Definition of the GCAIP Training Backend
SingleGPUTrainingGCAIPBackend(project: str, job_dir: str, gpu_type: str = 'K80', machine_type: str = 'n1-standard-4', image: str = 'eu.gcr.io/maiot-zenml/zenml:cuda-0.2.0', job_name: str = 'train_1611569655', region: str = 'europe-west1', python_version: str = '3.7', max_running_time: int = 7200, **kwargs)
: Runs a TrainerStep on Google Cloud AI Platform.
A training backend can be used to efficiently train a machine learning model on large amounts of data. This triggers a Training job on the Google Cloud AI Platform service: https://cloud.google.com/ai-platform. This backend is meant for a training job with a single GPU only. The user has a choice of three GPUs, specified in the GCPGPUTypes Enum. An opinionated wrapper around a GCAIP training job. Args: project: The GCP project in which to run the job. job_dir: A bucket where to store some metadata while training. gpu_type: The type of gpu. machine_type: The type of machine to use. This must conform to the GCP compatability matrix with the gpu_type. See details here: https://cloud.google.com/ai-platform/training/docs/using -gpus#compute-engine-machine-types-with-gpu image: The Docker image with which to run the job. job_name: The name of the job. region: The GCP region to run the job in. python_version: The Python version for the job. max_running_time: The maximum running time of the job in seconds. **kwargs: ### Ancestors (in MRO) * zenml.core.backends.training.training_local_backend.TrainingLocalBackend * zenml.core.backends.base_backend.BaseBackend ### Class variables `BACKEND_TYPE` : ### Methods `get_custom_config(self)` : Return a dict to be passed as a custom_config to the Trainer. `get_executor_spec(self)` : Return a TFX Executor spec for the Trainer Component.