notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train, tune, and deploy a custom ML model using the Time Series Forecasting (CfC) Algorithm from AWS Marketplace"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Time Series Forecasting (CfC) Algorithm from AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-7s4giphluwgta) performs time series forecasting with the Closed-Form Continuous-Depth (CfC) network. \n",
    "It implements both training and inference from CSV data and supports both CPU and GPU instances. \n",
    "The training and inference Docker images were built by extending the PyTorch 2.1.0 Python 3.10 SageMaker containers. \n",
    "The Docker images include software licensed under the Apache License 2.0, \n",
    "see the [LICENSE](https://github.com/fg-research/cfc-tsf-sagemaker/blob/master/LICENSE.txt) \n",
    "and [NOTICE](https://github.com/fg-research/cfc-tsf-sagemaker/blob/master/NOTICE.txt).\n",
    "\n",
    "**Model Overview**\n",
    "\n",
    "The closed-form continuous-depth network (CfC) is a neural network architecture for sequential data. CfCs belong to the class of continuous-time recurrent neural networks (CT-RNN), where the evolution of the hidden state over time is described by an Ordinary Differential Equations (ODE). \n",
    "\n",
    "CfCs use the Liquid Time Constant (LTC) ODE, where both the derivative and the time constant of the hidden state are determined by a neural network. Differently from other CT-RNNs (including LTCs), which use a numerical solver to approximate the ODE solution, CfCs use an approximate closed-form solution. As a results, CfCs achieve faster training and inference performance than other CT-RNNs.\n",
    "\n",
    "The hidden state $x$ of a CfC at time $t$ is given by\n",
    "\n",
    "$$x(t) = \\sigma(-f(x, I; \\theta_f)t) \\odot g(x, I; \\theta_g) + [1 - \\sigma(-[f(x, I; \\theta_f)]t)] \\odot h(x, I; \\theta_h)$$\n",
    "\n",
    "where $\\odot$ is the Hadamard product, $\\sigma$ is the sigmoid function, $I$ is the input sequence, while $f$, $g$ and $h$ are neural networks. The three neural networks $f$, $g$ and $h$ share a common backbone, which is a stack of fully-connected layers with non-linear activation. \n",
    "\n",
    "The backbone is followed by three separate neural network heads. The head of the $g$ and $h$ neural networks is a fully-connected layer with hyperbolic tangent activation. The head of the $f$ neural network is an affine function $b + a(\\Delta t)$ where $\\Delta t$ is the time span (or time increment) between consecutive time steps while the intercept $b$ and slope $a$ are the outputs of two fully-connected layers with linear activation.\n",
    "\n",
    "<img src=https://fg-research-assets.s3.eu-west-1.amazonaws.com/cfc-diagram.png style=\"width:80%;margin-top:20px;margin-bottom:20px\"/>\n",
    "\n",
    "*CfC architecture (source: [doi:10.1038/s42256–022–00556–7](https://doi.org/10.1038/s42256-022-00556-7))*\n",
    "\n",
    "**Model Resources**\n",
    "\n",
    "- **Paper:** [Closed-form continuous-time neural networks](https://doi.org/10.1038/s42256-022-00556-7).\n",
    "\n",
    "This sample notebook shows you how to train a custom ML model using the [Time Series Forecasting (CfC) Algorithm from AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-7s4giphluwgta).\n",
    "\n",
    "**Note: This is a reference notebook and it cannot run unless you make the changes suggested in the notebook.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pre-requisites\n",
    "1. This notebook contains elements which render correctly in Jupyter interface. Open this notebook from an Amazon SageMaker Notebook Instance or Amazon SageMaker Studio.\n",
    "1. Ensure that IAM role used has **AmazonSageMakerFullAccess**\n",
    "1. Some hands-on experience using [Amazon SageMaker](https://aws.amazon.com/sagemaker/).\n",
    "1. To use this algorithm successfully, ensure that:\n",
    "    1. Either your IAM role has these three permissions and you have authority to make AWS Marketplace subscriptions in the AWS account used: \n",
    "        1. **aws-marketplace:ViewSubscriptions**\n",
    "        1. **aws-marketplace:Unsubscribe**\n",
    "        1. **aws-marketplace:Subscribe**  \n",
    "    2. or your AWS account has a subscription to the [Time Series Forecasting (CfC) Algorithm from AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-7s4giphluwgta)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "1. [Subscribe to the algorithm](#1.-Subscribe-to-the-algorithm)\n",
    "1. [Prepare dataset](#2.-Prepare-dataset)\n",
    "\t1. [Dataset format expected by the algorithm](#A.-Dataset-format-expected-by-the-algorithm)\n",
    "\t1. [Configure and visualize train and test dataset](#B.-Configure-and-visualize-train-and-test-dataset)\n",
    "\t1. [Upload datasets to Amazon S3](#C.-Upload-datasets-to-Amazon-S3)\n",
    "1. [Train a machine learning model](#3:-Train-a-machine-learning-model)\n",
    "\t1. [Set up environment](#3.1-Set-up-environment)\n",
    "\t1. [Train a model](#3.2-Train-a-model)\n",
    "1. [Deploy model and verify results](#4:-Deploy-model-and-verify-results)\n",
    "    1. [Deploy trained model](#A.-Deploy-trained-model)\n",
    "    1. [Create input payload](#B.-Create-input-payload)\n",
    "    1. [Perform real-time inference](#C.-Perform-real-time-inference)\n",
    "    1. [Visualize output](#D.-Visualize-output)\n",
    "    1. [Calculate relevant metrics](#E.-Calculate-relevant-metrics)\n",
    "    1. [Delete the endpoint](#F.-Delete-the-endpoint)\n",
    "1. [Tune your model! (optional)](#5:-Tune-your-model!-(optional))\n",
    "\t1. [Tuning Guidelines](#A.-Tuning-Guidelines)\n",
    "\t1. [Define Tuning configuration](#B.-Define-Tuning-configuration)\n",
    "\t1. [Run a model tuning job](#C.-Run-a-model-tuning-job)\n",
    "1. [Perform Batch inference](#6.-Perform-Batch-inference)\n",
    "1. [Clean-up](#7.-Clean-up)\n",
    "\t1. [Delete the model](#A.-Delete-the-model)\n",
    "\t1. [Unsubscribe to the listing (optional)](#B.-Unsubscribe-to-the-listing-(optional))\n",
    "\n",
    "\n",
    "## Usage instructions\n",
    "You can run this notebook one cell at a time (by using Shift+Enter for running a cell)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Subscribe to the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To subscribe to the algorithm:\n",
    "1. Open the algorithm listing page.\n",
    "1. On the AWS Marketplace listing, click on the **Continue to subscribe** button.\n",
    "1. On the **Subscribe to this software** page, review and click on **\"Accept Offer\"** if you agree with EULA, pricing, and support terms. \n",
    "1. Once you click on the **Continue to configuration** button and then choose a **region**, you will see a **Product ARN**. This is the algorithm ARN that you need to specify while training a custom ML model. **Copy the ARN corresponding to your region and specify the same in the following cell.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# SageMaker algorithm ARN from AWS Marketplace\n",
    "algo_arn = \"arn:aws:sagemaker:<...>\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Prepare dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "import datetime\n",
    "import boto3\n",
    "import sagemaker\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# SageMaker session\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "# S3 bucket\n",
    "bucket = sagemaker_session.default_bucket()\n",
    "\n",
    "# EC2 instance\n",
    "instance_type = \"ml.m5.2xlarge\"\n",
    "\n",
    "# fixed hyperparameters\n",
    "context_length = 200     # number of timesteps used as input\n",
    "prediction_length = 100  # number of timesteps to output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Dataset format expected by the algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training and validation datasets should be provided as CSV files. \n",
    "Each column represents a time series, while each row represents a time step.\n",
    "All the time series should have the same length and should not contain missing values.\n",
    "The column headers should be formatted as follows:\n",
    "\n",
    "- The column names of the (mandatory) target time series should start with `\"y\"` (e.g. `\"y1\"`, `\"y2\"`, ...). \n",
    "- The column names of the (optional) feature time series should start with `\"x\"` (e.g. `\"x1\"`, `\"x2\"`, ...). \n",
    "- The column containing the time spans (optional) should be named `\"ts\"`.\n",
    "\n",
    "If the features are not provided, the algorithm will only use the past values of the target time series as input.      \n",
    "If the time spans are not provided, the algorithm will assume that the observations are equally spaced.\n",
    "\n",
    "You can find more information about dataset format in the **Usage Information** section of the algorithm listing page."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Configure and visualize train and test dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sample data provided in the algorithm's [GitHub repository](https://github.com/fg-research/cfc-tsf-sagemaker) consists of four artificially generated time series including two targets (`\"y1\"`, `\"y2\"`) and two features (`\"x1\"`, `\"x2\"`) observed at equally spaced time intervals (`\"ts\"`). The training dataset contains 1000 samples, while the validation and test datasets contain 500 samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "**Training dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_dataset = pd.read_csv(\"https://raw.githubusercontent.com/fg-research/cfc-tsf-sagemaker/master/data/training/train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.121642</td>\n",
       "      <td>-0.992107</td>\n",
       "      <td>2.627251</td>\n",
       "      <td>-21.043495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.046251</td>\n",
       "      <td>-0.899425</td>\n",
       "      <td>-3.134639</td>\n",
       "      <td>-20.881941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>0.016789</td>\n",
       "      <td>-0.845949</td>\n",
       "      <td>-3.418829</td>\n",
       "      <td>-16.630003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.020171</td>\n",
       "      <td>-1.000907</td>\n",
       "      <td>-7.383935</td>\n",
       "      <td>-20.160302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.313692</td>\n",
       "      <td>-1.002584</td>\n",
       "      <td>-11.523211</td>\n",
       "      <td>-18.012853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts        x1        x2         y1         y2\n",
       "0  0.0000  0.121642 -0.992107   2.627251 -21.043495\n",
       "1  0.0005 -0.046251 -0.899425  -3.134639 -20.881941\n",
       "2  0.0005  0.016789 -0.845949  -3.418829 -16.630003\n",
       "3  0.0005 -0.020171 -1.000907  -7.383935 -20.160302\n",
       "4  0.0005 -0.313692 -1.002584 -11.523211 -18.012853"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len([c for c in training_dataset.columns if c != \"ts\"]), ncols=1, sharex=True, figsize=(6, 8))\n",
    "for i, c in enumerate([c for c in training_dataset.columns if c != \"ts\"]):\n",
    "    axs[i].plot(training_dataset[c], color=\"#7f8ea3\", lw=1)\n",
    "    axs[i].set_title(c, size=10)\n",
    "    axs[i].set(xlabel=\"Time\", ylabel=\"Value\")\n",
    "    axs[i].xaxis.set_tick_params(labelbottom=True)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=7)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"minor\", labelsize=7)\n",
    "fig.suptitle(\"Training Dataset\")\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Validation dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "validation_dataset = pd.read_csv(\"https://raw.githubusercontent.com/fg-research/cfc-tsf-sagemaker/master/data/training/valid.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 5)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.102191</td>\n",
       "      <td>-1.020255</td>\n",
       "      <td>-1.629867</td>\n",
       "      <td>-18.293319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.196894</td>\n",
       "      <td>-0.825729</td>\n",
       "      <td>-5.875837</td>\n",
       "      <td>-15.153669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.103916</td>\n",
       "      <td>-0.909815</td>\n",
       "      <td>-9.099113</td>\n",
       "      <td>-18.314596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.128711</td>\n",
       "      <td>-1.021893</td>\n",
       "      <td>-10.430847</td>\n",
       "      <td>-17.330995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.331239</td>\n",
       "      <td>-0.987491</td>\n",
       "      <td>-15.354902</td>\n",
       "      <td>-15.939409</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts        x1        x2         y1         y2\n",
       "0  0.0005 -0.102191 -1.020255  -1.629867 -18.293319\n",
       "1  0.0005 -0.196894 -0.825729  -5.875837 -15.153669\n",
       "2  0.0005 -0.103916 -0.909815  -9.099113 -18.314596\n",
       "3  0.0005 -0.128711 -1.021893 -10.430847 -17.330995\n",
       "4  0.0005 -0.331239 -0.987491 -15.354902 -15.939409"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len([c for c in validation_dataset.columns if c != \"ts\"]), ncols=1, sharex=True, figsize=(6, 8))\n",
    "for i, c in enumerate([c for c in validation_dataset.columns if c != \"ts\"]):\n",
    "    axs[i].plot(validation_dataset[c], color=\"#7f8ea3\", lw=1)\n",
    "    axs[i].set_title(c, size=10)\n",
    "    axs[i].set(xlabel=\"Time\", ylabel=\"Value\")\n",
    "    axs[i].xaxis.set_tick_params(labelbottom=True)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=7)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"minor\", labelsize=7)\n",
    "fig.suptitle(\"Validation Dataset\")\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Upload datasets to Amazon S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_data = sagemaker_session.upload_string_as_file_body(\n",
    "    body=training_dataset.to_csv(index=False),\n",
    "    bucket=bucket,\n",
    "    key=\"data/training/train.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-eu-west-1-661670223746/data/training/train.csv'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "validation_data = sagemaker_session.upload_string_as_file_body(\n",
    "    body=validation_dataset.to_csv(index=False),\n",
    "    bucket=bucket,\n",
    "    key=\"data/training/valid.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-eu-west-1-661670223746/data/training/valid.csv'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Train a machine learning model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that dataset is available in an accessible Amazon S3 bucket, we are ready to train a machine learning model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Set up environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Train a model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can find more information about the model's hyperparameters in the **Hyperparameters** section of the algorithm listing page."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameters = {\n",
    "    \"context-length\": context_length,\n",
    "    \"prediction-length\": prediction_length,\n",
    "    \"sequence-stride\": 1,\n",
    "    \"backbone-layers\": 1,\n",
    "    \"backbone-units\": 128,\n",
    "    \"backbone-activation\": \"silu\",\n",
    "    \"backbone-dropout\": 0,\n",
    "    \"hidden-size\": 64,\n",
    "    \"minimal\": 0,\n",
    "    \"no-gate\": 0,\n",
    "    \"use-ltc\": 0,\n",
    "    \"use-mixed\": 0,\n",
    "    \"lr\": 0.001,\n",
    "    \"lr-decay\": 0.999,\n",
    "    \"batch-size\": 64,\n",
    "    \"epochs\": 50,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For information on creating an `Estimator` object, see the [documentation](https://sagemaker.readthedocs.io/en/stable/api/training/estimators.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: cfc-tsf-training-2024-02-01-18-33-21-523\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-02-01 18:33:21 Starting - Starting the training job...\n",
      "2024-02-01 18:33:36 Starting - Preparing the instances for training......\n",
      "2024-02-01 18:34:41 Downloading - Downloading input data...\n",
      "2024-02-01 18:35:06 Downloading - Downloading the training image...............\n",
      "2024-02-01 18:37:26 Training - Training image download completed. Training in progress...\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,852 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,853 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,853 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,853 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,863 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:56,865 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,186 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,187 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,187 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,198 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,199 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,199 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,210 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,210 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,211 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,221 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "\u001b[34mTraining Env:\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"training\": \"/opt/ml/input/data/training\",\n",
      "        \"validation\": \"/opt/ml/input/data/validation\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"current_instance_group\": \"homogeneousCluster\",\n",
      "    \"current_instance_group_hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"current_instance_type\": \"ml.m5.2xlarge\",\n",
      "    \"distribution_hosts\": [],\n",
      "    \"distribution_instance_groups\": [],\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"backbone-activation\": \"silu\",\n",
      "        \"backbone-dropout\": 0.0,\n",
      "        \"backbone-layers\": 1,\n",
      "        \"backbone-units\": 128,\n",
      "        \"batch-size\": 64,\n",
      "        \"context-length\": 200,\n",
      "        \"epochs\": 50,\n",
      "        \"hidden-size\": 64,\n",
      "        \"lr\": 0.001,\n",
      "        \"lr-decay\": 0.999,\n",
      "        \"minimal\": 0,\n",
      "        \"no-gate\": 0,\n",
      "        \"prediction-length\": 100,\n",
      "        \"sequence-stride\": 1,\n",
      "        \"use-ltc\": 0,\n",
      "        \"use-mixed\": 0\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"training\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"validation\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"instance_groups\": [\n",
      "        \"homogeneousCluster\"\n",
      "    ],\n",
      "    \"instance_groups_dict\": {\n",
      "        \"homogeneousCluster\": {\n",
      "            \"instance_group_name\": \"homogeneousCluster\",\n",
      "            \"instance_type\": \"ml.m5.2xlarge\",\n",
      "            \"hosts\": [\n",
      "                \"algo-1\"\n",
      "            ]\n",
      "        }\n",
      "    },\n",
      "    \"is_hetero\": false,\n",
      "    \"is_master\": true,\n",
      "    \"is_modelparallel_enabled\": null,\n",
      "    \"is_smddpmprun_installed\": false,\n",
      "    \"is_smddprun_installed\": false,\n",
      "    \"job_name\": \"cfc-tsf-training-2024-02-01-18-33-21-523\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"/opt/ml/code\",\n",
      "    \"module_name\": \"training\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 8,\n",
      "    \"num_gpus\": 0,\n",
      "    \"num_neurons\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.m5.2xlarge\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.m5.2xlarge\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"training.py\"\u001b[0m\n",
      "\u001b[34m}\u001b[0m\n",
      "\u001b[34mEnvironment variables:\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"backbone-activation\":\"silu\",\"backbone-dropout\":0.0,\"backbone-layers\":1,\"backbone-units\":128,\"batch-size\":64,\"context-length\":200,\"epochs\":50,\"hidden-size\":64,\"lr\":0.001,\"lr-decay\":0.999,\"minimal\":0,\"no-gate\":0,\"prediction-length\":100,\"sequence-stride\":1,\"use-ltc\":0,\"use-mixed\":0}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=training.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"training\",\"validation\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.m5.2xlarge\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}}\u001b[0m\n",
      "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n",
      "\u001b[34mSM_IS_HETERO=false\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=training\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=8\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[34mSM_NUM_NEURONS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"training\":\"/opt/ml/input/data/training\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.m5.2xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"backbone-activation\":\"silu\",\"backbone-dropout\":0.0,\"backbone-layers\":1,\"backbone-units\":128,\"batch-size\":64,\"context-length\":200,\"epochs\":50,\"hidden-size\":64,\"lr\":0.001,\"lr-decay\":0.999,\"minimal\":0,\"no-gate\":0,\"prediction-length\":100,\"sequence-stride\":1,\"use-ltc\":0,\"use-mixed\":0},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"is_smddprun_installed\":false,\"job_name\":\"cfc-tsf-training-2024-02-01-18-33-21-523\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"training\",\"network_interface_name\":\"eth0\",\"num_cpus\":8,\"num_gpus\":0,\"num_neurons\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"training.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--backbone-activation\",\"silu\",\"--backbone-dropout\",\"0.0\",\"--backbone-layers\",\"1\",\"--backbone-units\",\"128\",\"--batch-size\",\"64\",\"--context-length\",\"200\",\"--epochs\",\"50\",\"--hidden-size\",\"64\",\"--lr\",\"0.001\",\"--lr-decay\",\"0.999\",\"--minimal\",\"0\",\"--no-gate\",\"0\",\"--prediction-length\",\"100\",\"--sequence-stride\",\"1\",\"--use-ltc\",\"0\",\"--use-mixed\",\"0\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_VALIDATION=/opt/ml/input/data/validation\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-ACTIVATION=silu\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-DROPOUT=0.0\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-LAYERS=1\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-UNITS=128\u001b[0m\n",
      "\u001b[34mSM_HP_BATCH-SIZE=64\u001b[0m\n",
      "\u001b[34mSM_HP_CONTEXT-LENGTH=200\u001b[0m\n",
      "\u001b[34mSM_HP_EPOCHS=50\u001b[0m\n",
      "\u001b[34mSM_HP_HIDDEN-SIZE=64\u001b[0m\n",
      "\u001b[34mSM_HP_LR=0.001\u001b[0m\n",
      "\u001b[34mSM_HP_LR-DECAY=0.999\u001b[0m\n",
      "\u001b[34mSM_HP_MINIMAL=0\u001b[0m\n",
      "\u001b[34mSM_HP_NO-GATE=0\u001b[0m\n",
      "\u001b[34mSM_HP_PREDICTION-LENGTH=100\u001b[0m\n",
      "\u001b[34mSM_HP_SEQUENCE-STRIDE=1\u001b[0m\n",
      "\u001b[34mSM_HP_USE-LTC=0\u001b[0m\n",
      "\u001b[34mSM_HP_USE-MIXED=0\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python310.zip:/opt/conda/lib/python3.10:/opt/conda/lib/python3.10/lib-dynload:/opt/conda/lib/python3.10/site-packages\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.10 training.py --backbone-activation silu --backbone-dropout 0.0 --backbone-layers 1 --backbone-units 128 --batch-size 64 --context-length 200 --epochs 50 --hidden-size 64 --lr 0.001 --lr-decay 0.999 --minimal 0 --no-gate 0 --prediction-length 100 --sequence-stride 1 --use-ltc 0 --use-mixed 0\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,221 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:37:58,221 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTraining on 1000 samples.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTimespans: ['ts']\u001b[0m\n",
      "\u001b[34mFeatures: ['x1', 'x2']\u001b[0m\n",
      "\u001b[34mTargets: ['y1', 'y2']\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mValidating on 500 samples.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mBuilding the model.\u001b[0m\n",
      "\u001b[34mNumber of parameters: 135256\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTraining the model.\u001b[0m\n",
      "\u001b[34mepoch: 1, train_mse: 0.12068529 train_mae: 0.28418443 valid_mse: 0.12068000 valid_mae: 0.28228116\u001b[0m\n",
      "\u001b[34mepoch: 2, train_mse: 0.08023614 train_mae: 0.24563971 valid_mse: 0.08130899 valid_mae: 0.24740779\u001b[0m\n",
      "\u001b[34mepoch: 3, train_mse: 0.09868914 train_mae: 0.27235261 valid_mse: 0.09966919 valid_mae: 0.27450350\u001b[0m\n",
      "\u001b[34mepoch: 4, train_mse: 0.06985513 train_mae: 0.22779581 valid_mse: 0.07094488 valid_mae: 0.22996975\u001b[0m\n",
      "\u001b[34mepoch: 5, train_mse: 0.08256885 train_mae: 0.25025684 valid_mse: 0.08370662 valid_mae: 0.25245759\u001b[0m\n",
      "\u001b[34mepoch: 6, train_mse: 0.07349806 train_mae: 0.23455326 valid_mse: 0.07548398 valid_mae: 0.23829611\u001b[0m\n",
      "\u001b[34mepoch: 7, train_mse: 0.01807132 train_mae: 0.11023771 valid_mse: 0.01833377 valid_mae: 0.11113464\u001b[0m\n",
      "\u001b[34mepoch: 8, train_mse: 0.01057533 train_mae: 0.08168544 valid_mse: 0.01111105 valid_mae: 0.08418547\u001b[0m\n",
      "\u001b[34mepoch: 9, train_mse: 0.01025623 train_mae: 0.07925885 valid_mse: 0.01165863 valid_mae: 0.08514431\u001b[0m\n",
      "\u001b[34mepoch: 10, train_mse: 0.00765529 train_mae: 0.07162765 valid_mse: 0.00889300 valid_mae: 0.07711266\u001b[0m\n",
      "\u001b[34mepoch: 11, train_mse: 0.00746253 train_mae: 0.07024598 valid_mse: 0.00869290 valid_mae: 0.07727282\u001b[0m\n",
      "\u001b[34mepoch: 12, train_mse: 0.00756647 train_mae: 0.06912784 valid_mse: 0.00781988 valid_mae: 0.07003402\u001b[0m\n",
      "\u001b[34mepoch: 13, train_mse: 0.00645405 train_mae: 0.06368653 valid_mse: 0.00648975 valid_mae: 0.06341866\u001b[0m\n",
      "\u001b[34mepoch: 14, train_mse: 0.00562133 train_mae: 0.05969170 valid_mse: 0.00570672 valid_mae: 0.05930911\u001b[0m\n",
      "\u001b[34mepoch: 15, train_mse: 0.00617432 train_mae: 0.06220018 valid_mse: 0.00679536 valid_mae: 0.06558553\u001b[0m\n",
      "\u001b[34mepoch: 16, train_mse: 0.00532083 train_mae: 0.05865078 valid_mse: 0.00559985 valid_mae: 0.05977132\u001b[0m\n",
      "\u001b[34mepoch: 17, train_mse: 0.00491507 train_mae: 0.05639569 valid_mse: 0.00512951 valid_mae: 0.05686541\u001b[0m\n",
      "\u001b[34mepoch: 18, train_mse: 0.00336720 train_mae: 0.04672312 valid_mse: 0.00343639 valid_mae: 0.04699455\u001b[0m\n",
      "\u001b[34mepoch: 19, train_mse: 0.00223063 train_mae: 0.03797396 valid_mse: 0.00268917 valid_mae: 0.04102977\u001b[0m\n",
      "\u001b[34mepoch: 20, train_mse: 0.00183941 train_mae: 0.03447602 valid_mse: 0.00176050 valid_mae: 0.03312059\u001b[0m\n",
      "\u001b[34mepoch: 21, train_mse: 0.00183916 train_mae: 0.03442649 valid_mse: 0.00203750 valid_mae: 0.03603446\u001b[0m\n",
      "\u001b[34mepoch: 22, train_mse: 0.00175831 train_mae: 0.03364845 valid_mse: 0.00165017 valid_mae: 0.03185481\u001b[0m\n",
      "\u001b[34mepoch: 23, train_mse: 0.00247737 train_mae: 0.03973755 valid_mse: 0.00185001 valid_mae: 0.03403313\u001b[0m\n",
      "\u001b[34mepoch: 24, train_mse: 0.00187375 train_mae: 0.03503774 valid_mse: 0.00219722 valid_mae: 0.03766986\u001b[0m\n",
      "\u001b[34mepoch: 25, train_mse: 0.00183118 train_mae: 0.03422142 valid_mse: 0.00163285 valid_mae: 0.03161573\u001b[0m\n",
      "\u001b[34mepoch: 26, train_mse: 0.00166602 train_mae: 0.03257403 valid_mse: 0.00179031 valid_mae: 0.03362770\u001b[0m\n",
      "\u001b[34mepoch: 27, train_mse: 0.00163402 train_mae: 0.03210958 valid_mse: 0.00161182 valid_mae: 0.03172970\u001b[0m\n",
      "\u001b[34mepoch: 28, train_mse: 0.00170130 train_mae: 0.03297295 valid_mse: 0.00149671 valid_mae: 0.03050099\u001b[0m\n",
      "\u001b[34mepoch: 29, train_mse: 0.00170860 train_mae: 0.03327660 valid_mse: 0.00174313 valid_mae: 0.03323456\u001b[0m\n",
      "\u001b[34mepoch: 30, train_mse: 0.00177293 train_mae: 0.03372765 valid_mse: 0.00154935 valid_mae: 0.03078048\u001b[0m\n",
      "\u001b[34mepoch: 31, train_mse: 0.00169085 train_mae: 0.03310926 valid_mse: 0.00178674 valid_mae: 0.03329010\u001b[0m\n",
      "\u001b[34mepoch: 32, train_mse: 0.00172527 train_mae: 0.03331079 valid_mse: 0.00149872 valid_mae: 0.03031064\u001b[0m\n",
      "\u001b[34mepoch: 33, train_mse: 0.00160841 train_mae: 0.03206582 valid_mse: 0.00169519 valid_mae: 0.03255885\u001b[0m\n",
      "\u001b[34mepoch: 34, train_mse: 0.00166200 train_mae: 0.03270791 valid_mse: 0.00161776 valid_mae: 0.03184753\u001b[0m\n",
      "\u001b[34mepoch: 35, train_mse: 0.00160340 train_mae: 0.03221637 valid_mse: 0.00173045 valid_mae: 0.03274304\u001b[0m\n",
      "\u001b[34mepoch: 36, train_mse: 0.00163260 train_mae: 0.03203315 valid_mse: 0.00147711 valid_mae: 0.03065355\u001b[0m\n",
      "\u001b[34mepoch: 37, train_mse: 0.00157098 train_mae: 0.03166762 valid_mse: 0.00172354 valid_mae: 0.03282288\u001b[0m\n",
      "\u001b[34mepoch: 38, train_mse: 0.00160321 train_mae: 0.03203998 valid_mse: 0.00157565 valid_mae: 0.03083136\u001b[0m\n",
      "\u001b[34mepoch: 39, train_mse: 0.00158866 train_mae: 0.03190655 valid_mse: 0.00158936 valid_mae: 0.03125420\u001b[0m\n",
      "\u001b[34mepoch: 40, train_mse: 0.00158703 train_mae: 0.03197277 valid_mse: 0.00158553 valid_mae: 0.03124658\u001b[0m\n",
      "\u001b[34mepoch: 41, train_mse: 0.00158737 train_mae: 0.03180421 valid_mse: 0.00158873 valid_mae: 0.03160955\u001b[0m\n",
      "\u001b[34mepoch: 42, train_mse: 0.00156166 train_mae: 0.03159969 valid_mse: 0.00161246 valid_mae: 0.03147442\u001b[0m\n",
      "\u001b[34mepoch: 43, train_mse: 0.00156792 train_mae: 0.03173860 valid_mse: 0.00160950 valid_mae: 0.03159217\u001b[0m\n",
      "\u001b[34mepoch: 44, train_mse: 0.00153109 train_mae: 0.03136345 valid_mse: 0.00153349 valid_mae: 0.03050141\u001b[0m\n",
      "\u001b[34mepoch: 45, train_mse: 0.00157376 train_mae: 0.03170116 valid_mse: 0.00148705 valid_mae: 0.03006047\u001b[0m\n",
      "\u001b[34mepoch: 46, train_mse: 0.00158969 train_mae: 0.03211030 valid_mse: 0.00164357 valid_mae: 0.03169649\u001b[0m\n",
      "\u001b[34mepoch: 47, train_mse: 0.00175780 train_mae: 0.03346444 valid_mse: 0.00153665 valid_mae: 0.03065360\u001b[0m\n",
      "\u001b[34mepoch: 48, train_mse: 0.00157637 train_mae: 0.03188729 valid_mse: 0.00158689 valid_mae: 0.03071157\u001b[0m\n",
      "\u001b[34mepoch: 49, train_mse: 0.00152993 train_mae: 0.03127953 valid_mse: 0.00155667 valid_mae: 0.03042088\u001b[0m\n",
      "\u001b[34mepoch: 50, train_mse: 0.00160497 train_mae: 0.03204753 valid_mse: 0.00160730 valid_mae: 0.03091688\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mScoring the model.\u001b[0m\n",
      "\u001b[34mtrain:mse 0.00160497\u001b[0m\n",
      "\u001b[34mtrain:mae 0.03204753\u001b[0m\n",
      "\u001b[34mvalid:mse 0.00160730\u001b[0m\n",
      "\u001b[34mvalid:mae 0.03091688\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m2024-02-01 18:40:35,264 sagemaker-training-toolkit INFO     Waiting for the process to finish and give a return code.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:40:35,264 sagemaker-training-toolkit INFO     Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:40:35,265 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2024-02-01 18:40:48 Uploading - Uploading generated training model\n",
      "2024-02-01 18:40:48 Completed - Training job completed\n",
      "Training seconds: 368\n",
      "Billable seconds: 368\n"
     ]
    }
   ],
   "source": [
    "estimator = sagemaker.algorithm.AlgorithmEstimator(\n",
    "    algorithm_arn=algo_arn,\n",
    "    base_job_name=\"cfc-tsf-training\",\n",
    "    role=role,\n",
    "    instance_count=1,\n",
    "    instance_type=instance_type,\n",
    "    input_mode=\"File\",\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    hyperparameters=hyperparameters,\n",
    ")\n",
    "\n",
    "estimator.fit({\"training\": training_data, \"validation\": validation_data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "See this [blog-post](https://aws.amazon.com/blogs/machine-learning/easily-monitor-and-visualize-metrics-while-training-models-on-amazon-sagemaker/) for more information how to visualize metrics during the process. You can also open the training job from [Amazon SageMaker console](https://console.aws.amazon.com/sagemaker/home?#/jobs/) and monitor the metrics/logs in **Monitor** section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the model artifacts generated by the training job to continue training the model on the same dataset or to fine-tune the model on a different dataset (**incremental training**). You still need to provide all the hyperparameters to the estimator, but the training job will only use the sequence stride, learning rate decay factor, batch size and number of epochs, while it will overwrite all the other hyperparameters with the ones saved in the pre-trained model checkpoint. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: cfc-tsf-fine-tune-2024-02-01-18-41-07-039\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-02-01 18:41:07 Starting - Starting the training job...\n",
      "2024-02-01 18:41:21 Starting - Preparing the instances for training......\n",
      "2024-02-01 18:42:22 Downloading - Downloading input data...\n",
      "2024-02-01 18:42:46 Downloading - Downloading the training image...............\n",
      "2024-02-01 18:45:12 Training - Training image download completed. Training in progress..\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,676 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,677 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,677 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,678 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,688 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:39,689 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,065 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,066 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,066 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,077 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,078 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,078 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,089 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,090 sagemaker-training-toolkit INFO     No Neurons detected (normal if no neurons installed)\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,090 sagemaker-training-toolkit INFO     Failed to parse hyperparameter backbone-activation value silu to Json.\u001b[0m\n",
      "\u001b[34mReturning the value itself\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,100 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "\u001b[34mTraining Env:\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"model\": \"/opt/ml/input/data/model\",\n",
      "        \"training\": \"/opt/ml/input/data/training\",\n",
      "        \"validation\": \"/opt/ml/input/data/validation\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"current_instance_group\": \"homogeneousCluster\",\n",
      "    \"current_instance_group_hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"current_instance_type\": \"ml.m5.2xlarge\",\n",
      "    \"distribution_hosts\": [],\n",
      "    \"distribution_instance_groups\": [],\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"backbone-activation\": \"silu\",\n",
      "        \"backbone-dropout\": 0.0,\n",
      "        \"backbone-layers\": 1,\n",
      "        \"backbone-units\": 128,\n",
      "        \"batch-size\": 64,\n",
      "        \"context-length\": 200,\n",
      "        \"epochs\": 50,\n",
      "        \"hidden-size\": 64,\n",
      "        \"lr\": 0.001,\n",
      "        \"lr-decay\": 0.999,\n",
      "        \"minimal\": 0,\n",
      "        \"no-gate\": 0,\n",
      "        \"prediction-length\": 100,\n",
      "        \"sequence-stride\": 1,\n",
      "        \"use-ltc\": 0,\n",
      "        \"use-mixed\": 0\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"model\": {\n",
      "            \"ContentType\": \"application/x-sagemaker-model\",\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"training\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"validation\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"instance_groups\": [\n",
      "        \"homogeneousCluster\"\n",
      "    ],\n",
      "    \"instance_groups_dict\": {\n",
      "        \"homogeneousCluster\": {\n",
      "            \"instance_group_name\": \"homogeneousCluster\",\n",
      "            \"instance_type\": \"ml.m5.2xlarge\",\n",
      "            \"hosts\": [\n",
      "                \"algo-1\"\n",
      "            ]\n",
      "        }\n",
      "    },\n",
      "    \"is_hetero\": false,\n",
      "    \"is_master\": true,\n",
      "    \"is_modelparallel_enabled\": null,\n",
      "    \"is_smddpmprun_installed\": false,\n",
      "    \"is_smddprun_installed\": false,\n",
      "    \"job_name\": \"cfc-tsf-fine-tune-2024-02-01-18-41-07-039\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"/opt/ml/code\",\n",
      "    \"module_name\": \"training\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 8,\n",
      "    \"num_gpus\": 0,\n",
      "    \"num_neurons\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.m5.2xlarge\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.m5.2xlarge\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"training.py\"\u001b[0m\n",
      "\u001b[34m}\u001b[0m\n",
      "\u001b[34mEnvironment variables:\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"backbone-activation\":\"silu\",\"backbone-dropout\":0.0,\"backbone-layers\":1,\"backbone-units\":128,\"batch-size\":64,\"context-length\":200,\"epochs\":50,\"hidden-size\":64,\"lr\":0.001,\"lr-decay\":0.999,\"minimal\":0,\"no-gate\":0,\"prediction-length\":100,\"sequence-stride\":1,\"use-ltc\":0,\"use-mixed\":0}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=training.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"model\":{\"ContentType\":\"application/x-sagemaker-model\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"model\",\"training\",\"validation\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.m5.2xlarge\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}}\u001b[0m\n",
      "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n",
      "\u001b[34mSM_IS_HETERO=false\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=training\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=8\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[34mSM_NUM_NEURONS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"model\":\"/opt/ml/input/data/model\",\"training\":\"/opt/ml/input/data/training\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.m5.2xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"backbone-activation\":\"silu\",\"backbone-dropout\":0.0,\"backbone-layers\":1,\"backbone-units\":128,\"batch-size\":64,\"context-length\":200,\"epochs\":50,\"hidden-size\":64,\"lr\":0.001,\"lr-decay\":0.999,\"minimal\":0,\"no-gate\":0,\"prediction-length\":100,\"sequence-stride\":1,\"use-ltc\":0,\"use-mixed\":0},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"model\":{\"ContentType\":\"application/x-sagemaker-model\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"is_smddpmprun_installed\":false,\"is_smddprun_installed\":false,\"job_name\":\"cfc-tsf-fine-tune-2024-02-01-18-41-07-039\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"training\",\"network_interface_name\":\"eth0\",\"num_cpus\":8,\"num_gpus\":0,\"num_neurons\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.2xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.2xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"training.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--backbone-activation\",\"silu\",\"--backbone-dropout\",\"0.0\",\"--backbone-layers\",\"1\",\"--backbone-units\",\"128\",\"--batch-size\",\"64\",\"--context-length\",\"200\",\"--epochs\",\"50\",\"--hidden-size\",\"64\",\"--lr\",\"0.001\",\"--lr-decay\",\"0.999\",\"--minimal\",\"0\",\"--no-gate\",\"0\",\"--prediction-length\",\"100\",\"--sequence-stride\",\"1\",\"--use-ltc\",\"0\",\"--use-mixed\",\"0\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_MODEL=/opt/ml/input/data/model\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_VALIDATION=/opt/ml/input/data/validation\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-ACTIVATION=silu\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-DROPOUT=0.0\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-LAYERS=1\u001b[0m\n",
      "\u001b[34mSM_HP_BACKBONE-UNITS=128\u001b[0m\n",
      "\u001b[34mSM_HP_BATCH-SIZE=64\u001b[0m\n",
      "\u001b[34mSM_HP_CONTEXT-LENGTH=200\u001b[0m\n",
      "\u001b[34mSM_HP_EPOCHS=50\u001b[0m\n",
      "\u001b[34mSM_HP_HIDDEN-SIZE=64\u001b[0m\n",
      "\u001b[34mSM_HP_LR=0.001\u001b[0m\n",
      "\u001b[34mSM_HP_LR-DECAY=0.999\u001b[0m\n",
      "\u001b[34mSM_HP_MINIMAL=0\u001b[0m\n",
      "\u001b[34mSM_HP_NO-GATE=0\u001b[0m\n",
      "\u001b[34mSM_HP_PREDICTION-LENGTH=100\u001b[0m\n",
      "\u001b[34mSM_HP_SEQUENCE-STRIDE=1\u001b[0m\n",
      "\u001b[34mSM_HP_USE-LTC=0\u001b[0m\n",
      "\u001b[34mSM_HP_USE-MIXED=0\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python310.zip:/opt/conda/lib/python3.10:/opt/conda/lib/python3.10/lib-dynload:/opt/conda/lib/python3.10/site-packages\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.10 training.py --backbone-activation silu --backbone-dropout 0.0 --backbone-layers 1 --backbone-units 128 --batch-size 64 --context-length 200 --epochs 50 --hidden-size 64 --lr 0.001 --lr-decay 0.999 --minimal 0 --no-gate 0 --prediction-length 100 --sequence-stride 1 --use-ltc 0 --use-mixed 0\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,100 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker Debugger as it is not installed.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:45:41,101 sagemaker-training-toolkit INFO     Exceptions not imported for SageMaker TF as Tensorflow is not installed.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mLoading the pre-trained model.\u001b[0m\n",
      "\u001b[34mNumber of parameters: 135256\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mInitial learning rate: 0.0009512056281970313\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTraining on 1000 samples.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTimespans: ['ts']\u001b[0m\n",
      "\u001b[34mFeatures: ['x1', 'x2']\u001b[0m\n",
      "\u001b[34mTargets: ['y1', 'y2']\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mValidating on 500 samples.\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mTraining the model.\u001b[0m\n",
      "\u001b[34mepoch: 1 train_mse: 0.00165850 train_mae: 0.03270865 valid_mse: 0.00189398 valid_mae: 0.03455433\u001b[0m\n",
      "\u001b[34mepoch: 2 train_mse: 0.00158123 train_mae: 0.03174635 valid_mse: 0.00144544 valid_mae: 0.02992907\u001b[0m\n",
      "\u001b[34mepoch: 3 train_mse: 0.00150772 train_mae: 0.03102301 valid_mse: 0.00150598 valid_mae: 0.03068204\u001b[0m\n",
      "\u001b[34mepoch: 4 train_mse: 0.00152299 train_mae: 0.03102097 valid_mse: 0.00159566 valid_mae: 0.03149957\u001b[0m\n",
      "\u001b[34mepoch: 5 train_mse: 0.00157401 train_mae: 0.03174371 valid_mse: 0.00144517 valid_mae: 0.03006982\u001b[0m\n",
      "\u001b[34mepoch: 6 train_mse: 0.00154908 train_mae: 0.03165507 valid_mse: 0.00166413 valid_mae: 0.03260175\u001b[0m\n",
      "\u001b[34mepoch: 7 train_mse: 0.00156260 train_mae: 0.03149528 valid_mse: 0.00152104 valid_mae: 0.03099539\u001b[0m\n",
      "\u001b[34mepoch: 8 train_mse: 0.00167203 train_mae: 0.03260295 valid_mse: 0.00144762 valid_mae: 0.02968096\u001b[0m\n",
      "\u001b[34mepoch: 9 train_mse: 0.00150729 train_mae: 0.03111560 valid_mse: 0.00166629 valid_mae: 0.03225084\u001b[0m\n",
      "\u001b[34mepoch: 10 train_mse: 0.00159285 train_mae: 0.03172994 valid_mse: 0.00153663 valid_mae: 0.03093936\u001b[0m\n",
      "\u001b[34mepoch: 11 train_mse: 0.00152042 train_mae: 0.03110301 valid_mse: 0.00158139 valid_mae: 0.03103180\u001b[0m\n",
      "\u001b[34mepoch: 12 train_mse: 0.00152208 train_mae: 0.03127310 valid_mse: 0.00167898 valid_mae: 0.03207760\u001b[0m\n",
      "\u001b[34mepoch: 13 train_mse: 0.00157185 train_mae: 0.03171847 valid_mse: 0.00161764 valid_mae: 0.03187329\u001b[0m\n",
      "\u001b[34mepoch: 14 train_mse: 0.00149217 train_mae: 0.03096229 valid_mse: 0.00149480 valid_mae: 0.02991742\u001b[0m\n",
      "\u001b[34mepoch: 15 train_mse: 0.00156989 train_mae: 0.03182970 valid_mse: 0.00174835 valid_mae: 0.03322399\u001b[0m\n",
      "\u001b[34mepoch: 16 train_mse: 0.00167704 train_mae: 0.03272274 valid_mse: 0.00157129 valid_mae: 0.03123409\u001b[0m\n",
      "\u001b[34mepoch: 17 train_mse: 0.00152880 train_mae: 0.03155186 valid_mse: 0.00176181 valid_mae: 0.03305090\u001b[0m\n",
      "\u001b[34mepoch: 18 train_mse: 0.00152371 train_mae: 0.03155930 valid_mse: 0.00158558 valid_mae: 0.03097085\u001b[0m\n",
      "\u001b[34mepoch: 19 train_mse: 0.00152885 train_mae: 0.03157039 valid_mse: 0.00164186 valid_mae: 0.03204239\u001b[0m\n",
      "\u001b[34mepoch: 20 train_mse: 0.00146067 train_mae: 0.03081095 valid_mse: 0.00153711 valid_mae: 0.03117762\u001b[0m\n",
      "\u001b[34mepoch: 21 train_mse: 0.00152691 train_mae: 0.03123011 valid_mse: 0.00159372 valid_mae: 0.03173821\u001b[0m\n",
      "\u001b[34mepoch: 22 train_mse: 0.00158284 train_mae: 0.03208486 valid_mse: 0.00155470 valid_mae: 0.03081271\u001b[0m\n",
      "\u001b[34mepoch: 23 train_mse: 0.00148989 train_mae: 0.03090389 valid_mse: 0.00158260 valid_mae: 0.03155847\u001b[0m\n",
      "\u001b[34mepoch: 24 train_mse: 0.00154438 train_mae: 0.03150337 valid_mse: 0.00163846 valid_mae: 0.03223418\u001b[0m\n",
      "\u001b[34mepoch: 25 train_mse: 0.00147916 train_mae: 0.03088236 valid_mse: 0.00156799 valid_mae: 0.03103638\u001b[0m\n",
      "\u001b[34mepoch: 26 train_mse: 0.00157599 train_mae: 0.03175170 valid_mse: 0.00145753 valid_mae: 0.02976840\u001b[0m\n",
      "\u001b[34mepoch: 27 train_mse: 0.00167760 train_mae: 0.03283114 valid_mse: 0.00185204 valid_mae: 0.03372250\u001b[0m\n",
      "\u001b[34mepoch: 28 train_mse: 0.00158325 train_mae: 0.03174951 valid_mse: 0.00145921 valid_mae: 0.02979705\u001b[0m\n",
      "\u001b[34mepoch: 29 train_mse: 0.00147628 train_mae: 0.03086103 valid_mse: 0.00150418 valid_mae: 0.03047716\u001b[0m\n",
      "\u001b[34mepoch: 30 train_mse: 0.00150618 train_mae: 0.03112632 valid_mse: 0.00149430 valid_mae: 0.03037616\u001b[0m\n",
      "\u001b[34mepoch: 31 train_mse: 0.00148108 train_mae: 0.03091062 valid_mse: 0.00156592 valid_mae: 0.03142953\u001b[0m\n",
      "\u001b[34mepoch: 32 train_mse: 0.00151836 train_mae: 0.03127162 valid_mse: 0.00157999 valid_mae: 0.03136576\u001b[0m\n",
      "\u001b[34mepoch: 33 train_mse: 0.00148788 train_mae: 0.03096350 valid_mse: 0.00162644 valid_mae: 0.03181744\u001b[0m\n",
      "\u001b[34mepoch: 34 train_mse: 0.00146350 train_mae: 0.03064016 valid_mse: 0.00151835 valid_mae: 0.03050476\u001b[0m\n",
      "\u001b[34mepoch: 35 train_mse: 0.00155418 train_mae: 0.03169972 valid_mse: 0.00161822 valid_mae: 0.03128898\u001b[0m\n",
      "\u001b[34mepoch: 36 train_mse: 0.00148207 train_mae: 0.03096903 valid_mse: 0.00161745 valid_mae: 0.03175686\u001b[0m\n",
      "\u001b[34mepoch: 37 train_mse: 0.00159264 train_mae: 0.03188047 valid_mse: 0.00172818 valid_mae: 0.03285148\u001b[0m\n",
      "\u001b[34mepoch: 38 train_mse: 0.00152503 train_mae: 0.03135984 valid_mse: 0.00160220 valid_mae: 0.03113578\u001b[0m\n",
      "\u001b[34mepoch: 39 train_mse: 0.00144700 train_mae: 0.03048203 valid_mse: 0.00155954 valid_mae: 0.03088082\u001b[0m\n",
      "\u001b[34mepoch: 40 train_mse: 0.00144700 train_mae: 0.03057811 valid_mse: 0.00157674 valid_mae: 0.03135356\u001b[0m\n",
      "\u001b[34mepoch: 41 train_mse: 0.00143019 train_mae: 0.03019820 valid_mse: 0.00149317 valid_mae: 0.03055436\u001b[0m\n",
      "\u001b[34mepoch: 42 train_mse: 0.00148030 train_mae: 0.03072629 valid_mse: 0.00144123 valid_mae: 0.02971745\u001b[0m\n",
      "\u001b[34mepoch: 43 train_mse: 0.00147977 train_mae: 0.03101400 valid_mse: 0.00146837 valid_mae: 0.02996918\u001b[0m\n",
      "\u001b[34mepoch: 44 train_mse: 0.00154769 train_mae: 0.03161357 valid_mse: 0.00165494 valid_mae: 0.03179083\u001b[0m\n",
      "\u001b[34mepoch: 45 train_mse: 0.00151619 train_mae: 0.03113612 valid_mse: 0.00149904 valid_mae: 0.03035837\u001b[0m\n",
      "\u001b[34mepoch: 46 train_mse: 0.00148357 train_mae: 0.03077584 valid_mse: 0.00140337 valid_mae: 0.02922306\u001b[0m\n",
      "\u001b[34mepoch: 47 train_mse: 0.00149164 train_mae: 0.03081281 valid_mse: 0.00140816 valid_mae: 0.02959981\u001b[0m\n",
      "\u001b[34mepoch: 48 train_mse: 0.00150607 train_mae: 0.03100047 valid_mse: 0.00142321 valid_mae: 0.02928051\u001b[0m\n",
      "\u001b[34mepoch: 49 train_mse: 0.00144502 train_mae: 0.03058623 valid_mse: 0.00162418 valid_mae: 0.03137566\u001b[0m\n",
      "\u001b[34mepoch: 50 train_mse: 0.00148739 train_mae: 0.03099960 valid_mse: 0.00156958 valid_mae: 0.03044634\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34mScoring the model.\u001b[0m\n",
      "\u001b[34mtrain:mse 0.00148739\u001b[0m\n",
      "\u001b[34mtrain:mae 0.03099960\u001b[0m\n",
      "\u001b[34mvalid:mse 0.00156958\u001b[0m\n",
      "\u001b[34mvalid:mae 0.03044634\u001b[0m\n",
      "\u001b[34m--------------------------------------\u001b[0m\n",
      "\u001b[34m2024-02-01 18:47:23,882 sagemaker-training-toolkit INFO     Waiting for the process to finish and give a return code.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:47:23,882 sagemaker-training-toolkit INFO     Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n",
      "\u001b[34m2024-02-01 18:47:23,882 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2024-02-01 18:47:39 Uploading - Uploading generated training model\n",
      "2024-02-01 18:47:39 Completed - Training job completed\n",
      "Training seconds: 317\n",
      "Billable seconds: 317\n"
     ]
    }
   ],
   "source": [
    "estimator = sagemaker.algorithm.AlgorithmEstimator(\n",
    "    model_uri=f's3://{bucket}/{estimator.latest_training_job.name}/output/model.tar.gz',\n",
    "    algorithm_arn=algo_arn,\n",
    "    base_job_name=\"cfc-tsf-fine-tune\",\n",
    "    role=role,\n",
    "    instance_count=1,\n",
    "    instance_type=instance_type,\n",
    "    input_mode=\"File\",\n",
    "    sagemaker_session=sagemaker_session,\n",
    "    hyperparameters={\n",
    "        \"context-length\": context_length,\n",
    "        \"prediction-length\": prediction_length,\n",
    "        \"sequence-stride\": 1,\n",
    "        \"backbone-layers\": 1,\n",
    "        \"backbone-units\": 128,\n",
    "        \"backbone-activation\": \"silu\",\n",
    "        \"backbone-dropout\": 0,\n",
    "        \"hidden-size\": 64,\n",
    "        \"minimal\": 0,\n",
    "        \"no-gate\": 0,\n",
    "        \"use-ltc\": 0,\n",
    "        \"use-mixed\": 0,\n",
    "        \"lr\": 0.001,\n",
    "        \"lr-decay\": 0.999,\n",
    "        \"batch-size\": 64,\n",
    "        \"epochs\": 50,\n",
    "    },\n",
    ")\n",
    "\n",
    "estimator.fit({\"training\": training_data, \"validation\": validation_data})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Deploy model and verify results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can deploy the model for performing real-time inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "serializer = sagemaker.serializers.CSVSerializer(content_type=\"text/csv\")\n",
    "deserializer = sagemaker.base_deserializers.PandasDeserializer(accept=\"text/csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Deploy trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model package with name: cfc-tsf-model-2024-02-01-18-47-51-566201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: cfc-tsf-model-2024-02-01-18-47-51-566201\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating endpoint-config with name cfc-tsf-endpoint-2024-02-01-18-47-51-566232\n",
      "INFO:sagemaker:Creating endpoint with name cfc-tsf-endpoint-2024-02-01-18-47-51-566232\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------!"
     ]
    }
   ],
   "source": [
    "predictor = estimator.deploy(\n",
    "    initial_instance_count=1,\n",
    "    instance_type=instance_type,\n",
    "    model_name=f\"cfc-tsf-model-{datetime.datetime.now().strftime(format='%Y-%m-%d-%H-%M-%S-%f')}\",\n",
    "    endpoint_name=f\"cfc-tsf-endpoint-{datetime.datetime.now().strftime(format='%Y-%m-%d-%H-%M-%S-%f')}\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the endpoint is in service, you can perform real-time inference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Create input payload"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inference algorithm takes as input a CSV file containing the time series. The CSV file should have the same format as the one used for training."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Test dataset:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_dataset = pd.read_csv(\"https://raw.githubusercontent.com/fg-research/cfc-tsf-sagemaker/master/data/inference/input/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(500, 5)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.124333</td>\n",
       "      <td>-0.888155</td>\n",
       "      <td>-3.611156</td>\n",
       "      <td>-21.167402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.124550</td>\n",
       "      <td>-0.972857</td>\n",
       "      <td>-7.578829</td>\n",
       "      <td>-21.938505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.071144</td>\n",
       "      <td>-1.087489</td>\n",
       "      <td>-7.008032</td>\n",
       "      <td>-19.818385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.165431</td>\n",
       "      <td>-0.965825</td>\n",
       "      <td>-8.733201</td>\n",
       "      <td>-16.944541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0005</td>\n",
       "      <td>-0.330729</td>\n",
       "      <td>-0.940734</td>\n",
       "      <td>-15.859760</td>\n",
       "      <td>-17.673990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       ts        x1        x2         y1         y2\n",
       "0  0.0005 -0.124333 -0.888155  -3.611156 -21.167402\n",
       "1  0.0005 -0.124550 -0.972857  -7.578829 -21.938505\n",
       "2  0.0005 -0.071144 -1.087489  -7.008032 -19.818385\n",
       "3  0.0005 -0.165431 -0.965825  -8.733201 -16.944541\n",
       "4  0.0005 -0.330729 -0.940734 -15.859760 -17.673990"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x800 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len([c for c in test_dataset.columns if c != \"ts\"]), ncols=1, sharex=True, figsize=(6, 8))\n",
    "for i, c in enumerate([c for c in test_dataset.columns if c != \"ts\"]):\n",
    "    axs[i].plot(test_dataset[c], color=\"#7f8ea3\", lw=1)\n",
    "    axs[i].set_title(c, size=10)\n",
    "    axs[i].set(xlabel=\"Time\", ylabel=\"Value\")\n",
    "    axs[i].xaxis.set_tick_params(labelbottom=True)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=7)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"minor\", labelsize=7)\n",
    "fig.suptitle(\"Test Dataset\")\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_data = sagemaker_session.upload_string_as_file_body(\n",
    "    body=test_dataset.to_csv(index=False),\n",
    "    bucket=bucket,\n",
    "    key=\"data/inference/input/test.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-eu-west-1-661670223746/data/inference/input/test.csv'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given that the test dataset used in this experiment is relatively small, we invoke the endpoint using all the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "payload = test_dataset.to_csv(index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Perform real-time inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "response = sagemaker_session.sagemaker_runtime_client.invoke_endpoint(\n",
    "    EndpointName=predictor.endpoint_name,\n",
    "    ContentType=\"text/csv\",\n",
    "    Body=payload\n",
    ")\n",
    "\n",
    "real_time_predictions = deserializer.deserialize(response[\"Body\"], content_type=\"text/csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'ResponseMetadata': {'RequestId': '6457899a-a780-49a8-be21-c75fd8b1b964',\n",
       "  'HTTPStatusCode': 200,\n",
       "  'HTTPHeaders': {'x-amzn-requestid': '6457899a-a780-49a8-be21-c75fd8b1b964',\n",
       "   'x-amzn-invoked-production-variant': 'AllTraffic',\n",
       "   'date': 'Thu, 01 Feb 2024 18:53:11 GMT',\n",
       "   'content-type': 'application/json',\n",
       "   'content-length': '30911',\n",
       "   'connection': 'keep-alive'},\n",
       "  'RetryAttempts': 0},\n",
       " 'ContentType': 'application/json',\n",
       " 'InvokedProductionVariant': 'AllTraffic',\n",
       " 'Body': <botocore.response.StreamingBody at 0x7fc6651fa290>}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_time_results = sagemaker_session.upload_string_as_file_body(\n",
    "    body=real_time_predictions.to_csv(index=False),\n",
    "    bucket=bucket,\n",
    "    key=\"data/inference/output/real-time/real_time_predictions.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-eu-west-1-661670223746/data/inference/output/real-time/real_time_predictions.csv'"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_time_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Visualize output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The inference algorithm outputs the predicted values of the time series and the standard deviation of the predictions. \n",
    "\n",
    "**Notes:** \n",
    "\n",
    "a) The model predicts the time series sequence by sequence. For instance, if the `context-length` is set equal to 200, and the `prediction-length` is set equal to 100, then the first 200 data points (from 1 to 200) are used as input to predict the next 100 data points (from 201 to 300). As a result, the algorithm does not return the predicted values of the first 200 data points, which are set to missing in the output CSV file.\n",
    "\n",
    "b) The outputs include the out-of-sample forecasts beyond the last time step of the inputs. For instance, if the number of input samples is 500, and the `prediction-length` is 100, then the output CSV file will contain 600 samples, where the last 100 samples are the out-of-sample forecasts.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 4)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_time_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1_mean</th>\n",
       "      <th>y1_std</th>\n",
       "      <th>y2_mean</th>\n",
       "      <th>y2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y1_mean  y1_std  y2_mean  y2_std\n",
       "0      NaN     NaN      NaN     NaN\n",
       "1      NaN     NaN      NaN     NaN\n",
       "2      NaN     NaN      NaN     NaN\n",
       "3      NaN     NaN      NaN     NaN\n",
       "4      NaN     NaN      NaN     NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_time_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1_mean</th>\n",
       "      <th>y1_std</th>\n",
       "      <th>y2_mean</th>\n",
       "      <th>y2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>11.077118</td>\n",
       "      <td>2.327890</td>\n",
       "      <td>-16.994116</td>\n",
       "      <td>2.207869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>7.974410</td>\n",
       "      <td>2.280835</td>\n",
       "      <td>-18.382240</td>\n",
       "      <td>2.148462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>4.983987</td>\n",
       "      <td>2.252448</td>\n",
       "      <td>-18.028560</td>\n",
       "      <td>2.304434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2.382181</td>\n",
       "      <td>2.389343</td>\n",
       "      <td>-19.242182</td>\n",
       "      <td>2.237700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>-1.046188</td>\n",
       "      <td>2.341685</td>\n",
       "      <td>-19.737051</td>\n",
       "      <td>2.224695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y1_mean    y1_std    y2_mean    y2_std\n",
       "595  11.077118  2.327890 -16.994116  2.207869\n",
       "596   7.974410  2.280835 -18.382240  2.148462\n",
       "597   4.983987  2.252448 -18.028560  2.304434\n",
       "598   2.382181  2.389343 -19.242182  2.237700\n",
       "599  -1.046188  2.341685 -19.737051  2.224695"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_time_predictions.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y1_mean    200\n",
       "y1_std     200\n",
       "y2_mean    200\n",
       "y2_std     200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real_time_predictions.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len([c for c in test_dataset.columns if c.startswith(\"y\")]), ncols=1, sharex=True, figsize=(6, 5))\n",
    "for i, c in enumerate([c for c in test_dataset.columns if c.startswith(\"y\")]):\n",
    "    axs[i].plot(test_dataset.index, test_dataset[c].values, color=\"#7f8ea3\", lw=1, label=\"Actual\" if i == 0 else None)\n",
    "    axs[i].plot(real_time_predictions.index, real_time_predictions[f\"{c}_mean\"].values, color=\"#009ad3\", lw=1, label=\"Predicted\" if i == 0 else None)\n",
    "    axs[i].fill_between(real_time_predictions.index, real_time_predictions[f\"{c}_mean\"].values + real_time_predictions[f\"{c}_std\"].values, real_time_predictions[f\"{c}_mean\"].values - real_time_predictions[f\"{c}_std\"].values, color=\"#009ad3\", alpha=0.2, lw=1, label=\"Predicted +/- 1 Std. Dev.\" if i == 0 else None)\n",
    "    axs[i].fill_between(real_time_predictions.index, real_time_predictions[f\"{c}_mean\"].values + 2 * real_time_predictions[f\"{c}_std\"].values, real_time_predictions[f\"{c}_mean\"].values - 2 * real_time_predictions[f\"{c}_std\"].values, color=\"#009ad3\", alpha=0.1, lw=1, label=\"Predicted +/- 2 Std. Dev.\" if i == 0 else None)\n",
    "    axs[i].set_title(c, size=10)\n",
    "    axs[i].set(xlabel=\"Time\", ylabel=\"Value\")\n",
    "    axs[i].xaxis.set_tick_params(labelbottom=True)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=7)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"minor\", labelsize=7)\n",
    "fig.suptitle(\"Real Time Predictions on Test Dataset\")\n",
    "fig.legend(bbox_to_anchor=(1, 0, 0.4, 1), frameon=False)\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### E. Calculate relevant metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we assess the model's forecasting performance against the ground truth time series values. Note that in this section we calculate the mean squared error (MSE) and mean absolute error (MAE) separately for each time series using unscaled data. These are different from the metrics returned by the algorithm, which are the average MSE and MAE across all time series calculated using scaled data.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time series: y1, MSE: 5.74, MAE: 1.96\n",
      "Time series: y2, MSE: 3.99, MAE: 1.62\n"
     ]
    }
   ],
   "source": [
    "for c in [c for c in test_dataset.columns if c.startswith(\"y\")]:\n",
    "    mse = mean_squared_error(test_dataset[c].iloc[context_length:], real_time_predictions[f\"{c}_mean\"].iloc[context_length: - prediction_length])\n",
    "    mae = mean_absolute_error(test_dataset[c].iloc[context_length:], real_time_predictions[f\"{c}_mean\"].iloc[context_length: -prediction_length])\n",
    "    print(f\"Time series: {c}, MSE: {format(mse, '.2f')}, MAE: {format(mae, '.2f')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If [Amazon SageMaker Model Monitor](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html) supports the type of problem you are trying to solve using this algorithm, use the following examples to add Model Monitor support to your product.\n",
    "For sample code to enable and monitor the model, see following notebooks:\n",
    "1. [Enable Amazon SageMaker Model Monitor](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_model_monitor/enable_model_monitor/SageMaker-Enable-Model-Monitor.ipynb)\n",
    "2. [Amazon SageMaker Model Monitor - visualizing monitoring results](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker_model_monitor/visualization/SageMaker-Model-Monitor-Visualize.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### F. Delete the endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you have successfully performed a real-time inference, you do not need the endpoint any more. You can terminate the same to avoid being charged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Deleting endpoint configuration with name: cfc-tsf-endpoint-2024-02-01-18-47-51-566232\n",
      "INFO:sagemaker:Deleting endpoint with name: cfc-tsf-endpoint-2024-02-01-18-47-51-566232\n"
     ]
    }
   ],
   "source": [
    "predictor.delete_endpoint(delete_endpoint_config=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this is an experiment, you do not need to run a hyperparameter tuning job. However, if you would like to see how to tune a model trained using a third-party algorithm with Amazon SageMaker's hyperparameter tuning functionality, you can run the optional tuning step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Tune your model! (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Tuning Guidelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has the following hyperparameters, all of which are tunable:\n",
    "- `context-length`: `int`. The length of the input sequences.\n",
    "- `prediction-length`: `int`. The length of the output sequences.\n",
    "- `sequence-stride`: `int`. The period between consecutive output sequences.\n",
    "- `backbone-layers`: `int`. The number of hidden layers of the backbone neural network. \n",
    "- `backbone-units`: `int`. The number of hidden units of the backbone neural network.\n",
    "- `backbone-activation`: `str`. The activation function of the backbone neural network.\n",
    "- `backbone-dropout`: `float`. The dropout rate of the backbone neural network.\n",
    "- `hidden-size`: `int`. The number of hidden units of the neural network heads.\n",
    "- `minimal`: `bool`. If set to 1, the model will use the CfC direct solution.\n",
    "- `no-gate`: `bool`. If set to 1, the model will use a CfC without the (1 - sigmoid) part.\n",
    "- `use-ltc`: `bool`. If set to 1, the model will use an LTC instead of a CfC.\n",
    "- `use-mixed`: `bool`. If set to 1, the model will mix the CfC RNN-state with an LSTM.\n",
    "- `lr`: `float`. The learning rate used for training.\n",
    "- `lr-decay`: `float`. The decay factor applied to the learning rate.\n",
    "- `batch-size`: `int`. The batch size used for training.\n",
    "- `epochs`: `int`. The number of training epochs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Define Tuning configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "hyperparameter_ranges = {\n",
    "    \"backbone-layers\": sagemaker.parameter.IntegerParameter(1, 3),\n",
    "    \"backbone-units\": sagemaker.parameter.CategoricalParameter([32, 64, 128, 256]),\n",
    "    \"backbone-activation\": sagemaker.parameter.CategoricalParameter([\"silu\", \"relu\", \"tanh\", \"gelu\", \"lecun\"]),\n",
    "    \"backbone-dropout\": sagemaker.parameter.ContinuousParameter(0, 0.5),\n",
    "    \"hidden-size\": sagemaker.parameter.CategoricalParameter([32, 64, 128, 256]),\n",
    "    \"lr\": sagemaker.parameter.ContinuousParameter(0.0001, 0.01),\n",
    "    \"lr-decay\": sagemaker.parameter.ContinuousParameter(0.9, 1.0),\n",
    "    \"batch-size\": sagemaker.parameter.CategoricalParameter([32, 64, 128, 256]),\n",
    "    \"epochs\": sagemaker.parameter.IntegerParameter(20, 200),\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the validation mean absolute error (MAE) as the objective to be minimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "objective_metric_name = \"valid_mae\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "objective_type = \"Minimize\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Run a model tuning job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure to detach the pre-trained model before tuning, otherwise the algorithm will keep using the same hyperparameters with the exception of the batch size, learning rate decay factor and number of epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "estimator.model_uri = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the interest of time, we run the tuner only for a few iterations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tuner = sagemaker.tuner.HyperparameterTuner(\n",
    "    estimator=estimator,\n",
    "    base_tuning_job_name=\"cfc-tsf-tuning\",\n",
    "    objective_metric_name=objective_metric_name,\n",
    "    objective_type=objective_type,\n",
    "    hyperparameter_ranges=hyperparameter_ranges,\n",
    "    max_jobs=4,\n",
    "    max_parallel_jobs=4,\n",
    "    random_seed=100,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating hyperparameter tuning job with name: cfc-tsf-tuning-240201-1853\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................................................................................................................................................................................................!\n"
     ]
    }
   ],
   "source": [
    "tuner.fit({\"training\": training_data, \"validation\": validation_data})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>backbone-activation</th>\n",
       "      <th>backbone-dropout</th>\n",
       "      <th>backbone-layers</th>\n",
       "      <th>backbone-units</th>\n",
       "      <th>batch-size</th>\n",
       "      <th>epochs</th>\n",
       "      <th>hidden-size</th>\n",
       "      <th>lr</th>\n",
       "      <th>lr-decay</th>\n",
       "      <th>TrainingJobName</th>\n",
       "      <th>TrainingJobStatus</th>\n",
       "      <th>FinalObjectiveValue</th>\n",
       "      <th>TrainingStartTime</th>\n",
       "      <th>TrainingEndTime</th>\n",
       "      <th>TrainingElapsedTimeSeconds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>gelu</td>\n",
       "      <td>0.295607</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>197.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.006777</td>\n",
       "      <td>0.978245</td>\n",
       "      <td>cfc-tsf-tuning-240201-1853-003-c6dafd7c</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.028875</td>\n",
       "      <td>2024-02-01 18:54:28+00:00</td>\n",
       "      <td>2024-02-01 19:04:36+00:00</td>\n",
       "      <td>608.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tanh</td>\n",
       "      <td>0.410043</td>\n",
       "      <td>1.0</td>\n",
       "      <td>256.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>194.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.001237</td>\n",
       "      <td>0.974435</td>\n",
       "      <td>cfc-tsf-tuning-240201-1853-002-1ce22ccd</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.029234</td>\n",
       "      <td>2024-02-01 18:54:37+00:00</td>\n",
       "      <td>2024-02-01 19:03:51+00:00</td>\n",
       "      <td>554.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>lecun</td>\n",
       "      <td>0.494782</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0.000346</td>\n",
       "      <td>0.976463</td>\n",
       "      <td>cfc-tsf-tuning-240201-1853-004-a05b1a4b</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.076882</td>\n",
       "      <td>2024-02-01 18:54:29+00:00</td>\n",
       "      <td>2024-02-01 19:09:49+00:00</td>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>relu</td>\n",
       "      <td>0.124774</td>\n",
       "      <td>1.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.906963</td>\n",
       "      <td>cfc-tsf-tuning-240201-1853-001-0e197d69</td>\n",
       "      <td>Completed</td>\n",
       "      <td>0.237890</td>\n",
       "      <td>2024-02-01 18:54:43+00:00</td>\n",
       "      <td>2024-02-01 19:01:31+00:00</td>\n",
       "      <td>408.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  backbone-activation  backbone-dropout  backbone-layers  backbone-units  \\\n",
       "0                gelu          0.295607              1.0            64.0   \n",
       "1                tanh          0.410043              1.0           256.0   \n",
       "2               lecun          0.494782              3.0            32.0   \n",
       "3                relu          0.124774              1.0            64.0   \n",
       "\n",
       "   batch-size  epochs  hidden-size        lr  lr-decay  \\\n",
       "0        64.0   197.0         32.0  0.006777  0.978245   \n",
       "1       128.0   194.0         32.0  0.001237  0.974435   \n",
       "2        32.0   122.0         32.0  0.000346  0.976463   \n",
       "3        32.0    52.0         64.0  0.000195  0.906963   \n",
       "\n",
       "                           TrainingJobName TrainingJobStatus  \\\n",
       "0  cfc-tsf-tuning-240201-1853-003-c6dafd7c         Completed   \n",
       "1  cfc-tsf-tuning-240201-1853-002-1ce22ccd         Completed   \n",
       "2  cfc-tsf-tuning-240201-1853-004-a05b1a4b         Completed   \n",
       "3  cfc-tsf-tuning-240201-1853-001-0e197d69         Completed   \n",
       "\n",
       "   FinalObjectiveValue         TrainingStartTime           TrainingEndTime  \\\n",
       "0             0.028875 2024-02-01 18:54:28+00:00 2024-02-01 19:04:36+00:00   \n",
       "1             0.029234 2024-02-01 18:54:37+00:00 2024-02-01 19:03:51+00:00   \n",
       "2             0.076882 2024-02-01 18:54:29+00:00 2024-02-01 19:09:49+00:00   \n",
       "3             0.237890 2024-02-01 18:54:43+00:00 2024-02-01 19:01:31+00:00   \n",
       "\n",
       "   TrainingElapsedTimeSeconds  \n",
       "0                       608.0  \n",
       "1                       554.0  \n",
       "2                       920.0  \n",
       "3                       408.0  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuner.analytics().dataframe().sort_values(by=\"FinalObjectiveValue\", ascending=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tuning_job_result = sagemaker_session.sagemaker_client.describe_hyper_parameter_tuning_job(\n",
    "    HyperParameterTuningJobName=tuner.latest_tuning_job.name\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best hyperparameters:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'backbone-activation': 'gelu',\n",
       " 'backbone-dropout': '0.2956067352669055',\n",
       " 'backbone-layers': '1',\n",
       " 'backbone-units': '64',\n",
       " 'batch-size': '64',\n",
       " 'epochs': '197',\n",
       " 'hidden-size': '32',\n",
       " 'lr': '0.006776639721916928',\n",
       " 'lr-decay': '0.9782453713778745'}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Best hyperparameters:\")\n",
    "tuning_job_result[\"BestTrainingJob\"][\"TunedHyperParameters\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best score:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.028874510899186134"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Best score:\")\n",
    "tuning_job_result[\"BestTrainingJob\"][\"FinalHyperParameterTuningJobObjectiveMetric\"][\"Value\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have completed a tuning job (or even while the job is still running), you can [clone and use this notebook](https://github.com/awslabs/amazon-sagemaker-examples/blob/master/hyperparameter_tuning/analyze_results/HPO_Analyze_TuningJob_Results.ipynb) to analyze the results to understand how each hyperparameter effects the quality of the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Perform Batch inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, you will perform batch inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model package with name: cfc-tsf-v1-6-2024-02-01-19-10-58-316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........."
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: cfc-tsf-v1-6-2024-02-01-19-10-58-316-2024-02-01-19-11-43-836\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "transformer = estimator.transformer(\n",
    "    instance_count=1,\n",
    "    instance_type=instance_type,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "transformer.base_transform_job_name = \"cfc-tsf-transform\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating transform job with name: cfc-tsf-transform-2024-02-01-19-11-44-573\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".............................................\n",
      "\u001b[34mWarning: TorchServe is using non-default JVM parameters: -XX:-UseContainerSupport\u001b[0m\n",
      "\u001b[34mWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:11,891 [WARN ] main org.pytorch.serve.util.ConfigManager - Your torchserve instance can access any URL to load models. When deploying to production, make sure to limit the set of allowed_urls in config.properties\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:11,893 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Initializing plugins manager...\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:11,967 [INFO ] main org.pytorch.serve.metrics.configuration.MetricConfiguration - Successfully loaded metrics configuration from /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,070 [INFO ] main org.pytorch.serve.ModelServer - \u001b[0m\n",
      "\u001b[34mTorchserve version: 0.8.2\u001b[0m\n",
      "\u001b[34mTS Home: /opt/conda/lib/python3.10/site-packages\u001b[0m\n",
      "\u001b[34mCurrent directory: /\u001b[0m\n",
      "\u001b[34mTemp directory: /home/model-server/tmp\u001b[0m\n",
      "\u001b[34mMetrics config path: /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml\u001b[0m\n",
      "\u001b[34mNumber of GPUs: 0\u001b[0m\n",
      "\u001b[34mNumber of CPUs: 8\u001b[0m\n",
      "\u001b[34mMax heap size: 7924 M\u001b[0m\n",
      "\u001b[34mPython executable: /opt/conda/bin/python3.10\u001b[0m\n",
      "\u001b[34mConfig file: /etc/sagemaker-ts.properties\u001b[0m\n",
      "\u001b[34mInference address: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[34mManagement address: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[34mMetrics address: http://127.0.0.1:8082\u001b[0m\n",
      "\u001b[34mModel Store: /.sagemaker/ts/models\u001b[0m\n",
      "\u001b[34mInitial Models: model=/opt/ml/model\u001b[0m\n",
      "\u001b[35mWarning: TorchServe is using non-default JVM parameters: -XX:-UseContainerSupport\u001b[0m\n",
      "\u001b[35mWARNING: sun.reflect.Reflection.getCallerClass is not supported. This will impact performance.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:11,891 [WARN ] main org.pytorch.serve.util.ConfigManager - Your torchserve instance can access any URL to load models. When deploying to production, make sure to limit the set of allowed_urls in config.properties\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:11,893 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager - Initializing plugins manager...\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:11,967 [INFO ] main org.pytorch.serve.metrics.configuration.MetricConfiguration - Successfully loaded metrics configuration from /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,070 [INFO ] main org.pytorch.serve.ModelServer - \u001b[0m\n",
      "\u001b[35mTorchserve version: 0.8.2\u001b[0m\n",
      "\u001b[35mTS Home: /opt/conda/lib/python3.10/site-packages\u001b[0m\n",
      "\u001b[35mCurrent directory: /\u001b[0m\n",
      "\u001b[35mTemp directory: /home/model-server/tmp\u001b[0m\n",
      "\u001b[35mMetrics config path: /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml\u001b[0m\n",
      "\u001b[35mNumber of GPUs: 0\u001b[0m\n",
      "\u001b[35mNumber of CPUs: 8\u001b[0m\n",
      "\u001b[35mMax heap size: 7924 M\u001b[0m\n",
      "\u001b[35mPython executable: /opt/conda/bin/python3.10\u001b[0m\n",
      "\u001b[35mConfig file: /etc/sagemaker-ts.properties\u001b[0m\n",
      "\u001b[35mInference address: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[35mManagement address: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[35mMetrics address: http://127.0.0.1:8082\u001b[0m\n",
      "\u001b[35mModel Store: /.sagemaker/ts/models\u001b[0m\n",
      "\u001b[35mInitial Models: model=/opt/ml/model\u001b[0m\n",
      "\u001b[34mLog dir: /logs\u001b[0m\n",
      "\u001b[34mMetrics dir: /logs\u001b[0m\n",
      "\u001b[34mNetty threads: 0\u001b[0m\n",
      "\u001b[34mNetty client threads: 0\u001b[0m\n",
      "\u001b[34mDefault workers per model: 8\u001b[0m\n",
      "\u001b[34mBlacklist Regex: N/A\u001b[0m\n",
      "\u001b[34mMaximum Response Size: 6553500\u001b[0m\n",
      "\u001b[34mMaximum Request Size: 6553500\u001b[0m\n",
      "\u001b[34mLimit Maximum Image Pixels: true\u001b[0m\n",
      "\u001b[34mPrefer direct buffer: false\u001b[0m\n",
      "\u001b[34mAllowed Urls: [file://.*|http(s)?://.*]\u001b[0m\n",
      "\u001b[34mCustom python dependency for model allowed: false\u001b[0m\n",
      "\u001b[34mEnable metrics API: true\u001b[0m\n",
      "\u001b[34mMetrics mode: log\u001b[0m\n",
      "\u001b[34mDisable system metrics: true\u001b[0m\n",
      "\u001b[34mWorkflow Store: /.sagemaker/ts/models\u001b[0m\n",
      "\u001b[34mModel config: N/A\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,077 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager -  Loading snapshot serializer plugin...\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,096 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: /opt/ml/model\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,100 [WARN ] main org.pytorch.serve.archive.model.ModelArchive - Model archive version is not defined. Please upgrade to torch-model-archiver 0.2.0 or higher\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,100 [WARN ] main org.pytorch.serve.archive.model.ModelArchive - Model archive createdOn is not defined. Please upgrade to torch-model-archiver 0.2.0 or higher\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,102 [INFO ] main org.pytorch.serve.wlm.ModelManager - Model model loaded.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,114 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,377 [INFO ] main org.pytorch.serve.ModelServer - Inference API bind to: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,377 [INFO ] main org.pytorch.serve.ModelServer - Initialize Metrics server with: EpollServerSocketChannel.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:12,456 [INFO ] main org.pytorch.serve.ModelServer - Metrics API bind to: http://127.0.0.1:8082\u001b[0m\n",
      "\u001b[34mModel server started.\u001b[0m\n",
      "\u001b[35mLog dir: /logs\u001b[0m\n",
      "\u001b[35mMetrics dir: /logs\u001b[0m\n",
      "\u001b[35mNetty threads: 0\u001b[0m\n",
      "\u001b[35mNetty client threads: 0\u001b[0m\n",
      "\u001b[35mDefault workers per model: 8\u001b[0m\n",
      "\u001b[35mBlacklist Regex: N/A\u001b[0m\n",
      "\u001b[35mMaximum Response Size: 6553500\u001b[0m\n",
      "\u001b[35mMaximum Request Size: 6553500\u001b[0m\n",
      "\u001b[35mLimit Maximum Image Pixels: true\u001b[0m\n",
      "\u001b[35mPrefer direct buffer: false\u001b[0m\n",
      "\u001b[35mAllowed Urls: [file://.*|http(s)?://.*]\u001b[0m\n",
      "\u001b[35mCustom python dependency for model allowed: false\u001b[0m\n",
      "\u001b[35mEnable metrics API: true\u001b[0m\n",
      "\u001b[35mMetrics mode: log\u001b[0m\n",
      "\u001b[35mDisable system metrics: true\u001b[0m\n",
      "\u001b[35mWorkflow Store: /.sagemaker/ts/models\u001b[0m\n",
      "\u001b[35mModel config: N/A\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,077 [INFO ] main org.pytorch.serve.servingsdk.impl.PluginsManager -  Loading snapshot serializer plugin...\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,096 [INFO ] main org.pytorch.serve.ModelServer - Loading initial models: /opt/ml/model\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,100 [WARN ] main org.pytorch.serve.archive.model.ModelArchive - Model archive version is not defined. Please upgrade to torch-model-archiver 0.2.0 or higher\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,100 [WARN ] main org.pytorch.serve.archive.model.ModelArchive - Model archive createdOn is not defined. Please upgrade to torch-model-archiver 0.2.0 or higher\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,102 [INFO ] main org.pytorch.serve.wlm.ModelManager - Model model loaded.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,114 [INFO ] main org.pytorch.serve.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,377 [INFO ] main org.pytorch.serve.ModelServer - Inference API bind to: http://0.0.0.0:8080\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,377 [INFO ] main org.pytorch.serve.ModelServer - Initialize Metrics server with: EpollServerSocketChannel.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:12,456 [INFO ] main org.pytorch.serve.ModelServer - Metrics API bind to: http://127.0.0.1:8082\u001b[0m\n",
      "\u001b[35mModel server started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,688 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9001, pid=64\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,689 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9001\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,766 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,767 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - [PID]64\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,768 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,775 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9001\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,779 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,800 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9004, pid=67\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,801 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9004\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,807 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9003, pid=66\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,808 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9003\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,814 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9001.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,819 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154819\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,880 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,884 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9005, pid=68\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,885 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9005\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,885 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,886 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - [PID]67\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,886 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,688 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9001, pid=64\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,689 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9001\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,766 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,767 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - [PID]64\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,768 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,775 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9001\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,779 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,800 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9004, pid=67\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,801 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9004\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,807 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9003, pid=66\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,808 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9003\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,814 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9001.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,819 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154819\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,880 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,884 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9005, pid=68\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,885 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9005\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,885 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,886 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - [PID]67\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,886 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,887 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,888 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9004\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,896 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9004.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,897 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154897\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,908 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9006, pid=62\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,909 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9006\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - [PID]66\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,918 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,918 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9003\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,937 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9003.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,938 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154938\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,940 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:14,996 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,887 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,888 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9004\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,896 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9004.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,897 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154897\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,908 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9006, pid=62\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,909 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9006\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - [PID]66\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,917 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,918 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,918 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9003\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,937 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9003.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,938 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815154938\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,940 [INFO ] W-9004-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:14,996 [INFO ] W-9003-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,044 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,048 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,058 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - [PID]68\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,044 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,048 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,058 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - [PID]68\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,059 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,059 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9005\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,060 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,064 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - [PID]62\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,066 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,067 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9005.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,066 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9006\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,067 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,072 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155072\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,093 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9006.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,093 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155093\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,144 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,160 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,166 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9007, pid=69\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,171 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9007\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,176 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9002, pid=65\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,176 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9002\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,345 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,346 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - [PID]69\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,347 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,347 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9007\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,351 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,352 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9007.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,353 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155353\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - [PID]65\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,423 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,059 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,059 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9005\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,060 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,064 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - [PID]62\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,066 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,067 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9005.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,066 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9006\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,067 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,072 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155072\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,093 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9006.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,093 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155093\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,144 [INFO ] W-9006-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,160 [INFO ] W-9005-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,166 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9007, pid=69\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,171 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9007\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,176 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9002, pid=65\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,176 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9002\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,345 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,346 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - [PID]69\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,347 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,347 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9007\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,351 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,352 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9007.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,353 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155353\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - [PID]65\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,421 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,423 [INFO ] W-9007-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,424 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9002\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,439 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9002.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,439 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155439\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,488 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,535 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 629\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,536 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3426.0|#WorkerName:W-9001-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,539 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:91.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,554 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 594\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,555 [INFO ] W-9004-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3445.0|#WorkerName:W-9004-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,555 [INFO ] W-9004-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:64.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,778 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9000, pid=63\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,779 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9000\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,814 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 663\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,814 [INFO ] W-9005-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3704.0|#WorkerName:W-9005-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,815 [INFO ] W-9005-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:79.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 860\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3737.0|#WorkerName:W-9003-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:49.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,865 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,873 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - [PID]63\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,873 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 719\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,873 [INFO ] W-9006-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3761.0|#WorkerName:W-9006-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,874 [INFO ] W-9006-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:62.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9000\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,881 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9000.\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,883 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155883\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,424 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9002\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,439 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9002.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,439 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155439\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,488 [INFO ] W-9002-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,535 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 629\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,536 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3426.0|#WorkerName:W-9001-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,539 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:91.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,554 [INFO ] W-9004-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 594\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,555 [INFO ] W-9004-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3445.0|#WorkerName:W-9004-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,555 [INFO ] W-9004-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:64.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,778 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - s_name_part0=/home/model-server/tmp/.ts.sock, s_name_part1=9000, pid=63\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,779 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Listening on port: /home/model-server/tmp/.ts.sock.9000\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,814 [INFO ] W-9005-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 663\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,814 [INFO ] W-9005-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3704.0|#WorkerName:W-9005-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,815 [INFO ] W-9005-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:79.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 860\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3737.0|#WorkerName:W-9003-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,847 [INFO ] W-9003-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:49.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,865 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Successfully loaded /opt/conda/lib/python3.10/site-packages/ts/configs/metrics.yaml.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,873 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - [PID]63\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,873 [INFO ] W-9006-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 719\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,873 [INFO ] W-9006-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3761.0|#WorkerName:W-9006-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Torch worker started.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,874 [INFO ] W-9006-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:62.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815155\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Connecting to: /home/model-server/tmp/.ts.sock.9000\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,874 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Python runtime: 3.10.9\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,881 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - Connection accepted: /home/model-server/tmp/.ts.sock.9000.\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,883 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd LOAD to backend at: 1706815155883\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:15,928 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:15,928 [INFO ] W-9000-model_1.0-stdout MODEL_LOG - model_name: model, batchSize: 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,073 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 639\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,074 [INFO ] W-9007-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3962.0|#WorkerName:W-9007-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,075 [INFO ] W-9007-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:83.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,089 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 601\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,090 [INFO ] W-9002-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3980.0|#WorkerName:W-9002-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,090 [INFO ] W-9002-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:50.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,309 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 382\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,310 [INFO ] W-9000-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:4202.0|#WorkerName:W-9000-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:16,310 [INFO ] W-9000-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:45.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,073 [INFO ] W-9007-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 639\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,074 [INFO ] W-9007-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3962.0|#WorkerName:W-9007-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,075 [INFO ] W-9007-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:83.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,089 [INFO ] W-9002-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 601\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,090 [INFO ] W-9002-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:3980.0|#WorkerName:W-9002-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,090 [INFO ] W-9002-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:50.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,309 [INFO ] W-9000-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 382\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,310 [INFO ] W-9000-model_1.0 TS_METRICS - WorkerLoadTime.Milliseconds:4202.0|#WorkerName:W-9000-model_1.0,Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:16,310 [INFO ] W-9000-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:45.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815156\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,605 [INFO ] pool-2-thread-9 ACCESS_LOG - /169.254.255.130:58212 \"GET /ping HTTP/1.1\" 200 14\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,606 [INFO ] pool-2-thread-9 TS_METRICS - Requests2XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,621 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:58216 \"GET /execution-parameters HTTP/1.1\" 404 1\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,622 [INFO ] epollEventLoopGroup-3-2 TS_METRICS - Requests4XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,688 [INFO ] epollEventLoopGroup-3-3 TS_METRICS - ts_inference_requests_total.Count:1.0|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,689 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd PREDICT to backend at: 1706815157689\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,690 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Backend received inference at: 1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,860 [INFO ] W-9001-model_1.0-stdout org.pytorch.serve.wlm.WorkerLifeCycle - result=[METRICS]PredictionTime.Milliseconds:169.4|#ModelName:model,Level:Model|#hostname:36163f706ff0,1706815157,832237e0-dbee-4c4f-a239-22ab0bc52313, pattern=[METRICS]\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,861 [INFO ] W-9001-model_1.0-stdout MODEL_METRICS - PredictionTime.ms:169.4|#ModelName:model,Level:Model|#hostname:36163f706ff0,requestID:832237e0-dbee-4c4f-a239-22ab0bc52313,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,861 [INFO ] W-9001-model_1.0 ACCESS_LOG - /169.254.255.130:58228 \"POST /invocations HTTP/1.1\" 200 174\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - Requests2XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - ts_inference_latency_microseconds.Microseconds:172391.008|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - ts_queue_latency_microseconds.Microseconds:157.041|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 TS_METRICS - QueueTime.Milliseconds:0.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 171\u001b[0m\n",
      "\u001b[34m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:3.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,605 [INFO ] pool-2-thread-9 ACCESS_LOG - /169.254.255.130:58212 \"GET /ping HTTP/1.1\" 200 14\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,606 [INFO ] pool-2-thread-9 TS_METRICS - Requests2XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,621 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:58216 \"GET /execution-parameters HTTP/1.1\" 404 1\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,622 [INFO ] epollEventLoopGroup-3-2 TS_METRICS - Requests4XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,688 [INFO ] epollEventLoopGroup-3-3 TS_METRICS - ts_inference_requests_total.Count:1.0|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,689 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Flushing req.cmd PREDICT to backend at: 1706815157689\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,690 [INFO ] W-9001-model_1.0-stdout MODEL_LOG - Backend received inference at: 1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,860 [INFO ] W-9001-model_1.0-stdout org.pytorch.serve.wlm.WorkerLifeCycle - result=[METRICS]PredictionTime.Milliseconds:169.4|#ModelName:model,Level:Model|#hostname:36163f706ff0,1706815157,832237e0-dbee-4c4f-a239-22ab0bc52313, pattern=[METRICS]\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,861 [INFO ] W-9001-model_1.0-stdout MODEL_METRICS - PredictionTime.ms:169.4|#ModelName:model,Level:Model|#hostname:36163f706ff0,requestID:832237e0-dbee-4c4f-a239-22ab0bc52313,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,861 [INFO ] W-9001-model_1.0 ACCESS_LOG - /169.254.255.130:58228 \"POST /invocations HTTP/1.1\" 200 174\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - Requests2XX.Count:1.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - ts_inference_latency_microseconds.Microseconds:172391.008|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,862 [INFO ] W-9001-model_1.0 TS_METRICS - ts_queue_latency_microseconds.Microseconds:157.041|#model_name:model,model_version:default|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 TS_METRICS - QueueTime.Milliseconds:0.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 org.pytorch.serve.wlm.WorkerThread - Backend response time: 171\u001b[0m\n",
      "\u001b[35m2024-02-01T19:19:17,863 [INFO ] W-9001-model_1.0 TS_METRICS - WorkerThreadTime.Milliseconds:3.0|#Level:Host|#hostname:36163f706ff0,timestamp:1706815157\u001b[0m\n",
      "\u001b[32m2024-02-01T19:19:17.626:[sagemaker logs]: MaxConcurrentTransforms=1, MaxPayloadInMB=6, BatchStrategy=MULTI_RECORD\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "transformer.transform(\n",
    "    data=test_data,\n",
    "    content_type=\"text/csv\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch_predictions = sagemaker_session.read_s3_file(\n",
    "    bucket=bucket,\n",
    "    key_prefix=f\"{transformer.latest_transform_job.name}/test.csv.out\"\n",
    ")\n",
    "\n",
    "batch_predictions = pd.read_csv(io.StringIO(batch_predictions), dtype=float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(600, 4)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1_mean</th>\n",
       "      <th>y1_std</th>\n",
       "      <th>y2_mean</th>\n",
       "      <th>y2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   y1_mean  y1_std  y2_mean  y2_std\n",
       "0      NaN     NaN      NaN     NaN\n",
       "1      NaN     NaN      NaN     NaN\n",
       "2      NaN     NaN      NaN     NaN\n",
       "3      NaN     NaN      NaN     NaN\n",
       "4      NaN     NaN      NaN     NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_predictions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>y1_mean</th>\n",
       "      <th>y1_std</th>\n",
       "      <th>y2_mean</th>\n",
       "      <th>y2_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>11.077118</td>\n",
       "      <td>2.327890</td>\n",
       "      <td>-16.994116</td>\n",
       "      <td>2.207869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>7.974410</td>\n",
       "      <td>2.280835</td>\n",
       "      <td>-18.382240</td>\n",
       "      <td>2.148462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>4.983987</td>\n",
       "      <td>2.252448</td>\n",
       "      <td>-18.028560</td>\n",
       "      <td>2.304434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2.382181</td>\n",
       "      <td>2.389343</td>\n",
       "      <td>-19.242182</td>\n",
       "      <td>2.237700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>-1.046188</td>\n",
       "      <td>2.341685</td>\n",
       "      <td>-19.737051</td>\n",
       "      <td>2.224695</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       y1_mean    y1_std    y2_mean    y2_std\n",
       "595  11.077118  2.327890 -16.994116  2.207869\n",
       "596   7.974410  2.280835 -18.382240  2.148462\n",
       "597   4.983987  2.252448 -18.028560  2.304434\n",
       "598   2.382181  2.389343 -19.242182  2.237700\n",
       "599  -1.046188  2.341685 -19.737051  2.224695"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_predictions.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y1_mean    200\n",
       "y1_std     200\n",
       "y2_mean    200\n",
       "y2_std     200\n",
       "dtype: int64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_predictions.isna().sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(np.isclose(batch_predictions.dropna().values, real_time_predictions.dropna().values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=len([c for c in test_dataset.columns if c.startswith(\"y\")]), ncols=1, sharex=True, figsize=(6, 5))\n",
    "for i, c in enumerate([c for c in test_dataset.columns if c.startswith(\"y\")]):\n",
    "    axs[i].plot(test_dataset.index, test_dataset[c].values, color=\"#7f8ea3\", lw=1, label=\"Actual\" if i == 0 else None)\n",
    "    axs[i].plot(batch_predictions.index, batch_predictions[f\"{c}_mean\"].values, color=\"#009ad3\", lw=1, label=\"Predicted\" if i == 0 else None)\n",
    "    axs[i].fill_between(batch_predictions.index, batch_predictions[f\"{c}_mean\"].values + batch_predictions[f\"{c}_std\"].values, batch_predictions[f\"{c}_mean\"].values - batch_predictions[f\"{c}_std\"].values, color=\"#009ad3\", alpha=0.2, lw=1, label=\"Predicted +/- 1 Std. Dev.\" if i == 0 else None)\n",
    "    axs[i].fill_between(batch_predictions.index, batch_predictions[f\"{c}_mean\"].values + 2 * batch_predictions[f\"{c}_std\"].values, batch_predictions[f\"{c}_mean\"].values - 2 * batch_predictions[f\"{c}_std\"].values, color=\"#009ad3\", alpha=0.1, lw=1, label=\"Predicted +/- 2 Std. Dev.\" if i == 0 else None)\n",
    "    axs[i].set_title(c, size=10)\n",
    "    axs[i].set(xlabel=\"Time\", ylabel=\"Value\")\n",
    "    axs[i].xaxis.set_tick_params(labelbottom=True)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"major\", labelsize=7)\n",
    "    axs[i].tick_params(axis=\"both\", which=\"minor\", labelsize=7)\n",
    "fig.suptitle(\"Batch Predictions on Test Dataset\")\n",
    "fig.legend(bbox_to_anchor=(1, 0, 0.4, 1), frameon=False)\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "batch_results = sagemaker_session.upload_string_as_file_body(\n",
    "    body=batch_predictions.to_csv(index=False),\n",
    "    bucket=bucket,\n",
    "    key=\"data/inference/output/batch/batch_predictions.csv\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-eu-west-1-661670223746/data/inference/output/batch/batch_predictions.csv'"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "batch_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Clean-up"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Delete the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Deleting model with name: cfc-tsf-v1-6-2024-02-01-19-10-58-316-2024-02-01-19-11-43-836\n"
     ]
    }
   ],
   "source": [
    "transformer.delete_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Unsubscribe to the listing (optional)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to unsubscribe to the algorithm, follow these steps. Before you cancel the subscription, ensure that you do not have any [deployable model](https://console.aws.amazon.com/sagemaker/home#/models) created from the model package or using the algorithm. Note - You can find this information by looking at the container name associated with the model. \n",
    "\n",
    "**Steps to unsubscribe to product from AWS Marketplace**:\n",
    "1. Navigate to __Machine Learning__ tab on [__Your Software subscriptions page__](https://aws.amazon.com/marketplace/ai/library?productType=ml&ref_=mlmp_gitdemo_indust)\n",
    "2. Locate the listing that you want to cancel the subscription for, and then choose __Cancel Subscription__  to cancel the subscription.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_p310",
   "language": "python",
   "name": "conda_pytorch_p310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}