mirror of
https://github.com/elastic/eland.git
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120 lines
4.2 KiB
Plaintext
120 lines
4.2 KiB
Plaintext
[[machine-learning]]
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== Machine Learning
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[discrete]
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[[ml-trained-models]]
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=== Trained models
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Eland allows transforming trained models from scikit-learn, XGBoost,
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and LightGBM libraries to be serialized and used as an inference
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model in {es}.
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[source,python]
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------------------------
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>>> from xgboost import XGBClassifier
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>>> from eland.ml import MLModel
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# Train and exercise an XGBoost ML model locally
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>>> xgb_model = XGBClassifier(booster="gbtree")
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>>> xgb_model.fit(training_data[0], training_data[1])
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>>> xgb_model.predict(training_data[0])
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[0 1 1 0 1 0 0 0 1 0]
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# Import the model into Elasticsearch
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>>> es_model = MLModel.import_model(
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es_client="http://localhost:9200",
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model_id="xgb-classifier",
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model=xgb_model,
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feature_names=["f0", "f1", "f2", "f3", "f4"],
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)
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# Exercise the ML model in Elasticsearch with the training data
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>>> es_model.predict(training_data[0])
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[0 1 1 0 1 0 0 0 1 0]
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------------------------
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[discrete]
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[[ml-nlp-pytorch]]
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=== Natural language processing (NLP) with PyTorch
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For NLP tasks, Eland enables you to import PyTorch trained BERT models into {es}.
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Models can be either plain PyTorch models, or supported
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https://huggingface.co/transformers[transformers] models from the
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https://huggingface.co/models[Hugging Face model hub]. For example:
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[source,bash]
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------------------------
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$ eland_import_hub_model <authentication> \ <1>
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--url http://localhost:9200/ \ <2>
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--hub-model-id elastic/distilbert-base-cased-finetuned-conll03-english \ <3>
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--task-type ner \ <4>
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--start
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------------------------
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<1> Use an authentication method to access your cluster. Refer to <<ml-nlp-pytorch-auth>>.
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<2> The cluster URL. Alternatively, use `--cloud-id`.
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<3> Specify the identifier for the model in the Hugging Face model hub.
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<4> Specify the type of NLP task. Supported values are `fill_mask`, `ner`,
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`text_classification`, `text_embedding`, and `zero_shot_classification`.
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[source,python]
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------------------------
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>>> import elasticsearch
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>>> from pathlib import Path
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>>> from eland.ml.pytorch import PyTorchModel
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>>> from eland.ml.pytorch.transformers import TransformerModel
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# Load a Hugging Face transformers model directly from the model hub
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>>> tm = TransformerModel("elastic/distilbert-base-cased-finetuned-conll03-english", "ner")
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Downloading: 100%|██████████| 257/257 [00:00<00:00, 108kB/s]
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Downloading: 100%|██████████| 954/954 [00:00<00:00, 372kB/s]
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Downloading: 100%|██████████| 208k/208k [00:00<00:00, 668kB/s]
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Downloading: 100%|██████████| 112/112 [00:00<00:00, 43.9kB/s]
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Downloading: 100%|██████████| 249M/249M [00:23<00:00, 11.2MB/s]
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# Export the model in a TorchScript representation which Elasticsearch uses
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>>> tmp_path = "models"
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>>> Path(tmp_path).mkdir(parents=True, exist_ok=True)
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>>> model_path, config_path, vocab_path = tm.save(tmp_path)
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# Import model into Elasticsearch
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>>> es = elasticsearch.Elasticsearch("http://elastic:mlqa_admin@localhost:9200", timeout=300) # 5 minute timeout
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>>> ptm = PyTorchModel(es, tm.elasticsearch_model_id())
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>>> ptm.import_model(model_path, config_path, vocab_path)
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100%|██████████| 63/63 [00:12<00:00, 5.02it/s]
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------------------------
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[discrete]
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[[ml-nlp-pytorch-auth]]
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==== Authentication methods
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The following authentication options are available when using the import script:
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* username and password authentication (specified with the `-u` and `-p` options):
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+
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--
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[source,bash]
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--------------------------------------------------
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eland_import_hub_model -u <username> -p <password> --cloud-id <cloud-id> ...
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--------------------------------------------------
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These `-u` and `-p` options also work when you use `--url`.
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--
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* username and password authentication (embedded in the URL):
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+
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--
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[source,bash]
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--------------------------------------------------
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eland_import_hub_model --url https://<user>:<password>@<hostname>:<port> ...
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--------------------------------------------------
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--
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* API key authentication:
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+
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--
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[source,bash]
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--------------------------------------------------
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eland_import_hub_model --es-api-key <api-key> --url https://<hostname>:<port> ...
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--------------------------------------------------
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--
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