameter to the stop deployment API.
This allows a user to forcefully stop a deployment. Currently,
this specifically allows stopping a deployment that is in use
by ingest processors.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
Removes `testenv` annotations and related code. These annotations originally let you skip x-pack snippet tests in the docs. However, that's no longer possible.
Relates to #79309, #31619
Adds `start_time` to the get deployment stats API for the deployment
and each allocation.
Co-authored-by: Elastic Machine <elasticmachine@users.noreply.github.com>
the infer endpoint has changed its format.
Also, the results format for the various tasks have changed. This updates the docs to match what is currently in 8.0.0.
This commit makes the two following changes (along with some
refactoring) - Nlp results will now indicate if the input was truncated
or not - The default truncation is now `none` instead of `first`
David Roberts
Lisa Cawley
Tobias Stadler
David Kyle
István Zoltán Szabó
Ugo Sangiorgi
Dimitris Athanasiou
Ed Savage
Benjamin Trent
James Rodewig
Mark Vieira