Intro_Data_Parallelism (EN).ipynb

{"cells":[{"cell_type":"markdown","metadata":{"execution":{"iopub.execute_input":"2023-02-19T04:37:57.926336Z","iopub.status.busy":"2023-02-19T04:37:57.925962Z","iopub.status.idle":"2023-02-19T04:37:57.93554Z","shell.execute_reply":"2023-02-19T04:37:57.934078Z","shell.execute_reply.started":"2023-02-19T04:37:57.926302Z"}},"source":["# Introduction to Data Parallelism\n","\n","The goal is to train deep learning models in parallel and take advantage of more computing power.\n","\n","<blockquote>\n","    \n","*  What is data parallelism ?\n","\n","*  Train a deep learning model with 2 GPUs\n","</blockquote>\n"]},{"cell_type":"markdown","metadata":{},"source":["## Data parallelism \n","\n","This approach can be explain in 3 steps: \n","\n","\n","1. We split the data into $n$ number of partitions, where $n$ is the total number of available workers in the compute cluster.\n","\n","2. We copy the model in each worker node and each one of them performs the training on its own subset of the data.\n","\n","3. We synchronize the model parameters, or gradients, at the end of the batch computation.\n","\n","\n","\n","<img src=\"https://frankdenneman.nl/wp-content/uploads/2020/02/Data-Parallelism.png\" width=\"850\" height=\"380\">\n","\n","\n","\n","### Synchronous training \n","\n","Data parallelism involves dividing the data into partitions and distributing each part to a worker. Each worker trains on a portion of the data using a full replica of the model.\n","\n","\n","1.  During synchronous training, all workers perform the forward pass simultaneously, producing different outputs and gradients. The workers then wait for the other workers to complete their training loops and calculate gradients.\n","\n","2.  Next, the workers communicate with each other and use the all-reduce algorithm to aggregate the gradients. Now after all the workers have completed computing gradients, all of them start communicating with each other and aggregate the gradients using the all-reduce algorithm, which we will discuss ahead. \n","\n","3.  Once the gradients are combined, the updated gradients are sent to all workers. Each worker can then perform the backward pass and update the local copy of the weights.\n","\n","4.  The next forward pass does not begin until all workers have updated their weights, making this approach synchronous.\n","\n","It is important to note that each worker produces different gradients because they train on different subsets of the data. However, at any given time, all workers have the same weights.\n"]},{"cell_type":"markdown","metadata":{},"source":["### GPUs Specifications"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:20.432625Z","iopub.status.busy":"2024-04-14T14:05:20.432142Z","iopub.status.idle":"2024-04-14T14:05:21.593709Z","shell.execute_reply":"2024-04-14T14:05:21.592551Z","shell.execute_reply.started":"2024-04-14T14:05:20.432510Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["/bin/bash: /opt/conda/lib/libtinfo.so.6: no version information available (required by /bin/bash)\n","Sun Apr 14 14:05:21 2024       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n","| N/A   46C    P8              12W /  70W |      0MiB / 15360MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","|   1  Tesla T4                       Off | 00000000:00:05.0 Off |                    0 |\n","| N/A   45C    P8              11W /  70W |      0MiB / 15360MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","|  No running processes found                                                           |\n","+---------------------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"markdown","metadata":{},"source":["Lets see how to train a model in Keras with both GPUs"]},{"cell_type":"markdown","metadata":{},"source":["## MNIST"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:21.596413Z","iopub.status.busy":"2024-04-14T14:05:21.596037Z","iopub.status.idle":"2024-04-14T14:05:28.881289Z","shell.execute_reply":"2024-04-14T14:05:28.879953Z","shell.execute_reply.started":"2024-04-14T14:05:21.596364Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n","29515/29515 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n","26421880/26421880 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n","5148/5148 [==============================] - 0s 0us/step\n","Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n","4422102/4422102 [==============================] - 0s 0us/step\n","X_train Before: (60000, 28, 28)\n","X_train After: (60000, 28, 28, 1)\n","X_test Before: (10000, 28, 28)\n","X_test After: (10000, 28, 28, 1)\n"]}],"source":["import tensorflow as tf\n","import matplotlib.pyplot as plt\n","import time\n","\n","## Data Loading new fashion_mnist dataset\n","(X_train, y_train), (X_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()\n","\n","img_rows, img_cols = 28, 28\n","input_shape = (img_rows, img_cols, 1)\n","\n","print(f\"X_train Before: {X_train.shape}\")\n","X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)\n","print(f\"X_train After: {X_train.shape}\")\n","\n","print(f\"X_test Before: {X_test.shape}\")\n","X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)\n","print(f\"X_test After: {X_test.shape}\")\n","\n","## converting the images to float32\n","X_train = X_train.astype('float32')\n","X_test = X_test.astype('float32')\n","\n","## converting the range to [0,1]\n","X_train /= 255\n","X_test /= 255"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:28.883864Z","iopub.status.busy":"2024-04-14T14:05:28.883066Z","iopub.status.idle":"2024-04-14T14:05:28.894017Z","shell.execute_reply":"2024-04-14T14:05:28.892887Z","shell.execute_reply.started":"2024-04-14T14:05:28.883825Z"},"trusted":true},"outputs":[{"data":{"text/plain":["60000"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["len(X_train)"]},{"cell_type":"markdown","metadata":{},"source":["Let's take a quick look at the data"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:28.897309Z","iopub.status.busy":"2024-04-14T14:05:28.896907Z","iopub.status.idle":"2024-04-14T14:05:30.029696Z","shell.execute_reply":"2024-04-14T14:05:30.028722Z","shell.execute_reply.started":"2024-04-14T14:05:28.897262Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x1000 with 25 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10,10))\n","for i in range(25):\n","    plt.subplot(5,5,i+1)\n","    plt.xticks([])\n","    plt.yticks([])\n","    plt.grid(False)\n","    plt.imshow(X_train[i], cmap=plt.cm.binary)\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["To make things better, lets put the data on a TF.dataset.\n","\n","One important detail here is the BATCH_SIZE. To get the benefits of multiple GPUs, we have to optimize their use, otherwise you won't see the gains."]},{"cell_type":"code","execution_count":5,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:30.031257Z","iopub.status.busy":"2024-04-14T14:05:30.030918Z","iopub.status.idle":"2024-04-14T14:05:33.510171Z","shell.execute_reply":"2024-04-14T14:05:33.508958Z","shell.execute_reply.started":"2024-04-14T14:05:30.031223Z"},"trusted":true},"outputs":[],"source":["BUFFER_SIZE = 10000\n","BATCH_SIZE_PER_REPLICA = 1024\n","\n","def get_batch_size(num_gpus=1):\n","    batch_size = BATCH_SIZE_PER_REPLICA * num_gpus\n","    print(f'BATCH_SIZE = {batch_size}')\n","    return batch_size\n","\n","# Wrap data in Dataset objects.\n","train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n","test_data = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n","\n","# the batch size will be defined later based on the number of used GPUs\n","options = tf.data.Options()\n","options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF\n","train_data = train_data.with_options(options).cache().shuffle(BUFFER_SIZE).prefetch(tf.data.AUTOTUNE)\n","test_data = test_data.with_options(options)"]},{"cell_type":"markdown","metadata":{},"source":["## Defining a Distribution Strategy\n","\n","To use Multi GPU, we are going to use the `tf.distribute.Strategy` API, \n","more specifically `tf.distribute.MirroredStrategy`\n","\n","This strategy will do the spliting of the work between both T4s for you"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:33.512194Z","iopub.status.busy":"2024-04-14T14:05:33.511743Z","iopub.status.idle":"2024-04-14T14:05:33.821820Z","shell.execute_reply":"2024-04-14T14:05:33.820621Z","shell.execute_reply.started":"2024-04-14T14:05:33.512149Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Number of devices: 2\n"]}],"source":["strategy = tf.distribute.MirroredStrategy()\n","print('Number of devices: {}'.format(strategy.num_replicas_in_sync))"]},{"cell_type":"markdown","metadata":{},"source":["## Building the model\n","\n","The model has to be created inside a Strategy scope. The scope will make sure to place the model's variables on each processor. Then, it uses all-reduce to combine the gradients from all processors, and applies the combined value to all copies of the model."]},{"cell_type":"code","execution_count":7,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:33.823366Z","iopub.status.busy":"2024-04-14T14:05:33.823055Z","iopub.status.idle":"2024-04-14T14:05:33.833659Z","shell.execute_reply":"2024-04-14T14:05:33.832295Z","shell.execute_reply.started":"2024-04-14T14:05:33.823336Z"},"trusted":true},"outputs":[],"source":["def build_model():\n","    model = tf.keras.Sequential([\n","        tf.keras.layers.Conv2D(32, [3, 3], activation='relu', input_shape=(28, 28, 1)),\n","        tf.keras.layers.Conv2D(64, [3, 3], activation='relu'),\n","        tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n","        tf.keras.layers.Dropout(0.25),\n","        tf.keras.layers.Flatten(),\n","        tf.keras.layers.Dense(128, activation='relu'),\n","        tf.keras.layers.Dropout(0.5),\n","        tf.keras.layers.Dense(10, activation='softmax')\n","    ])\n","\n","    model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n","                optimizer=tf.keras.optimizers.Adam(),\n","                metrics=['accuracy'])\n","\n","    return model"]},{"cell_type":"code","execution_count":8,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:33.835853Z","iopub.status.busy":"2024-04-14T14:05:33.835068Z","iopub.status.idle":"2024-04-14T14:05:34.248205Z","shell.execute_reply":"2024-04-14T14:05:34.247011Z","shell.execute_reply.started":"2024-04-14T14:05:33.835818Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," conv2d (Conv2D)             (None, 26, 26, 32)        320       \n","                                                                 \n"," conv2d_1 (Conv2D)           (None, 24, 24, 64)        18496     \n","                                                                 \n"," max_pooling2d (MaxPooling2D  (None, 12, 12, 64)       0         \n"," )                                                               \n","                                                                 \n"," dropout (Dropout)           (None, 12, 12, 64)        0         \n","                                                                 \n"," flatten (Flatten)           (None, 9216)              0         \n","                                                                 \n"," dense (Dense)               (None, 128)               1179776   \n","                                                                 \n"," dropout_1 (Dropout)         (None, 128)               0         \n","                                                                 \n"," dense_1 (Dense)             (None, 10)                1290      \n","                                                                 \n","=================================================================\n","Total params: 1,199,882\n","Trainable params: 1,199,882\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["# buiding a model inside the strategy scope\n","with strategy.scope():\n","    multi_gpu_model = build_model()\n","\n","multi_gpu_model.summary()\n","\n","# buiding a regular model on 1 GPU for performance comparison\n","regular_model = build_model()"]},{"cell_type":"markdown","metadata":{},"source":["## Warmup\n","\n","The framework will load aditional libraries not yet in memory and this would impact the timing of training the model."]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:34.249908Z","iopub.status.busy":"2024-04-14T14:05:34.249566Z","iopub.status.idle":"2024-04-14T14:05:34.257234Z","shell.execute_reply":"2024-04-14T14:05:34.256042Z","shell.execute_reply.started":"2024-04-14T14:05:34.249879Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["BATCH_SIZE = 2048\n"]},{"data":{"text/plain":["2048"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["get_batch_size(strategy.num_replicas_in_sync)"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:34.261277Z","iopub.status.busy":"2024-04-14T14:05:34.260891Z","iopub.status.idle":"2024-04-14T14:05:55.780152Z","shell.execute_reply":"2024-04-14T14:05:55.778855Z","shell.execute_reply.started":"2024-04-14T14:05:34.261245Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["BATCH_SIZE = 2048\n","Epoch 1/5\n"]},{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py:1082: UserWarning: \"`sparse_categorical_crossentropy` received `from_logits=True`, but the `output` argument was produced by a sigmoid or softmax activation and thus does not represent logits. Was this intended?\"\n","  return dispatch_target(*args, **kwargs)\n"]},{"name":"stdout","output_type":"stream","text":["30/30 [==============================] - 13s 70ms/step - loss: 0.9909 - accuracy: 0.6525\n","Epoch 2/5\n","30/30 [==============================] - 2s 58ms/step - loss: 0.5368 - accuracy: 0.8074\n","Epoch 3/5\n","30/30 [==============================] - 2s 55ms/step - loss: 0.4428 - accuracy: 0.8444\n","Epoch 4/5\n","30/30 [==============================] - 2s 54ms/step - loss: 0.3987 - accuracy: 0.8600\n","Epoch 5/5\n","30/30 [==============================] - 2s 55ms/step - loss: 0.3653 - accuracy: 0.8712\n"]}],"source":["EPOCHS = 5\n","\n","d_train_data = train_data.batch(get_batch_size(strategy.num_replicas_in_sync))\n","history = multi_gpu_model.fit(d_train_data, epochs=EPOCHS)"]},{"cell_type":"markdown","metadata":{},"source":["## Training on 2 GPUs"]},{"cell_type":"code","execution_count":11,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:05:55.782516Z","iopub.status.busy":"2024-04-14T14:05:55.782153Z","iopub.status.idle":"2024-04-14T14:06:57.196035Z","shell.execute_reply":"2024-04-14T14:06:57.194880Z","shell.execute_reply.started":"2024-04-14T14:05:55.782482Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["BATCH_SIZE = 2048\n","Epoch 1/30\n","30/30 [==============================] - 2s 57ms/step - loss: 0.3411 - accuracy: 0.8802\n","Epoch 2/30\n","30/30 [==============================] - 2s 54ms/step - loss: 0.3241 - accuracy: 0.8855\n","Epoch 3/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.3070 - accuracy: 0.8906\n","Epoch 4/30\n","30/30 [==============================] - 2s 57ms/step - loss: 0.2949 - accuracy: 0.8960\n","Epoch 5/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2826 - accuracy: 0.9011\n","Epoch 6/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.2734 - accuracy: 0.9029\n","Epoch 7/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.2637 - accuracy: 0.9056\n","Epoch 8/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.2560 - accuracy: 0.9063\n","Epoch 9/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2472 - accuracy: 0.9111\n","Epoch 10/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2395 - accuracy: 0.9123\n","Epoch 11/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2389 - accuracy: 0.9135\n","Epoch 12/30\n","30/30 [==============================] - 2s 59ms/step - loss: 0.2261 - accuracy: 0.9180\n","Epoch 13/30\n","30/30 [==============================] - 2s 58ms/step - loss: 0.2218 - accuracy: 0.9199\n","Epoch 14/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.2194 - accuracy: 0.9205\n","Epoch 15/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2124 - accuracy: 0.9236\n","Epoch 16/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.2040 - accuracy: 0.9250\n","Epoch 17/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.1977 - accuracy: 0.9273\n","Epoch 18/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.1928 - accuracy: 0.9293\n","Epoch 19/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.1884 - accuracy: 0.9307\n","Epoch 20/30\n","30/30 [==============================] - 2s 57ms/step - loss: 0.1864 - accuracy: 0.9315\n","Epoch 21/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.1799 - accuracy: 0.9341\n","Epoch 22/30\n","30/30 [==============================] - 2s 56ms/step - loss: 0.1778 - accuracy: 0.9349\n","Epoch 23/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.1728 - accuracy: 0.9370\n","Epoch 24/30\n","30/30 [==============================] - 2s 58ms/step - loss: 0.1662 - accuracy: 0.9379\n","Epoch 25/30\n","30/30 [==============================] - 2s 54ms/step - loss: 0.1623 - accuracy: 0.9403\n","Epoch 26/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.1607 - accuracy: 0.9398\n","Epoch 27/30\n","30/30 [==============================] - 2s 54ms/step - loss: 0.1543 - accuracy: 0.9427\n","Epoch 28/30\n","30/30 [==============================] - 2s 59ms/step - loss: 0.1547 - accuracy: 0.9425\n","Epoch 29/30\n","30/30 [==============================] - 2s 55ms/step - loss: 0.1503 - accuracy: 0.9443\n","Epoch 30/30\n","30/30 [==============================] - 2s 58ms/step - loss: 0.1455 - accuracy: 0.9454\n","--- 61.40555667877197 seconds ---\n"]}],"source":["EPOCHS = 30\n","\n","d_train_data = train_data.batch(get_batch_size(strategy.num_replicas_in_sync))\n","\n","start_time_multi = time.time()\n","history_multi = multi_gpu_model.fit(d_train_data, epochs=EPOCHS)\n","final_time_multi = time.time() - start_time_multi\n","print(\"--- %s seconds ---\" % (final_time_multi))"]},{"cell_type":"markdown","metadata":{},"source":["## Training on 1 GPU"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:06:57.197565Z","iopub.status.busy":"2024-04-14T14:06:57.197257Z","iopub.status.idle":"2024-04-14T14:06:57.205508Z","shell.execute_reply":"2024-04-14T14:06:57.204284Z","shell.execute_reply.started":"2024-04-14T14:06:57.197529Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["BATCH_SIZE = 1024\n"]},{"data":{"text/plain":["1024"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["get_batch_size()"]},{"cell_type":"code","execution_count":13,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:06:57.207229Z","iopub.status.busy":"2024-04-14T14:06:57.206899Z","iopub.status.idle":"2024-04-14T14:08:23.053956Z","shell.execute_reply":"2024-04-14T14:08:23.052806Z","shell.execute_reply.started":"2024-04-14T14:06:57.207199Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["BATCH_SIZE = 1024\n","Epoch 1/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.8431 - accuracy: 0.7010\n","Epoch 2/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.4712 - accuracy: 0.8352\n","Epoch 3/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.3938 - accuracy: 0.8623\n","Epoch 4/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.3544 - accuracy: 0.8747\n","Epoch 5/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.3329 - accuracy: 0.8813\n","Epoch 6/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.3094 - accuracy: 0.8898\n","Epoch 7/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2947 - accuracy: 0.8941\n","Epoch 8/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2772 - accuracy: 0.9009\n","Epoch 9/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2708 - accuracy: 0.9028\n","Epoch 10/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2554 - accuracy: 0.9084\n","Epoch 11/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2460 - accuracy: 0.9111\n","Epoch 12/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2375 - accuracy: 0.9143\n","Epoch 13/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2267 - accuracy: 0.9176\n","Epoch 14/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2198 - accuracy: 0.9202\n","Epoch 15/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2163 - accuracy: 0.9210\n","Epoch 16/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2065 - accuracy: 0.9245\n","Epoch 17/30\n","59/59 [==============================] - 3s 47ms/step - loss: 0.2015 - accuracy: 0.9257\n","Epoch 18/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1976 - accuracy: 0.9266\n","Epoch 19/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1864 - accuracy: 0.9307\n","Epoch 20/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1818 - accuracy: 0.9338\n","Epoch 21/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1795 - accuracy: 0.9345\n","Epoch 22/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1684 - accuracy: 0.9366\n","Epoch 23/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1673 - accuracy: 0.9388\n","Epoch 24/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1609 - accuracy: 0.9406\n","Epoch 25/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1584 - accuracy: 0.9394\n","Epoch 26/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1497 - accuracy: 0.9438\n","Epoch 27/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1482 - accuracy: 0.9450\n","Epoch 28/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1449 - accuracy: 0.9461\n","Epoch 29/30\n","59/59 [==============================] - 3s 45ms/step - loss: 0.1400 - accuracy: 0.9472\n","Epoch 30/30\n","59/59 [==============================] - 3s 46ms/step - loss: 0.1369 - accuracy: 0.9488\n","--- 85.83570432662964 seconds ---\n"]}],"source":["regular_train_data = train_data.batch(get_batch_size())\n","\n","\n","start_time_single = time.time()\n","history_regular = regular_model.fit(regular_train_data, epochs=EPOCHS)\n","final_time_single = time.time() - start_time_single\n","print(\"--- %s seconds ---\" % (final_time_single))"]},{"cell_type":"markdown","metadata":{},"source":["## Performance Comparison"]},{"cell_type":"markdown","metadata":{},"source":["### Accuracy Comparison"]},{"cell_type":"code","execution_count":14,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:08:23.055874Z","iopub.status.busy":"2024-04-14T14:08:23.055470Z","iopub.status.idle":"2024-04-14T14:08:23.536557Z","shell.execute_reply":"2024-04-14T14:08:23.535470Z","shell.execute_reply.started":"2024-04-14T14:08:23.055841Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1800x800 with 2 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["fig, axs = plt.subplots(1, 2,figsize=(18,8))\n","axs[0].plot(history_multi.history['accuracy'],label = '2 GPUs')\n","axs[0].plot(history_regular.history['accuracy'],label = '1 GPU')\n","axs[0].set_title('model accuracy')\n","axs[0].set_ylabel('accuracy')\n","axs[0].set_xlabel('epoch')\n","\n","\n","axs[1].plot(history_multi.history['loss'],label = '2 GPUs')\n","axs[1].plot(history_regular.history['loss'],label = '1 GPU')\n","axs[1].set_title('model loss')\n","axs[1].set_ylabel('loss')\n","axs[1].set_xlabel('epoch')\n","\n","plt.legend()\n","plt.show()"]},{"cell_type":"markdown","metadata":{},"source":["### Computing Power Comparison"]},{"cell_type":"code","execution_count":15,"metadata":{"execution":{"iopub.execute_input":"2024-04-14T14:08:23.538298Z","iopub.status.busy":"2024-04-14T14:08:23.537980Z","iopub.status.idle":"2024-04-14T14:08:23.861802Z","shell.execute_reply":"2024-04-14T14:08:23.860779Z","shell.execute_reply.started":"2024-04-14T14:08:23.538267Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["We achieve 69.89 percent of scaling\n"]},{"data":{"image/png":"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size 1800x800 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(18,8))\n","nb_images = len(train_data)\n","plt.plot(['1GPU','2 GPUS'],[nb_images/(final_time_single/EPOCHS),nb_images/(final_time_multi/EPOCHS)],label = 'real scaling')\n","plt.plot(['1GPU','2 GPUS'],[nb_images/(final_time_single/EPOCHS),2*nb_images/(final_time_single/EPOCHS)],label = 'theoritical scaling')\n","plt.title('Nb of images processed per EPOCH')\n","plt.ylabel('Nb of images processed per epoch')\n","plt.xlabel('nb of GPUs')\n","plt.legend()\n","print(\"We achieve %s percent of scaling\"% round((final_time_single/(2*final_time_multi))*100,2))"]},{"cell_type":"markdown","metadata":{},"source":["When going from training with a single GPU to multiple GPUs on the same host, ideally we should experience the performance scaling with only the additional overhead of gradient communication and increased host thread utilization. Because of this overhead, we don't have an exact 2x speedup if we move from 1 to 2 GPUs."]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[],"dockerImageVersionId":30397,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.12"}},"nbformat":4,"nbformat_minor":4}