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name="my-in-coop2",
image="library/bash:4.4.23",
command=["sh", "-c"],
arguments=["echo op2 %s" % item.B_b],
)
op_out = dsl.ContainerOp(
name="my-out-cop",
image="library/bash:4.4.23",
command=["sh", "-c"],
arguments=["echo %s" % my_pipe_param],
)
if __name__ == '__main__':
kfp.compiler.Compiler().compile(pipeline, __file__ + '.yaml')
name="create-pvc",
resource_name="my-pvc",
modes=dsl.VOLUME_MODE_RWO,
size=size
)
cop = dsl.ContainerOp(
name="cop",
image="library/bash:4.4.23",
command=["sh", "-c"],
arguments=["echo foo > /mnt/file1"],
pvolumes={"/mnt": vop.volume}
)
if __name__ == '__main__':
kfp.compiler.Compiler().compile(volumeop_basic, __file__ + '.yaml')
resource_name="vol3",
data_source=step2_snap.snapshot,
size=step2_snap.outputs["size"]
)
step3 = dsl.ContainerOp(
name="step3_output",
image="library/bash:4.4.23",
command=["cat", "/data/full"],
pvolumes={"/data": vop3.volume}
)
if __name__ == "__main__":
import kfp.compiler as compiler
compiler.Compiler().compile(volume_snapshotop_rokurl, __file__ + ".tar.gz")
output_dir: An optional output directory into which to output the pipeline
definition files. Defaults to the current working directory.
output_filename: An optional output file name for the pipeline definition
file. Defaults to pipeline_name.tar.gz when compiling a TFX pipeline.
Currently supports .tar.gz, .tgz, .zip, .yaml, .yml formats. See
https://github.com/kubeflow/pipelines/blob/181de66cf9fa87bcd0fe9291926790c400140783/sdk/python/kfp/compiler/compiler.py#L851
for format restriction.
config: An optional KubeflowDagRunnerConfig object to specify runtime
configuration when running the pipeline under Kubeflow.
"""
if config and not isinstance(config, KubeflowDagRunnerConfig):
raise TypeError('config must be type of KubeflowDagRunnerConfig.')
super(KubeflowDagRunner, self).__init__(config or KubeflowDagRunnerConfig())
self._output_dir = output_dir or os.getcwd()
self._output_filename = output_filename
self._compiler = compiler.Compiler()
self._params = [] # List of dsl.PipelineParam used in this pipeline.
self._deduped_parameter_names = set() # Set of unique param names used.
command=['sh', '-c'],
arguments=['echo "$0"', text]
)
@dsl.pipeline(
name='Sequential pipeline',
description='A pipeline with two sequential steps.'
)
def sequential_pipeline(url='gs://ml-pipeline-playground/shakespeare1.txt'):
"""A pipeline with two sequential steps."""
download_task = gcs_download_op(url)
echo_task = echo_op(download_task.output)
if __name__ == '__main__':
kfp.compiler.Compiler().compile(sequential_pipeline, __file__ + '.yaml')
image='gcr.io/constant-cubist-173123/inference_server/ml_predict:5',
command=['python3', 'predict.py'],
arguments=[
'--model_bin', model_bin,
'--model_xml', model_xml,
'--input_numpy_file', input_numpy_file,
'--label_numpy_file', label_numpy_file,
'--batch_size', batch_size,
'--scale_div', scale_div,
'--scale_sub', scale_sub,
'--output_folder', generated_model_dir],
file_outputs={})
if __name__ == '__main__':
import kfp.compiler as compiler
compiler.Compiler().compile(openvino_predict, __file__ + '.tar.gz')
def deploy_pipeline_to_kfp(self):
import kfp.compiler as compiler
import kfp
# import the generated pipeline code
# add temp folder to PYTHONPATH
sys.path.append(self.temp_dirdirpath)
from pipeline_code import auto_generated_pipeline
pipeline_filename = self.pipeline_name + '.pipeline.tar.gz'
compiler.Compiler().compile(auto_generated_pipeline, pipeline_filename)
# Get or create an experiment and submit a pipeline run
client = kfp.Client(host=self.kfp_url)
list_experiments_response = client.list_experiments()
experiments = list_experiments_response.experiments
print(experiments)
if not experiments:
# The user does not have any experiments available. Creating a new one
experiment = client.create_experiment(self.pipeline_name + ' experiment')
else:
experiment = experiments[-1] # Using the last experiment
# Submit a pipeline run
run_name = self.pipeline_name + ' run'
}
""")
seldon_serving_json = seldon_serving_json_template.substitute({ 'dockerreposerving': str(docker_repo_serving),'dockertagserving': str(docker_tag_serving),'modelpvc': modelvolop.outputs["name"]})
seldon_deployment = json.loads(seldon_serving_json)
serve = dsl.ResourceOp(
name='serve',
k8s_resource=seldon_deployment,
success_condition='status.state == Available'
).after(train)
if __name__ == "__main__":
import kfp.compiler as compiler
compiler.Compiler().compile(mnist_tf_volume, __file__ + ".tar.gz")