shunping commented on code in PR #35844:
URL: https://github.com/apache/beam/pull/35844#discussion_r2276415140
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examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_timesfm.ipynb:
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@@ -0,0 +1,2503 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "collapsed_sections": [
+ "cAgnGkn3GFVb",
+ "VgyZHICtuRMz",
+ "aP8LqLobuViH",
+ "WSl5lV_9ugQY"
+ ],
+ "gpuType": "T4"
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "source": [
+ "# @title ###### Licensed to the Apache Software Foundation (ASF),
Version 2.0 (the \"License\")\n",
+ "\n",
+ "# Licensed to the Apache Software Foundation (ASF) under one\n",
+ "# or more contributor license agreements. See the NOTICE file\n",
+ "# distributed with this work for additional information\n",
+ "# regarding copyright ownership. The ASF licenses this file\n",
+ "# to you under the Apache License, Version 2.0 (the\n",
+ "# \"License\"); you may not use this file except in compliance\n",
+ "# with the License. You may obtain a copy of the License at\n",
+ "#\n",
+ "# http://www.apache.org/licenses/LICENSE-2.0\n";,
+ "#\n",
+ "# Unless required by applicable law or agreed to in writing,\n",
+ "# software distributed under the License is distributed on an\n",
+ "# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n",
+ "# KIND, either express or implied. See the License for the\n",
+ "# specific language governing permissions and limitations\n",
+ "# under the License"
+ ],
+ "metadata": {
+ "id": "eMMlVe_Gukos"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# TimesFM Anomaly Detection Pipeline Diagram\n",
+ "Time series data is a sequence of data points indexed by time, where
each data point is recorded at a specific interval. TimesFM is a foundation
model pretrained on a large corpus of time series data. Its architecture is a
decoder-only transformer, similar to LLMs, which learns to predict the next
part of a time series from previous data. We can use the follow pipeline to
detect anomalies in time series data and periodically learn from incoming data
to improve our timesfm predictions.\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "cAgnGkn3GFVb"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "oCgmuQtdrSkG"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install timesfm[torch]\n",
+ "!pip install 'apache_beam[gcp, test, interactive] == 2.67.0rc2'\n",
Review Comment:
This is rc2 of 2.67.0. You will need to remove "rc2" in the version there.
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