docs: tutorial on reusing fine-tuned models

[jmoralez]

Adds a tutorial on how to save, use and delete fine-tuned models.

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Experiment Results

Experiment 1: air-passengers

Description:

variable	experiment
h	12
season_length	12
freq	MS
level	None
n_windows	1

Results:

metric	timegpt-1	timegpt-1-long-horizon	SeasonalNaive	Naive
mae	12.6793	11.0623	47.8333	76
mape	0.027	0.0232	0.0999	0.1425
mse	213.936	199.132	2571.33	10604.2
total_time	1.6901	1.1235	0.0042	0.0032

Plot:

Experiment 2: air-passengers

Description:

variable	experiment
h	24
season_length	12
freq	MS
level	None
n_windows	1

Results:

metric	timegpt-1	timegpt-1-long-horizon	SeasonalNaive	Naive
mae	58.1031	58.4587	71.25	115.25
mape	0.1257	0.1267	0.1552	0.2358
mse	4040.21	4110.79	5928.17	18859.2
total_time	0.7642	0.7424	0.0036	0.0032

Plot:

Experiment 3: electricity-multiple-series

Description:

variable	experiment
h	24
season_length	24
freq	H
level	None
n_windows	1

Results:

metric	timegpt-1	timegpt-1-long-horizon	SeasonalNaive	Naive
mae	178.293	268.129	269.23	1331.02
mape	0.0234	0.0311	0.0304	0.1692
mse	121589	219467	213677	4.68961e+06
total_time	0.7579	1.2174	0.0046	0.0042

Plot:

Experiment 4: electricity-multiple-series

Description:

variable	experiment
h	168
season_length	24
freq	H
level	None
n_windows	1

Results:

metric	timegpt-1	timegpt-1-long-horizon	SeasonalNaive	Naive
mae	465.497	346.972	398.956	1119.26
mape	0.062	0.0436	0.0512	0.1583
mse	835021	403760	656723	3.17316e+06
total_time	0.9292	0.9546	0.0048	0.0042

Plot:

Experiment 5: electricity-multiple-series

Description:

variable	experiment
h	336
season_length	24
freq	H
level	None
n_windows	1

Results:

metric	timegpt-1	timegpt-1-long-horizon	SeasonalNaive	Naive
mae	558.673	459.764	602.926	1340.95
mape	0.0697	0.0565	0.0787	0.17
mse	1.22723e+06	739132	1.61572e+06	6.04619e+06
total_time	0.9568	1.3328	0.0049	0.0044

Plot:

[ngupta23]

Can we just rename the method to dump? Appending model_ seems redundant.

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[jmoralez]

That's a method of pydantic's BaseModel, so I have no control over that. What we could do is something like NixltaClient.finetuned_models(as_df=True) and have that return the dataframe instead. WDYT?

[ngupta23]

Yes, that is a good idea.

[ngupta23]

Nice enhancement!

[ngupta23]

Do we need to elaborate on what the limit is for storing finetuned models?

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[jmoralez]

We haven't settled on the limit, Max wants it to be related to the plan, but right now it's 50 for everyone. I'll set that in the doc for now.