huggingface_pretrained_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "24bf4b10",
   "metadata": {},
   "source": [
    "# Get Huggingface pretrained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fb062dfe",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install transformers --upgrade --quiet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ac6e63d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import transformers\n",
    "import torch\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27f6977b",
   "metadata": {},
   "source": [
    "## 1. Get pretrained model, test and save it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d64ac0ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to distilbert-base-uncased-finetuned-sst-2-english (https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67cb620f3c3b4cfb887fd052cd597029",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6de98edeceaa4d01b71f76dc8ef7393b",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ace4b3443edd41b4818daddb94feb8a2",
       "version_major": 2,
       "version_minor": 0
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a97d82317fc40b7b206b3cd2d490423",
       "version_major": 2,
       "version_minor": 0
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pretrained_classifier = pipeline(\"sentiment-analysis\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c00da9de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998599290847778},\n",
       " {'label': 'NEGATIVE', 'score': 0.9997907280921936}]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_classifier([\n",
    "    \"I really like this place!\",\n",
    "    \"The food was bad!\"\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4ed20b90",
   "metadata": {},
   "outputs": [],
   "source": [
    "pretrained_classifier.save_pretrained(\"./model/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d11c76c3",
   "metadata": {},
   "source": [
    "## 2. Check if we can successfully load our saved model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "679855ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "local_classifier = pipeline('sentiment-analysis', model=\"./model/\", tokenizer=\"./model/\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b09d47f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'label': 'POSITIVE', 'score': 0.9998599290847778},\n",
       " {'label': 'NEGATIVE', 'score': 0.9997907280921936}]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "local_classifier([\n",
    "    \"I really like this place!\",\n",
    "    \"The food was bad!\"\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82995986",
   "metadata": {},
   "source": [
    "ok this works!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb9124d1",
   "metadata": {},
   "source": [
    "## 3. Build container for Lambda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b62def20",
   "metadata": {},
   "source": [
    "Let's build a container that holds our huggingface model \n",
    "and our lambda code that handles requests and forwards the data to our huggingface model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4ccba52a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "look at the versions for our dependencies:\n",
      "\n",
      "Python 3.6.13\n",
      "Torch version 1.7.1\n",
      "Transformers version 4.16.0\n"
     ]
    }
   ],
   "source": [
    "print(\"look at the versions for our dependencies:\")\n",
    "print(\"\")\n",
    "!python --version\n",
    "print(f\"Torch version {torch.__version__}\")\n",
    "print(f\"Transformers version {transformers.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "037d4c64",
   "metadata": {},
   "source": [
    "#### Create requirements.txt with dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "80aa6d90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting requirements.txt\n"
     ]
    }
   ],
   "source": [
    "%%writefile requirements.txt\n",
    "--find-links  https://download.pytorch.org/whl/torch_stable.html \n",
    " \n",
    "torch==1.7.1+cpu\n",
    "transformers==4.9.2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4906f748",
   "metadata": {},
   "source": [
    "#### Create dockerfile\n",
    "https://docs.aws.amazon.com/lambda/latest/dg/images-create.html\n",
    "\n",
    "- start from the official lambda docker container.\n",
    "- copy the requirements.txt to the docker container and isntall.\n",
    "- copy our model we saved locally in the model/ folder to the docker container\n",
    "- execcute the lambda handler function on start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7c097435",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting Dockerfile\n"
     ]
    }
   ],
   "source": [
    "%%writefile Dockerfile\n",
    "FROM public.ecr.aws/lambda/python:3.8\n",
    "\n",
    "COPY requirements.txt ./requirements.txt\n",
    "RUN pip install -r requirements.txt \n",
    "\n",
    "COPY ./model/   ./model/\n",
    "COPY ./app/app.py   ./\n",
    "CMD [\"app.handler\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2044a69a",
   "metadata": {},
   "source": [
    "#### Build dockerfile and push to ECR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f24cb3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install sagemaker-studio-image-build --quiet"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1fd7ae72",
   "metadata": {},
   "source": [
    "make sure this role has a trust relationship with codebuild\n",
    "\n",
    "https://github.com/aws-samples/sagemaker-studio-image-build-cli"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "08a303f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "...[Container] 2022/01/19 18:00:07 Waiting for agent ping\n",
      "\n",
      "[Container] 2022/01/19 18:00:08 Waiting for DOWNLOAD_SOURCE\n",
      "[Container] 2022/01/19 18:00:14 Phase is DOWNLOAD_SOURCE\n",
      "[Container] 2022/01/19 18:00:14 CODEBUILD_SRC_DIR=/codebuild/output/src038408758/src\n",
      "[Container] 2022/01/19 18:00:14 YAML location is /codebuild/output/src038408758/src/buildspec.yml\n",
      "[Container] 2022/01/19 18:00:14 Processing environment variables\n",
      "[Container] 2022/01/19 18:00:14 No runtime version selected in buildspec.\n",
      "[Container] 2022/01/19 18:00:15 Moving to directory /codebuild/output/src038408758/src\n",
      "[Container] 2022/01/19 18:00:15 Configuring ssm agent with target id: codebuild:4ddcd472-c66a-48f7-a824-43e918906fad\n",
      "[Container] 2022/01/19 18:00:15 Successfully updated ssm agent configuration\n",
      "[Container] 2022/01/19 18:00:15 Registering with agent\n",
      "[Container] 2022/01/19 18:00:15 Phases found in YAML: 3\n",
      "[Container] 2022/01/19 18:00:15  PRE_BUILD: 9 commands\n",
      "[Container] 2022/01/19 18:00:15  BUILD: 4 commands\n",
      "[Container] 2022/01/19 18:00:15  POST_BUILD: 3 commands\n",
      "[Container] 2022/01/19 18:00:15 Phase complete: DOWNLOAD_SOURCE State: SUCCEEDED\n",
      "[Container] 2022/01/19 18:00:15 Phase context status code:  Message:\n",
      "[Container] 2022/01/19 18:00:15 Entering phase INSTALL\n",
      "[Container] 2022/01/19 18:00:15 Phase complete: INSTALL State: SUCCEEDED\n",
      "[Container] 2022/01/19 18:00:15 Phase context status code:  Message:\n",
      "[Container] 2022/01/19 18:00:15 Entering phase PRE_BUILD\n",
      "[Container] 2022/01/19 18:00:15 Running command echo Logging in to Amazon ECR...\n",
      "Logging in to Amazon ECR...\n",
      "\n",
      "[Container] 2022/01/19 18:00:15 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:20 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 763104351884)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:21 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 217643126080)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:21 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 727897471807)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:22 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 626614931356)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:22 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 683313688378)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:23 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 520713654638)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:23 Running command $(aws ecr get-login --no-include-email --region $AWS_DEFAULT_REGION --registry-ids 462105765813)\n",
      "WARNING! Using --password via the CLI is insecure. Use --password-stdin.\n",
      "WARNING! Your password will be stored unencrypted in /root/.docker/config.json.\n",
      "Configure a credential helper to remove this warning. See\n",
      "https://docs.docker.com/engine/reference/commandline/login/#credentials-store\n",
      "\n",
      "Login Succeeded\n",
      "\n",
      "[Container] 2022/01/19 18:00:24 Phase complete: PRE_BUILD State: SUCCEEDED\n",
      "[Container] 2022/01/19 18:00:24 Phase context status code:  Message:\n",
      "[Container] 2022/01/19 18:00:24 Entering phase BUILD\n",
      "[Container] 2022/01/19 18:00:24 Running command echo Build started on `date`\n",
      "Build started on Wed Jan 19 18:00:24 UTC 2022\n",
      "\n",
      "[Container] 2022/01/19 18:00:24 Running command echo Building the Docker image...\n",
      "Building the Docker image...\n",
      "\n",
      "[Container] 2022/01/19 18:00:24 Running command docker build -t $IMAGE_REPO_NAME:$IMAGE_TAG .\n",
      "Sending build context to Docker daemon  268.8MB\n",
      "Step 1/6 : FROM public.ecr.aws/lambda/python:3.8\n",
      "3.8: Pulling from lambda/python\n",
      "060999a19daa: Pulling fs layer\n",
      "ccd1f0c44b60: Pulling fs layer\n",
      "d64053590116: Pulling fs layer\n",
      "cbc023ce6397: Pulling fs layer\n",
      "f81ef1c45229: Pulling fs layer\n",
      "04a95b502b45: Pulling fs layer\n",
      "cbc023ce6397: Waiting\n",
      "f81ef1c45229: Waiting\n",
      "04a95b502b45: Waiting\n",
      "d64053590116: Download complete\n",
      "ccd1f0c44b60: Download complete\n",
      "cbc023ce6397: Verifying Checksum\n",
      "cbc023ce6397: Download complete\n",
      "04a95b502b45: Verifying Checksum\n",
      "04a95b502b45: Download complete\n",
      "f81ef1c45229: Verifying Checksum\n",
      "f81ef1c45229: Download complete\n",
      "060999a19daa: Verifying Checksum\n",
      "060999a19daa: Download complete\n",
      "060999a19daa: Pull complete\n",
      "ccd1f0c44b60: Pull complete\n",
      "d64053590116: Pull complete\n",
      "cbc023ce6397: Pull complete\n",
      "f81ef1c45229: Pull complete\n",
      "04a95b502b45: Pull complete\n",
      "Digest: sha256:aa2db5526341b0f15cc065d9791548f2611f295e4f3cd8e9379eff889952ee13\n",
      "Status: Downloaded newer image for public.ecr.aws/lambda/python:3.8\n",
      " ---> f435dc94536f\n",
      "Step 2/6 : COPY requirements.txt ./requirements.txt\n",
      " ---> 764be2a89d4c\n",
      "Step 3/6 : RUN pip install -r requirements.txt\n",
      " ---> Running in d7dd9b6db0d7\n",
      "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
      "Collecting torch==1.7.1+cpu\n",
      "  Downloading https://download.pytorch.org/whl/cpu/torch-1.7.1%2Bcpu-cp38-cp38-linux_x86_64.whl (159.4 MB)\n",
      "Collecting transformers==4.9.2\n",
      "  Downloading transformers-4.9.2-py3-none-any.whl (2.6 MB)\n",
      "Collecting numpy\n",
      "  Downloading numpy-1.22.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.8 MB)\n",
      "Collecting typing-extensions\n",
      "  Downloading typing_extensions-4.0.1-py3-none-any.whl (22 kB)\n",
      "Collecting tqdm>=4.27\n",
      "  Downloading tqdm-4.62.3-py2.py3-none-any.whl (76 kB)\n",
      "Collecting tokenizers<0.11,>=0.10.1\n",
      "  Downloading tokenizers-0.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (3.3 MB)\n",
      "Collecting packaging\n",
      "  Downloading packaging-21.3-py3-none-any.whl (40 kB)\n",
      "Collecting huggingface-hub==0.0.12\n",
      "  Downloading huggingface_hub-0.0.12-py3-none-any.whl (37 kB)\n",
      "Collecting requests\n",
      "  Downloading requests-2.27.1-py2.py3-none-any.whl (63 kB)\n",
      "Collecting sacremoses\n",
      "  Downloading sacremoses-0.0.47-py2.py3-none-any.whl (895 kB)\n",
      "Collecting filelock\n",
      "  Downloading filelock-3.4.2-py3-none-any.whl (9.9 kB)\n",
      "Collecting pyyaml>=5.1\n",
      "  Downloading PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (701 kB)\n",
      "Collecting regex!=2019.12.17\n",
      "  Downloading regex-2022.1.18-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (764 kB)\n",
      "Collecting pyparsing!=3.0.5,>=2.0.2\n",
      "  Downloading pyparsing-3.0.6-py3-none-any.whl (97 kB)\n",
      "Collecting idna<4,>=2.5\n",
      "  Downloading idna-3.3-py3-none-any.whl (61 kB)\n",
      "Collecting charset-normalizer~=2.0.0\n",
      "  Downloading charset_normalizer-2.0.10-py3-none-any.whl (39 kB)\n",
      "Collecting urllib3<1.27,>=1.21.1\n",
      "  Downloading urllib3-1.26.8-py2.py3-none-any.whl (138 kB)\n",
      "Collecting certifi>=2017.4.17\n",
      "  Downloading certifi-2021.10.8-py2.py3-none-any.whl (149 kB)\n",
      "Collecting click\n",
      "  Downloading click-8.0.3-py3-none-any.whl (97 kB)\n",
      "Collecting joblib\n",
      "  Downloading joblib-1.1.0-py2.py3-none-any.whl (306 kB)\n",
      "Collecting six\n",
      "  Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)\n",
      "Installing collected packages: urllib3, pyparsing, idna, charset-normalizer, certifi, typing-extensions, tqdm, six, requests, regex, packaging, joblib, filelock, click, tokenizers, sacremoses, pyyaml, numpy, huggingface-hub, transformers, torch\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed certifi-2021.10.8 charset-normalizer-2.0.10 click-8.0.3 filelock-3.4.2 huggingface-hub-0.0.12 idna-3.3 joblib-1.1.0 numpy-1.22.1 packaging-21.3 pyparsing-3.0.6 pyyaml-6.0 regex-2022.1.18 requests-2.27.1 sacremoses-0.0.47 six-1.16.0 tokenizers-0.10.3 torch-1.7.1+cpu tqdm-4.62.3 transformers-4.9.2 typing-extensions-4.0.1 urllib3-1.26.8\n",
      "\u001b[91mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\n",
      "\u001b[0m\u001b[91mWARNING: You are using pip version 21.1.1; however, version 21.3.1 is available.\n",
      "You should consider upgrading via the '/var/lang/bin/python3.8 -m pip install --upgrade pip' command.\n",
      "\u001b[0mRemoving intermediate container d7dd9b6db0d7\n",
      " ---> 0c9c465b71ef\n",
      "Step 4/6 : COPY ./model/   ./model/\n",
      " ---> abbdbad1b955\n",
      "Step 5/6 : COPY ./app/app.py   ./\n",
      " ---> b3043aa6b0e5\n",
      "Step 6/6 : CMD [\"app.handler\"]\n",
      " ---> Running in d5a9003eff71\n",
      "Removing intermediate container d5a9003eff71\n",
      " ---> 33e9e0cccb4e\n",
      "Successfully built 33e9e0cccb4e\n",
      "Successfully tagged huggingface-on-lambda:1.0\n",
      "\n",
      "[Container] 2022/01/19 18:01:41 Running command docker tag $IMAGE_REPO_NAME:$IMAGE_TAG $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/$IMAGE_REPO_NAME:$IMAGE_TAG\n",
      "\n",
      "[Container] 2022/01/19 18:01:41 Phase complete: BUILD State: SUCCEEDED\n",
      "[Container] 2022/01/19 18:01:41 Phase context status code:  Message:\n",
      "[Container] 2022/01/19 18:01:41 Entering phase POST_BUILD\n",
      "[Container] 2022/01/19 18:01:41 Running command echo Build completed on `date`\n",
      "Build completed on Wed Jan 19 18:01:41 UTC 2022\n",
      "\n",
      "[Container] 2022/01/19 18:01:41 Running command echo Pushing the Docker image...\n",
      "Pushing the Docker image...\n",
      "\n",
      "[Container] 2022/01/19 18:01:41 Running command docker push $AWS_ACCOUNT_ID.dkr.ecr.$AWS_DEFAULT_REGION.amazonaws.com/$IMAGE_REPO_NAME:$IMAGE_TAG\n",
      "The push refers to repository [077590795309.dkr.ecr.eu-west-1.amazonaws.com/huggingface-on-lambda]\n",
      "a38bb14de046: Preparing\n",
      "8a4e26be04af: Preparing\n",
      "91cd6ed7661d: Preparing\n",
      "5fd9724c2da6: Preparing\n",
      "d04826348e4c: Preparing\n",
      "8ecc72c5be6a: Preparing\n",
      "1b1312f842d8: Preparing\n",
      "30f2e1fe3f91: Preparing\n",
      "a1e48692d03f: Preparing\n",
      "64752117c824: Preparing\n",
      "8ecc72c5be6a: Waiting\n",
      "1b1312f842d8: Waiting\n",
      "30f2e1fe3f91: Waiting\n",
      "a1e48692d03f: Waiting\n",
      "64752117c824: Waiting\n",
      "a38bb14de046: Pushed\n",
      "5fd9724c2da6: Pushed\n",
      "1b1312f842d8: Pushed\n",
      "30f2e1fe3f91: Pushed\n",
      "a1e48692d03f: Pushed\n",
      "8a4e26be04af: Pushed\n",
      "d04826348e4c: Pushed\n",
      "8ecc72c5be6a: Pushed\n",
      "64752117c824: Pushed\n",
      "91cd6ed7661d: Pushed\n",
      "1.0: digest: sha256:568b5b9008ab99f3ed45dc87d7c0501899db3a8406d69381618d9dd1190d0367 size: 2421\n",
      "\n",
      "[Container] 2022/01/19 18:02:40 Phase complete: POST_BUILD State: SUCCEEDED\n",
      "[Container] 2022/01/19 18:02:40 Phase context status code:  Message:\n",
      "\n",
      "Image URI: 077590795309.dkr.ecr.eu-west-1.amazonaws.com/huggingface-on-lambda:1.0\n"
     ]
    }
   ],
   "source": [
    "!sm-docker build . --repository huggingface-on-lambda:1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef46e5fd",
   "metadata": {},
   "source": [
    "## 4. Deploy Container within a Lambda Function"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68c18d17",
   "metadata": {},
   "source": [
    "Create a serverless.yml file that points to the docker image uri + digest.\n",
    "\n",
    "We expect a POST request on the path \"prediction/\"\n",
    "\n",
    "More configuration can be found in the serverless file ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a6ee03af",
   "metadata": {},
   "outputs": [],
   "source": [
    "ACCOUNT_NUMBER = \"\"\n",
    "REGION = \"\"\n",
    "# {ACCOUNT_NUMBER}.dkr.ecr.{REGION}.amazonaws.com/huggingface-on-lambda\n",
    "image_uri= \"077590795309.dkr.ecr.eu-west-1.amazonaws.com/huggingface-on-lambda\"\n",
    "# sha256:f0e2ae3aee2cceb6d93d...\n",
    "image_digest = \"sha256:568b5b9008ab99f3ed45dc87d7c0501899db3a8406d69381618d9dd1190d0367\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e79f261",
   "metadata": {},
   "source": [
    "__serverless file__:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3d9edf9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "service: huggingface-on-lambda\n",
      "\n",
      "provider:\n",
      "  name: aws\n",
      "  region: eu-west-1 \n",
      "\n",
      "functions:\n",
      "  huggingface:\n",
      "    image: 077590795309.dkr.ecr.eu-west-1.amazonaws.com/huggingface-on-lambda@sha256:568b5b9008ab99f3ed45dc87d7c0501899db3a8406d69381618d9dd1190d0367\n",
      "    # 2 minutes before we throw a timeout\n",
      "    timeout: 120\n",
      "    # have 1 hot lambda available\n",
      "    provisionedConcurrency: 0\n",
      "    # our model is less than 1GB, so 1024MB is enough\n",
      "    memorySize: 1024 \n",
      "    events:\n",
      "      - http:\n",
      "          path: prediction\n",
      "          method: post\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "service: huggingface-on-lambda\n",
    "\n",
    "provider:\n",
    "  name: aws\n",
    "  region: eu-west-1 \n",
    "\n",
    "functions:\n",
    "  huggingface:\n",
    "    image: {image_uri}@{image_digest}\n",
    "    # 2 minutes before we throw a timeout\n",
    "    timeout: 120\n",
    "    # have 1 hot lambda available\n",
    "    provisionedConcurrency: 0\n",
    "    # our model is less than 1GB, so 1024MB is enough\n",
    "    memorySize: 1024 \n",
    "    events:\n",
    "      - http:\n",
    "          path: prediction\n",
    "          method: post\n",
    "\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7814733",
   "metadata": {},
   "source": [
    "open an AWS cloudshell and install the serverless framework"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b9357b7",
   "metadata": {},
   "source": [
    "    npm install serverless"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c99c983",
   "metadata": {},
   "source": [
    "create the serverless.yml file in the cloudshell\n",
    "    \n",
    "    cat > serverless.yml\n",
    "\n",
    "paste the contents and press ctrl + c to close and save the file.\n",
    "To deploy the stack run\n",
    "\n",
    "    npm install --prefix ./ serverless\n",
    "    node_modules/serverless/bin/serverless.js deploy\n",
    "    \n",
    "    \n",
    "            Output:\n",
    "    \n",
    "            Serverless: Packaging service...\n",
    "            Serverless: WARNING: Function huggingface has timeout of 120 seconds, however, \n",
    "            it's attached to API Gateway so it's automatically limited to 30 seconds.\n",
    "            Serverless: Uploading CloudFormation file to S3...\n",
    "            Serverless: Uploading artifacts...\n",
    "            Serverless: Validating template...\n",
    "            Serverless: Updating Stack...\n",
    "            Serverless: Checking Stack update progress...\n",
    "            ........................\n",
    "            Serverless: Stack update finished...\n",
    "            Service Information\n",
    "            service: huggingface-on-lambda\n",
    "            stage: dev\n",
    "            region: eu-west-1\n",
    "            stack: huggingface-on-lambda-dev\n",
    "            resources: 12\n",
    "            api keys:\n",
    "              None\n",
    "            endpoints:\n",
    "              POST - https://8af9ar02gi.execute-api.eu-west-1.amazonaws.com/dev/prediction\n",
    "            functions:\n",
    "              huggingface: huggingface-on-lambda-dev-huggingface\n",
    "            layers:\n",
    "              None\n",
    "            Serverless: Removing old service artifacts from S3...\n",
    "\n",
    "if deploy succeeds, call the endpoint\n",
    "\n",
    "    curl -d '{\"data\":\"some very much wow positive text!\"}' -H \"Content-Type: application/json\" -X POST https://8af9ar02gi.execute-api.eu-west-1.amazonaws.com/dev/prediction\n",
    "            \n",
    "            Output:\n",
    "            \n",
    "            [{\"label\": \"POSITIVE\", \"score\": 0.9998674392700195}]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d33e588",
   "metadata": {},
   "source": [
    "## 5. Call Endpoint\n",
    "### First call to a cold Lambda"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "898f0f52",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"label\": \"POSITIVE\", \"score\": 0.9998674392700195}]CPU times: user 4.16 ms, sys: 14 ms, total: 18.1 ms\n",
      "Wall time: 311 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "!curl -d '{\"data\":\"some very much wow positive text!\"}' -H \"Content-Type: application/json\" -X POST  https://293z5epjvk.execute-api.eu-west-1.amazonaws.com/dev/prediction\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97923b80",
   "metadata": {},
   "source": [
    "### Burst of 10 000 calls to Lambda\n",
    "\n",
    "- The min latency is 57 ms, the max is 4651 ms, and the average is 139 ms.\n",
    "- 0 errors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "9498d6d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.2 s, sys: 1.46 s, total: 14.6 s\n",
      "Wall time: 1min 3s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from joblib import Parallel, delayed\n",
    "def process(i):\n",
    "    print(\".\",)\n",
    "    subprocess.check_call(shlex.split(cmd))\n",
    "    \n",
    "results = Parallel(n_jobs=100)(delayed(process)(i) for i in range(10000))"
   ]
  },
  {
   "attachments": {
    "Screen%20Shot%202022-01-27%20at%2021.06.28.png": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "fa38785c",
   "metadata": {},
   "source": [
    "![Screen%20Shot%202022-01-27%20at%2021.06.28.png](attachment:Screen%20Shot%202022-01-27%20at%2021.06.28.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "862ca7b5",
   "metadata": {},
   "source": [
    "## 6. Remove endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4e7291c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "conda_pytorch_latest_p36",
   "language": "python",
   "name": "conda_pytorch_latest_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}