docs/advanced_examples/Deployment.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "e8019f56",
   "metadata": {},
   "source": [
    "# Deployment\n",
    "\n",
    "In this notebook we display how to serve a model in FHE through AWS using python.\n",
    "\n",
    "In the first block of code we train, compile and serialize our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dff1ab8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No CUDA runtime is found, using CUDA_HOME='/usr/local/cuda'\n"
     ]
    }
   ],
   "source": [
    "import io\n",
    "import shutil\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy\n",
    "import requests\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from tqdm import tqdm\n",
    "\n",
    "from concrete.ml.deployment import FHEModelClient, FHEModelDev\n",
    "from concrete.ml.deployment.deploy_to_aws import AWSInstance, deploy_to_aws\n",
    "from concrete.ml.sklearn import XGBClassifier\n",
    "\n",
    "# Let's first get some data and train a model.\n",
    "X, y = load_breast_cancer(return_X_y=True)\n",
    "\n",
    "assert isinstance(X, numpy.ndarray)\n",
    "assert isinstance(y, numpy.ndarray)\n",
    "\n",
    "# Split X into X_model_owner and X_client\n",
    "X_train = X[:-10]\n",
    "y_train = y[:-10]\n",
    "\n",
    "X_test = X[-10:]\n",
    "y_test = y[-10:]\n",
    "\n",
    "# Train the model and compile it\n",
    "model = XGBClassifier(n_bits=2, n_estimators=8, max_depth=3)\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "\n",
    "path_to_model = Path(\"./serialized_model\").resolve()\n",
    "if path_to_model.exists():\n",
    "    shutil.rmtree(path_to_model)\n",
    "model.compile(X_train)\n",
    "dev = FHEModelDev(path_to_model, model)\n",
    "dev.save(via_mlir=True)  # Needed for cross-platform deployment"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55d13d30",
   "metadata": {},
   "source": [
    "In Concrete ML we expose a context manager called `AWSDeploy` that deploys the model to a AWS EC2 instance.\n",
    "\n",
    "Using a context manager allows for proper termination of the instance once the computation is finished.\n",
    "\n",
    "For persisting the instance/server consider using the `deploy_to_aws` method instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "506a0e28",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Security Group Created sg-07f05840eb7ade459 in vpc vpc-0677a14476c51231f.\n",
      "Instance took 15.56025743484497 seconds to start running\n",
      "Waiting for SSH connexion to be available...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "9it [00:15,  1.78s/it]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SSH connexion available.\n",
      "upload files...\n",
      "upload finished.\n",
      "ssh -i /home/luis/Documents/3068-can-we-call-your-deployment-script-directly-from-py-notebooks/src/concrete/ml/deployment/ssh_keys/cml_notebook_demo-2023_03_21_10_41_02-keypair.pem -o StrictHostKeyChecking=no -o IdentitiesOnly=yes -o RequestTTY=yes ubuntu@13.37.225.25 \"tmux new-session -d -s cml_notebook_demo-2023_03_21_10_41_02 sudo chmod -R 777 /home/ubuntu/venv && sudo apt install -y python3.8 && sudo apt install -y python3.8-distutils && virtualenv deployment_venv --python=python3.8 && source deployment_venv/bin/activate && python -m pip install -r server_requirements.txt && python -m pip install concrete-ml==0.6.1 && python -m pip install concrete-compiler==0.24.0rc5 && PORT=5000 PATH_TO_MODEL=\"./serialized_model\" python ./server.py\"\n",
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      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 86.8/86.8 KB 22.1 MB/s eta 0:00:00\n",
      "\n",
      "Collecting mpmath>=0.19\n",
      "\n",
      "  Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
      "\n",
      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 536.2/536.2 KB 68.8 MB/s eta 0:00:00\n",
      "\n",
      "Installing collected packages: mpmath, flatbuffers, zipp, typing-extensions, tqdm, tokenize-rt, threadpoolctl, tabulate, sympy, six, setuptools, PyYAML, pyparsing, psutil, protobuf, Pillow, packaging, numpy, networkx, kiwisolver, joblib, humanfriendly, fonttools, dill, dependencies, cycler, torch, scipy, python-dateutil, onnx, importlib-resources, future-annotations, contourpy, concrete-compiler, coloredlogs, xgboost, scikit-learn, onnxruntime, onnxoptimizer, onnxconverter-common, matplotlib, concrete-ml-extensions-brevitas, skorch, skl2onnx, hummingbird-ml, concrete-numpy, onnxmltools, concrete-ml\n",
      "\n",
      "  Attempting uninstall: typing-extensions\n",
      "\n",
      "    Found existing installation: typing_extensions 4.5.0\n",
      "\n",
      "    Uninstalling typing_extensions-4.5.0:\n",
      "\n",
      "      Successfully uninstalled typing_extensions-4.5.0\n",
      "\n",
      "  Attempting uninstall: setuptools\n",
      "\n",
      "    Found existing installation: setuptools 59.6.0\n",
      "\n",
      "    Uninstalling setuptools-59.6.0:\n",
      "\n",
      "      Successfully uninstalled setuptools-59.6.0\n",
      "\n",
      "Successfully installed Pillow-9.4.0 PyYAML-6.0 coloredlogs-15.0.1 concrete-compiler-0.23.4 concrete-ml-0.6.1 concrete-ml-extensions-brevitas-0.1.0 concrete-numpy-0.9.0 contourpy-1.0.7 cycler-0.11.0 dependencies-2.0.1 dill-0.3.6 flatbuffers-23.3.3 fonttools-4.39.2 future-annotations-1.0.0 humanfriendly-10.0 hummingbird-ml-0.4.4 importlib-resources-5.12.0 joblib-1.2.0 kiwisolver-1.4.4 matplotlib-3.7.1 mpmath-1.3.0 networkx-2.8.8 numpy-1.21.6 onnx-1.12.0 onnxconverter-common-1.13.0 onnxmltools-1.11.2 onnxoptimizer-0.2.7 onnxruntime-1.14.1 packaging-23.0 protobuf-3.19.4 psutil-5.9.4 pyparsing-3.0.9 python-dateutil-2.8.2 scikit-learn-1.0.2 scipy-1.7.3 setuptools-65.6.3 six-1.16.0 skl2onnx-1.14.0 skorch-0.11.0 sympy-1.11.1 tabulate-0.9.0 threadpoolctl-3.1.0 tokenize-rt-5.0.0 torch-1.12.1 tqdm-4.65.0 typing-extensions-4.4.0 xgboost-1.6.2 zipp-3.15.0\n",
      "\n",
      "Collecting concrete-compiler==0.24.0rc5\n",
      "\n",
      "  Downloading concrete_compiler-0.24.0rc5-cp38-cp38-manylinux_2_28_x86_64.whl (67.1 MB)\n",
      "\n",
      "     ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 67.1/67.1 MB 8.5 MB/s eta 0:00:00\n",
      "\n",
      "Requirement already satisfied: numpy in ./deployment_venv/lib/python3.8/site-packages (from concrete-compiler==0.24.0rc5) (1.21.6)\n",
      "\n",
      "Requirement already satisfied: PyYAML in ./deployment_venv/lib/python3.8/site-packages (from concrete-compiler==0.24.0rc5) (6.0)\n",
      "\n",
      "Requirement already satisfied: setuptools in ./deployment_venv/lib/python3.8/site-packages (from concrete-compiler==0.24.0rc5) (65.6.3)\n",
      "\n",
      "Installing collected packages: concrete-compiler\n",
      "\n",
      "  Attempting uninstall: concrete-compiler\n",
      "\n",
      "    Found existing installation: concrete-compiler 0.23.4\n",
      "\n",
      "    Uninstalling concrete-compiler-0.23.4:\n",
      "\n",
      "      Successfully uninstalled concrete-compiler-0.23.4\n",
      "\n",
      "Successfully installed concrete-compiler-0.24.0rc5\n",
      "\n",
      "ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "\n",
      "concrete-numpy 0.9.0 requires concrete-compiler<0.24.0,>=0.23.1, but you have concrete-compiler 0.24.0rc5 which is incompatible.\n",
      "\n",
      "INFO:     Started server process [884]\n",
      "\n",
      "INFO:     Waiting for application startup.\n",
      "\n",
      "INFO:     Application startup complete.\n",
      "\n",
      "INFO:     Uvicorn running on http://0.0.0.0\n",
      "Evaluation keys size: 72.23 MB\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 10/10 [00:16<00:00,  1.63s/it]\n"
     ]
    }
   ],
   "source": [
    "status_ok = 200\n",
    "client_folder = Path(\"./client_folder\")\n",
    "client_folder.mkdir(exist_ok=True)\n",
    "\n",
    "with AWSInstance(\n",
    "    instance_type=\"c5.large\",\n",
    "    verbose=True,\n",
    "    instance_name=\"cml_notebook_demo\",\n",
    ") as instance_metadata:\n",
    "    deploy_to_aws(\n",
    "        path_to_model=path_to_model,\n",
    "        instance_metadata=instance_metadata,\n",
    "        verbose=True,\n",
    "        wait_bar=True,\n",
    "    )\n",
    "    url = instance_metadata[\"URL\"]\n",
    "\n",
    "    zip_response = requests.get(f\"{url}/get_client\")\n",
    "    assert zip_response.status_code == status_ok\n",
    "    with open(client_folder / \"client.zip\", \"wb\") as file:\n",
    "        file.write(zip_response.content)\n",
    "\n",
    "    # Let's create the client using what we got from the server\n",
    "    client = FHEModelClient(path_dir=client_folder, key_dir=client_folder / \"keys\")\n",
    "\n",
    "    # The client first need to create the private and evaluation keys.\n",
    "    client.generate_private_and_evaluation_keys()\n",
    "\n",
    "    # Get the serialized evaluation keys\n",
    "    serialized_evaluation_keys = client.get_serialized_evaluation_keys()\n",
    "    assert isinstance(serialized_evaluation_keys, bytes)\n",
    "\n",
    "    # Evaluation keys can be quite large files but only have to be shared once with the server.\n",
    "\n",
    "    # Check the size of the evaluation keys (in MB)\n",
    "    print(f\"Evaluation keys size: {len(serialized_evaluation_keys) / (10**6):.2f} MB\")\n",
    "\n",
    "    # Let's send this evaluation key to the server (this has to be done only once)\n",
    "    # send_evaluation_key_to_server(serialized_evaluation_keys)\n",
    "\n",
    "    # Now we have everything for the client to interact with the server\n",
    "\n",
    "    # We create a loop to send the input to the server and receive the encrypted prediction\n",
    "    execution_time = []\n",
    "    encrypted_input = None\n",
    "    clear_input = None\n",
    "\n",
    "    response = requests.post(\n",
    "        f\"{url}/add_key\", files={\"key\": io.BytesIO(initial_bytes=serialized_evaluation_keys)}\n",
    "    )\n",
    "    assert response.status_code == status_ok\n",
    "    uid = response.json()[\"uid\"]\n",
    "\n",
    "    inferences = []\n",
    "    for i in tqdm(range(len(X_test))):\n",
    "        clear_input = X_test[[i], :]\n",
    "\n",
    "        assert isinstance(clear_input, numpy.ndarray)\n",
    "        encrypted_input = client.quantize_encrypt_serialize(clear_input)\n",
    "        assert isinstance(encrypted_input, bytes)\n",
    "\n",
    "        inferences.append(\n",
    "            requests.post(\n",
    "                f\"{url}/compute\",\n",
    "                files={\n",
    "                    \"model_input\": io.BytesIO(encrypted_input),\n",
    "                },\n",
    "                data={\n",
    "                    \"uid\": uid,\n",
    "                },\n",
    "            )\n",
    "        )\n",
    "\n",
    "# Unpack the results\n",
    "decrypted_predictions = []\n",
    "for result in inferences:\n",
    "    assert result.status_code == status_ok\n",
    "    encrypted_result = result.content\n",
    "    decrypted_prediction = client.deserialize_decrypt_dequantize(encrypted_result)[0]\n",
    "    decrypted_predictions.append(decrypted_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bcd9b24",
   "metadata": {},
   "source": [
    "We can now check that the predictions from the server are actually the same as the model in clear."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dbb5814d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(numpy.array(decrypted_predictions) == model.predict_proba(X_test)).all()"
   ]
  }
 ],
 "metadata": {
  "execution": {
   "timeout": 10800
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}