> ## Documentation Index > Fetch the complete documentation index at: https://docs.platform.toloka.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Projects > Use projects as the shared workspace for datasets, Data Labeling pipelines, Training drafts, models, files, and secrets. A project is the workspace shared by Data Labeling and Train. It belongs to one team and holds the resources for one body of work: datasets, pipelines and their runs, project files and secrets, Training drafts and Training Runs, and Models with their Model Versions. The same project can connect the products. You can label examples in a project dataset, prepare those rows for Training, and train from that dataset without creating a separate account or balance. ## Ownership and access Every project belongs to one [team](https://docs.platform.toloka.ai/docs/core/teams-and-balance.md#teams). The team's access level applies to the project and its resources: * **Full access** members can create and change project resources. * **Read only** members can open projects, datasets, pipelines, runs, and results but cannot change them. All projects in the team use the team's single balance. See [Billing](https://docs.platform.toloka.ai/docs/core/billing.md) for the different ways pipeline runs and Training Runs use it. ## What a project contains | Resource | Product scope | Purpose | | - | - | - | | Datasets | Shared | Store rows used by pipelines or Training. Any pipeline in the project can bind one of its datasets, and a Training draft reads a frozen slice of one of them. | | Pipelines and pipeline runs | Data Labeling | Define labeling work, process dataset items, and write results back. | | Trainings and Training Runs | Train | Define fine-tuning or gisting work and execute an immutable billed run. | | Models and Model Versions | Train | Organize the immutable outputs published by completed Training Runs. | | Secrets | Data Labeling | Keep credentials used by Code nodes out of pipeline source. | | Project files | Data Labeling | Store files attached for the project's agent workflow. | ## Create or select a project Choose the surface that matches your work: * **Web app for Data Labeling.** From the team's project list, click **+ Project**, describe the labeling task, and attach data if needed. The project opens with the agent building a pipeline from that description. * **CLI for either product.** Run `toloka project create --team-id ` in a new checkout. Use `toloka project add ` to select an existing project instead. * **HTTP API for either product.** Create or list projects through the beta API, then use the project ID with Data Labeling or Train resources. The CLI records one selected project in `toloka.json` for a checkout. A second project needs a separate checkout or an explicit project ID with its state files kept separate. See [CLI project selection](https://docs.platform.toloka.ai/docs/cli.md#how-the-cli-finds-a-project) for the precedence rules. ### Empty draft projects Choosing a product or template on the home screen creates a draft project. The platform automatically deletes a draft project that owns nothing once it is more than a day old, measured from when the draft was created. A draft owns nothing when it has no dataset, pipeline, secret, Training, Model, or other project resource. Deletion also removes the draft's agent chat thread and any project files uploaded to it. If you return to an old browser tab for an empty draft, the project is gone. Start a new draft from the home screen instead. Drafts that own a dataset or any other resource are kept. ## Use a project for Data Labeling A Data Labeling project combines the agent chat, datasets, and pipelines. Start with [Get your first labeled dataset](https://docs.platform.toloka.ai/docs/data/get-started/first-labeled-dataset.md) to create a project in the web app and run its first pipeline. For source-controlled authoring, the [TypeScript SDK](https://docs.platform.toloka.ai/docs/sdk/pipelines.md) defines pipeline source and the [CLI](https://docs.platform.toloka.ai/docs/cli.md#data-labeling) imports, checks, plans, and applies it. ## Use a project for Train Train uses the same project and dataset resources. A Training draft names a model source, a method, a frozen slice of a project dataset with its split column, and settings. A billed Training Run copies that configuration and publishes a Model Version when it completes. Start with [Train your first model](https://docs.platform.toloka.ai/docs/train/get-started/first-trained-model.md). If your examples come from labeling results, follow [Prepare training data from labels](https://docs.platform.toloka.ai/docs/train/guides/prepare-training-data.md). ## Related * [Create and manage datasets](https://docs.platform.toloka.ai/docs/core/datasets.md) — load and maintain shared project data * [Teams, access, and balance](https://docs.platform.toloka.ai/docs/core/teams-and-balance.md) — project ownership and permissions * [HTTP API](https://docs.platform.toloka.ai/docs/api-reference.md) — automate projects and both product surfaces This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.