custom container

Related topics

• custom container is an alternative to prebuilt container From the official documentation: “Choose whether you plan to use a [custom container](/gemini-enterprise-agent-platform/machine-learning/training/create-custom-container) or a [prebuilt container](/gemini-enterprise-agent-platform/machine-learning/training/pre-built-containers) for training:”
• Cloud Storage works with custom container From the official documentation: “The container can write results to a file named `results.json` (in JSON format), located in the Cloud Storage bucket provided by Cloud Deploy.”
• custom container is an alternative to prebuilt containers From the official documentation: “You can choose to run your training code with one of these containers, or create a custom container that has your training code and dependencies pre-installed.”
• Agent Platform works with Cloud Storage From the official documentation: “Load the model's `state_dict` object into Cloud Storage by using a Agent Platform custom job:”
• Cloud Storage works with Gemini Enterprise Agent Platform From the official documentation: “This page describes how to use Gemini Enterprise Agent Platform to export your image AutoML Edge models to Cloud Storage.”
• prebuilt containers is part of Gemini Enterprise Agent Platform From the official documentation: “Gemini Enterprise Agent Platform provides Docker container images that you run as *prebuilt containers* for serverless training.”
• Gemini Enterprise Agent Platform depends on Cloud Storage From the official documentation: “Certain tasks in Agent Platform require that you use additional Google Cloud products besides Agent Platform. For example, in most cases, you must use Cloud Storage and Artifact Registry when you create a custom training pipeline.”
• prebuilt containers is part of Agent Platform From the official documentation: “Agent Platform provides prebuilt containers with the following machine learning (ML) frameworks: PyTorch, scikit-learn, TensorFlow, and XGBoost.”