Chapter 3. Serving large models

1. The composite Caikit-TGIS runtime is based on Caikit and Text Generation Inference Server (TGIS). To use this runtime, you must convert your models to Caikit format. For an example, see Converting Hugging Face Hub models to Caikit format in the caikit-tgis-serving repository.
2. The Caikit Standalone runtime is based on Caikit NLP. To use this runtime, you must convert your models to the Caikit embeddings format. For an example, see Tests for text embedding module.
3. Text Generation Inference Server (TGIS) is based on an early fork of Hugging Face TGI. Red Hat will continue to develop the standalone TGIS runtime to support TGI models. If a model is incompatible in the current version of OpenShift AI, support might be added in a future version. In the meantime, you can also add your own custom runtime to support a TGI model. For more information, see Adding a custom model-serving runtime for the single-model serving platform.

Procedure

1. Enable the single-model serving platform as follows:

   1. In the left menu, click Settings Cluster settings.
   2. Locate the Model serving platforms section.
   3. To enable the single-model serving platform for projects, select the Single-model serving platform checkbox.
   4. Click Save changes.

2. Enable preinstalled runtimes for the single-model serving platform as follows:

   1. In the left menu of the OpenShift AI dashboard, click Settings Serving runtimes.

      The Serving runtimes page shows preinstalled runtimes and any custom runtimes that you have added.

      For more information about preinstalled runtimes, see Supported runtimes.

   2. Set the runtime that you want to use to Enabled.

      The single-model serving platform is now available for model deployments.

Procedure

1. From the OpenShift AI dashboard, click Settings > Serving runtimes.

   The Serving runtimes page opens and shows the model-serving runtimes that are already installed and enabled.

2. To add a custom runtime, choose one of the following options:

   • To start with an existing runtime (for example, TGIS Standalone ServingRuntime for KServe), click the action menu (⋮) next to the existing runtime and then click Duplicate.
   • To add a new custom runtime, click Add serving runtime.
3. In the Select the model serving platforms this runtime supports list, select Single-model serving platform.
4. In the Select the API protocol this runtime supports list, select REST or gRPC.

5. Optional: If you started a new runtime (rather than duplicating an existing one), add your code by choosing one of the following options:

   • Upload a YAML file

     1. Click Upload files.

     2. In the file browser, select a YAML file on your computer.

        The embedded YAML editor opens and shows the contents of the file that you uploaded.

   • Enter YAML code directly in the editor

     1. Click Start from scratch.
     2. Enter or paste YAML code directly in the embedded editor.
   Note

   In many cases, creating a custom runtime will require adding new or custom parameters to the env section of the ServingRuntime specification.

6. Click Add.

   The Serving runtimes page opens and shows the updated list of runtimes that are installed. Observe that the custom runtime that you added is automatically enabled. The API protocol that you specified when creating the runtime is shown.

7. Optional: To edit your custom runtime, click the action menu (⋮) and select Edit.

Procedure

1. In the left menu, click Data Science Projects.

   The Data Science Projects page opens.

2. Click the name of the project that you want to deploy a model in.

   A project details page opens.

3. Click the Models tab.

4. Perform one of the following actions:

   • If you see a ​​Single-model serving platform tile, click Deploy model on the tile.
   • If you do not see any tiles, click the Deploy model button.

   The Deploy model dialog opens.

5. In the Model name field, enter a unique name for the model that you are deploying.
6. In the Serving runtime field, select an enabled runtime.
7. From the Model framework list, select a value.
8. In the Number of model replicas to deploy field, specify a value.
9. From the Model server size list, select a value.
10. Optional: In the Model route section, select the Make deployed models available through an external route checkbox to make your deployed models available to external clients.

11. To require token authorization for inference requests to the deployed model, perform the following actions:

    1. Select Require token authorization.
    2. In the Service account name field, enter the service account name that the token will be generated for.

12. To specify the location of your model, perform one of the following sets of actions:

    • To use an existing data connection

      1. Select Existing data connection.
      2. From the Name list, select a data connection that you previously defined.

      3. In the Path field, enter the folder path that contains the model in your specified data source.

         Important

         The OpenVINO Model Server runtime has specific requirements for how you specify the model path. For more information, see known issue RHOAIENG-3025 in the OpenShift AI release notes.

    • To use a new data connection

      1. To define a new data connection that your model can access, select New data connection.
      2. In the Name field, enter a unique name for the data connection.
      3. In the Access key field, enter the access key ID for your S3-compatible object storage provider.
      4. In the Secret key field, enter the secret access key for the S3-compatible object storage account that you specified.
      5. In the Endpoint field, enter the endpoint of your S3-compatible object storage bucket.
      6. In the Region field, enter the default region of your S3-compatible object storage account.
      7. In the Bucket field, enter the name of your S3-compatible object storage bucket.

      8. In the Path field, enter the folder path in your S3-compatible object storage that contains your data file.

         Important

         The OpenVINO Model Server runtime has specific requirements for how you specify the model path. For more information, see known issue RHOAIENG-3025 in the OpenShift AI release notes.

13. Click Deploy.

Procedure

1. From the OpenShift AI dashboard, click Data Science Projects.

   The Data Science Projects page opens.

2. Click the name of the project that contains your deployed model.

   A project details page opens.

3. Click the Models tab.

4. In the Models and model servers list, expand the section for your model.

   Your authorization token is shown in the Token authorization section, in the Token secret field.

5. Optional: To copy the authorization token for use in an inference request, click the Copy button ( ) next to the token value.

Procedure

1. From the OpenShift AI dashboard, click Model Serving.

   The inference endpoint for the model is shown in the Inference endpoint field.

2. Depending on what action you want to perform with the model (and if the model supports that action), copy the inference endpoint and then add a path to the end of the URL.
3. Use the endpoint to make API requests to your deployed model.

Procedure

1. From the OpenShift AI dashboard navigation menu, click Data Science Projects.

   The Data Science Projects page opens.

2. Click the name of the project that contains the data science models that you want to monitor.
3. In the project details page, click the Models tab.
4. Select the model that you are interested in.

5. On the Endpoint performance tab, set the following options:

   • Time range - Specifies how long to track the metrics. You can select one of these values: 1 hour, 24 hours, 7 days, and 30 days.
   • Refresh interval - Specifies how frequently the graphs on the metrics page are refreshed (to show the latest data). You can select one of these values: 15 seconds, 30 seconds, 1 minute, 5 minutes, 15 minutes, 30 minutes, 1 hour, 2 hours, and 1 day.
6. Scroll down to view data graphs for number of requests, average response time, CPU utilization, and memory utilization.

Procedure

1. From the OpenShift AI dashboard, click Settings > Serving runtimes.

   The Serving runtimes page opens and shows the model-serving runtimes that are already installed and enabled.

2. Find the custom vLLM model-serving runtime that you created, click the action menu (⋮) next to the runtime and select Edit.

   The embedded YAML editor opens and shows the contents of the custom model-serving runtime.

3. To configure the vLLM model-serving runtime for speculative decoding by matching n-grams in the prompt:

   1. Add the following arguments:

      containers:
        - args:
            - --speculative-model=[ngram]
            - --num-speculative-tokens=<NUM_SPECULATIVE_TOKENS>
            - --ngram-prompt-lookup-max=<NGRAM_PROMPT_LOOKUP_MAX>
            - --use-v2-block-manager

   2. Replace <NUM_SPECULATIVE_TOKENS> and <NGRAM_PROMPT_LOOKUP_MAX> with your own values.

      Note

      Inferencing throughput varies depending on the model used for speculating with n-grams.

4. To configure the vLLM model-serving runtime for speculative decoding with a draft model:

   1. Remove the --model argument:

      containers:
        - args:
            - --model=/mnt/models

   2. Add the following arguments:

      containers:
        - args:
            - --port=8080
            - --served-model-name={{.Name}}
            - --distributed-executor-backend=mp
            - --model=/mnt/models/<path_to_original_model>
            - --speculative-model=/mnt/models/<path_to_speculative_model>
            - --num-speculative-tokens=<NUM_SPECULATIVE_TOKENS>
            - --use-v2-block-manager

   3. Replace <path_to_speculative_model> and <path_to_original_model> with the paths to the speculative model and original model on your S3-compatible object storage.
   4. Replace <NUM_SPECULATIVE_TOKENS> with your own value.

5. To configure the vLLM model-serving runtime for multi-modal inferencing:

   1. Add the following arguments:

      containers:
        - args:
            - --trust-remote-code

      Note

      Only use the --trust-remote-code argument with models from trusted sources.

6. Click Update.

   The Serving runtimes page opens and shows the list of runtimes that are installed. Confirm that the custom model-serving runtime you updated is shown.

7. Deploy the model by using the custom runtime as described in Deploying models on the single-model serving platform.

Procedure

1. From the OpenShift AI dashboard, click Settings > Serving runtimes.

   The Serving runtimes page opens and shows the model-serving runtimes that are already installed and enabled.

2. Based on the runtime that you used to deploy your model, perform one of the following actions:

   • If you used the pre-installed TGIS Standalone ServingRuntime for KServe runtime, duplicate the runtime to create a custom version and then follow the remainder of this procedure. For more information about duplicating the pre-installed TGIS runtime, see Adding a custom model-serving runtime for the single-model serving platform.

   • If you were already using a custom TGIS runtime, click the action menu (⋮) next to the runtime and select Edit.

     The embedded YAML editor opens and shows the contents of the custom model-serving runtime.

3. Add or update the BATCH_SAFETY_MARGIN environment variable and set the value to 30. Similarly, add or update the ESTIMATE_MEMORY_BATCH_SIZE environment variable and set the value to 8.

   spec:
     containers:
       env:
       - name: BATCH_SAFETY_MARGIN
         value: 30
       - name: ESTIMATE_MEMORY_BATCH
         value: 8

   Note

   The BATCH_SAFETY_MARGIN parameter sets a percentage of free GPU memory to hold back as a safety margin to avoid OOM conditions. The default value of BATCH_SAFETY_MARGIN is 20. The ESTIMATE_MEMORY_BATCH_SIZE parameter sets the batch size used in the memory auto-tuning algorithm. The default value of ESTIMATE_MEMORY_BATCH_SIZE is 16.

4. Click Update.

   The Serving runtimes page opens and shows the list of runtimes that are installed. Observe that the custom model-serving runtime you updated is shown.

5. To redeploy the model for the parameter updates to take effect, perform the following actions:

   1. From the OpenShift AI dashboard, click Model Serving > Deployed Models.
   2. Find the model you want to redeploy, click the action menu (⋮) next to the model, and select Delete.
   3. Redeploy the model as described in Deploying models on the single-model serving platform.