SaschaHeyer

.add_node_selector_constraint(
  label_name="cloud.google.com/gke-accelerator", 
  value="NVIDIA_TESLA_T4"
)
.set_gpu_limit(1)

SaschaHeyer

def train_model(
    parameter_a: str,
    dataset_artifact: Input[Dataset],
    model_artifact: Output[Model]
):
    data = pd.read_csv(dataset_artifact.path)
    model.save_model(model_artifact.path)

SaschaHeyer

@component()
def train(metrics: Output[Metrics]):
  metrics.log_metric("f1", 0.94) # static value for demo purpose

SaschaHeyer

@component()
def evaluate(
    smetrics: Output[Metrics]
    metrics: Output[ClassificationMetrics],
):
  smetrics.log_metric("f1", float(f1))

  metrics.log_confusion_matrix(...)
  metrics.log_roc_curve(...)

SaschaHeyer

todo

SaschaHeyer

from google.cloud.aiplatform import pipeline_jobs

aiplatform.init(project='sascha-playground-doit',
                location='us-central1')

job = pipeline_jobs.PipelineJob(
    display_name="basic-pipeline",
    template_path="basic_pipeline.json"
)

SaschaHeyer

@component(output_component_file="gpu_training.yaml", 
           base_image="gcr.io/deeplearning-platform-release/tf2-gpu.2-6") 

SaschaHeyer

@component(
    packages_to_install=["google-cloud-aiplatform==1.3.0", "pandas==1.3.4"],
)

SaschaHeyer

job = pipeline_jobs.PipelineJob(
    display_name="basic-pipeline",
    template_path="basic_pipeline.json",
    parameter_values={"a": "stres", "b": "sed"},
)

SaschaHeyer

@dsl.pipeline()
def pipeline():
    reverse_op = kfp.components.load_component_from_file('...')
    reverse_io = kfp.components.load_component_from_url('...')