From 751f5f5f342d31249d6fea65fc0a4abd2f47d3c6 Mon Sep 17 00:00:00 2001 From: Abdul Fatir Date: Sun, 15 Dec 2024 01:32:19 +0100 Subject: [PATCH] Fix training install instructions (#236) *Issue #, if available:* Fixes #235 *Description of changes:* By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice. --- scripts/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/scripts/README.md b/scripts/README.md index 56bc60e..7c60ae1 100644 --- a/scripts/README.md +++ b/scripts/README.md @@ -4,7 +4,7 @@ - Install this package with with the `training` extra: ``` - pip install "chronos[training] @ git+https://github.com/amazon-science/chronos-forecasting.git" + pip install "chronos-forecasting[training] @ git+https://github.com/amazon-science/chronos-forecasting.git" ``` - Run `kernel-synth.py`: ```sh @@ -21,7 +21,7 @@ ## Pretraining (and fine-tuning) Chronos models - Install this package with with the `training` extra: ``` - pip install "chronos[training] @ git+https://github.com/amazon-science/chronos-forecasting.git" + pip install "chronos-forecasting[training] @ git+https://github.com/amazon-science/chronos-forecasting.git" ``` - Convert your time series dataset into a GluonTS-compatible file dataset. We recommend using the arrow format. You may use the `convert_to_arrow` function from the following snippet for that. Optionally, you may use [synthetic data from KernelSynth](#generating-synthetic-time-series-kernelsynth) to follow along. ```py @@ -113,7 +113,7 @@ Follow these steps to compute the WQL and MASE values for the in-domain and zero - Install this package with with the `evaluation` extra: ``` - pip install "chronos[evaluation] @ git+https://github.com/amazon-science/chronos-forecasting.git" + pip install "chronos-forecasting[evaluation] @ git+https://github.com/amazon-science/chronos-forecasting.git" ``` - Run the evaluation script: ```sh