tfds and Google Cloud Storage

Google Cloud Storage (GCS) can be used with tfds for multiple reasons:

• Storing preprocessed data
• Accessing datasets that have data stored on GCS

Access through TFDS GCS bucket

Some datasets are available directly in our GCS bucket gs://tfds-data/datasets/ without any authentication:

• If tfds.load(..., try_gcs=False) (default), the dataset will be copied locally in ~/tensorflow_datasets during download_and_prepare.
• If tfds.load(..., try_gcs=True), the dataset will be streamed directly from GCS (download_and_prepare will be skipped).

You can check whether a dataset is hosted on the public bucket with tfds.is_dataset_on_gcs('mnist').

Authentication

Before starting, you should decide on how you want to authenticate. There are three options:

• no authentication (a.k.a anonymous access)
• using your Google account
• using a service account (can be easily shared with others in your team)

You can find detailed information in Google Cloud documentation

Simplified instructions

If you run from colab, you can authenticate with your account, but running:

from google.colab import auth
auth.authenticate_user()

If you run on your local machine (or in VM), you can authenticate with your account by running:

gcloud auth application-default login

If you want to login with service account, download the JSON file key and set

export GOOGLE_APPLICATION_CREDENTIALS=<JSON_FILE_PATH>

Using Google Cloud Storage to store preprocessed data

Normally when you use TensorFlow Datasets, the downloaded and prepared data will be cached in a local directory (by default ~/tensorflow_datasets).

In some environments where local disk may be ephemeral (a temporary cloud server or a Colab notebook) or you need the data to be accessible by multiple machines, it's useful to set data_dir to a cloud storage system, like a Google Cloud Storage (GCS) bucket.

How?

Create a GCS bucket and ensure you (or your service account) have read/write permissions on it (see authorization instructions above)

When you use tfds, you can set data_dir to "gs://YOUR_BUCKET_NAME"

ds_train, ds_test = tfds.load(name="mnist", split=["train", "test"], data_dir="gs://YOUR_BUCKET_NAME")

Caveats:

• This approach works for datasets that only use tf.io.gfile for data access. This is true for most datasets, but not all.
• Remember that accessing GCS is accessing a remote server and streaming data from it, so you may incur network costs.

Accessing datasets stored on GCS

If dataset owners allowed anonymous access, you can just go ahead and run the tfds.load code - and it would work like a normal internet download.

If dataset requires authentication, please use the instructions above to decide on which option you want (own account vs service account) and communicate the account name (a.k.a email) to the dataset owner. After they enable you access to the GCS directory, you should be able to run the tfds download code.