mirror of
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Ayush GuptaandGitHub
1c19d3a6eb
* Fix Istio sidecar injection by moving from annotations to labels Signed-off-by: madmecodes <[email protected]> * Update Istio sidecar injection from annotations to labels across the codebase Replace annotations with labels for Istio sidecar injection according to Istio recommendations. Update conformance tests, examples, constants, composers, and utilities to use the new label-based approach consistently. Signed-off-by: madmecodes <[email protected]> * fix: Update SuggestionLabels function and composer implementation for Istio label injection Signed-off-by: madmecodes <[email protected]> * Fix linting issues in mpi-job-horovod.py Signed-off-by: madmecodes <[email protected]> * update: function moved from annotations to labels Signed-off-by: madmecodes <[email protected]> --------- Signed-off-by: madmecodes <[email protected]>
219 KiB
219 KiB
In [1]:
# Install required packages (Kubeflow Pipelines and Katib SDK).
!pip install kfp==1.8.12
!pip install kubeflow-katib==0.13.0Requirement already satisfied: kfp==1.8.12 in /opt/conda/lib/python3.8/site-packages (1.8.12) Requirement already satisfied: tabulate<1,>=0.8.6 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (0.8.9) Requirement already satisfied: google-cloud-storage<2,>=1.20.0 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (1.44.0) Requirement already satisfied: google-api-core!=2.0.*,!=2.1.*,!=2.2.*,!=2.3.0,<3.0.0dev,>=1.31.5 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (2.7.1) Requirement already satisfied: google-auth<2,>=1.6.1 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (1.35.0) Requirement already satisfied: cloudpickle<3,>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (2.0.0) Requirement already satisfied: strip-hints<1,>=0.1.8 in /opt/conda/lib/python3.8/site-packages (from kfp==1.8.12) (0.1.10) Requirement already satisfied: typer<1.0,>=0.3.2 in 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In [2]:
import kfp
import kfp.dsl as dsl
from kfp import components
from kubeflow.katib import ApiClient
from kubeflow.katib import V1beta1ExperimentSpec
from kubeflow.katib import V1beta1AlgorithmSpec
from kubeflow.katib import V1beta1ObjectiveSpec
from kubeflow.katib import V1beta1ParameterSpec
from kubeflow.katib import V1beta1FeasibleSpace
from kubeflow.katib import V1beta1TrialTemplate
from kubeflow.katib import V1beta1TrialParameterSpecIn [3]:
# You should define the Experiment name, namespace and number of training steps in the arguments.
def create_katib_experiment_task(experiment_name, experiment_namespace, training_steps):
# Trial count specification.
max_trial_count = 5
max_failed_trial_count = 3
parallel_trial_count = 2
# Objective specification.
objective = V1beta1ObjectiveSpec(
type="minimize",
goal=0.001,
objective_metric_name="loss"
)
# Algorithm specification.
algorithm = V1beta1AlgorithmSpec(
algorithm_name="random",
)
# Experiment search space.
# In this example we tune learning rate and batch size.
parameters = [
V1beta1ParameterSpec(
name="learning_rate",
parameter_type="double",
feasible_space=V1beta1FeasibleSpace(
min="0.01",
max="0.05"
),
),
V1beta1ParameterSpec(
name="batch_size",
parameter_type="int",
feasible_space=V1beta1FeasibleSpace(
min="80",
max="100"
),
)
]
# Experiment Trial template.
# TODO (andreyvelich): Use community image for the mnist example.
trial_spec = {
"apiVersion": "kubeflow.org/v1",
"kind": "TFJob",
"spec": {
"tfReplicaSpecs": {
"Chief": {
"replicas": 1,
"restartPolicy": "OnFailure",
"template": {
"metadata": {
"labels": {
"sidecar.istio.io/inject": "false"
}
},
"spec": {
"containers": [
{
"name": "tensorflow",
"image": "docker.io/liuhougangxa/tf-estimator-mnist",
"command": [
"python",
"/opt/model.py",
"--tf-train-steps=" + str(training_steps),
"--tf-learning-rate=${trialParameters.learningRate}",
"--tf-batch-size=${trialParameters.batchSize}"
]
}
]
}
}
},
"Worker": {
"replicas": 1,
"restartPolicy": "OnFailure",
"template": {
"metadata": {
"labels": {
"sidecar.istio.io/inject": "false"
}
},
"spec": {
"containers": [
{
"name": "tensorflow",
"image": "docker.io/liuhougangxa/tf-estimator-mnist",
"command": [
"python",
"/opt/model.py",
"--tf-train-steps=" + str(training_steps),
"--tf-learning-rate=${trialParameters.learningRate}",
"--tf-batch-size=${trialParameters.batchSize}"
]
}
]
}
}
}
}
}
}
# Configure parameters for the Trial template.
trial_template = V1beta1TrialTemplate(
primary_container_name="tensorflow",
trial_parameters=[
V1beta1TrialParameterSpec(
name="learningRate",
description="Learning rate for the training model",
reference="learning_rate"
),
V1beta1TrialParameterSpec(
name="batchSize",
description="Batch size for the model",
reference="batch_size"
),
],
trial_spec=trial_spec
)
# Create an Experiment from the above parameters.
experiment_spec = V1beta1ExperimentSpec(
max_trial_count=max_trial_count,
max_failed_trial_count=max_failed_trial_count,
parallel_trial_count=parallel_trial_count,
objective=objective,
algorithm=algorithm,
parameters=parameters,
trial_template=trial_template
)
# Create the KFP task for the Katib Experiment.
# Experiment Spec should be serialized to a valid Kubernetes object.
katib_experiment_launcher_op = components.load_component_from_url(
"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/katib-launcher/component.yaml")
op = katib_experiment_launcher_op(
experiment_name=experiment_name,
experiment_namespace=experiment_namespace,
experiment_spec=ApiClient().sanitize_for_serialization(experiment_spec),
experiment_timeout_minutes=60,
delete_finished_experiment=False)
return opIn [4]:
# This function converts Katib Experiment HP results to args.
def convert_katib_results(katib_results) -> str:
import json
import pprint
katib_results_json = json.loads(katib_results)
print("Katib results:")
pprint.pprint(katib_results_json)
best_hps = []
for pa in katib_results_json["currentOptimalTrial"]["parameterAssignments"]:
if pa["name"] == "learning_rate":
best_hps.append("--tf-learning-rate=" + pa["value"])
elif pa["name"] == "batch_size":
best_hps.append("--tf-batch-size=" + pa["value"])
print("Best Hyperparameters: {}".format(best_hps))
return " ".join(best_hps)In [5]:
# You should define the TFJob name, namespace, number of training steps, output of Katib and model volume tasks in the arguments.
def create_tfjob_task(tfjob_name, tfjob_namespace, training_steps, katib_op, model_volume_op):
import json
# Get parameters from the Katib Experiment.
# Parameters are in the format "--tf-learning-rate=0.01 --tf-batch-size=100"
convert_katib_results_op = components.func_to_container_op(convert_katib_results)
best_hp_op = convert_katib_results_op(katib_op.output)
best_hps = str(best_hp_op.output)
# Create the TFJob Chief and Worker specification with the best Hyperparameters.
# TODO (andreyvelich): Use community image for the mnist example.
tfjob_chief_spec = {
"replicas": 1,
"restartPolicy": "OnFailure",
"template": {
"metadata": {
"labels": {
"sidecar.istio.io/inject": "false"
}
},
"spec": {
"containers": [
{
"name": "tensorflow",
"image": "docker.io/liuhougangxa/tf-estimator-mnist",
"command": [
"sh",
"-c"
],
"args": [
"python /opt/model.py --tf-export-dir=/mnt/export --tf-train-steps={} {}".format(training_steps, best_hps)
],
"volumeMounts": [
{
"mountPath": "/mnt/export",
"name": "model-volume"
}
]
}
],
"volumes": [
{
"name": "model-volume",
"persistentVolumeClaim": {
"claimName": str(model_volume_op.outputs["name"])
}
}
]
}
}
}
tfjob_worker_spec = {
"replicas": 1,
"restartPolicy": "OnFailure",
"template": {
"metadata": {
"labels": {
"sidecar.istio.io/inject": "false"
}
},
"spec": {
"containers": [
{
"name": "tensorflow",
"image": "docker.io/liuhougangxa/tf-estimator-mnist",
"command": [
"sh",
"-c",
],
"args": [
"python /opt/model.py --tf-export-dir=/mnt/export --tf-train-steps={} {}".format(training_steps, best_hps)
],
}
],
}
}
}
# Create the KFP task for the TFJob.
tfjob_launcher_op = components.load_component_from_url(
"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/launcher/component.yaml")
op = tfjob_launcher_op(
name=tfjob_name,
namespace=tfjob_namespace,
chief_spec=json.dumps(tfjob_chief_spec),
worker_spec=json.dumps(tfjob_worker_spec),
tfjob_timeout_minutes=60,
delete_finished_tfjob=False)
return opIn [6]:
# You should define the model name, namespace, output of the TFJob and model volume tasks in the arguments.
def create_kfserving_task(model_name, model_namespace, tfjob_op, model_volume_op):
inference_service = '''
apiVersion: "serving.kubeflow.org/v1beta1"
kind: "InferenceService"
metadata:
name: {}
namespace: {}
annotations:
"sidecar.istio.io/inject": "false"
spec:
predictor:
tensorflow:
storageUri: "pvc://{}/"
'''.format(model_name, model_namespace, str(model_volume_op.outputs["name"]))
kfserving_launcher_op = components.load_component_from_url(
'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml')
kfserving_launcher_op(action="create", inferenceservice_yaml=inference_service).after(tfjob_op)In [7]:
name="mnist-e2e"
namespace="kubeflow-user-example-com"
training_steps="200"
@dsl.pipeline(
name="End to End Pipeline",
description="An end to end mnist example including hyperparameter tuning, train and inference"
)
def mnist_pipeline(name=name, namespace=namespace, training_steps=training_steps):
# Run the hyperparameter tuning with Katib.
katib_op = create_katib_experiment_task(name, namespace, training_steps)
# Create volume to train and serve the model.
model_volume_op = dsl.VolumeOp(
name="model-volume",
resource_name="model-volume",
size="1Gi",
modes=dsl.VOLUME_MODE_RWO
)
# Run the distributive training with TFJob.
tfjob_op = create_tfjob_task(name, namespace, training_steps, katib_op, model_volume_op)
# Create the KFServing inference.
create_kfserving_task(name, namespace, tfjob_op, model_volume_op)
# Run the Kubeflow Pipeline in the user's namespace.
kfp_client=kfp.Client()
run_id = kfp_client.create_run_from_pipeline_func(mnist_pipeline, namespace=namespace, arguments={}).run_id
print("Run ID: ", run_id)The finished Pipeline should look as follows.
In [8]:
import numpy as np
from PIL import Image
import requests
# Pipeline Run should be succeeded.
kfp_run = kfp_client.get_run(run_id=run_id)
if kfp_run.run.status == "Succeeded":
print("Run {} has been Succeeded\n".format(run_id))
# Specify the image URL here.
image_url = "https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/kubeflow-pipelines/images/9.bmp"
image = Image.open(requests.get(image_url, stream=True).raw)
data = np.array(image.convert('L').resize((28, 28))).astype(np.float).reshape(-1, 28, 28, 1)
data_formatted = np.array2string(data, separator=",", formatter={"float": lambda x: "%.1f" % x})
json_request = '{{ "instances" : {} }}'.format(data_formatted)
# Specify the prediction URL. If you are runing this notebook outside of Kubernetes cluster, you should set the Cluster IP.
url = "http://{}-predictor-default.{}.svc.cluster.local/v1/models/{}:predict".format(name, namespace, name)
response = requests.post(url, data=json_request)
print("Prediction for the image")
display(image)
print(response.json())
Run a6b4800d-6b45-43d0-979d-7c2e2dcc38aa has been Succeeded Prediction for the image
<ipython-input-8-e6f2009e4aa7>:13: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.
Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
data = np.array(image.convert('L').resize((28, 28))).astype(np.float).reshape(-1, 28, 28, 1)
{'predictions': [{'predictions': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], 'classes': 9}]}
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