# Dataset Loading – Axolotl

# Dataset Loading

Understanding how to load datasets from different sources

## Overview

Datasets can be loaded in a number of different ways depending on the how it is saved (the extension of the file) and where it is stored.

## Loading Datasets

We use the `datasets` library to load datasets and a mix of `load_dataset` and `load_from_disk` to load them.

You may recognize the similar named configs between `load_dataset` and the `datasets` section of the config file.

    datasets:
      - path:
        name:
        data_files:
        split:
        revision:
        trust_remote_code:

Tip

Do not feel overwhelmed by the number of options here. A lot of them are optional. In fact, the most common config to use would be `path` and sometimes `data_files`.

This matches the API of `datasets.load_dataset`, so if you’re familiar with that, you will feel right at home.

For HuggingFace’s guide to load different dataset types, see here.

For full details on the config, see config-reference.qmd.

Note

You can set multiple datasets in the config file by more than one entry under `datasets`.

    datasets:
      - path: /path/to/your/dataset
      - path: /path/to/your/other/dataset

### Local dataset

#### Files

To load a JSON file, you would do something like this:

    from datasets import load_dataset

    dataset = load_dataset("json", data_files="data.json")

Which translates to the following config:

    datasets:
      - path: data.json
        ds_type: json

In the example above, it can be seen that we can just point the `path` to the file or directory along with the `ds_type` to load the dataset.

This works for CSV, JSON, Parquet, and Arrow files.

Tip

If `path` points to a file and `ds_type` is not specified, we will automatically infer the dataset type from the file extension, so you could omit `ds_type` if you’d like.

#### Directory

If you’re loading a directory, you can point the `path` to the directory.

Then, you have two options:

##### Loading entire directory

You do not need any additional configs.

We will attempt to load in the following order: - datasets saved with `datasets.save_to_disk` - loading entire directory of files (such as with parquet/arrow files)

    datasets:
      - path: /path/to/your/directory

##### Loading specific files in directory

Provide `data_files` with a list of files to load.

    datasets:
        # single file
      - path: /path/to/your/directory
        ds_type: csv
        data_files: file1.csv

        # multiple files
      - path: /path/to/your/directory
        ds_type: json
        data_files:
          - file1.jsonl
          - file2.jsonl

        # multiple files for parquet
      - path: /path/to/your/directory
        ds_type: parquet
        data_files:
          - file1.parquet
          - file2.parquet

### HuggingFace Hub

The method you use to load the dataset depends on how the dataset was created, whether a folder was uploaded directly or a HuggingFace Dataset was pushed.

Note

If you’re using a private dataset, you will need to enable the `hf_use_auth_token` flag in the root-level of the config file.

#### Folder uploaded

This would mean that the dataset is a single file or file(s) uploaded to the Hub.

    datasets:
      - path: org/dataset-name
        data_files:
          - file1.jsonl
          - file2.jsonl

#### HuggingFace Dataset

This means that the dataset is created as a HuggingFace Dataset and pushed to the Hub via `datasets.push_to_hub`.

    datasets:
      - path: org/dataset-name

Note

There are some other configs which may be required like `name`, `split`, `revision`, `trust_remote_code`, etc depending on the dataset.

### Remote Filesystems

Via the `storage_options` config under `load_dataset`, you can load datasets from remote filesystems like S3, GCS, Azure, and OCI.

Warning

This is currently experimental. Please let us know if you run into any issues!

The only difference between the providers is that you need to prepend the path with the respective protocols.

    datasets:
        # Single file
      - path: s3://bucket-name/path/to/your/file.jsonl

        # Directory
      - path: s3://bucket-name/path/to/your/directory

For directory, we load via `load_from_disk`.

#### S3

Prepend the path with `s3://`.

The credentials are pulled in the following order:

- `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, and `AWS_SESSION_TOKEN` environment variables
- from the `~/.aws/credentials` file
- for nodes on EC2, the IAM metadata provider

Note

We assume you have credentials setup and not using anonymous access. If you want to use anonymous access, let us know! We may have to open a config option for this.

Other environment variables that can be set can be found in boto3 docs

#### GCS

Prepend the path with `gs://` or `gcs://`.

The credentials are loaded in the following order:

- gcloud credentials
- for nodes on GCP, the google metadata service
- anonymous access

#### Azure

##### Gen 1

Prepend the path with `adl://`.

Ensure you have the following environment variables set:

- `AZURE_STORAGE_TENANT_ID`
- `AZURE_STORAGE_CLIENT_ID`
- `AZURE_STORAGE_CLIENT_SECRET`

##### Gen 2

Prepend the path with `abfs://` or `az://`.

Ensure you have the following environment variables set:

- `AZURE_STORAGE_ACCOUNT_NAME`
- `AZURE_STORAGE_ACCOUNT_KEY`

Other environment variables that can be set can be found in adlfs docs

#### OCI

Prepend the path with `oci://`.

It would attempt to read in the following order:

- `OCIFS_IAM_TYPE`, `OCIFS_CONFIG_LOCATION`, and `OCIFS_CONFIG_PROFILE` environment variables
- when on OCI resource, resource principal

Other environment variables:

- `OCI_REGION_METADATA`

Please see the ocifs docs.

### HTTPS

The path should start with `https://`.

    datasets:
      - path: https://path/to/your/dataset/file.jsonl

This must be publically accessible.

## Next steps

Now that you know how to load datasets, you can learn more on how to load your specific dataset format into your target output format dataset formats docs.