Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 88, in _split_generators
inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "pyarrow/table.pxi", line 6321, in pyarrow.lib.concat_tables
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<manifests: list<item: struct<annotations: struct<containerd.io/distribution.source.docker.io: string, io.containerd.image.name: string, org.opencontainers.image.ref.name: string>, digest: string, mediaType: string, size: int64>>, mediaType: string, schemaVersion: int64> vs list<item: struct<Config: string, Layers: list<item: string>, RepoTags: list<item: string>>>
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Docker Backup Archive
This repository contains backup archives of Docker Images and Docker Containers saved as .tar files. These archives are hosted here for disaster recovery and environment restoration purposes.
📁 Repository Structure
.
├── images/
│ └── <image_name>.tar # Raw image backups (created via `docker save`)
└── containers/
└── <container_name>.tar # Container filesystem snapshots (created via `docker export`)
⚙️ Archive Types Comparison
| Archive Type | Location | Created With | Restoration Command | Data State |
|---|---|---|---|---|
| Docker Image | images/ |
docker save |
docker load |
Restores full image with all original layers, tags, and commit history. |
| Docker Container | containers/ |
docker export |
docker import |
Restores container filesystem snapshot into a new base image. |
🚀 Step-by-Step Restoration Guide
1. Download Files from Hugging Face
Use the huggingface-cli or Git LFS to download the required .tar files to your local machine:
# Download the entire dataset repository
huggingface-cli download nadyaputriast/docker --repo-type dataset --local-dir ./docker-backup
2. Restore Docker Images (images/)
Use docker load to restore image archives back to your local Docker engine:
# Navigate to the images directory
cd docker-backup/images
# Load the image archive
docker load -i <image_file_name>.tar
Verify restored image:
docker images
3. Restore Docker Containers (containers/)
Use docker import to convert container filesystem snapshots into a new Docker image:
# Navigate to the containers directory
cd docker-backup/containers
# Import container snapshot as a new image (e.g., cc_restored:latest)
docker import cc_container.tar cc_restored:latest
Verify created image:
docker images
4. Run Containers (Example: n8n Setup)
Once the images are restored, you can instantiate new running containers. Below is an example workflow for pulling, running, and custom-building an n8n environment:
A. Run Official n8n Image
# Pull official image if needed
docker pull docker.n8n.io/n8nio/n8n
# Run n8n container with persistent volume storage
docker run --name n8n-docker \
-p 5678:5678 \
-v n8n_data:/home/node/.n8n \
-d docker.n8n.io/n8nio/n8n
B. Build Custom n8n Image
If you have a local custom Dockerfile for n8n:
# Build custom image tagged as v2
docker build -t nadyaputriast/n8n-custom:v2 .
📝 Important Notes
- Ensure your local Docker daemon has sufficient disk space before restoring large
.tarfiles. - For services requiring data persistence (like n8n), ensure volume flags (
-v) map correctly to your host environment.
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