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toto

Datadog Toto-2, 4M to 2.5B parameters, plus every hub model.

extra CPU tag GPU tag families models example
toto :toto :toto-gpu Toto-2 5 toto_2_0_4m

Start a server

docker run --rm -p 8000:8000 sktime/tserve:toto toto_2_0_4m

uv pip install "tserve[server,toto]"

uv run tserve toto_2_0_4m

pip install "tserve[server,toto]"

tserve toto_2_0_4m

Check what loaded:

curl -s http://127.0.0.1:8000/models

Predict

Python needs the client extra on the caller.

curl -s http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{
  "past": {
    "timestamp": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05"],
    "sales": [120, 135, 128, 142, 138]
  },
  "time": "timestamp",
  "target": ["sales"],
  "fh": 3,
  "model": "toto_2_0_4m"
}'

curl.exe -s http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"past":{"timestamp":["2024-01-01","2024-01-02","2024-01-03","2024-01-04","2024-01-05"],"sales":[120,135,128,142,138]},"time":"timestamp","target":["sales"],"fh":3,"model":"toto_2_0_4m"}'

from tserve.client import Client

past = {
    "timestamp": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05"],
    "sales": [120, 135, 128, 142, 138],
}

with Client("http://127.0.0.1:8000") as client:
    result = client.predict(
        past=past,
        time="timestamp",
        target=["sales"],
        fh=3,
        model="toto_2_0_4m",
    )
print(result.predictions)

Models

Toto-2

Toto2Forecaster extra toto 5 models
multivariate exogenous quantiles
model checkpoint
toto_2_0_4m Datadog/Toto-2.0-4m
toto_2_0_22m Datadog/Toto-2.0-22m
toto_2_0_313m Datadog/Toto-2.0-313m
toto_2_0_1b Datadog/Toto-2.0-1B
toto_2_0_2_5b Datadog/Toto-2.0-2.5B

Also loadable here

Next steps