Laminar datasets CLI

The lmnr-cli dataset command manages datasets in Laminar from your terminal: list them, create new ones from local files, and push or pull datapoints. It ships in the standalone lmnr-cli npm package, so you do not need the @lmnr-ai/lmnr SDK installed to use it.

lmnr-cli replaces the dataset commands that used to ship with the @lmnr-ai/lmnr SDK (lmnr datasets ...). The old SDK-bundled CLI is deprecated; use lmnr-cli dataset instead.

Install

# Run directly with npx (no install)
npx lmnr-cli@latest dataset list

# Or install globally
npm install -g lmnr-cli

The rest of this page assumes a global install (lmnr-cli dataset ...). With npx, prefix each command with npx lmnr-cli@latest.

Usage

Creating a new dataset and iterating on it

1. Prepare input files

   Prepare input files for the dataset. Supported formats are: .json, .jsonl, .csv. Every datapoint must at least have a data field. Save this file as data.json (or data.jsonl or data.csv).

   For JSON, the file must contain one array of datapoints.

   For JSONL, the file must contain one datapoint per line.

   For CSV, the file must contain a header row and one datapoint per row.

   Examples:

   Show JSON

   [
       { "data": { "color": "red", "size": "large" } },
       { "data": { "color": "blue", "size": "small" } },
   ]
   

   Show JSONL

   {"data": {"color": "red", "size": "large"}}
   {"data": {"color": "blue", "size": "small"}}
   

   Show CSV

   data,target
   "{""color"": ""red"", ""size"": ""large""}","{""expected_output"": ""red""}"
   "{""color"": ""blue"", ""size"": ""small""}","{""expected_output"": ""blue""}"
   
2. Authenticate and link your project

   The CLI authenticates as you, the signed-in user. Run lmnr-cli setup once in your project: it logs you in (opens a browser to authorize the CLI) and links the current directory to a project by writing .lmnr/project.json. Every dataset command then runs against that linked project.

   lmnr-cli setup
   

   Alternatively, target a project explicitly with --project-id on any command:

   lmnr-cli dataset --project-id <your-project-id> list
   

   Unlike the old SDK-bundled CLI, lmnr-cli does not use a project API key. See Authenticate and Directory-scoped projects for details.

3. Create a new dataset

   Create a new dataset from the input file. This command will create a new dataset with the name my-cli-dataset and save the datapoints to the file my-cli-dataset.json.

   The datapoints are saved to a new file in order to:

   • Store datasets in the Laminar format. In particular, datapoint id is crucial for versioning (Learn more).
   • Not overwrite existing files.

   lmnr-cli dataset create my-cli-dataset data.json -o my-cli-dataset.json
   
4. Work on the dataset locally

   Make any changes required to the dataset by editing the file my-cli-dataset.json.

   Make sure to not edit the id field of the datapoints.

   If you delete a datapoint, this will not affect the dataset in Laminar. This is because the push operation only pushes new datapoint (versions) to the dataset.

5. Push the changes to Laminar

   Push the changes to Laminar.

   lmnr-cli dataset push my-cli-dataset.json -n my-cli-dataset
   

   This will push the changes to the dataset in Laminar.

6. Pull the changes from Laminar

   If you need to update the local dataset with the latest changes from Laminar, you can pull the changes.

   lmnr-cli dataset pull my-cli-dataset.json -n my-cli-dataset
   

   This will pull the changes from the dataset in Laminar to the local file my-cli-dataset.json.

   This will overwrite the contents of the current file my-cli-dataset.json.

Working on an existing dataset

1. Authenticate and link your project

   Run lmnr-cli setup once to log in and link the current directory to your project (see above):

   lmnr-cli setup
   
2. Select the dataset to work on

   List all datasets and select the one you want to work on.

   lmnr-cli dataset list
   
3. Pull the data from Laminar

   Pull the data from Laminar to a local file.

   lmnr-cli dataset pull my-dataset.json -n my-dataset
   

   This will pull the changes from the dataset in Laminar to the local file my-dataset.json.

   If my-dataset.json already exists, this will overwrite the contents of the file.

4. Work on the dataset locally

   Make any changes required to the dataset by editing the file my-dataset.json.

   Make sure to not edit the id field of the datapoints.

   If you delete a datapoint, this will not affect the dataset in Laminar. This is because the push operation only pushes new datapoint (versions) to the dataset.

5. Push the changes to Laminar

   Push the changes to Laminar.

   lmnr-cli dataset push my-dataset.json -n my-dataset
   

   This will push the changes to the dataset in Laminar.

Setting the CLI to call a local Laminar instance

The dataset command has optional arguments for pointing at a self-hosted instance:

• --base-url: The base URL of the Laminar instance. Do NOT include port here. Default is https://api.lmnr.ai (or the LMNR_BASE_URL env variable).
• --port: The HTTP port of the Laminar instance. Default is 443 (or the LMNR_HTTP_PORT env variable). For local self-hosted Laminar, use 8000.
• --project-id: The id of the project to target. If not provided, resolves from the nearest .lmnr/project.json.

lmnr-cli dataset --base-url http://localhost --port 8000 list

See Self-hosting for the full self-hosted setup.

Reference

lmnr-cli dataset [command]

General options

These are shared by every dataset subcommand.

  --project-id <id>   Target project id. Defaults to the linked .lmnr/project.json
  --base-url <url>    Base URL for the Laminar API. Defaults to https://api.lmnr.ai or LMNR_BASE_URL env variable
  --port <port>       Port for the Laminar API. Defaults to 443 or LMNR_HTTP_PORT env variable
  --json              Output structured JSON to stdout

Commands

List all datasets

List all datasets.

lmnr-cli dataset list

Create a new dataset

Create a dataset from input files.

lmnr-cli dataset create [options] <name> <paths...>

Arguments:
  name                      Name of the dataset to create
  paths                     Paths to files or directories containing data to push

Options:
  -o, --output-file <file>  Path to save the pulled data (required)
  --output-format <format>  Output format (json, csv, jsonl). Inferred from file extension if not provided
  -r, --recursive           Recursively read files in directories (default: false)
  --batch-size <size>       Batch size for pushing/pulling data (default: 100)

Push datapoints to a dataset

Push datapoints to an existing dataset from a file or files.

lmnr-cli dataset push [options] <paths...>

Arguments:
  paths                Paths to files or directories containing data to push

Options:
  -n, --name <name>    Name of the dataset (either name or id must be provided)
  --id <id>            ID of the dataset (either name or id must be provided)
  -r, --recursive      Recursively read files in directories (default: false)
  --batch-size <size>  Batch size for pushing data (default: 100)

Pull datapoints from a dataset

Pull datapoints from a dataset to a file or print them to the console.

lmnr-cli dataset pull [options] [output-path]

Arguments:
  output-path               Path to save the data. If not provided, prints to console

Options:
  -n, --name <name>         Name of the dataset (either name or id must be provided)
  --id <id>                 ID of the dataset (either name or id must be provided)
  --output-format <format>  Output format (json, csv, jsonl). Inferred from file extension if not provided
  --batch-size <size>       Batch size for pulling data (default: 100)
  --limit <limit>           Limit number of datapoints to pull
  --offset <offset>         Offset for pagination (default: 0)