Quickstart - Impulse Labs

Step 1 — Train a model

Go to app.impulselabs.ai and sign in.
Upload a CSV dataset under Datasets.
Click Train Model, pick your target column, and start training.
Once training finishes, note the Deployment ID shown on the model card — you’ll need it when calling /infer.

Deployment IDs look like clf-titanic-survived or a UUID. You can find them on the Models page in the dashboard.

Step 2 — Create an API key

In the dashboard, go to Settings → API Keys.
Click New API Key, give it a name (e.g. my-app), and click Create.
Copy the key immediately — it starts with imp_ and is shown only once.

Store your API key securely (e.g. in an environment variable). It cannot be retrieved after creation.

export IMPULSE_API_KEY="imp_your_key_here"
export DEPLOYMENT_ID="your-deployment-id"

Step 3 — Check deployment status

Before calling /infer, confirm the model is ACTIVE:

curl -s "https://inference.impulselabs.ai/deploy/status?deployment_id=$DEPLOYMENT_ID" \
  -H "Authorization: Bearer $IMPULSE_API_KEY" | jq .status

Expected response:

{
  "deployment_id": "your-deployment-id",
  "status": "ACTIVE"
}

If status is CREATED or BUILDING, wait a few seconds and retry — the model container is warming up.

Step 4 — Run your first inference

Replace the inputs fields with your actual feature column names and values:

curl -s -X POST "https://inference.impulselabs.ai/infer" \
  -H "Authorization: Bearer $IMPULSE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "deployment_id": "'$DEPLOYMENT_ID'",
    "inputs": {
      "Pclass": 3,
      "Sex": "male",
      "Age": 22,
      "SibSp": 1,
      "Parch": 0,
      "Fare": 7.25,
      "Embarked": "S"
    }
  }'

Response:

{
  "prediction": 0,
  "probability": 0.142,
  "target": "Survived"
}

Next steps

Use with Codex

Train, inspect, and deploy models from Codex

Authentication

Manage multiple API keys

Rate limits

Understand limits and headers

Error reference

Handle errors gracefully

Inference API

Full parameter reference

​Step 1 — Train a model

​Step 2 — Create an API key

​Step 3 — Check deployment status

​Step 4 — Run your first inference

​Next steps

Use with Codex

Authentication

Rate limits

Error reference

Inference API

Step 1 — Train a model

Step 2 — Create an API key

Step 3 — Check deployment status

Step 4 — Run your first inference

Next steps