Accelerated datasets#

Accelerated datasets and Accelerators are entirely different things

Accelerated datasets is a Union feature that enables quick access to large datasets from within a task. An accelerator, on the other hand, is a specialized hardware device that is used to accelerate the execution of a task. These concepts are entirely different and should not be confused.

Many of the workflows that you may want to run in Union will involve tasks that use large static assets such as reference genomes, training datasets, or pre-trained models. These assets are often stored in an object store and need to be downloaded to the task pod each time before the task can run. This can be a significant bottleneck, especially if the data must be loaded into memory to be randomly accessed and therefore cannot be streamed.

To remedy this, Union provides a way to pre-load large static assets into a shared object store that is mounted to all machine nodes in your cluster by default. This allows you to upload your data once and then access it from any task without needing to download it each time.

Data items stored in this way are called an accelerated datasets.

Only on S3

Currently, this feature is only available for AWS S3.

How it works#

Each customer has a dedicated S3 bucket where they can store their accelerated datasets.
The naming and set up of this bucket must be coordinated with the Union team, in order that a suitable name is chosen. In general it will usually be something like s3://union-<org-name>-persistent.
You can upload any data you wish to this bucket.
The bucket will be automatically mounted into every node in your cluster.
To your task logic, it will appear to be a local directory in the task container.
To use it, initialize a FlyteFile object with the path to the data file and pass it into a task as an input.
- Note that in order for the system to recognize the file as an accelerated dataset, it must be created as a FlyteFile and that FLyteFile must be passed into a task. If you try to access the file directly from the object store, it will not be recognized as an accelerated dataset and the data will not be found.

Example usage#

Assuming that your organization is called my-company and the file you want to access is called my_data.csv, you would first need to upload the file to the persistent bucket. See Upload a File to Your Amazon S3 Bucket.

The code to access the data looks like this:

from flytekit.types.file import FlyteFile

@task
def my_task(f: FlyteFile) -> int:
    with open(f, newline="\n") as input_file:
        data = input_file.read()
        // Do something with the data

@workflow
def my_wf()
    my_task(f=FlyteFile("s3://union-my-company-persistent/my_data.csv"))

Note that you do not have to invoke FlyteFile.download() because the file will already have been made available locally within the container.

Considerations#

Caching#

While the persistent bucket appears to your task as a locally mounted volume, the data itself will not be resident in the local file system until after the first access. After the first access it will be cached locally. This fact should be taken into account when using this feature.

Storage consumption#

Data cached during the use of accelerated datasets will consume local storage on the nodes in your cluster. This should be taken into account when selecting and sizing your cluster nodes.