notebooks-3/3-multiple-gpus-gluon.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Concise Implementation of Multi-GPU Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:50:25.762210Z",
     "start_time": "2019-07-03T22:50:22.940185Z"
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     "classes": [],
     "id": "",
     "n": "1"
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   },
   "outputs": [],
   "source": [
    "import d2l\n",
    "from mxnet import autograd, gluon, init, np, npx\n",
    "from mxnet.gluon import nn\n",
    "npx.set_np()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Initialize model parameters on multiple GPUs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:50:32.537906Z",
     "start_time": "2019-07-03T22:50:25.764263Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "3"
    }
   },
   "outputs": [],
   "source": [
    "net = d2l.resnet18(10)\n",
    "ctx = d2l.try_all_gpus()\n",
    "net.initialize(init=init.Normal(sigma=0.01), ctx=ctx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:50:32.772457Z",
     "start_time": "2019-07-03T22:50:32.539859Z"
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     "n": "4"
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   "outputs": [
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       "         -3.36428002e-06,  1.04560286e-07, -6.10012194e-07,\n",
       "          2.03278501e-06]], ctx=gpu(0)),\n",
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       "         -6.0733169e-07,  1.9712008e-06]], ctx=gpu(1)))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.uniform(size=(4, 1, 28, 28))\n",
    "gpu_x = gluon.utils.split_and_load(x, ctx)\n",
    "net(gpu_x[0]), net(gpu_x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Evaluating accuracy on multiple GPUs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:50:32.779294Z",
     "start_time": "2019-07-03T22:50:32.774319Z"
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   "source": [
    "def evaluate_accuracy_gpus(net, data_iter):\n",
    "    # Query the list of devices.\n",
    "    ctx_list = list(net.collect_params().values())[0].list_ctx()\n",
    "    metric = d2l.Accumulator(2)  # num_corrected_examples, num_examples\n",
    "    for features, labels in data_iter:\n",
    "        Xs, ys = d2l.split_batch(features, labels, ctx_list)\n",
    "        pys = [net(X) for X in Xs]  # run in parallel\n",
    "        metric.add(sum(float(d2l.accuracy(py, y)) for py, y in zip(pys, ys)), \n",
    "                   labels.size)\n",
    "    return metric[0]/metric[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The training function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:50:32.788932Z",
     "start_time": "2019-07-03T22:50:32.780660Z"
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   "outputs": [],
   "source": [
    "def train(num_gpus, batch_size, lr):\n",
    "    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)\n",
    "    ctx_list = [d2l.try_gpu(i) for i in range(num_gpus)]\n",
    "    net.initialize(init=init.Normal(sigma=0.01),\n",
    "                   ctx=ctx_list, force_reinit=True)\n",
    "    trainer = gluon.Trainer(\n",
    "        net.collect_params(), 'sgd', {'learning_rate': lr})\n",
    "    loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
    "    timer, num_epochs = d2l.Timer(), 10\n",
    "    animator = d2l.Animator('epoch', 'test acc', xlim=[1, num_epochs])\n",
    "    for epoch in range(num_epochs):\n",
    "        timer.start()\n",
    "        for features, labels in train_iter:\n",
    "            Xs, ys = d2l.split_batch(features, labels, ctx_list)\n",
    "            with autograd.record():\n",
    "                ls = [loss(net(X), y) for X, y in zip(Xs, ys)]\n",
    "            for l in ls:\n",
    "                l.backward()\n",
    "            trainer.step(batch_size)\n",
    "        npx.waitall()\n",
    "        timer.stop()\n",
    "        animator.add(epoch+1, (evaluate_accuracy_gpus(net, test_iter),))\n",
    "    print('test acc: %.2f, %.1f sec/epoch on %s' % (\n",
    "        animator.Y[0][-1], timer.avg(), ctx_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Use a single GPU for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-03T22:52:56.382631Z",
     "start_time": "2019-07-03T22:50:32.790237Z"
    },
    "attributes": {
     "classes": [],
     "id": "",
     "n": "8"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test acc: 0.93, 13.2 sec/epoch on [gpu(0)]\n"
     ]
    },
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