From 76c2ed03fcefb664f0784fb09241339681359970 Mon Sep 17 00:00:00 2001 From: kolchfa-aws <105444904+kolchfa-aws@users.noreply.github.com> Date: Fri, 23 Feb 2024 14:45:25 -0500 Subject: [PATCH] Add unsupported warning for local models (#6489) * Add unsupported warning for local models Signed-off-by: Fanit Kolchina * Reword to be more generic Signed-off-by: Fanit Kolchina --------- Signed-off-by: Fanit Kolchina --- _ml-commons-plugin/custom-local-models.md | 5 +++++ _ml-commons-plugin/opensearch-assistant.md | 2 +- _ml-commons-plugin/pretrained-models.md | 2 ++ _ml-commons-plugin/using-ml-models.md | 3 +++ 4 files changed, 11 insertions(+), 1 deletion(-) diff --git a/_ml-commons-plugin/custom-local-models.md b/_ml-commons-plugin/custom-local-models.md index 13cae7fc..d701dad3 100644 --- a/_ml-commons-plugin/custom-local-models.md +++ b/_ml-commons-plugin/custom-local-models.md @@ -18,6 +18,11 @@ As of OpenSearch 2.6, OpenSearch supports local text embedding models. As of OpenSearch 2.11, OpenSearch supports local sparse encoding models. +As of OpenSearch 2.12, OpenSearch supports local cross-encoder models. + +Running local models on the CentOS 7 operating system is not supported. Moreover, not all local models can run on all hardware and operating systems. +{: .important} + ## Preparing a model For both text embedding and sparse encoding models, you must provide a tokenizer JSON file within the model zip file. diff --git a/_ml-commons-plugin/opensearch-assistant.md b/_ml-commons-plugin/opensearch-assistant.md index 9dbfab51..3a8e0c87 100644 --- a/_ml-commons-plugin/opensearch-assistant.md +++ b/_ml-commons-plugin/opensearch-assistant.md @@ -15,7 +15,7 @@ This is an experimental feature and is not recommended for use in a production e The OpenSearch Assistant Toolkit helps you create AI-powered assistants for OpenSearch Dashboards. The toolkit includes the following elements: -- [**Agents and tools**]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/index/): _Agents_ interface with a large language model (LLM) and execute high-level tasks, such as summarization or generating Piped Processing Language (PPL) from natural language. The agent's high-level tasks consist of low-level tasks called _tools_, which can be reused by multiple agents. +- [**Agents and tools**]({{site.url}}{{site.baseurl}}/ml-commons-plugin/agents-tools/index/): _Agents_ interface with a large language model (LLM) and execute high-level tasks, such as summarization or generating Piped Processing Language (PPL) queries from natural language. The agent's high-level tasks consist of low-level tasks called _tools_, which can be reused by multiple agents. - [**Configuration automation**]({{site.url}}{{site.baseurl}}/automating-configurations/index/): Uses templates to set up infrastructure for artificial intelligence and machine learning (AI/ML) applications. For example, you can automate configuring agents to be used for chat or generating PPL queries from natural language. - [**OpenSearch Assistant for OpenSearch Dashboards**]({{site.url}}{{site.baseurl}}/dashboards/dashboards-assistant/index/): This is the OpenSearch Dashboards UI for the AI-powered assistant. The assistant's workflow is configured with various agents and tools. diff --git a/_ml-commons-plugin/pretrained-models.md b/_ml-commons-plugin/pretrained-models.md index 785f1450..cbcb11bf 100644 --- a/_ml-commons-plugin/pretrained-models.md +++ b/_ml-commons-plugin/pretrained-models.md @@ -256,6 +256,8 @@ To learn how to set up a vector index and use sparse encoding models for search, OpenSearch supports the following models, categorized by type. Text embedding models are sourced from [Hugging Face](https://huggingface.co/). Sparse encoding models are trained by OpenSearch. Although models with the same type will have similar use cases, each model has a different model size and will perform differently depending on your cluster setup. For a performance comparison of some pretrained models, see the [SBERT documentation](https://www.sbert.net/docs/pretrained_models.html#model-overview). +Running local models on the CentOS 7 operating system is not supported. Moreover, not all local models can run on all hardware and operating systems. +{: .important} ### Sentence transformers diff --git a/_ml-commons-plugin/using-ml-models.md b/_ml-commons-plugin/using-ml-models.md index 5090b5a9..5c23e19a 100644 --- a/_ml-commons-plugin/using-ml-models.md +++ b/_ml-commons-plugin/using-ml-models.md @@ -19,6 +19,9 @@ To integrate machine learning (ML) models into your OpenSearch cluster, you can - **Custom models** such as PyTorch deep learning models: To learn more, see [Custom models]({{site.url}}{{site.baseurl}}/ml-commons-plugin/custom-local-models/). +Running local models on the CentOS 7 operating system is not supported. Moreover, not all local models can run on all hardware and operating systems. +{: .important} + ## GPU acceleration For better performance, you can take advantage of GPU acceleration on your ML node. For more information, see [GPU acceleration]({{site.url}}{{site.baseurl}}/ml-commons-plugin/gpu-acceleration/).