You can use one or multiple buckets on Google Cloud Storage (GCS) to access data directly on your machine learning experiments and jobs.

Create a Google cloud storage bucket

You should create a google cloud storage bucket (e.g. plx-data), and you have to assign permission to the bucket.

Google cloud storage provides an easy way to download the access key as a JSON file. You should create a secret based on that JSON file.

Create a secret on Kubernetes

You can create a secret with an env var of the content of the gcs-key.json:

  • GC_KEYFILE_DICT or GOOGLE_KEYFILE_DICT

Or you can create a secret to be mounted as a volume:

  • kubectl create secret generic gcs-secret --from-file=gcs-secret.json=path/to/gcs-key.json -n polyaxon

Use the secret name and secret key in your data persistence definition

You can use the default mount path /plx-context/.gc/gc-secret.json.

connections:
- name: gcs-dataset1
  kind: gcs
  schema:
    bucket: "gs://gcs-datasets"
  secret:
    name: "gcs-secret"
    mountPath: /plx-context/.gc/gc-secret.json

You can also use a different mount path /etc/gcs/gc-secret.json, in which case you need to provide an env var to tell the SDK where to look:

kubectl create configmap gcs-key-path --from-literal GC_KEY_PATH="/etc/gcs/gcs-secret.json" -n polyaxon
connections:
- name: gcs-dataset1
  kind: gcs
  schema:
    bucket: "gs://gcs-datasets"
  secret:
    name: "gcs-secret"
    mountPath: /etc/gcs
  configMap:
    name: gcs-key-path

If you want ot access multiple datasets using the same secret:

persistence:
- name: gcs-dataset1
  kind: gcs
  schema:
    bucket: "gs://gcs-datasets/path1"
  secret:
    name: "gcs-secret"
    mountPath: /etc/gcs
  configMap:
    name: gcs-key-path
- name: gcs-dataset2
  kind: gcs
  schema:
    bucket: "gs://gcs-datasets/path2"
  secret:
    name: "gcs-secret"
    mountPath: /etc/gcs
  configMap:
    name: gcs-key-path

Update/Install Polyaxon deployment

You can deploy/upgrade your Polyaxon CE or Polyaxon Agent deployment with access to data on GCS.

Access to data in your experiments/jobs

To expose the connection secret to one of the containers in your jobs or services:

run:
  kind: job
  connections: [gcs-dataset1]

Or

run:
  kind: job
  connections: [gcs-dataset1, s3-dataset1]

Use the initializer to load the dataset

To use the artifacts initializer to load the dataset

run:
  kind: job
  init:
   - artifacts: [dirs: [...], files: [...]]
     connection: "gcs-dataset1"

Use Polyaxon to access the dataset

This is optional, you can use any language or logic to interacts with Azure Storage.

Polyaxon has some built-in logic that you can leverage if you want.

To use that logic: 

pip install polyaxon[gcs]

All possible functions to use:

from polyaxon.connections.gcp.gcs import GCSService

store = GCSService(...)

store.delete()
store.ls()
store.upload_file()
store.upload_dir()
store.download_file()
store.download_dir()