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kernels_metadata.md

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To upload and run a kernel, a special kernel-metadata.json file must be specified.

Here's a basic example for kernel-metadata.json:

{
  "id": "timoboz/my-awesome-kernel",
  "id_no": 12345,
  "title": "My Awesome Kernel",
  "code_file": "my-awesome-kernel.ipynb",
  "language": "python",
  "kernel_type": "notebook",
  "is_private": "false",
  "enable_gpu": "false",
  "enable_internet": "false",
  "machine_shape": "",
  "dataset_sources": ["timoboz/my-awesome-dataset"],
  "competition_sources": [],
  "kernel_sources": [],
  "model_sources": []
}

You can also use the API command kaggle kernels init -p /path/to/kernel to have the API create this file for you for a new kernel. If you wish to get the metadata for an existing kernel, you can use kaggle kernels pull -p /path/to/download -k username/kernel-slug -m.

Contents

We currently support the following metadata fields for kernels.

  • id: The URL slug of your kernel. One of id or id_no must be specified. If both are, id_no will be preferred.
    1. Your username slug
    2. A unique kernel slug
  • id_no: The kernel's numeric ID. One of id or id_no must be specified. If both are, id_no will be preferred.
  • title: The title of the kernel. Required for new kernels - optional for existing ones. Please be aware that kernel titles and slugs are linked to each other. A kernel slug is always the title lowercased with dashes (-) replacing spaces.
    • If you wish to rename your kernel, you may change the title within the metadata. However, you will need to update the id as well AFTER the rename is complete.
  • code_file: The path to your kernel source code. Required. If not an absolute path, it should be relative to the location of kernel-metadata.json.
  • language: The language your kernel is written in. Valid options are python, r, and rmarkdown. Required.
  • kernel_type: The type of kernel. Valid options are script and notebook. Required.
  • is_private: Whether or not the kernel should be private. If not specified, will be true.
  • enable_gpu: Whether or not the kernel should run on a GPU. If not specified, will be false.
  • enable_internet: Whether or not the kernel should be able to access the internet. If not specified, will be false.
  • machine_shape: The accelerator/GPU type to use (e.g., NvidiaTeslaT4, NvidiaTeslaP100, or Tpu1VmV38).
  • dataset_sources: A list of dataset sources, specified as "username/dataset-slug"
  • competition_sources: A list of competition sources, specified as "competition-slug"
  • kernel_sources: A list of kernel sources, specified as "username/kernel-slug"
  • model_sources: A list of model sources, specified as "username/model-slug/framework/variation-slug/version-number"

We will add further metadata processing in upcoming versions of the API.