> For the complete documentation index, see [llms.txt](https://docs.tonic.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tonic.ai/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/textual-config-ml-roberta-worker-count.md). # Configuring the number of workers for machine learning and RoBERTa ## Recommended RAM per worker For each worker, we recommend 3GB of GPU RAM. ## Configuring the number of machine learning workers The `TEXTUAL_ML_WORKERS` environment variable specifies the number of workers to use within the `textual-ml` container. The default value is 1. Having multiple workers allows for parallelization of machine learning tasks. The number of required workers is also affected by the [number of jobs that each worker can run simultaneously](/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/config-processing-parallelism.md). ## Configuring the number of RoBERTa workers The `TEXTUAL_ROBERTA_WORKERS` environment variable specifies the number of workers to use within the `textual-roberta` container. The default value is 1. Having multiple workers allows for parallelization of inferences with NER models. When you deploy Textual with Kubernetes on GPUs, parallelization allows the `textual-roberta` container to fully utilize the GPU. The number of required workers is also affected by the [number of jobs that each worker can run simultaneously](https://docs.tonic.ai/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/config-processing-parallelism). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.tonic.ai/textual/textual-install-administer/configuring-textual/general-instance-and-processing-settings/textual-config-ml-roberta-worker-count.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.