Picture by Creator
The Kaggle CLI (Command Line Interface) means that you can work together with Kaggle’s datasets, competitions, notebooks, and fashions straight out of your terminal. That is helpful for automating downloads, submissions, and dataset administration with no need an online browser. Most of my GitHub Motion workflows use Kaggle CLI for downloading or pushing datasets, as it’s the quickest and best method.
1. Set up & Setup
Be sure you have Python 3.10+ put in. Then, run the next command in your terminal to put in the official Kaggle API:
To acquire your Kaggle credentials, obtain the kaggle.json file out of your Kaggle account settings by clicking “Create New Token.”
Subsequent, set the surroundings variables in your native system:
- KAGGLE_USERNAME=
- KAGGLE_API_KEY=
- KAGGLE_API_KEY=
2. Competitions
Kaggle Competitions are hosted challenges the place you’ll be able to remedy machine studying issues, obtain knowledge, submit predictions, and see your outcomes on the leaderboard.
The CLI helps you automate every little thing: looking competitions, downloading information, submitting options, and extra.
Checklist Competitions
kaggle competitions listing -s Reveals a listing of Kaggle competitions, optionally filtered by a search time period. Helpful for locating new challenges to hitch.
Checklist Competitors Recordsdata
kaggle competitions information Shows all information accessible for a particular competitors, so you understand what knowledge is offered.
Obtain Competitors Recordsdata
kaggle competitions obtain (-f ) (-p ) Downloads all or particular information from a contest to your native machine. Use -f to specify a file, -p to set the obtain folder.
Undergo a Competitors
kaggle competitions submit -f -m "" Add your answer file to a contest with an elective message describing your submission.
Checklist Your Submissions
kaggle competitions submissions Reveals all of your earlier submissions for a contest, together with scores and timestamps.
View Leaderboard
kaggle competitions leaderboard (-s) Shows the present leaderboard for a contest. Use -s to indicate solely the highest entries.
3. Datasets
Kaggle Datasets are collections of knowledge shared by the group. The dataset CLI instructions show you how to discover, obtain, and add datasets, in addition to handle dataset variations.
Checklist Datasets
Finds datasets on Kaggle, optionally filtered by a search time period. Nice for locating knowledge in your tasks.
Checklist Recordsdata in a Dataset
Reveals all information included in a particular dataset, so you’ll be able to see what’s accessible earlier than downloading.
Obtain Dataset Recordsdata
kaggle datasets obtain / (-f ) (--unzip) Downloads all or particular information from a dataset. Use –unzip to robotically extract zipped information.
Initialize Dataset Metadata
Creates a metadata file in a folder, making ready it for dataset creation or versioning.
Create a New Dataset
kaggle datasets create -p Uploads a brand new dataset from a folder containing your knowledge and metadata.
Create a New Dataset Model
kaggle datasets model -p -m "" Uploads a brand new model of an present dataset, with a message describing the modifications.
4. Notebooks
Kaggle Notebooks are executable code snippets or notebooks. The CLI means that you can listing, obtain, add, and examine the standing of those notebooks, which is helpful for sharing or automating evaluation.
Checklist Kernels
Finds public Kaggle notebooks (kernels) matching your search time period.
Get Kernel Code
Downloads the code for a particular kernel to your native machine.
Initialize Kernel Metadata
Creates a metadata file in a folder, making ready it for kernel creation or updates.
Replace Kernel
Uploads new code and runs the kernel, updating it on Kaggle.
Get Kernel Output
kaggle kernels output / -p Downloads the output information generated by a kernel run.
Examine Kernel Standing
Reveals the present standing (e.g., operating, full, failed) of a kernel.
5. Fashions
Kaggle Fashions are versioned machine studying fashions you’ll be able to share, reuse, or deploy. The CLI helps handle these fashions, from itemizing and downloading to creating and updating them.
Checklist Fashions
Finds public fashions on Kaggle matching your search time period.
Get a Mannequin
Downloads a mannequin and its metadata to your native machine.
Initialize Mannequin Metadata
Creates a metadata file in a folder, making ready it for mannequin creation.
Create a New Mannequin
Uploads a brand new mannequin to Kaggle out of your native folder.
Replace a Mannequin
Uploads a brand new model of an present mannequin.
Delete a Mannequin
Removes a mannequin from Kaggle.
6. Config
Kaggle CLI configuration instructions management default behaviors, reminiscent of obtain places and your default competitors. Alter these settings to make your workflow smoother.
View Config
Shows your present Kaggle CLI configuration settings (e.g., default competitors, obtain path).
Set Config
Units a configuration worth, reminiscent of default competitors or obtain path.
Unset Config
Removes a configuration worth, reverting to default habits.
7. Ideas
- Use -h or –help after any command for detailed choices and utilization
- Use -v for CSV output, -q for quiet mode
- You have to settle for competitors guidelines on the Kaggle web site earlier than downloading or submitting to competitions
Abid Ali Awan (@1abidaliawan) is an authorized knowledge scientist skilled who loves constructing machine studying fashions. At the moment, he’s specializing in content material creation and writing technical blogs on machine studying and knowledge science applied sciences. Abid holds a Grasp’s diploma in expertise administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids scuffling with psychological sickness.
