Run TimeGPT distributedly on top of Ray
TimeGPT
on top of Ray.
Outline:
Note You can installIf executing on a distributedfugue
withpip
:
Ray
cluster, ensure that the nixtla
library is installed across all the workers.
pandas
DataFrame. In this tutorial, we
will use a dataset that contains hourly electricity prices from
different markets.
unique_id | ds | y | |
---|---|---|---|
0 | BE | 2016-10-22 00:00:00 | 70.00 |
1 | BE | 2016-10-22 01:00:00 | 37.10 |
2 | BE | 2016-10-22 02:00:00 | 37.10 |
3 | BE | 2016-10-22 03:00:00 | 44.75 |
4 | BE | 2016-10-22 04:00:00 | 37.10 |
Ray
and convert the pandas DataFrame to a Ray
DataFrame.
TimeGPT
on top of Ray
is almost identical to the
non-distributed case. The only difference is that you need to use a
Ray
DataFrame.
First, instantiate the
NixtlaClient
class.
👍 Use an Azure AI endpoint To use an Azure AI endpoint, set theThen use any method from thebase_url
argument:nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")
NixtlaClient
class such as
forecast
or
cross_validation
.
📘 Available models in Azure AI If you are using an Azure AI endpoint, please be sure to setTo visualize the result, use themodel="azureai"
:nixtla_client.forecast(..., model="azureai")
For the public API, we support two models:timegpt-1
andtimegpt-1-long-horizon
. By default,timegpt-1
is used. Please see this tutorial on how and when to usetimegpt-1-long-horizon
.
to_pandas
method to convert the
output of Ray
to a pandas
DataFrame.
unique_id | ds | TimeGPT | |
---|---|---|---|
55 | NP | 2018-12-24 07:00:00 | 55.387066 |
56 | NP | 2018-12-24 08:00:00 | 56.115517 |
57 | NP | 2018-12-24 09:00:00 | 56.090714 |
58 | NP | 2018-12-24 10:00:00 | 55.813717 |
59 | NP | 2018-12-24 11:00:00 | 55.528519 |
unique_id | ds | cutoff | TimeGPT | |
---|---|---|---|---|
295 | NP | 2018-12-23 19:00:00 | 2018-12-23 11:00:00 | 53.632019 |
296 | NP | 2018-12-23 20:00:00 | 2018-12-23 11:00:00 | 52.512775 |
297 | NP | 2018-12-23 21:00:00 | 2018-12-23 11:00:00 | 51.894035 |
298 | NP | 2018-12-23 22:00:00 | 2018-12-23 11:00:00 | 51.06572 |
299 | NP | 2018-12-23 23:00:00 | 2018-12-23 11:00:00 | 50.32592 |
TimeGPT
on top of Ray
. To
do this, please refer to the Exogenous
Variables
tutorial. Just keep in mind that instead of using a pandas DataFrame,
you need to use a Ray
DataFrame instead.
Ray
session.