Configuring Access to Docker and Cloud Storage

When the model is trained, all the model files are packed into a Docker image, which is then used for the training itself, and for hyperparameter tuning later on. In order to build that image, the SDK must be provided with Docker credentials, so that it can publish the resulting images to the registry specified in Model.image attribute.

As the image building happens on the cluster, the model files are first uploaded to a blob storage such as S3, GCS, or MinIO and then used by the builder. By default, the SDK uses a cluster-local MinIO installation which doesn’t require any additional configuration. If users wish to use a specific S3 location instead, then appropriate AWS credentials need to be provided.

This guide focuses on two main aspects of credential distribution and configuration:

  • Secure automatic mounting of credential files and environment variables to notebook containers
  • Using the SDK API for creating and modifying access credentials and parameters.

Secure mounting of credentials

The standard way of secure sharing of sensitive information in Kubernetes is by using Secret objects. More information about Secret objects is available in the official Kubernetes documentation. A Secret can be created from a file and can be accessed only by the service account which created it, and by users with administrative privileges.

After a Secret is created it can be mounted to a Notebook container as a file or used to populate environment variables. To simplify the process, you can create all the necessary secrets once and then reuse them across notebooks. To make Secrets automatically available for mounting, create a PodDefault for them. For more information about PodDefault follow the Kubeflow Notebook Setup Guide or check the [PodDefault manifest][poddefault_ manifest].

Docker credentials

In order to make Docker credentials available as a Secret, create a config.json file which has the following standard layout:

{
    "auths": {
            "https://index.docker.io/v1/": {
                    "auth": "<username and password in base64>"
            }
    }
}

The auth field is a base64-encoded string of the form <username>:<password> where <username> and <password> are the actual username and password used to login to the Docker registry. To generate the value for auth field, use the following command: echo -n "<username>:<password>" | base64.

NOTE: It is important that the file with Docker credentials is named config.json because it is the default name used and recognized by a variety of tools and components.

To create a Secret from the credentials file config.json run the following command:

kubectl create secret generic docker-secret -n <kaptain_namespace> --from-file=config.json

Be sure to replace <kaptain_namespace> with the namespace you use for creating notebooks. In this example, we used a namespace named ‘user’

Verify the Secret is created:

kubectl get secret docker-secret -n <kaptain_namespace> -o yaml

the output should look like this:

apiVersion: v1
data:
  config.json: ewogICJhdXRocyI6IH...
kind: Secret
metadata:
  name: docker-secret
  namespace: user
type: Opaque

To make this Secret available for selection in the Notebook creation dialogue, create a file named pod_default.yaml with the following contents:

apiVersion: "kubeflow.org/v1alpha1"
kind: PodDefault
metadata:
  name: docker-config
  namespace: user
spec:
  selector:
    matchLabels:
      docker-config: "true"
  desc: "Add Docker registry credentials"
  volumeMounts:
    - name: docker-secret-volume
      mountPath: /home/kubeflow/.docker/
  volumes:
    - name: docker-secret-volume
      secret:
        secretName: docker-secret

WARNING: The volume name and mountPath must be unique across all PodDefault objects to avoid conflicts when mounting Secrets to Pods.

Create the PodDefault resource from the file using the following command:

kubectl create -f pod_default.yaml

After that, the Docker credentials secret will be available for selection in the Notebook Spawner UI and, if selected, will be mounted to /home/kubeflow/.docker/:

image

AWS credentials

File-based and environment variable based configuration

There are two ways to make AWS credentials available in a notebook:

  • As a configuration file mounted to the Pod from a Secret
  • As environment variables injected into the Pod from a Secret

NOTE: Which method to use depends on what AWS settings you need to configure:

  • The configuration file method is recommended when working with the default account settings, i.e. when only credentials such as AWS Access Key ID, AWS Secret Access Key, and AWS Session Token are needed to access associated S3 storage.
  • The environment variables method is recommended when additional configuration is required, such as AWS Region, S3 Endpoint URL, S3 Bucket Access style (url or path-style), and Protocol Signature version. These additional properties are often required when working with non-standard S3-compatible storage solutions such as MinIO.

File-based AWS credentials

Making an AWS credentials file available as a Secret follows the same steps as with Docker credentials. First, create an AWS credentials file with the standard layout:

[default]
aws_access_key_id     = <your AWS Access Key ID>
aws_secret_access_key = <your AWS Secret Access Key>
# optional
aws_session_token     = <your AWS Session Token>

NOTE: It is important that the file with AWS credentials is named credentials because it is the default name used and recognized by AWS-related tools and components.

To create a Secret from the file credentials run the following command:

kubectl create secret generic aws-credentials -n <kaptain_namespace> --from-file=credentials

Be sure to replace <kaptain_namespace> with the namespace you use for creating notebooks. In this example, we used a namespace named ‘user’

Verify that the Secret is created:

kubectl get secret aws-credentials -o yaml

the output should look like this:

apiVersion: v1
data:
  credentials: W2RlZmF1bHRdCmF3c19hY2...
kind: Secret
metadata:
  name: aws-credentials
  namespace: user
type: Opaque

To make this Secret available for selection in the Notebook creation dialogue, create a PodDefault referencing it. Create a file named pod_default.yaml with the following contents:

apiVersion: "kubeflow.org/v1alpha1"
kind: PodDefault
metadata:
  name: aws-credentials
  namespace: user
spec:
  selector:
    matchLabels:
      docker-config: "true"
  desc: "Add AWS credentials"
  volumeMounts:
    - name: aws-secret-volume
      mountPath: /home/kubeflow/.aws/
  volumes:
    - name: aws-secret-volume
      secret:
        secretName: aws-credentials

NOTE: Volume name and mountPath must be unique across all PodDefault objects to avoid conflicts when mounting Secrets to Pods.

Create a PodDefault resource from the file using the following command:

kubectl create -f pod_default.yaml

After creating this resource, the AWS credentials secret will be available for selection in the Notebook Spawner UI and, if selected, will be mounted to /home/kubeflow/.aws/credentials:

image

Environment variable based AWS configuration

Making AWS configuration and credentials available as environment variables requires creating a Secret from manifest. The following environment variables are supported and recognized by the SDK:

  • AWS_ACCESS_KEY_ID - The access key to authenticate with S3.
  • AWS_SECRET_ACCESS_KEY - The secret key to authenticate with S3.
  • AWS_SESSION_TOKEN - The session token to authenticate with S3.
  • AWS_REGION - The name of AWS region.
  • S3_ENDPOINT - The complete URL of S3 endpoint. This parameter is required when working with non-standard, S3-compatible storage solutions such as MinIO. It should be set to the resolvable address of the running server.
  • S3_SIGNATURE_VERSION - The signature version when signing requests
  • S3_FORCE_PATH_STYLE - When enabled, clients will use path style instead of URL style for accessing buckets. Supported values: true|false.

Creating a Secret with environment variables requires a YAML specification file (e.g. secret.yaml) with the following contents:

apiVersion: v1
kind: Secret
metadata:
  name: aws-configuration
data:
  AWS_ACCESS_KEY_ID: <base64-encoded value>
  AWS_SECRET_ACCESS_KEY: <base64-encoded value>
  AWS_SESSION_TOKEN: <base64-encoded value>
  AWS_REGION: <base64-encoded value>
  S3_ENDPOINT: <base64-encoded value>
  S3_SIGNATURE_VERSION: <base64-encoded value>
  S3_FORCE_PATH_STYLE: <base64-encoded value>
type: Opaque

<base64-encoded value> should contain the actual property value encoded in base64. To encode a specific value in base64 use the following command: echo -n "<AWS configuration property value>" | base64.

To create a Secret from the YAML specification file (e.g. secret.yaml) run the following command:

kubectl create -f -n <kaptain_namespace> secret.yaml

Be sure to replace <kaptain_namespace> with the namespace you use for creating notebooks. In this example, we used a namespace named ‘user’.

Verify the Secret is created:

kubectl get secret aws-configuration -o yaml

# the output should look like this:

apiVersion: v1
data:
  AWS_ACCESS_KEY_ID: QVdTX0FDQ0VTU19LRVlfSUQK
  AWS_REGION: QVdTX1JFR0lPTgo=
  AWS_SECRET_ACCESS_KEY: QVdTX1NFQ1JFVF9BQ0NFU1NfS0VZCg==
  AWS_SESSION_TOKEN: QVdTX1NFU1NJT05fVE9LRU4K
  S3_ENDPOINT: UzNfRU5EUE9JTlQK
  S3_FORCE_PATH_STYLE: dHJ1ZQo=
  S3_SIGNATURE_VERSION: UzNfU0lHTkFUVVJFX1ZFUlNJT04K
kind: Secret
metadata:
  name: aws-configuration
  namespace: user
type: Opaque

To make this Secret available for selection in the Notebook creation dialogue, create a PodDefault referencing it. Create the file pod_default.yaml with the following contents:

apiVersion: "kubeflow.org/v1alpha1"
kind: PodDefault
metadata:
  name: aws-configuration
  namespace: user
spec:
  selector:
    matchLabels:
      aws-configuration: "true"
  desc: "Add AWS Environment variables"
  envFrom:
  - secretRef:
      name: aws-configuration

Create a PodDefault resource from file using the following command:

kubectl create -f pod_default.yaml

After that, the AWS configuration secret will be available for selection in the Notebook Spawner UI and, if selected, will make all the environment variables available in the Notebook:

image

SDK API for Configuring access to Docker and cloud storage

The Model class serves as the main API for training, tuning, and deploying model to serving. It uses a Docker registry for publishing images with model files and also requires S3-compatible storage for storing trained models and transient data. For configuring access to the Docker registry and storage, Model exposes a config argument which allows users to fine-tune their configurations. This section covers the available configuration providers and their defaults.

NOTE: If no Config is provided, the Model constructor will use Docker credentials from the file $HOME/.docker/config.json, and the in-cluster MinIO server configuration for storage. A Docker config.json is mandatory for Model instantiation.

Config.default() in the example below is initialized and expects a Docker configuration file to be present at /home/kubeflow/.docker/config.json when run from the Notebook:

model = Model(
    id="Model id",
    name="Model name",
    description="Model description",
    version="Model version",
    framework="tensorflow",
    framework_version="2.3.0",
    main_file="train.py",
    config=Config.default(),
)

Users can customize the Config to provide custom Docker and storage configuration for example:

config = Config(
    docker_config_provider=<instance of ConfigurationProvider>,
    storage_config_provider=<instance of ConfigurationProvider>,
)

The SDK comes with the convenience implementations for Docker and S3-compatible storage configuration providers.

Docker Configuration

DockerConfigurationProvider supports Docker credentials reading from a file only.

DockerConfigurationProvider.default() loads a configuration from /home/kubeflow/.docker/config.json and fails with an error if the file is not present.

DockerConfigurationProvider.from_file(<path/to/config.json>) loads a configuration from the specified path, and can be used when the config Secret is mounted to a non-default path or created by the user.

Example:

# building global Config object using custom location of configuration file
docker_config=DockerConfigurationProvider.from_file("config.json")

config = Config(
  docker_config_provider=docker_config,
  storage_config_provider=S3ConfigurationProvider.default(),
)

# use the created config with the Model
model = Model(
  id="Model id",
  name="Model name",
  description="Model description",
  version="Model version",
  framework="tensorflow",
  framework_version="2.3.0",
  main_file="train.py",
  config=config,
)

AWS Configuration

S3ConfigurationProvider supports reading Docker credentials only from a file.

S3ConfigurationProvider.default() loads configuration from /home/kubeflow/.aws/credentials and fails with an error if the file is not present.

S3ConfigurationProvider.from_file(<path/to/aws/credentials>) loads configuration from the specified path and can be used when the config Secret is mounted to a non-default path or created by the user.

S3ConfigurationProvider.from_env() loads configuration from the environment variables and can be used when the configuration Secret is mounted as environment variables or environment variables are set by the user.

Example:

# building global Config object using environment variables
storage_config=S3ConfigurationProvider.from_env()

config = Config(
  docker_config_provider=DockerConfigurationProvider.default(),
  storage_config_provider=storage_config,
)

# use the created config with the Model
model = Model(
  id="Model id",
  name="Model name",
  description="Model description",
  version="Model version",
  framework="tensorflow",
  framework_version="2.3.0",
  main_file="train.py",
  config=config,
)

MinIO Configuration

DefaultMinioConfigurationProvider is a special configuration provider pre-configured for an in-cluster MinIO instance.

DefaultMinioConfigurationProvider.default() returns a configured instance ready to be used with it. It is used by default when no S3 provider is specified. DefaultMinioConfigurationProvider extends S3ConfigurationProvider and supports all the same methods.

Example:

# building global Config object using MinIO for storage
storage_config=DefaultMinioConfigurationProvider.default()

config = Config(
  docker_config_provider=DockerConfigurationProvider.default(),
  storage_config_provider=storage_config,
)

# use the created config with the Model
model = Model(
  id="Model id",
  name="Model name",
  description="Model description",
  version="Model version",
  framework="tensorflow",
  framework_version="2.3.0",
  main_file="train.py",
  config=config,
)

NOTE: The above example is the default behavior of the SDK for Config instantiation when no config is provided to the Model constructor.