[[machine-learning]]
== Machine Learning

[discrete]
[[ml-trained-models]]
=== Trained models

Eland allows transforming trained models from scikit-learn, XGBoost,
and LightGBM libraries to be serialized and used as an inference
model in {es}.

[source,python]
------------------------
>>> from xgboost import XGBClassifier
>>> from eland.ml import MLModel

# Train and exercise an XGBoost ML model locally
>>> xgb_model = XGBClassifier(booster="gbtree")
>>> xgb_model.fit(training_data[0], training_data[1])

>>> xgb_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]

# Import the model into Elasticsearch
>>> es_model = MLModel.import_model(
    es_client="http://localhost:9200",
    model_id="xgb-classifier",
    model=xgb_model,
    feature_names=["f0", "f1", "f2", "f3", "f4"],
)

# Exercise the ML model in Elasticsearch with the training data
>>> es_model.predict(training_data[0])
[0 1 1 0 1 0 0 0 1 0]
------------------------

[discrete]
[[ml-nlp-pytorch]]
=== Natural language processing (NLP) with PyTorch


IMPORTANT: You need to use PyTorch `1.11.0` or earlier to import an NLP model. 
Run `pip install torch==1.11` to install the aproppriate version of PyTorch.

For NLP tasks, Eland enables you to import PyTorch trained BERT models into {es}. 
Models can be either plain PyTorch models, or supported 
https://huggingface.co/transformers[transformers] models from the
https://huggingface.co/models[Hugging Face model hub]. For example:

[source,bash]
------------------------
$ eland_import_hub_model <authentication> \ <1>
  --url http://localhost:9200/ \ <2>
  --hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \ <3>
  --task-type ner \ <4>
  --start
------------------------
<1> Use an authentication method to access your cluster. Refer to <<ml-nlp-pytorch-auth>>.
<2> The cluster URL. Alternatively, use `--cloud-id`.
<3> Specify the identifier for the model in the Hugging Face model hub.
<4> Specify the type of NLP task. Supported values are `fill_mask`, `ner`,
`question_answering`, `text_classification`, `text_embedding`, and `zero_shot_classification`.

[source,python]
------------------------
>>> import elasticsearch
>>> from pathlib import Path
>>> from eland.ml.pytorch import PyTorchModel
>>> from eland.ml.pytorch.transformers import TransformerModel

# Load a Hugging Face transformers model directly from the model hub
>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s] 
Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]

# Export the model in a TorchScript representation which Elasticsearch uses
>>> tmp_path = "models"
>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
>>> model_path, config, vocab_path = tm.save(tmp_path)

# Import model into Elasticsearch
>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300)  # 5 minute timeout
>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
>>> ptm.import_model(model_path=model_path, config_path=None, vocab_path=vocab_path, config=config)
100%|██████████| 63/63 [00:12<00:00,  5.02it/s]
------------------------

[discrete]
[[ml-nlp-pytorch-docker]]
==== Import model with Docker

IMPORTANT: To use the Docker container, you need to clone the Eland repository: https://github.com/elastic/eland

If you want to use Eland without installing it, you can build the Docker container to run the available scripts:

```bash
$ docker build -t elastic/eland .
```

You can now use the container interactively:

```bash
$ docker run -it --rm --network host elastic/eland
```

Running installed scripts is also possible without an interactive shell, for example:

```bash
docker run -it --rm elastic/eland \
    eland_import_hub_model \
      --url $ELASTICSEARCH_URL \
      --hub-model-id elastic/distilbert-base-uncased-finetuned-conll03-english \
      --start
```

Replace the `$ELASTICSEARCH_URL` with the URL for your Elasticsearch cluster. For authentication purposes, include an administrator username and password in the URL in the following format: `https://username:password@host:port`.

[discrete]
[[ml-nlp-pytorch-auth]]
==== Authentication methods

The following authentication options are available when using the import script:

* username and password authentication (specified with the `-u` and `-p` options):
+
--
[source,bash]
--------------------------------------------------
eland_import_hub_model -u <username> -p <password> --cloud-id <cloud-id> ...
--------------------------------------------------
These `-u` and `-p` options also work when you use `--url`.
--

* username and password authentication (embedded in the URL):
+
--
[source,bash]
--------------------------------------------------
eland_import_hub_model --url https://<user>:<password>@<hostname>:<port> ...
--------------------------------------------------
--

* API key authentication:
+
--
[source,bash]
--------------------------------------------------
eland_import_hub_model --es-api-key <api-key> --url https://<hostname>:<port> ...
--------------------------------------------------
--