shunping commented on code in PR #35844:
URL: https://github.com/apache/beam/pull/35844#discussion_r2276415140
##########
examples/notebooks/beam-ml/anomaly_detection/anomaly_detection_timesfm.ipynb:
##########
@@ -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 KitKat need to remove "rc2" in the version there.
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
