This page covers
clearml-data, ClearML's file-based data management solution.
See Hyper-Datasets for ClearML's advanced queryable dataset management solution.
In Machine Learning, you are very likely dealing with a gargantuan amount of data that you need to put in a dataset, which you then need to be able to share, reproduce, and track.
ClearML Data Management solves two important challenges:
- Accessibility - Making data easily accessible from every machine,
- Versioning - Linking data and experiments for better traceability.
We believe Data is not code. It should not be stored in a git tree, because progress on datasets is not always linear. Moreover, it can be difficult and inefficient to find on a git tree the commit associated with a certain version of a dataset.
Use ClearML Data to create, manage, and version your datasets. Store your files in any storage location of your choice
(S3 / GS / Azure / Network Storage) by setting the dataset’s upload destination (see
CLI option or
Datasets can be set up to inherit from other datasets, so data lineages can be created, and users can track when and how their data changes. Dataset changes are stored using differentiable storage, meaning a version will store the change-set from its previous dataset parents.
You can get a local copy of your dataset on any machine. Local copies of datasets are always cached, so the same data never needs to be downloaded twice. When a dataset is pulled it will automatically pull all parent datasets and merge them into one output folder for you to work with.
clearml-data comes built-in with the
clearml python package! Just check out the Getting Started
guide for more info!
ClearML Data offers two interfaces:
clearml-data- A CLI utility for creating, uploading, and managing datasets. See CLI for a reference of
clearml.Dataset- A python interface for creating, retrieving, managing, and using datasets. See SDK for an overview of the basic methods of the
For an overview of our recommendations for ClearML Data workflows and practices, see Best Practices.
The following table displays the possible states for a dataset version.
|Uploading||Dataset creation is in progress|
|Failed||Dataset creation was terminated with an error|
|Aborted||Dataset creation was aborted by user before it was finalization|
|Final||A dataset was created and finalized successfully|
|Published||The dataset is read-only. Publish a dataset to prevent changes to it|