VenkatManda/KaggleQuestions huggingface dataset:
Category: noteboook
Question: How to add data sources to Kaggle Notebooks?
Answer: One of the advantages of using Notebooks as your data science workbench is that you can easily add data sources from thousands of publicly available Datasets or even upload your own. You can also use output files from another Notebook as a data source. You can add multiple data sources to your Notebook’s environment, allowing you to join together interesting datasets.

## Datasets

Kaggle Datasets provide a rich mix of interesting datasets for any kind of data science project.

There are two ways of loading a Dataset in a Notebook. The first is to navigate to a chosen dataset’s landing page, then click on the “[New Notebook](https://www.kaggle.com/notebooks?modal=true)” button. This will launch a new Notebook session with the dataset in question spun up and ready to go.

Alternatively, you may wish to add datasets after creating your Notebook. To do that, navigate to the “Data” pane in a Notebook editor and click the “Add Data” button. This will open a modal that lets you select Datasets to add to your Notebook.

## Competitions

You can also add Competition data sources to your Notebook environment using the same steps as above.

The main difference is that you need to accept the rules for any Competition data sources you add to your Notebook. Whether you start a new Notebook from the “Notebooks” tab of a Competition or add a Competition data source from an existing Notebook editor, you’ll be prompted to read and accept the rules first.

You can mix Competitions and Datasets data sources in the same Notebook, but please be sure to abide by the rules of the specific Competition with respect to using external data sources. If you don’t, you risk consequences for rule-breaking in the Competition.

## Notebooks

You will notice that there is a third option in the “Add Data” modal: Notebook Output Files.

Up to 20 GBs of output from a Notebook may be saved to disk in `/kaggle/working`. This data is saved automatically and you can then reuse that data in any future Notebook: just navigate to the “Data” pane in a Notebook editor, click on “Add Data”, click on the "Notebook Output Files" tab, find a Notebook of interest, and then click to add it to your current Notebook.

By chaining Notebooks as data sources in this way, it’s possible to build pipelines and generate more and better content than you could in a single notebook alone.

“[Minimal LSTM + NB-SVM baseline ensemble](https://www.kaggle.com/jhoward/minimal-lstm-nb-svm-baseline-ensemble/notebook)”, written by Jeremy Howard, is one example of a great Notebook using this feature. Click on the “Data” tab to view the data sources he uses.
Category: noteboook
Question: How to collaborate on Kaggle Notebooks?
Answer: Notebooks collaboration is a powerful feature. It allows multiple users to co-own and edit a Notebook. For example, you can work with Competition teammates to iterate on a model or collaborate with classmates on a data science project.

## Inviting Collaborators

From your Notebook editor or viewer, public or private, you may navigate to the 'Share' or 'Sharing' button in the Notebook’s menu to expose, among other settings, the Collaborators options. There, use the search box to find and add other users as Notebook collaborators.

If your Notebook is private, you may choose between giving Collaborators either viewing privileges (“Can view”) or editing privileges (“Can edit”). If your Notebook is public, Collaborators can only be added with editing privileges (“Can edit”), as anyone can view it already.

When you add a collaborator, they will receive a notification via email.

“[Creating, Reading & Writing Data](https://www.kaggle.com/residentmario/creating-reading-writing-data)”, a Notebook from the Advanced Pandas Kaggle Learn track, is one example of great collaborative Notebook.

## Collaborating on Datasets

Using Notebooks is a powerful way to work with your collaborators on Datasets, too.

Datasets created on Kaggle also have privacy settings, and these settings are distinct from the sharing settings on your Notebook meaning each can be shared with a different group of users. That is, your Notebook collaborators won’t automatically have the same access to any private Datasets as you unless they are explicitly invited to collaborate on the Dataset. Anyone has access to Datasets shared publicly.

To learn more about how to use Datasets collaboratively, read more [here](https://www.kaggle.com/docs/datasets#collaborating-on-datasets).
Category: noteboook
Question: How to add GPU to Kaggle Notebook?
Answer: You can add a single NVIDIA Tesla P100 or two NVIDIA T4 GPUs to your Notebook for free. GPU environments have lower CPU and main memory, but are a great way to achieve significant speed-ups for certain types of work like training neural networks on image data. One of the major benefits to using Notebooks as opposed to a local machine or your own VM is that the Notebook environment is already pre-configured with GPU-ready software and packages which can be time-consuming and frustrating to set up. Free GPU availability is limited: in busy times, you might be placed in a queue.

To add a GPU, navigate to the “Settings” pane from the Notebook editor and click the “Accelerator" > GPU option. Your session will restart which may take a few moments to several minutes if you don’t need to wait in a queue to access a GPU-enabled machine.

To learn more about getting the most out of using a GPU in Notebooks, check out this tutorial Notebook by Dan Becker [here](https://www.kaggle.com/dansbecker/running-kaggle-kernels-with-a-gpu).
Category: noteboook
Question: How can I add TPU to a notebook on Kaggle?
Answer: You can add a TPU v3-8 to your Notebook for free. TPUs are hardware accelerators specialized in deep learning tasks. They are supported in TensorFlow 2.1 both through the Keras high-level API and, at a lower level, in models using a custom training loop. Free TPU availability is limited: in busy times, you might be placed in a queue. To learn more about getting the most out of using a TPU in Notebooks, check out this in-depth guide [here](https://www.kaggle.com/docs/tpu).

To add a TPU, navigate to the “Settings” pane from the Notebook editor and click the “Accelerator" > TPU v3-8 option. Your session will restart which may take a few moments to several minutes if you don’t need to wait in a queue to access a TPU-enabled machine.
Category: noteboook
Question: What are the technical specifications of Kaggle Notebooks?
Answer: Kaggle Notebooks run in a remote computational environment. We provide the hardware—you need only worry about the code.

At the time of writing, each Notebook editing session is provided with the following resources:

- 12 hours execution time for CPU and GPU notebook sessions and 9 hours for TPU notebook sessions
- 20 Gigabytes of auto-saved disk space (/kaggle/working)
- Additional scratchpad disk space (outside /kaggle/working) that will not be saved outside of the current session

## CPU Specifications

- 4 CPU cores
- 30 Gigabytes of RAM

## P100 GPU Specifications

- 1 Nvidia Tesla P100 GPU
- 4 CPU cores
- 29 Gigabytes of RAM

## T4 x2 GPU Specifications

- 2 Nvidia Tesla T4 GPUs
- 4 CPU cores
- 29 Gigabytes of RAM

## TPU 1VM Specifications

- 96 CPU cores
- 330 Gigabytes of RAM

**NOTE:** CPU Platforms (ex. Intel Skylake, Broadwell, AMD) may be variable during regular notebook runs; however, submissions runs (for code competitions or when submissions are rerun in bulk) are always run on Intel Skylake CPUs.

### CPU Specifications

While editing a Notebook, you are provided with 20 minutes of idle time for your interactive session. If the code is not modified or executed in that time, the current interactive session will end. If this happens, you will need to click the Edit button again to continue editing. If you want to run a computation that takes longer, you can Save a Version of your Notebook from top to bottom by selecting the "Save & Run All" option in the "Save Version" menu (see below).

Once you are satisfied with the contents of the Notebook, you can click "Save Version" to save your changes. From there, you will have two options for creating a new version:

- **Quick Save**: skips the top-to-bottom notebook execution and just takes a snapshot of your notebook exactly as it’s displayed in the editor. This is a great option for taking a bunch of versions while you’re still actively experimenting. Quick Save is a brand new way of saving work on Kaggle.

- **Save & Run All**: creates a new session with a completely clean state and runs your notebook from top to bottom. This is perfect for major milestones or when you want to share your work, as it gives you (and anyone else who reads your notebook) the confidence that your notebook can be run reproducibly. In order to save successfully, the entire Notebook must execute within 12 hours (9 hours for TPU notebooks). Save & Run All is identical to the “Commit” behavior you may have used previously on Kaggle.
Category: organization
Question: What is organization profiles on Kaggle?
Answer: Anyone can create an organization profile on Kaggle. Organization
profiles allow anyone in the community can find your organization's
datasets, models, and competitions in one place.
Category: organization
Question: How do organization profiles work?
Answer: ## What are organizations for?

Organization profiles are a "landing page" for your organization's published competitions, models, and datasets. For example, it gives you an easy way to share (and other users to find) all of the datasets and models that your team has published with a single link.

## What are organizations NOT for?

Currently, organizations are not meant to be used as a tool for collaboration with a group of people. While all members of an organization can create competitions, datasets, and models as an organization, this does not give other members of the organization the ability to manage that content (edit, delete, update, or view private resources). Read more about organization permissions below.

## Who should create and use organization profiles?

There are a number of groups for whom organization profiles can be helpful! For professors, an organization profile can make it easier to see and manage the community competitions that you host for your classes. For research labs, whether part of a university or industry corporation, organization profiles provide a way to organize the models and datasets your team has published in one place. For large companies, an organization profile will display all of the competitions you've hosted.
Category: organization
Question: How to create a new organization profile on Kaggle?

Answer: ## Creation

Anyone can create an organization profile. To create one, click on the "+Create" button in the upper lefthand corner on any page on Kaggle. This will open up the creation flow.

On this page you'll fill out the following information:

- **Name**: The name of your organization
- **Tagline**: A short description of your organization
- **URL**: You should edit this to something that's short. All links to this organization page will start with this URL, e.g., any datasets or models it owns.
- **Website**: A URL to your organization website
- **Image**: A 400 x 400px image of your organization logo
- **Moderation Details**: Information you share here won't appear on your organization profile page, but will be used by our team to review your organization for approval.

You'll be able to change your organization Name, Tagline, Website, and Image among other things once you've clicked "Create organization". You will also be able to add a bio and invite members to your organization and more.

Once you click "Create organization", your organization will be reviewed by Kaggle's moderation team for approval before it's made public. Continue to the next section "Review" to learn more about the next steps.

## Review

While your organization is being reviewed by Kaggle's moderation team it's in a "pending" state.

While your organization is in a pending state, you are able to invite members but you won't be able to start creating competitions, datasets, or models under your organization profile until it's approved.

While your organization is in a pending state, the organization profile will not be publicly visible to non-members.

At this point, Kaggle's moderation team will review your organization profile for approval. You will receive a notification when your organization profile's status changes.

If you have questions about the review process or you would like to appeal a review, please see our [contact page](https://www.kaggle.com/contact#/other/issue).

## Approval

Once your organization has been approved, you'll receive an email and/or site notification. You and other members of the organization can now create organization-owned datasets, models, or competitions including making them public. Anyone can also see your organization's profile page.
Category: organization
Question: What permissions do members of an organization have?
Answer: ## Abilities of organization members

Organization members can create datasets, models, and competitions under approved organization profiles.

Again, organizations are not currently meant to be used as a tool for collaboration with a group of people. While all members of an organization can create competitions, datasets, and models as an organization, this does not give other members of the organization the ability to manage that content (edit, delete, update, or view private resources).

If you want to share private datasets or models owned by an organization profile, you will need to use Collaboration features.

Similarly, organization members are NOT able to see any unlaunched competitions unless their user is the creator of the competition.

Members will not be able to add new members to an organization unless the organization owner shares the unique invitation link.

## Abilities of organization admins

Organization admins have the same abilities and permissions as organization members. In addition, they can add and remove members, transfer ownership of the organization to another member, and edit information about the organization (logo, tagline, description, etc.).
Category: organization
Question: How to create contents as an organization?
Answer: ## Competitions

Anyone can host a community competition by clicking the "+Create" button in the upper lefthand corner of any page on Kaggle and selecting "Competition." In order to associate your competition with an organization profile that you are an admin or member of, simply choose your organization from the "Creating As" dropdown.

When a competition is created under an organization profile, the competition will feature your organization's logo, and the competition will show up on the "Competitions" tab of your organization's profile page.

When a competition is created under an organization profile, there are NO changes to who can see or manage your competition. That is, other members of the organization cannot see an unlaunched competition, and they cannot manage the settings of your competition when it is launched.

## Datasets and Models

Anyone can publish datasets or models by clicking the "+Create" button in the upper lefthand corner of any page on Kaggle and selecting "Dataset" or "Model". In order to associate your dataset or model with an organization profile that you are an admin or member of, simply choose your organization from the "Creating As" dropdown.

When a dataset or model is created under an organization profile, the dataset or model will feature your organization's logo, and the dataset or model will show up on the "Datasets" or "Models" tab respectively of your organization's profile page.

When a dataset or model is created under an organization profile, other members will be able to see it while it's private. There are NO changes to who can see or manage your datasets or models created under an organization profile. That is, other members of the organization cannot edit, delete, or update the datasets or models unless they are separately added as edit collaborators on the "Settings" tab of the dataset or model.
Category: api
Question: How can one get started with installation and authentication for Kaggle's public API?
Answer: The easiest way to interact with Kaggle’s public API is via our command-line tool (CLI) implemented in Python. This section covers installation of the kaggle package and authentication.

## Installation

Ensure you have Python and the package manager pip installed. Run the following command to access the Kaggle API using the command line: `pip install kaggle` (You may need to do `pip install --user kaggle` on Mac/Linux. This is recommended if problems come up during the installation process.) Follow the authentication steps below and you’ll be able to use the `kaggle` CLI tool.

If you run into a `kaggle: command not found` error, ensure that your python binaries are on your path. You can see where kaggle is installed by doing `pip uninstall kaggle` and seeing where the binary is. For a local user install on Linux, the default location is `~/.local/bin`. On Windows, the default location is `$PYTHON_HOME/Scripts`.

## Authentication

In order to use the Kaggle’s public API [https://github.com/Kaggle/kaggle-api#api-credentials](https://github.com/Kaggle/kaggle-api#api-credentials), you must first authenticate using an API token. Go to the 'Account' tab of your user profile [https://www.kaggle.com/settings/account](https://www.kaggle.com/settings/account) and select 'Create New Token'. This will trigger the download of `kaggle.json`, a file containing your API credentials.

If you are using the Kaggle CLI tool, the tool will look for this token at `~/.kaggle/kaggle.json` on Linux, OSX, and other UNIX-based operating systems, and at `C:\Users\<Windows-username>\.kaggle\kaggle.json` on Windows. If the token is not there, an error will be raised. Hence, once you’ve downloaded the token, you should move it from your Downloads folder to this folder.

If you are using the Kaggle API directly, where you keep the token doesn’t matter, so long as you are able to provide your credentials at runtime.
Category: api
Question: How can one interacting with competitions using Kaggle API or CLI?
Answer: The Kaggle API and CLI tool provide easy ways to interact with Competitions on Kaggle. The commands available can make participating in competitions a seamless part of your model building workflow.

If you haven’t installed the package needed to use the command line tool or generated an API token, check out the getting started steps first.

Just like participating in a Competition normally through the user interface, you must read and accept the rules in order to download data or make submissions. You cannot accept Competition rules via the API. You must do this by visiting the Kaggle website and accepting the rules there.

Some of the commands for interacting with Competitions via CLI include:

- `kaggle competitions list`: list the currently active competitions
- `kaggle competitions download -c [COMPETITION]`: download files associated with a competition
- `kaggle competitions submit -c [COMPETITION] -f [FILE] -m [MESSAGE]`: make a competition submission

View all available commands on the official documentation on GitHub [here](https://github.com/Kaggle/kaggle-api#competitions) and keep up-to-date with the latest features and bug fixes in the [changelog](https://github.com/Kaggle/kaggle-api/blob/master/CHANGELOG.md).

To explore additional CLI arguments, remember that you can always append `-h` after any call to see the help menu for that command.

# Submitting to a Competition

Assuming that you have already accepted the terms of a Competition (this can only be done through the website, and not through the CLI), you may use the Kaggle CLI to submit predictions to the Competition and have them scored. To do so, run the command `kaggle competitions submit -c [COMPETITION NAME] -f [FILE PATH]`.

You can list all previous submissions to a Competition you have entered using the command `kaggle competitions submissions -c [COMPETITION NAME]`.

To explore some further CLI arguments, remember that you can always append `-h` after any call to see the help menu for that command.
Category: competition-setup
Question: Can I create a competition on Kaggle?
Answer: Anybody can launch a machine learning competition using Kaggle's
Community Competitions platform, including educators, researchers,
companies, meetup groups, hackathon hosts, or inquisitive individuals!
Category: competition-setup
Question: How do Kaggle competitions work?
Answer: ## Overview

Every competition has two things:

a) a clearly defined problem that participants need to solve using a machine learning model
b) a dataset that’s used both for training and evaluating the effectiveness of these models.

For example, in the [Store Sales – Time Series Forecasting](https://www.kaggle.com/competitions/store-sales-time-series-forecasting) competition, participants must accurately predict how many of each grocery item will sell using a dataset of past product and sales information from a grocery retailer.

Once the competition starts, participants can submit their predictions. Kaggle will score them for accuracy, and the team will be placed on a ranked leaderboard. The team at the top of the leaderboard at the deadline wins!

## Datasets, Submissions & Leaderboards

Every competition’s dataset is split into two smaller datasets.

- One of these smaller datasets will be given to participants to train their models, typically named `train.csv`.
- The other dataset will be mostly hidden from participants and used by Kaggle for testing and scoring, named `test.csv` and `solution.csv` (`test.csv` is the same as `solution.csv` except that `test.csv` contains the feature values and `solution.csv` contains the ground truth variable(s) – participants will never, ever see `solution.csv`).

When a participant feels ready to make a submission to the competition, they will use `test.csv` to generate a prediction and upload a CSV file. Kaggle will automatically score the submission for accuracy using the hidden `solution.csv` file.

Most competitions have a maximum number of submissions that a participant can make each day and a final deadline at which point the leaderboard will be frozen.

It’s conceivable that a participant could use the mechanics of a Kaggle competition to overfit a solution - which would be great for winning a competition, but not valuable for a real-world application.

To help prevent this, Kaggle has two leaderboards – the public and private leaderboard. The competition host splits the `solution.csv` dataset into two parts, using one part for the public leaderboard and another part for the private leaderboard. Participants generally will now know which samples are public vs private. The private leaderboard is kept a secret until after the competition deadline and is used as the official leaderboard for determining the final ranking.