Add ML fault tolerance (#3803)
* Add ML fault tolerance Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Rework Profile API sentence Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Fix link Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Add review feedback Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Add technical feedback for ML. Change API names Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Add final ML node setting Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Add more technical feedback Signed-off-by: Naarcha-AWS <naarcha@amazon.com> * Apply suggestions from code review Co-authored-by: Chris Moore <107723039+cwillum@users.noreply.github.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update cluster-settings.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/api.md Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/api.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Apply suggestions from code review Co-authored-by: Nathan Bower <nbower@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/api.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/api.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update _ml-commons-plugin/api.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> * Update api.md Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> --------- Signed-off-by: Naarcha-AWS <naarcha@amazon.com> Signed-off-by: Naarcha-AWS <97990722+Naarcha-AWS@users.noreply.github.com> Co-authored-by: Chris Moore <107723039+cwillum@users.noreply.github.com> Co-authored-by: Nathan Bower <nbower@amazon.com>
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@ -24,7 +24,7 @@ In order to train tasks through the API, three inputs are required.
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- Model hyper parameters: Adjust these parameters to make the model train better.
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- Input data: The data input that trains the ML model, or applies the ML models to predictions. You can input data in two ways, query against your index or use data frame.
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## Train model
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## Training a model
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Training can occur both synchronously and asynchronously.
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@ -96,7 +96,7 @@ For asynchronous responses, the API returns the task_id, which can be used to ge
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}
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```
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## Get model information
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## Getting model information
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You can retrieve information on your model using the model_id.
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@ -115,12 +115,12 @@ The API returns information on the model, the algorithm used, and the content fo
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}
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```
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## Upload a model
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## Registering a model
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Use the upload operation to upload a custom model to a model index. ML Commons splits the model into smaller chunks and saves those chunks in the model's index.
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Use the register operation to register a custom model to a model index. ML Commons splits the model into smaller chunks and saves those chunks in the model's index.
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```json
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POST /_plugins/_ml/models/_upload
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POST /_plugins/_ml/models/_register
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```
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### Request fields
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@ -137,10 +137,10 @@ Field | Data type | Description
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### Example
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The following example request uploads version `1.0.0` of an NLP sentence transformation model named `all-MiniLM-L6-v2`.
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The following example request registers a version `1.0.0` of an NLP sentence transformation model named `all-MiniLM-L6-v2`.
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```json
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POST /_plugins/_ml/models/_upload
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POST /_plugins/_ml/models/_register
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{
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"name": "all-MiniLM-L6-v2",
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"version": "1.0.0",
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@ -166,14 +166,14 @@ OpenSearch responds with the `task_id` and task `status`.
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}
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```
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To see the status of your model upload, enter the `task_id` into the [task API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#get-task-information). Use the `model_id` from the task response once the upload is complete. For example:
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To see the status of your model registration, enter the `task_id` in the [task API] ...
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```json
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{
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"model_id" : "WWQI44MBbzI2oUKAvNUt",
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"task_type" : "UPLOAD_MODEL",
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"function_name" : "TEXT_EMBEDDING",
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"state" : "COMPLETED",
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"state" : "REGISTERED",
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"worker_node" : "KzONM8c8T4Od-NoUANQNGg",
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"create_time" : 1665961344003,
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"last_update_time" : 1665961373047,
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@ -181,28 +181,28 @@ To see the status of your model upload, enter the `task_id` into the [task API](
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}
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```
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## Load model
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## Deploying a model
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The load model operation reads the model's chunks from the model index, then creates an instance of the model to cache into memory. This operation requires the `model_id`.
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The deploy model operation reads the model's chunks from the model index and then creates an instance of the model to cache into memory. This operation requires the `model_id`.
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```json
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POST /_plugins/_ml/models/<model_id>/_load
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POST /_plugins/_ml/models/<model_id>/_deploy
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```
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### Example: Load into all available ML nodes
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### Example: Deploying to all available ML nodes
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In this example request, OpenSearch loads the model into any available OpenSearch ML node:
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In this example request, OpenSearch deploys the model to any available OpenSearch ML node:
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```json
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_load
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
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```
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### Example: Load into a specific node
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### Example: Deploying to a specific node
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If you want to reserve the memory of other ML nodes within your cluster, you can load your model into a specific node(s) by specifying the `node_ids` in the request body:
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If you want to reserve the memory of other ML nodes within your cluster, you can deploy your model to a specific node(s) by specifying the `node_ids` in the request body:
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```json
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_load
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POST /_plugins/_ml/models/WWQI44MBbzI2oUKAvNUt/_deploy
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{
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"node_ids": ["4PLK7KJWReyX0oWKnBA8nA"]
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}
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```json
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{
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"task_id" : "hA8P44MBhyWuIwnfvTKP",
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"status" : "CREATED"
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"status" : "DEPLOYING"
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}
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```
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## Unload a model
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## Undeploying a model
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To unload a model from memory, use the unload operation.
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To undeploy a model from memory, use the undeploy operation:
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```json
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POST /_plugins/_ml/models/<model_id>/_unload
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POST /_plugins/_ml/models/<model_id>/_undeploy
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```
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### Example: Unload model from all ML nodes
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### Example: Undeploying model from all ML nodes
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```json
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POST /_plugins/_ml/models/MGqJhYMBbbh0ushjm8p_/_unload
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POST /_plugins/_ml/models/MGqJhYMBbbh0ushjm8p_/_undeploy
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```
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### Response: Unload model from all ML nodes
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### Response: Undeploying a model from all ML nodes
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```json
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{
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"s5JwjZRqTY6nOT0EvFwVdA": {
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"stats": {
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"MGqJhYMBbbh0ushjm8p_": "unloaded"
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"MGqJhYMBbbh0ushjm8p_": "UNDEPLOYED"
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}
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}
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}
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```
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### Example: Unload specific models from specific nodes
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### Example: Undeploying specific models from specific nodes
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```json
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POST /_plugins/_ml/models/_unload
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POST /_plugins/_ml/models/_undeploy
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{
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"node_ids": ["sv7-3CbwQW-4PiIsDOfLxQ"],
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"model_ids": ["KDo2ZYQB-v9VEDwdjkZ4"]
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```
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### Response: Unload specific models from specific nodes
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### Response: Undeploying specific models from specific nodes
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```json
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{
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"sv7-3CbwQW-4PiIsDOfLxQ" : {
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"stats" : {
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"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded"
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"KDo2ZYQB-v9VEDwdjkZ4" : "UNDEPLOYED"
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}
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}
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}
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```
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### Response: Unload all models from specific nodes
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### Response: Undeploying all models from specific nodes
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```json
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{
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"sv7-3CbwQW-4PiIsDOfLxQ" : {
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"stats" : {
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"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded",
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"-8o8ZYQBvrLMaN0vtwzN" : "unloaded"
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"KDo2ZYQB-v9VEDwdjkZ4" : "UNDEPLOYED",
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"-8o8ZYQBvrLMaN0vtwzN" : "UNDEPLOYED"
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}
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}
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}
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```
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### Example: Unload specific models from all nodes
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### Example: Undeploying specific models from all nodes
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```json
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{
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}
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```
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### Response: Unload specific models from all nodes
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### Response: Undeploying specific models from all nodes
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```json
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{
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"sv7-3CbwQW-4PiIsDOfLxQ" : {
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"stats" : {
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"KDo2ZYQB-v9VEDwdjkZ4" : "unloaded"
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"KDo2ZYQB-v9VEDwdjkZ4" : "UNDEPLOYED"
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}
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}
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}
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```
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## Search model
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## Searching for a model
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Use this command to search models you've already created.
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{query}
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```
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### Example: Query all models
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### Example: Querying all models
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```json
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POST /_plugins/_ml/models/_search
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}
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```
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### Example: Query models with algorithm "FIT_RCF"
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### Example: Querying models with algorithm "FIT_RCF"
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```json
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POST /_plugins/_ml/models/_search
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}
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```
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## Delete model
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## Deleting a model
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Deletes a model based on the model_id
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Deletes a model based on the `model_id`.
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```json
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DELETE /_plugins/_ml/models/<model_id>
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}
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```
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## Profile
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## Returning model profile information
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Returns runtime information on ML tasks and models. This operation can help debug issues with models at runtime.
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The profile operation returns runtime information on ML tasks and models. The profile operation can help debug issues with models at runtime.
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```json
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return_all_tasks | boolean | Determines whether or not a request returns all tasks. When set to `false` task profiles are left out of the response.
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return_all_models | boolean | Determines whether or not a profile request returns all models. When set to `false` model profiles are left out of the response.
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### Example: Return all tasks and models on a specific node
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### Example: Returning all tasks and models on a specific node
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```json
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GET /_plugins/_ml/profile
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}
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```
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### Response: Return all tasks and models on a specific node
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### Response: Returning all tasks and models on a specific node
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```json
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{
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"KzONM8c8T4Od-NoUANQNGg" : { # node id
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"models" : {
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"WWQI44MBbzI2oUKAvNUt" : { # model id
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"model_state" : "LOADED", # model status
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"model_state" : "DEPLOYED", # model status
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"predictor" : "org.opensearch.ml.engine.algorithms.text_embedding.TextEmbeddingModel@592814c9",
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"worker_nodes" : [ # routing table
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"KzONM8c8T4Od-NoUANQNGg"
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}
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```
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## Get task information
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## Getting task information
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You can retrieve information about a task using the task_id.
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}
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```
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## Search task
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## Searching for a task
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Search tasks based on parameters indicated in the request body.
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}
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```
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## Delete task
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## Deleting a task
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Delete a task based on the task_id.
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## Set number of ML models per node
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Sets the number of ML models that can be loaded on to each ML node. When set to `0`, no ML models can load on any node.
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Sets the number of ML models that can be deployed to each ML node. When set to `0`, no ML models can deploy on any node.
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### Setting
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## Set sync job intervals
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When returning runtime information with the [profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#profile), ML Commons will run a regular job to sync newly loaded or unloaded models on each node. When set to `0`, ML Commons immediately stops sync up jobs.
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When returning runtime information with the [Profile API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#profile), ML Commons will run a regular job to sync newly deployed or undeployed models on each node. When set to `0`, ML Commons immediately stops sync-up jobs.
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### Setting
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- Default value: 90
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- Value range: [0, 100]
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## Allow custom deployment plans
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When enabled, this setting grants users the ability to deploy models to specific ML nodes according to that user's permissions.
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### Setting
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```
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plugins.ml_commons.allow_custom_deployment_plan: false
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```
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### Values
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- Default value: false
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- Value range: [false, true]
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## Enable auto redeploy
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This setting automatically redeploys deployed or partially deployed models upon cluster failure. If all ML nodes inside a cluster crash, the model switches to the `DEPLOYED_FAILED` state, and the model must be deployed manually.
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### Setting
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```
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plugins.ml_commons.model_auto_redeploy.enable: false
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```
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### Values
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- Default value: false
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- Value range: [false, true]
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## Set retires for auto redeploy
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This setting sets the limit for the number of times a deployed or partially deployed model will try and redeploy when ML nodes in a cluster fail or new ML nodes join the cluster.
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### Setting
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```
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plugins.ml_commons.model_auto_redeploy.lifetime_retry_times: 3
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```
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### Values
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- Default value: 3
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- Value range: [0, 100]
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## Set auto redeploy success ratio
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This setting sets the ratio of success for the auto-redeployment of a model based on the available ML nodes in a cluster. For example, if ML nodes crash inside a cluster, the auto redeploy protocol adds another node or retires a crashed node. If the ratio is `0.7` and 70% of all ML nodes successfully redeploy the model on auto-redeploy activation, the redeployment is a success. If the model redeploys on fewer than 70% of available ML nodes, the auto-redeploy retries until the redeployment succeeds or OpenSearch reaches [the maximum number of retries](#set-retires-for-auto-redeploy).
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### Setting
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```
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plugins.ml_commons.model_auto_redeploy_success_ratio: 0.8
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```
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### Values
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- Default value: 0.8
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- Value range: [0, 1]
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