The AI Platform training service allows you to train models using a wide range of different customization options.
You can use the following features:
- Running a training job using many different machine types
- Use custom Docker containers with your preferred ML Framework
- Use GPUs
- Use TPUs
- Hyperparameter tuning
- Distributed training
You can also select different ways to customize your training application. You can submit your input data for AI Platform to train using a built-in algorithm (beta). If the built-in algorithms do not fit your use case, you can submit your own training application to run on AI Platform, or build a custom container (beta) with your training application and its dependencies to run on AI Platform.
This folder covers different functionality available in different frameworks:
This folder covers different functionality available AI Platform Training, the following samples reflect the available features in AI Platform:
The AI Platform training service allows you to train models using a wide range of different customization options. You can select many different machine types to power your training jobs, enable distributed training, use hyperparameter tuning, and accelerate with GPUs and TPUs.
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- base Standard code to perform AI Platform Training using TensorFlow Estimators using CPU.
- census TF Keras A Binary classification model using with TF Keras and AI Platform Trainining.
- TPU Uses Cloud TPU for Model Training.
- Hyperparameter tuning Use Hyperparameter tuning.
- Distributed training Uses Distributed Training using TensorFlow Distribution strategy.
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- base Standard code to perform AI Platform Training using Sci-kit learn using CPU.
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- base Standard code to perform AI Platform Training using XGBoost.