Kaptain DocumentationNOTE: All tutorials in Jupyter Notebook format are available for download. You can either download them to a local computer and upload to the running Jupyter Notebook or run the following command from a Jupyter Notebook Terminal running in your Kaptain installation:
curl -L https://downloads.d2iq.com/kaptain/d2iq-tutorials-1.3.0.tar.gz | tar xz
NOTE: These notebook tutorials have been built for and tested on D2iQ's Kaptain. Without the requisite Kubernetes operators and custom Docker images, these notebooks will likely not work.
Developing and deploying scikit-learn models with Kaptain SDK
Introduction
This notebook shows how to build and deploy ML models by using different popular machine learning frameworks. This example will be based on scikit-learn library, but because model building will be handled by Kubernetes Job, you can choose to use any other ML library (that can be installed with pip). Currently, the SDK supports XGBoost, Onnx and LightGBM as well as PyTorch and TensorFlow for model deployment. See Kaptain documentation for more info.
To perform distributed training on a cluster’s resources, conduct experiments with multiple parallel trials to obtain the best hyperparameters, and deploying a trained or tuned model typically requires additional steps, such as building a Docker image and providing framework-specific specifications for Kubernetes. This places the burden on each data scientist to learn all the details of all the components.
Instead of doing all the work, using the Kaptain SDK, you can train, tune, and deploy from within a notebook without having to worry about framework specifics, Kubeflow-native SDKs, or even thinking about Kubernetes
What You Will Learn
The Kaptain SDK provides a data science-friendly user experience from within a notebook that hides all the specifics, and focuses on the model as the main abstraction. A model can be trained, tuned, deployed, and tracked.
The example is based on scikit-learn, but it works equally for other data science frameworks. The original scikit-learn example code can be found in the tutorials Recognizing hand-written digits.
The SDK relies on MinIO, an open-source S3-compliant object storage tool, that is already included with your Kaptain installation.
What You Will Need
All you need is this notebook.
Prerequisites
NOTE: This notebook requires Kaptain SDK 1.0.0 or later.
Before proceeding, check you are using the correct notebook image, that is, scikit-learn is available:
%%sh
set -o errexit
pip list | grep scikit-learn
pip list | grep kaptain
How to Create a Docker Credentials File and Kubernetes Secret
For the tutorial you will need getpass to provide a password interactively without it being immediately visible.
It is a standard Python library, so there is no need to install it.
A simple import will suffice.
WARNING: Please do not store passwords directly in notebooks. Ideally, credentials are stored safely inside secrets management solutions or provided with service accounts. Please check the section on how to manage secrets in the official Kaptain documentation for more details on how to set up Docker credentials that you can attach to a notebook server. This notebook should be used for demonstration purposes only!
Please type in the container registry username by running the next cell:
import json
import getpass
import pathlib
from base64 import b64encode
docker_user = input()
Enter the password for the Docker container registry when prompted by executing the following code:
docker_password = getpass.getpass()
With these details, base64-encode the username and password and create a Docker configuration file as follows:
# Create a folder to store the Docker configuration file
docker_config_folder = pathlib.Path.joinpath(pathlib.Path.home(), ".docker")
docker_config_folder.mkdir(exist_ok=True)
# Write the base64-encoded credentials to the configuration file
docker_credentials = b64encode(f"{docker_user}:{docker_password}".encode()).decode()
config = {"auths": {"https://index.docker.io/v1/": {"auth": docker_credentials}}}
with open(f"{docker_config_folder}/config.json", "w") as outfile:
outfile.write(json.dumps(config))
Define the trainer file
This step writes a file that will be run inside of the training container rather than than inside of the notebook kernel. It takes the form of a parameterized training script. The training parameters default inside the training program with the user-defined values being passed in when the container is run. The script is responsible for accessing any datasets that are required for the training run into the container.
To use the Kaptain SDK, you need to add code to do three things to the original model code:
- In the block at the end of main() to save the trained model to the cluster’s built-in object storage, MinIO.
- Also in the block at the end of main(), to record the metrics of interest.
- Parameters that will change need to be retrieved from the CLI
%%writefile training.py
import os
import argparse
import time
from sklearn import datasets, svm, metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from joblib import dump
from kaptain.platform.model_export_util import ModelExportUtil
from kaptain.platform.metadata_util import MetadataUtil
def main():
parser = argparse.ArgumentParser()
# Arguments that will be passed to your training code
parser.add_argument(
"--gamma",
type=float,
default=0.001,
help="Kernel coefficient"
)
parser.add_argument(
"--c",
type=float,
default=1.0,
help="Regularization parameter"
)
args, _ = parser.parse_known_args()
# Load the MNIST digit dataset
digits = datasets.load_digits()
# flatten the images
n_samples = len(digits.images)
data = digits.images.reshape((n_samples, -1))
# Create a classifier: a support vector classifier
clf = svm.SVC(C=args.c, gamma=args.gamma)
# Split data into 50% train and 50% test subsets
X_train, X_test, y_train, y_test = train_test_split(
data, digits.target, test_size=0.5, shuffle=False
)
# Learn the digits on the train subset
clf.fit(X_train, y_train)
# Predict the value of the digit on the test subset
predicted = clf.predict(X_test)
print(
f"Classification report for classifier {clf}:\n"
f"{metrics.classification_report(y_test, predicted)}\n"
)
cm = metrics.confusion_matrix(y_test, predicted)
print(f"Confusion matrix:\n{cm}")
score = clf.score(X_test, y_test)
print(f"\nModel accuracy: {score}")
model_upload_path = os.getenv("TRAINED_MODEL_UPLOAD_PATH")
if model_upload_path:
model_file_name = "model.joblib"
print(f"Exporting model to {model_upload_path}/{model_file_name} ...")
dump(clf, model_file_name)
ModelExportUtil().upload_model(model_file_name)
print("Export completed.")
# Record model accuracy metrics
MetadataUtil.record_metrics({"accuracy": score})
# Because the job executes too quickly, we need to wait to allow metric collector container fetch the metrics
time.sleep(10)
if __name__ == "__main__":
main()
Describe the model
The central abstraction of the Kaptain SDK is a Model class that encapsulates all the configuration and high-level APIs required for the model training, tuning, and serving. Prior to creating an instance of the Model class, let’s consider an example where we need to specify additional dependecies required for model training, tuning and serving, and also provide minimal required configuration for the model server.
Model dependencies can be provided via pip requirements.txt. For example:
%%writefile requirements.txt
scikit-learn>0.24.2
Below is an example of the minimally required serving configuration for a scikit-learn model. To learn more about advanced configuration options, consult the Kaptain SDK documentation.
serving_config = {
"requirements_file": "requirements.txt", # the model dependenies file. You can provide a different file for serving-specific dependencies
}
Finally, a Model instance requires providing a base Docker image (base_image) to use for model training, a target Docker repository and image name (image_name) to publish trainer code to (packed in a Docker image too), and a list of additional files that are required for the model code or the trainer code (extra_files).
base_image = "mesosphere/kubeflow:1.3.0-base"
image_name = "mesosphere/kubeflow:mnist-sklearn-sdk"
# name of the file with additional python packages to install into the model image (e.g. "requirements.txt")
requirements = "requirements.txt"
import os
from kaptain import envs
from kaptain.model.models import Model
from kaptain.model.frameworks import ModelFramework
envs.VERBOSE = True
envs.DEBUG = True
model = Model(
id="dev/mnist",
name="MNIST",
description="MSNIT Model",
version="0.0.1",
framework=ModelFramework.SKLEARN,
framework_version="0.24.2",
main_file="training.py",
image_name=image_name,
base_image=base_image,
serving_config=serving_config,
requirements=requirements,
)
The id is a unique identifier of the model.
The identifier shown indicates it is an MNIST model in development.
The fields member and description are for humans: to inform your colleagues and yourself of what the model is about.
version is the models’ own version, so it is easy to identify models by their iteration.
The framework and framework_version are for the time being human-friendly metadata.
Since a Docker image is built in the background when you train or tune a Model instance, a base_image has to be provided.
The name of the final image image_name must be provided with or without image tag.
If the tag is omitted, a concatenation of model id, framework, and framework_version is used.
The main_file specifies the name of file that contains the model code, that is, trainer.py for the purposes of this tutorial.
To specify additional Python packages required for training or serving, provide the path to your requirements file via the requirements parameter of the Model class. Details on the format of the requirements file can be found in the pip official documentation.
More details are available with ?Model.
Train/tune the model
Training the model is as easy as the following function call:
memory = "1G"
cpu = "0.5"
model.train(cpu=cpu, memory=memory, hyperparameters={})
2021-12-10 19:29:31,544 kaptain-log[INFO]: Skipping image build for the model - the image 'mesosphere/kubeflow:mnist-sklearn-sdk' with the same contents has already been published to the registry.
2021-12-10 19:29:31,546 kaptain-log[INFO]: Creating secret train-d3adabf95994484a in namespace demo.
2021-12-10 19:29:31,562 kaptain-log[INFO]: Creating secret train-registry-068ad4ec6b93f037 in namespace demo.
2021-12-10 19:29:31,570 kaptain-log[INFO]: Submitting a new training job "mnist-job-17921389".
2021-12-10 19:29:31,571 kaptain-log[INFO]: Creating job mnist-job-17921389 in namespace demo.
2021-12-10 19:29:31,589 kaptain-log[INFO]: Waiting for the training job to complete...
2021-12-10 19:29:31,767 kaptain-log[INFO]: Waiting for Master Node Training Model to start...
2021-12-10 19:29:33,483 kaptain-log[INFO]: Master Node Training Model started in pod: mnist-job-17921389-c5jgc.
2021-12-10 19:29:36,367 kaptain-log[INFO]: [mnist-job-17921389-c5jgc/sklearn] logs:
Classification report for classifier SVC(gamma=0.001):
precision recall f1-score support
0 1.00 0.99 0.99 88
1 0.99 0.97 0.98 91
2 0.99 0.99 0.99 86
3 0.98 0.87 0.92 91
4 0.99 0.96 0.97 92
5 0.95 0.97 0.96 91
6 0.99 0.99 0.99 91
7 0.96 0.99 0.97 89
8 0.94 1.00 0.97 88
9 0.93 0.98 0.95 92
accuracy 0.97 899
macro avg 0.97 0.97 0.97 899
weighted avg 0.97 0.97 0.97 899
Confusion matrix:
[[87 0 0 0 1 0 0 0 0 0]
[ 0 88 1 0 0 0 0 0 1 1]
[ 0 0 85 1 0 0 0 0 0 0]
[ 0 0 0 79 0 3 0 4 5 0]
[ 0 0 0 0 88 0 0 0 0 4]
[ 0 0 0 0 0 88 1 0 0 2]
[ 0 1 0 0 0 0 90 0 0 0]
[ 0 0 0 0 0 1 0 88 0 0]
[ 0 0 0 0 0 0 0 0 88 0]
[ 0 0 0 1 0 1 0 0 0 90]]
Model accuracy: 0.9688542825361512
Exporting model to s3://kaptain/models/dev/mnist/trained/fc6f2d4e16fd4b2cbd5d85e49d5b0b55/model.joblib ...
Export completed.
2021-12-10 19:29:38,400 kaptain-log[INFO]: Deleting secret train-d3adabf95994484a in namespace demo.
2021-12-10 19:29:38,415 kaptain-log[INFO]: Deleting secret train-registry-068ad4ec6b93f037 in namespace demo.
2021-12-10 19:29:38,426 kaptain-log[INFO]: Model training is completed.
The default gpus argument is 0, but it is shown here as an explicit option.
Use ?Model.train to see all supported arguments.
NOTE: When resource quotas are set for a namespace, users have to specify cpu and memory explicitly in the SDK.
Otherwise, tasks such as training and tuning will fail with Error creating: pods ... is forbidden: failed quota: kf-resource-quota: must specify cpu,memory.
These fields are optional when resource quotas are not set.
In case the issue appears for other types of workloads, it is recommended to configure defaults for the user namespace using the Limit Range.
The low accuracy of the model is to make the demonstration of distributed training quicker, as in the next section the model’s hyperparameters are optimized anyway.
Run an experiment
trials = 12
parallel_trials = 2
from kaptain.hyperparameter.domains import Double, Discrete
hyperparams = {"--gamma": Double(0.0001, 0.001), "--c": Double(0.01, 1.00)}
model.tune(
trials=trials,
parallel_trials=parallel_trials,
cpu=cpu,
memory=memory,
hyperparameters=hyperparams,
objectives=["accuracy"],
objective_goal=0.99,
)
2021-12-10 19:29:39,806 kaptain-log[INFO]: Skipping image build for the model - the image 'mesosphere/kubeflow:mnist-sklearn-sdk' with the same contents has already been published to the registry.
2021-12-10 19:29:39,813 kaptain-log[INFO]: Creating secret tune-b07d30db2ea16212 in namespace demo.
2021-12-10 19:29:39,827 kaptain-log[INFO]: Creating secret tune-registry-a3a9649180ddb293 in namespace demo.
2021-12-10 19:29:39,835 kaptain-log[INFO]: Creating experiment mnist-tune-0174088b in namespace demo
2021-12-10 19:29:39,874 kaptain-log[INFO]: Experiment mnist-tune-0174088b has been created.
Progress: 100%|█████████████████████████|12/12 [time: 01:05, accuracy: 0.9666, trials running: 0, pending: 0, failed: 0, killed: 0]
2021-12-10 19:30:45,017 kaptain-log[INFO]: Model tuning completed, final status: Succeeded
2021-12-10 19:30:45,024 kaptain-log[INFO]: Deleting secret tune-b07d30db2ea16212 in namespace demo.
2021-12-10 19:30:45,039 kaptain-log[INFO]: Deleting secret tune-registry-a3a9649180ddb293 in namespace demo.
2021-12-10 19:30:45,048 kaptain-log[INFO]: Experiment results:
parameters: {'--gamma': '0.0009033059385183185', '--c': '0.8628663122572235'}, best_trial_name: mnist-tune-0174088b-sh2xfkbt
2021-12-10 19:30:45,048 kaptain-log[INFO]: Copying saved model with the best metrics from the trial to the target location.
2021-12-10 19:30:45,190 kaptain-log[INFO]: Removing intermediate trial models from the storage.
Verify the Model is Exported to MinIO
%%sh
set -o errexit
minio_accesskey=$(kubectl get secret minio-creds-secret -o jsonpath="{.data.accesskey}" | base64 --decode)
minio_secretkey=$(kubectl get secret minio-creds-secret -o jsonpath="{.data.secretkey}" | base64 --decode)
mc --no-color alias set minio http://minio.kubeflow ${minio_accesskey} ${minio_secretkey}
mc --no-color ls -r minio/kaptain/models
Added `minio` successfully.
[2021-12-09 23:26:38 UTC] 321KiB dev/mnist/deploy/25f6e33b86b54499820124695435e6f8/model.joblib
[2021-12-09 23:25:24 UTC] 337KiB dev/mnist/trained/b3ca1937766b4698b909bbd5f0f8073c/model.joblib
[2021-12-09 23:26:38 UTC] 321KiB dev/mnist/tuned/d039aa7b4bb64f2bb71490bb046cfd00/model.joblib
Deploy the Model
Model is ready to be deployed. A trained model can be deployed as an auto-scalable inference service with a single call. When providing additional serving dependencies, make sure to specify sufficient resources (mostly memory) in order for the server to install them without issues.
In this tutorial, we will be using V2 protocol for sklearn predictor.
serving_config = {"protocol_version": "v2"}
model.serving_config = serving_config
model.deploy(cpu="0.5", memory="500M", replace=True)
2021-12-10 19:30:45,529 kaptain-log[INFO]: Building deployment artifacts and uploading to s3://kaptain/models/dev/mnist/deploy/b7d7f70cee7b4918ba5a71d9784deff4
2021-12-10 19:30:45,641 kaptain-log[INFO]: Deploying model from s3://kaptain/models/dev/mnist/deploy/b7d7f70cee7b4918ba5a71d9784deff4
2021-12-10 19:30:45,644 kaptain-log[INFO]: Reading secrets dev-mnist-secret in namespace demo.
2021-12-10 19:30:45,656 kaptain-log[INFO]: Creating secret dev-mnist-secret in namespace demo.
2021-12-10 19:30:45,667 kaptain-log[INFO]: Reading service account dev-mnist-service-account in namespace demo.
2021-12-10 19:30:45,678 kaptain-log[INFO]: Creating service account dev-mnist-service-account in namespace demo.
NAME READY PREV LATEST URL
dev-mnist Unknown
dev-mnist Unknown
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist Unknown 0 100
dev-mnist True 0 100 http://dev-mnist.demo.example.com
2021-12-10 19:31:35,472 kaptain-log[INFO]: Model dev/mnist deployed successfully. Cluster URL: http://dev-mnist.demo.svc.cluster.local/v2/models/dev-mnist/infer
Test the Model Endpoint
import codecs, json
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
image_index = 7
digits = load_digits()
plt.matshow(digits.images[image_index], cmap="binary")
n_samples = len(digits.images)
data = digits.images.reshape(n_samples, -1).tolist()
serving_req = {
"inputs": [
{
"name": "predict",
"shape": [1, 64],
"datatype": "FP32",
"data": data[image_index],
}
]
}
with open("input.json", "w") as json_file:
json.dump(serving_req, json_file)
%%bash
set -o errexit
model_name="dev-mnist"
namespace=$(cat /var/run/secrets/kubernetes.io/serviceaccount/namespace)
url=http://${model_name}.${namespace}.svc.cluster.local/v2/models/${model_name}/infer
curl --location \
--silent \
--fail \
--retry 10 \
--retry-delay 10 \
$url \
-d@input.json | python -m json.tool
{
"model_name": "dev-mnist",
"model_version": null,
"id": "ce4bc183-4f26-4fed-9894-8beb0cf153e4",
"parameters": null,
"outputs": [
{
"name": "predict",
"shape": [
1
],
"datatype": "FP32",
"parameters": null,
"data": [
7
]
}
]
}
We can see that the class with label “7” has the largest probability; the neural network correctly predicts the image to be a number 7.
This tutorial image includes code from the MinIO Project (“MinIO”), which is © 2015-2021 MinIO, Inc. MinIO is made available subject to the terms and conditions of the GNU Affero General Public License 3.0. The complete source code for the versions of MinIO packaged with Kaptain 1.3.0 are available at these URLs: https://github.com/minio/minio/tree/RELEASE.2020-12-03T05-49-24Z and https://github.com/minio/minio/tree/RELEASE.2021-07-30T00-02-00Z.