Section 2 - Federated Learning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section: Federated Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Introducing Federated Learning\n",
    "\n",
    "Federated Learning is a technique for training Deep Learning models on data to which you do not have access. Basically:\n",
    "\n",
    "Federated Learning: Instead of bringing all the data to one machine and training a model, we bring the model to the data, train it locally, and merely upload \"model updates\" to a central server.\n",
    "\n",
    "Use Cases:\n",
    "\n",
    "    - app company (Texting prediction app)\n",
    "    - predictive maintenance (automobiles / industrial engines)\n",
    "    - wearable medical devices\n",
    "    - ad blockers / autotomplete in browsers (Firefox/Brave)\n",
    "    \n",
    "Challenge Description: data is distributed amongst sources but we cannot aggregated it because of:\n",
    "\n",
    "    - privacy concerns: legal, user discomfort, competitive dynamics\n",
    "    - engineering: the bandwidth/storage requirements of aggregating the larger dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Introducing / Installing PySyft\n",
    "\n",
    "In order to perform Federated Learning, we need to be able to use Deep Learning techniques on remote machines. This will require a new set of tools. Specifically, we will use an extensin of PyTorch called PySyft.\n",
    "\n",
    "### Install PySyft\n",
    "\n",
    "The easiest way to install the required libraries is with [Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/overview.html). Create a new environment, then install the dependencies in that environment. In your terminal:\n",
    "\n",
    "```bash\n",
    "conda create -n pysyft python=3\n",
    "conda activate pysyft # some older version of conda require \"source activate pysyft\" instead.\n",
    "conda install jupyter notebook\n",
    "pip install syft\n",
    "pip install numpy\n",
    "```\n",
    "\n",
    "If you have any errors relating to zstd - run the following (if everything above installed fine then skip this step):\n",
    "\n",
    "```\n",
    "pip install --upgrade --force-reinstall zstd\n",
    "```\n",
    "\n",
    "and then retry installing syft (pip install syft).\n",
    "\n",
    "If you are using Windows, I suggest installing [Anaconda and using the Anaconda Prompt](https://docs.anaconda.com/anaconda/user-guide/getting-started/) to work from the command line. \n",
    "\n",
    "With this environment activated and in the repo directory, launch Jupyter Notebook:\n",
    "\n",
    "```bash\n",
    "jupyter notebook\n",
    "```\n",
    "\n",
    "and re-open this notebook on the new Jupyter server.\n",
    "\n",
    "If any part of this doesn't work for you (or any of the tests fail) - first check the [README](https://github.com/OpenMined/PySyft.git) for installation help and then open a Github Issue or ping the #beginner channel in our slack! [slack.openmined.org](http://slack.openmined.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch as th"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = th.tensor([1,2,3,4,5])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 2,  4,  6,  8, 10])\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import syft as sy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hook = sy.TorchHook(th)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "th.tensor([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Basic Remote Execution in PySyft"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PySyft => Remote PyTorch\n",
    "\n",
    "The essence of Federated Learning is the ability to train models in parallel on a wide number of machines. Thus, we need the ability to tell remote machines to execute the operations required for Deep Learning.\n",
    "\n",
    "Thus, instead of using Torch tensors - we're now going to work with **pointers** to tensors. Let me show you what I mean. First, let's create a \"pretend\" machine owned by a \"pretend\" person - we'll call him Bob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob = sy.VirtualWorker(hook, id=\"bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{50844634909: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:bob #tensors:1>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50844634909"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.id_at_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85673375777"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:me #tensors:0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.owner"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:me #tensors:0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hook.local_worker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:85673375777 -> bob:50844634909]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.get()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Playing with Remote Tensors\n",
    "\n",
    "In this project, I want you to .send() and .get() a tensor to TWO workers by calling .send(bob,alice). This will first require the creation of another VirtualWorker called alice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try this project here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Introducing Remote Arithmetic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)\n",
    "y = th.tensor([1,1,1,1,1]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:7344279461 -> bob:74976771769]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:68830170639 -> bob:70981483926]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:79875157913 -> bob:79875157913]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = z.get()\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:28437210120 -> bob:28437210120]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = th.add(x,y)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([2, 3, 4, 5, 6])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = z.get()\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1.,2,3,4,5], requires_grad=True).send(bob)\n",
    "y = th.tensor([1.,1,1,1,1], requires_grad=True).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = (x + y).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "z.backward()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 2., 3., 4., 5.], requires_grad=True)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Learn a Simple Linear Model\n",
    "\n",
    "In this project, I'd like for you to create a simple linear model which will solve for the following dataset below. You should use only Variables and .backward() to do so (no optimizers or nn.Modules). Furthermore, you must do so with both the data and the model being located on Bob's machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try this project here!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Garbage Collection and Common Errors\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob = bob.clear_objects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{23831414651: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{92212512027: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = \"asdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:19384969793 -> bob:49166137090]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{49166137090: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = \"asdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{49166137090: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{49166137090: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob = bob.clear_objects()\n",
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(1000):\n",
    "    x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{17426510898: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)\n",
    "y = th.tensor([1,1,1,1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "ename": "TensorsNotCollocatedException",
     "evalue": "You tried to call a method involving two tensors where one tensor is actually locatedon another machine (is a PointerTensor). Call .get() on the PointerTensor or .send(bob) on the other tensor.\n\nTensor A: [PointerTensor | me:46419059800 -> bob:14412738960]\nTensor B: tensor([1, 1, 1, 1, 1])",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPureTorchTensorFoundError\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m                     new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(\n\u001b[0;32m--> 562\u001b[0;31m                         \u001b[0mmethod_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m                     )\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mhook_method_args\u001b[0;34m(attr, method_self, args)\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;31m# Try running it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0mnew_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mtwo_fold\u001b[0;34m(lambdas, args)\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtwo_fold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mtuple_one_fold\u001b[0;34m(lambdas, args)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtuple_one_fold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0;31m# Last if not, rule is probably == 1 so use type to return the right transformation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0;32melse\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mforward_func\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrules\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# And do this for all the args / rules provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"child\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPureTorchTensorFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"child\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPureTorchTensorFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPureTorchTensorFoundError\u001b[0m: tensor([1, 1, 1, 1, 1])",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTensorsNotCollocatedException\u001b[0m             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-d63d93af9bc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    564\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m                     \u001b[0;31m# we can make some errors more descriptive with this method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mroute_method_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m                 \u001b[0;31m# Send the new command to the appropriate class and get the response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTensorsNotCollocatedException\u001b[0m: You tried to call a method involving two tensors where one tensor is actually locatedon another machine (is a PointerTensor). Call .get() on the PointerTensor or .send(bob) on the other tensor.\n\nTensor A: [PointerTensor | me:46419059800 -> bob:14412738960]\nTensor B: tensor([1, 1, 1, 1, 1])"
     ]
    }
   ],
   "source": [
    "z = x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)\n",
    "y = th.tensor([1,1,1,1,1]).send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "ename": "TensorsNotCollocatedException",
     "evalue": "You tried to call a method involving two tensors where one tensor is actually locatedon another machine (is a PointerTensor). Call .get() on the PointerTensor or .send(bob) on the other tensor.\n\nTensor A: [PointerTensor | me:46419059800 -> bob:14412738960]\nTensor B: tensor([1, 1, 1, 1, 1])",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPureTorchTensorFoundError\u001b[0m                 Traceback (most recent call last)",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    561\u001b[0m                     new_self, new_args = syft.frameworks.torch.hook_args.hook_method_args(\n\u001b[0;32m--> 562\u001b[0;31m                         \u001b[0mmethod_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    563\u001b[0m                     )\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mhook_method_args\u001b[0;34m(attr, method_self, args)\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;31m# Try running it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 86\u001b[0;31m         \u001b[0mnew_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     87\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mtwo_fold\u001b[0;34m(lambdas, args)\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtwo_fold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 421\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    422\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 271\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    272\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36mtuple_one_fold\u001b[0;34m(lambdas, args)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtuple_one_fold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0;31m# Last if not, rule is probably == 1 so use type to return the right transformation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 249\u001b[0;31m         \u001b[0;32melse\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mforward_func\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    250\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrules\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# And do this for all the args / rules provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"child\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPureTorchTensorFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook_args.py\u001b[0m in \u001b[0;36m<genexpr>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"child\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m     \u001b[0;32melse\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mthrow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPureTorchTensorFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mParameter\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchild\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPureTorchTensorFoundError\u001b[0m: tensor([1, 1, 1, 1, 1])",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTensorsNotCollocatedException\u001b[0m             Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-66-d63d93af9bc6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/atrask/anaconda/lib/python3.6/site-packages/syft-0.1.2a1-py3.6.egg/syft/frameworks/torch/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    564\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mBaseException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    565\u001b[0m                     \u001b[0;31m# we can make some errors more descriptive with this method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 566\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mroute_method_exception\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    567\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    568\u001b[0m                 \u001b[0;31m# Send the new command to the appropriate class and get the response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTensorsNotCollocatedException\u001b[0m: You tried to call a method involving two tensors where one tensor is actually locatedon another machine (is a PointerTensor). Call .get() on the PointerTensor or .send(bob) on the other tensor.\n\nTensor A: [PointerTensor | me:46419059800 -> bob:14412738960]\nTensor B: tensor([1, 1, 1, 1, 1])"
     ]
    }
   ],
   "source": [
    "z = x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Toy Federated Learning\n",
    "\n",
    "Let's start by training a toy model the centralized way. This is about a simple as models get. We first need:\n",
    "\n",
    "- a toy dataset\n",
    "- a model\n",
    "- some basic training logic for training a model to fit the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch import nn, optim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A Toy Dataset\n",
    "data = th.tensor([[1.,1],[0,1],[1,0],[0,0]], requires_grad=True)\n",
    "target = th.tensor([[1.],[1], [0], [0]], requires_grad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A Toy Model\n",
    "model = nn.Linear(2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = optim.SGD(params=model.parameters(), lr=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0860)\n",
      "tensor(0.0586)\n",
      "tensor(0.0402)\n",
      "tensor(0.0278)\n",
      "tensor(0.0194)\n",
      "tensor(0.0136)\n",
      "tensor(0.0096)\n",
      "tensor(0.0069)\n",
      "tensor(0.0049)\n",
      "tensor(0.0036)\n",
      "tensor(0.0026)\n",
      "tensor(0.0019)\n",
      "tensor(0.0014)\n",
      "tensor(0.0010)\n",
      "tensor(0.0008)\n",
      "tensor(0.0006)\n",
      "tensor(0.0004)\n",
      "tensor(0.0003)\n",
      "tensor(0.0002)\n",
      "tensor(0.0002)\n"
     ]
    }
   ],
   "source": [
    "def train(iterations=20):\n",
    "    for iter in range(iterations):\n",
    "        opt.zero_grad()\n",
    "\n",
    "        pred = model(data)\n",
    "\n",
    "        loss = ((pred - target)**2).sum()\n",
    "\n",
    "        loss.backward()\n",
    "\n",
    "        opt.step()\n",
    "\n",
    "        print(loss.data)\n",
    "        \n",
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_bob = data[0:2].send(bob)\n",
    "target_bob = target[0:2].send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_alice = data[2:4].send(alice)\n",
    "target_alice = target[2:4].send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = [(data_bob, target_bob), (data_alice, target_alice)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(iterations=20):\n",
    "\n",
    "    model = nn.Linear(2,1)\n",
    "    opt = optim.SGD(params=model.parameters(), lr=0.1)\n",
    "    \n",
    "    for iter in range(iterations):\n",
    "\n",
    "        for _data, _target in datasets:\n",
    "\n",
    "            # send model to the data\n",
    "            model = model.send(_data.location)\n",
    "\n",
    "            # do normal training\n",
    "            opt.zero_grad()\n",
    "            pred = model(_data)\n",
    "            loss = ((pred - _target)**2).sum()\n",
    "            loss.backward()\n",
    "            opt.step()\n",
    "\n",
    "            # get smarter model back\n",
    "            model = model.get()\n",
    "\n",
    "            print(loss.get())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(0.0824, requires_grad=True)\n",
      "tensor(0.0006, requires_grad=True)\n",
      "tensor(5.0509e-06, requires_grad=True)\n",
      "tensor(1.8660e-07, requires_grad=True)\n",
      "tensor(1.2442e-07, requires_grad=True)\n",
      "tensor(1.0333e-07, requires_grad=True)\n",
      "tensor(8.5991e-08, requires_grad=True)\n",
      "tensor(7.1573e-08, requires_grad=True)\n",
      "tensor(5.9562e-08, requires_grad=True)\n",
      "tensor(4.9569e-08, requires_grad=True)\n",
      "tensor(4.1269e-08, requires_grad=True)\n",
      "tensor(3.4340e-08, requires_grad=True)\n",
      "tensor(2.8568e-08, requires_grad=True)\n",
      "tensor(2.3789e-08, requires_grad=True)\n",
      "tensor(1.9802e-08, requires_grad=True)\n",
      "tensor(1.6471e-08, requires_grad=True)\n",
      "tensor(1.3715e-08, requires_grad=True)\n",
      "tensor(1.1405e-08, requires_grad=True)\n",
      "tensor(9.4892e-09, requires_grad=True)\n",
      "tensor(7.9073e-09, requires_grad=True)\n",
      "tensor(6.5776e-09, requires_grad=True)\n",
      "tensor(5.4857e-09, requires_grad=True)\n",
      "tensor(4.5577e-09, requires_grad=True)\n",
      "tensor(3.8027e-09, requires_grad=True)\n",
      "tensor(3.1617e-09, requires_grad=True)\n",
      "tensor(2.6215e-09, requires_grad=True)\n",
      "tensor(2.1910e-09, requires_grad=True)\n",
      "tensor(1.8242e-09, requires_grad=True)\n",
      "tensor(1.5201e-09, requires_grad=True)\n",
      "tensor(1.2593e-09, requires_grad=True)\n",
      "tensor(1.0471e-09, requires_grad=True)\n",
      "tensor(8.7642e-10, requires_grad=True)\n",
      "tensor(7.2482e-10, requires_grad=True)\n",
      "tensor(6.0216e-10, requires_grad=True)\n",
      "tensor(5.0250e-10, requires_grad=True)\n",
      "tensor(4.2101e-10, requires_grad=True)\n",
      "tensor(3.5084e-10, requires_grad=True)\n",
      "tensor(2.8831e-10, requires_grad=True)\n",
      "tensor(2.4171e-10, requires_grad=True)\n",
      "tensor(2.0130e-10, requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Advanced Remote Execution Tools\n",
    "\n",
    "In the last section we trained a toy model using Federated Learning. We did this by calling .send() and .get() on our model, sending it to the location of training data, updating it, and then bringing it back. However, at the end of the example we realized that we needed to go a bit further to protect people privacy. Namely, we want to average the gradients BEFORE calling .get(). That way, we won't ever see anyone's exact gradient (thus better protecting their privacy!!!)\n",
    "\n",
    "But, in order to do this, we need a few more pieces:\n",
    "\n",
    "- use a pointer to send a Tensor directly to another worker\n",
    "\n",
    "And in addition, while we're here, we're going to learn about a few more advanced tensor operations as well which will help us both with this example and a few in the future!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:bob #tensors:0>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob.clear_objects()\n",
    "alice.clear_objects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:alice #tensors:0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{21931995238: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{55299383133: (Wrapper)>[PointerTensor | alice:55299383133 -> bob:21931995238]}"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = x + x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:48924169518 -> alice:48924169518]"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{21931995238: tensor([1, 2, 3, 4, 5]),\n",
       " 48924169518: tensor([ 2,  4,  6,  8, 10])}"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{48924169518: (Wrapper)>[PointerTensor | alice:48924169518 -> bob:48924169518],\n",
       " 55299383133: (Wrapper)>[PointerTensor | alice:55299383133 -> bob:21931995238]}"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "jon = sy.VirtualWorker(hook, id=\"jon\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob.clear_objects()\n",
    "alice.clear_objects()\n",
    "\n",
    "x = th.tensor([1,2,3,4,5]).send(bob).send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10539507281: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{40541026396: (Wrapper)>[PointerTensor | alice:40541026396 -> bob:10539507281]}"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:40541026396 -> bob:10539507281]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.get()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{10539507281: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.get()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "bob.clear_objects()\n",
    "alice.clear_objects()\n",
    "\n",
    "x = th.tensor([1,2,3,4,5]).send(bob).send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{28843833290: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{88814770977: (Wrapper)>[PointerTensor | alice:88814770977 -> bob:28843833290]}"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "del x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson: Pointer Chain Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VirtualWorker id:alice #tensors:0>"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob.clear_objects()\n",
    "alice.clear_objects()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{81966670653: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:547575813 -> alice:547575813]"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.move(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{547575813: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = th.tensor([1,2,3,4,5]).send(bob).send(alice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{75487377866: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{94092707138: (Wrapper)>[PointerTensor | alice:94092707138 -> bob:75487377866]}"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:87205391815 -> alice:94092707138]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.remote_get()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{}"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{94092707138: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:87205391815 -> bob:87205391815]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.move(bob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Wrapper)>[PointerTensor | me:87205391815 -> bob:87205391815]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{87205391815: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bob._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{94092707138: tensor([1, 2, 3, 4, 5])}"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alice._objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Section Project:\n",
    "\n",
    "For the final project for this section, you're going to train on the MNIST dataset using federated learning However the gradient should not come up to central server in raw form"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.datasets as datasets\n",
    "mnist_trainset = datasets.MNIST(root='./data', train=True, download=True, transform=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "language_info": {
   "codemirror_mode": {
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