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