[DOCS] Adds instructions on model install in air-gapped env (#542)

Co-authored-by: David Kyle <david.kyle@elastic.co>

docs/guide/machine-learning.asciidoc

@@ -39,12 +39,12 @@ model in {es}.
 === 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.
+IMPORTANT: You need to use PyTorch `1.13` or earlier to import an NLP model. 
+Run `pip install torch==1.13` 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
+For NLP tasks, Eland enables you to import PyTorch models into {es}. Use the 
+`eland_import_hub_model` script to download and install supported 
+https://huggingface.co/transformers[transformer models] from the
 https://huggingface.co/models[Hugging Face model hub]. For example:
 
 [source,bash]
@@ -61,32 +61,6 @@ $ eland_import_hub_model <authentication> \ <1>
 <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]]
@@ -118,6 +92,68 @@ docker run -it --rm elastic/eland \
 
 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-air-gapped]]
+==== Install models in an air-gapped environment 
+
+You can install models in a restricted or closed network by pointing the 
+`eland_import_hub_model` script to local files. 
+
+For an offline install of a Hugging Face model, the model first needs to be 
+cloned locally, Git and https://git-lfs.com/[Git Large File Storage] are 
+required to be installed in your system.
+
+1. Select a model you want to use from Hugging Face. Refer to the 
+{ml-docs}/ml-nlp-model-ref.html[compatible third party model] list for more 
+information on the supported architectures. 
+
+2. Clone the selected model from Hugging Face by using the model URL. For 
+example:
++
+--
+[source,bash]
+----
+git clone https://huggingface.co/dslim/bert-base-NER
+----
+This command results in a local copy of 
+of the model in the directory `bert-base-NER`.
+--
+
+3. Use the `eland_import_hub_model` script with the `--hub-model-id` set to the 
+directory of the cloned model to install it:
++
+--
+[source,bash]
+----
+eland_import_hub_model \
+      --url 'XXXX' \
+      --hub-model-id /PATH/TO/MODEL \
+      --task-type ner \
+      --es-username elastic --es-password XXX \
+      --es-model-id bert-base-ner
+----
+
+If you use the Docker image to run `eland_import_hub_model` you must bind mount 
+the model directory, so the container can read the files:
+
+[source,bash]
+----
+docker run --mount type=bind,source=/PATH/TO/MODELS,destination=/models,readonly -it --rm elastic/eland \
+    eland_import_hub_model \
+      --url 'XXXX' \
+      --hub-model-id /models/bert-base-NER \
+      --task-type ner \
+      --es-username elastic --es-password XXX \
+      --es-model-id bert-base-ner
+----
+Once it's uploaded to {es}, the model will have the ID specified by 
+`--es-model-id`. If it is not set, the model ID is derived from 
+`--hub-model-id`; spaces and path delimiters are converted to double 
+underscores `__`.
+
+--
+
+
 [discrete]
 [[ml-nlp-pytorch-auth]]
 ==== Authentication methods