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Build your Notebook

This notebook requires Kaptain SDK 1.3.x or later.

Kaptain supports training a model in one cluster, uploading the model and artifacts to a shared object storage (such as S3), and deploying in another cluster. In this notebook, you will go through building a simple model based off the MNIST dataset and uploading that model and training state to S3, so you can deploy that model in another cluster.

What You Need

Ensure you go through all steps included in the prerequisites page:

  1. Create a Docker secret and an AWS credentials secret.

  2. Create a PodDefault configuration referencing the created secrets.

  3. Launch a Jupyter notebook server with said PodDefault configuration.

You will be able to open this notebook after launching the notebook server.

Ensure You Are Ready to Start

Before proceeding, let's verify that the notebook server was configured and launched correctly:

  1. Ensure you are using the correct notebook image, by verifying that TensorFlow is available:

    CODE 
    %%sh
pip list | grep tensorflow

    Output:

    CODE 
    tensorflow                   2.9.1
tensorflow-datasets          4.5.2
tensorflow-estimator         2.9.0
tensorflow-io-gcs-filesystem 0.27.0
tensorflow-metadata          1.10.0

  2. Ensure that the docker secret is mounted. You should not see an error:

    CODE 
    %%sh
ls -la ~/.docker/config.json

    Output:

    CODE 
    lrwxrwxrwx 1 root istio 18 Oct  6 07:45 /home/kubeflow/.docker/config.json -> ..data/config.json

  3. Verify that the AWS environment variables are set. You should see AWS_ACCESS_KEY_ID, AWS_REGION, and AWS_SECRET_ACCESS_KEY:

    CODE 
    %%sh
set | egrep ^AWS_ | cut -f 1 -d '='

    Output:

    CODE 
    AWS_ACCESS_KEY_ID
AWS_REGION
AWS_SECRET_ACCESS_KEY

Adapt the Model Code

To use the Kaptain SDK, you need to add two lines of code to the original model code:

  1. One right after the model training (here: Keras' fit method), to save the trained model to the configured object storage, S3.

  2. Another right after the model evaluation (here: Keras' evaluate method), to record the metrics of interest.

    CODE 
    %%writefile trainer.py
import argparse
import logging

import tensorflow as tf
import tensorflow_datasets as tfds

from kaptain.platform.model_export_util import ModelExportUtil
from kaptain.platform.metadata_util import MetadataUtil

logging.getLogger().setLevel(logging.INFO)


def get_datasets(buffer_size):
    datasets, ds_info = tfds.load(name="mnist", data_dir="datasets", download=False, with_info=True, as_supervised=True)
    mnist_train, mnist_test = datasets["train"], datasets["test"]

    def scale(image, label):
        image = tf.cast(image, tf.float32) / 255.0
        return image, label

    train_dataset = mnist_train.map(scale).cache().shuffle(buffer_size).repeat()
    test_dataset = mnist_test.map(scale)

    return train_dataset, test_dataset


def compile_model(args):
    model = tf.keras.Sequential(
        [
            tf.keras.layers.Conv2D(32, 3, activation="relu", input_shape=(28, 28, 1)),
            tf.keras.layers.MaxPooling2D(),
            tf.keras.layers.Flatten(),
            tf.keras.layers.Dense(64, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
        ]
    )
    model.compile(
        loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
        optimizer=tf.keras.optimizers.SGD(
            learning_rate=args.learning_rate, momentum=args.momentum
        ),
        metrics=["accuracy"],
    )
    return model

def main():
    parser = argparse.ArgumentParser(description="TensorFlow MNIST Trainer")
    parser.add_argument(
        "--batch-size",
        type=int,
        default=64,
        metavar="N",
        help="Batch size for training (default: 64)",
    )
    parser.add_argument(
        "--buffer-size",
        type=int,
        default=10000,
        metavar="N",
        help="Number of training examples to buffer before shuffling" "default: 10000)",
    )
    parser.add_argument(
        "--epochs",
        type=int,
        default=5,
        metavar="N",
        help="Number of epochs to train (default: 5)",
    )
    parser.add_argument(
        "--steps",
        type=int,
        default=10,
        metavar="N",
        help="Number of batches to train the model on in each epoch (default: 10)",
    )
    parser.add_argument(
        "--learning-rate",
        type=float,
        default=0.5,
        metavar="N",
        help="Learning rate (default: 0.5)",
    )
    parser.add_argument(
        "--momentum",
        type=float,
        default=0.1,
        metavar="N",
        help="Accelerates SGD in the relevant direction and dampens oscillations (default: 0.1)",
    )

    args, _ = parser.parse_known_args()

    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
    logging.debug(f"num_replicas_in_sync: {strategy.num_replicas_in_sync}")
    global_batch_size = args.batch_size * strategy.num_replicas_in_sync

    train_dataset, test_dataset = get_datasets(buffer_size=args.buffer_size)
    train_dataset = train_dataset.batch(batch_size=global_batch_size)
    test_dataset = test_dataset.batch(batch_size=global_batch_size)

    dataset_options = tf.data.Options()
    dataset_options.experimental_distribute.auto_shard_policy = (
        tf.data.experimental.AutoShardPolicy.DATA
    )
    train_datasets_sharded = train_dataset.with_options(dataset_options)
    test_dataset_sharded = test_dataset.with_options(dataset_options)

    with strategy.scope():
        model = compile_model(args=args)

    # Train the model
    model.fit(train_datasets_sharded, epochs=args.epochs, steps_per_epoch=args.steps)

    # Save the trained model with the Kaptain SDK exporter utility
    model.save("mnist")
    ModelExportUtil().upload_model("mnist")

    eval_loss, eval_acc = model.evaluate(test_dataset_sharded, verbose=0, steps=args.steps)
    
    # Record the evaluation metrics for use with the hyperparameter tuner
    MetadataUtil.record_metrics({"loss": eval_loss, "accuracy": eval_acc})

if __name__ == "__main__":
    main()

Define the Model

The central abstraction of the Kaptain SDK is a model. The model contains configuration about how the model is built, and also contains state about what was built and where files were uploaded.

CODE 
extra_files = ["datasets/mnist"]
base_image = "mesosphere/kubeflow:2.2.0-tensorflow-2.9.1"
# Replace with your docker repository and a tag (optional), e.g. "repository/image"  or "repository/image:tag"
image_name = "mesosphere/kubeflow:mnist-sdk-example"
# Use the name of the file with additional python packages to install into model image (e.g. "requirements.txt")
requirements = None
CODE 
from kaptain.model.models import Model
from kaptain.model.frameworks import ModelFramework
from kaptain.config import Config
from kaptain.platform.config.s3 import S3ConfigurationProvider
from kaptain.platform.config.docker import DockerConfigurationProvider

config = Config(
  docker_config_provider=DockerConfigurationProvider.default(),
  storage_config_provider=S3ConfigurationProvider.from_env(),
)

model = Model(
    id="dev/mnist",
    name="MNIST",
    description="MNIST Model",
    version="0.0.1",
    framework=ModelFramework.TENSORFLOW,
    framework_version="2.8.0",
    main_file="trainer.py",
    extra_files=extra_files,
    image_name=image_name,
    base_image=base_image,
    requirements=requirements,
    config=config,
)

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 make the time metadata human-readable.

Since a Docker image is built in the background when you train or tune a Model instance, you must provide a base_image. Provide the name of the final image image_name with or without an 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. You can find details on the format of the requirements file in the pip official documentation.

Refer to ?Model for more details.

Train the Model

CODE 
workers = 2
gpus = 0
memory = "5G"
cpu = "1"
CODE 
model.train(
    workers=workers,
    cpu=cpu,
    memory=memory,
    gpus=gpus,
    hyperparameters={"--steps": 10, "--epochs": 5},
    args={}, # additional command line arguments for the training job. 
)
CODE 
[I 221006 06:22:58 image_builder:80] Skipping image build for the model - the image 'mesosphere/kubeflow:mnist-sdk-example' with the same contents has already been published to the registry.
[I 221006 06:22:58 job_runner:132] Submitting a new training job "mnist-tfjob-65818540".
[I 221006 06:22:58 job_runner:58] Waiting for the training job to complete...
[I 221006 06:23:02 kubernetes:268] Waiting for Master Node Training Model to start...
[I 221006 06:23:05 kubernetes:274] Master Node Training Model started in pod: mnist-tfjob-65818540-chief-0.
10/10 [==============================] - 4s 89ms/step - loss: 2.1121 - accuracy: 0.3289
[I 221006 06:23:17 kubernetes:334] [mnist-tfjob-65818540-chief-0/tensorflow] logs:
    Epoch 2/5
10/10 [==============================] - 1s 97ms/step - loss: 2.2689 - accuracy: 0.3844
    Epoch 3/5
10/10 [==============================] - 1s 97ms/step - loss: 2.0894 - accuracy: 0.2984
    Epoch 4/5
10/10 [==============================] - 1s 79ms/step - loss: 1.9951 - accuracy: 0.3484
[I 221006 06:23:22 kubernetes:334] [mnist-tfjob-65818540-chief-0/tensorflow] logs:
    Epoch 5/5
10/10 [==============================] - 1s 100ms/step - loss: 1.2166 - accuracy: 0.6273
[I 221006 06:23:54 models:418] Model training is completed.
[I 221006 06:23:54 model_util:48] Saved model to /tmp/tmprqsm360t
[I 221006 06:23:55 model_util:53] Model uploaded to s3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/.state.
[I 221006 06:23:55 model_util:54] Model state saved.

The output should be:

CODE 
True

If you set resource quotas 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, we recommended you 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.

Save Model State to S3

Saving the model state to S3 allows it to be imported to another cluster, so that the cluster can access the current training configuration, version information, and other necessary metadata.

CODE 
model.save_as_json()

Output:

CODE 
[I 221006 06:26:06 model_util:48] Saved model to /tmp/tmpkd7d5spr
[I 221006 06:26:07 model_util:53] Model uploaded to s3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/.state.
[I 221006 06:26:07 model_util:54] Model state saved.

Verify that the Model is Exported to S3, along with save state

Run the following command to see a list of files including, but not limited to, saved_model.pb and .state/model.json:

CODE 
from kaptain.platform.storage import storage_factory

storage_client = storage_factory.get_client(config.storage_config_provider)
storage_client.list(model.meta().saved_model_uri)

Output:

CODE 
['s3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/.state/model.json',
 's3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/keras_metadata.pb',
 's3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/saved_model.pb',
 's3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/variables/variables.data-00000-of-00001',
 's3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001/variables/variables.index']

Make Note of the Model URL, and Proceed to Deploy the Model on a New Cluster

You need this URL to load the model state in your target deployment cluster. Run the following command, and note the output:

CODE 
model.meta().saved_model_uri

Output:

CODE 
's3://kaptain/models/dev/mnist/trained/b69dc6f6e3c246858cf43a1eba8be5f5/0001'

Continue to Deploy your Notebook in your target model deployment cluster.

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