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Run TimeGPT in a distributed manner using Dask.
Open In Colab Dask is an open-source parallel computing library for Python. This guide explains how to use TimeGPT from Nixtla with Dask for distributed forecasting tasks.
Before proceeding, make sure you have an API key from Nixtla.

Highlights

• Simplify distributed computing with Fugue. • Run TimeGPT at scale on a Dask cluster. • Seamlessly convert pandas DataFrames to Dask.

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Outline

  1. Installation
  2. Load Your Data
  3. Import Dask
  4. Use TimeGPT on Dask
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Step 1: Installation

Fugue provides an easy-to-use interface for distributed computing over frameworks like Dask.
You can install fugue with:
Install Fugue and Dask
pip install fugue[dask]
If running on a distributed Dask cluster, ensure the nixtla library is installed on all worker nodes.
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Step 2: Load Your Data

You can start by loading data into a pandas DataFrame. In this example, we use hourly electricity prices from multiple markets:
Load Electricity Data
import pandas as pd

df = pd.read_csv(
    'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/electricity-short.csv',
    parse_dates=['ds'],
)
df.head()
Example pandas DataFrame:
unique_iddsy
0BE2016-10-22 00:00:0070.00
1BE2016-10-22 01:00:0037.10
2BE2016-10-22 02:00:0037.10
3BE2016-10-22 03:00:0044.75
4BE2016-10-22 04:00:0037.10
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Step 3: Import Dask

Convert the pandas DataFrame into a Dask DataFrame for parallel processing.
Convert to Dask DataFrame
import dask.dataframe as dd

dask_df = dd.from_pandas(df, npartitions=2)
dask_df
When converting to a Dask DataFrame, you can specify the number of partitions based on your data size or system resources.
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Step 4: Use TimeGPT on Dask

To use TimeGPT with Dask, provide a Dask DataFrame to Nixtla’s client methods instead of a pandas DataFrame.

Important Concept: NixtlaClient

Instantiate the NixtlaClient class to interact with Nixtla’s API.
Initialize NixtlaClient
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    api_key='my_api_key_provided_by_nixtla'
)
To use Azure AI, set the base_url parameter:
Azure AI Endpoint Setup
nixtla_client = NixtlaClient(
    base_url="your Azure AI endpoint",
    api_key="your api_key"
)
You can use any method from the NixtlaClient, such as forecast or cross_validation.
  • Forecast Example
  • Cross-validation Example
Forecast with TimeGPT and Dask
fcst_df = nixtla_client.forecast(dask_df, h=12)
fcst_df.compute().head()
unique_iddsTimeGPT
0BE2016-12-31 00:00:0045.190453
1BE2016-12-31 01:00:0043.244446
2BE2016-12-31 02:00:0041.958389
3BE2016-12-31 03:00:0039.796486
4BE2016-12-31 04:00:0039.204533
When using an Azure AI endpoint, set model to "azureai":
Azure AI Model Usage
nixtla_client.forecast(..., model="azureai")
For the public API, two models are available: • timegpt-1 (default) • timegpt-1-long-horizonSee the Long Horizon Forecasting Tutorial for details on timegpt-1-long-horizon.
TimeGPT with Dask also supports exogenous variables. Refer to the Exogenous Variables Tutorial for details. Substitute pandas DataFrames with Dask DataFrames as needed.
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