TimeGPT
enables you to use several distributed computing
frameworks to manage large datasets efficiently. TimeGPT
currently
supports Spark
, Dask
, and Ray
through Fugue
.
In this notebook, we will explain how to leverage these frameworks using
TimeGPT
.
Outline:
TimeGPT
with any of the supported distributed computing
frameworks, you first need an API Key, just as you would when not using
any distributed computing.
Upon registration, you will receive an
email asking you to confirm your signup. After confirming, you will
receive access to your dashboard. There, underAPI Keys
, you will find
your API Key. Next, you need to integrate your API Key into your
development workflow with the Nixtla SDK. For guidance on how to do
this, please refer to the Setting Up Your Authentication Key
tutorial.
TimeGPT
with any of the supported distributed computing
frameworks is straightforward and its usage is almost identical to the
non-distributed case.
NixtlaClient
class.pandas
DataFrame.NixtlaClient
class methods.TimeGPT
with any of the supported distributed computing frameworks.
For a detailed explanation and a complete example, please refer to the
guide for the specific framework linked above.
Important Parallelization in these frameworks is done along the various time series within your dataset. Therefore, it is essential that your dataset includes multiple time series, each with a unique id.
TimeGPT
can be used with any of the supported frameworks with minimal
code changes, choosing the right one should align with your specific
needs and resources. This will ensure that you leverage the full
potential of TimeGPT
while handling large datasets efficiently.