diff --git a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py b/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
index 628ea961..febab1f9 100644
--- a/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
+++ b/how-to-use-azureml/automated-machine-learning/continuous-retraining/check_data.py
@@ -36,7 +36,10 @@ except Exception:
     last_train_time = datetime.min.replace(tzinfo=pytz.UTC)
 
 train_ds = Dataset.get_by_name(ws, args.ds_name)
-dataset_changed_time = train_ds.data_changed_time
+dataset_changed_time = train_ds.data_changed_time.replace(tzinfo=pytz.UTC)
+
+print("dataset_changed_time=" + str(dataset_changed_time))
+print("last_train_time=" + str(last_train_time))
 
 if not dataset_changed_time > last_train_time:
     print("Cancelling run since there is no new data.")
diff --git a/how-to-use-azureml/deployment/onnx/mnist.py b/how-to-use-azureml/deployment/onnx/mnist.py
index 0262bceb..c2c344bf 100644
--- a/how-to-use-azureml/deployment/onnx/mnist.py
+++ b/how-to-use-azureml/deployment/onnx/mnist.py
@@ -94,15 +94,15 @@ def main():
     os.makedirs(output_dir, exist_ok=True)
 
     kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
-    # Use Azure Open Datasets for MNIST dataset
+    # MNIST dataset
     datasets.MNIST.resources = [
-        ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+        ("train-images-idx3-ubyte.gz",
          "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-        ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+        ("train-labels-idx1-ubyte.gz",
          "d53e105ee54ea40749a09fcbcd1e9432"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+        ("t10k-images-idx3-ubyte.gz",
          "9fb629c4189551a2d022fa330f9573f3"),
-        ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+        ("t10k-labels-idx1-ubyte.gz",
          "ec29112dd5afa0611ce80d1b7f02629c")
     ]
     train_loader = torch.utils.data.DataLoader(
diff --git a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
index 355dd853..ee84a916 100644
--- a/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
+++ b/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.ipynb
@@ -1,663 +1,659 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# MNIST Handwritten Digit Classification using ONNX and AzureML\n",
-        "\n",
-        "This example shows how to train a model on the MNIST data using PyTorch, save it as an ONNX model, and deploy it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
-        "\n",
-        "## What is ONNX\n",
-        "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
-        "\n",
-        "## MNIST Details\n",
-        "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). "
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
-        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
-        "    * install the AML SDK\n",
-        "    * create a workspace and its configuration file (`config.json`)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Resource group: ' + ws.resource_group, sep = '\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model\n",
-        "\n",
-        "### Create a remote compute target\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
-        "\n",
-        "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpu-cluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3', \n",
-        "                                                                max_nodes=6)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "# Use the 'status' property to get a detailed status for the current cluster. \n",
-        "print(compute_target.status.serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "project_folder = './pytorch-mnist'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copy the training script [`mnist.py`](mnist.py) into your project directory. Make sure the training script has the following code to create an ONNX file:\n",
-        "```python\n",
-        "dummy_input = torch.randn(1, 1, 28, 28, device=device)\n",
-        "model_path = os.path.join(output_dir, 'mnist.onnx')\n",
-        "torch.onnx.export(model, dummy_input, model_path)\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "shutil.copy('mnist.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'pytorch1-mnist'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a PyTorch estimator\n",
-        "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.train.dnn import PyTorch\n",
-        "\n",
-        "estimator = PyTorch(source_directory=project_folder, \n",
-        "                    script_params={'--output-dir': './outputs'},\n",
-        "                    compute_target=compute_target,\n",
-        "                    entry_script='mnist.py',\n",
-        "                    use_gpu=True)\n",
-        "\n",
-        "# upgrade to PyTorch 1.0 Preview, which has better support for ONNX\n",
-        "estimator.conda_dependencies.remove_conda_package('pytorch=0.4.0')\n",
-        "estimator.conda_dependencies.add_conda_package('pytorch-nightly')\n",
-        "estimator.conda_dependencies.add_channel('pytorch')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
-        "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
-        "\n",
-        "To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(estimator)\n",
-        "print(run.get_details())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "run.wait_for_completion(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Download the model (optional)\n",
-        "\n",
-        "Once the run completes, you can choose to download the ONNX model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# list all the files from the run\n",
-        "run.get_file_names()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model_path = os.path.join('outputs', 'mnist.onnx')\n",
-        "run.download_file(model_path, output_file_path=model_path)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Register the model\n",
-        "You can also register the model from your run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. You can then deploy this registered model as a web service through the AML SDK."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "model = run.register_model(model_name='mnist', model_path=model_path)\n",
-        "print(model.name, model.id, model.version, sep = '\\t')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "#### Displaying your registered models (optional)\n",
-        "\n",
-        "You can optionally list out all the models that you have registered in this workspace."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "models = ws.models\n",
-        "for name, m in models.items():\n",
-        "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploying as a web service\n",
-        "\n",
-        "### Write scoring file\n",
-        "\n",
-        "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "%%writefile score.py\n",
-        "import json\n",
-        "import time\n",
-        "import sys\n",
-        "import os\n",
-        "from azureml.core.model import Model\n",
-        "import numpy as np    # we're going to use numpy to process input and output data\n",
-        "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
-        "\n",
-        "def init():\n",
-        "    global session\n",
-        "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
-        "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
-        "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
-        "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'mnist.onnx')\n",
-        "    session = onnxruntime.InferenceSession(model)\n",
-        "\n",
-        "def preprocess(input_data_json):\n",
-        "    # convert the JSON data into the tensor input\n",
-        "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
-        "\n",
-        "def postprocess(result):\n",
-        "    # We use argmax to pick the highest confidence label\n",
-        "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
-        "\n",
-        "def run(input_data_json):\n",
-        "    try:\n",
-        "        start = time.time()   # start timer\n",
-        "        input_data = preprocess(input_data_json)\n",
-        "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
-        "        result = session.run([], {input_name: input_data})\n",
-        "        end = time.time()     # stop timer\n",
-        "        return {\"result\": postprocess(result),\n",
-        "                \"time\": end - start}\n",
-        "    except Exception as e:\n",
-        "        result = str(e)\n",
-        "        return {\"error\": result}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create inference configuration\n",
-        "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.conda_dependencies import CondaDependencies \n",
-        "\n",
-        "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime==1.15.1\",\"azureml-core\", \"azureml-defaults\"])\n",
-        "\n",
-        "with open(\"myenv.yml\",\"w\") as f:\n",
-        "    f.write(myenv.serialize_to_string())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Then we setup the inference configuration "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.environment import Environment\n",
-        "\n",
-        "\n",
-        "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
-        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Deploy the model"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
-        "                                               memory_gb = 1, \n",
-        "                                               tags = {'demo': 'onnx'}, \n",
-        "                                               description = 'web service for MNIST ONNX model')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The following cell will likely take a few minutes to run as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.model import Model\n",
-        "from random import randint\n",
-        "\n",
-        "aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n",
-        "print(\"Service\", aci_service_name)\n",
-        "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
-        "aci_service.wait_for_deployment(True)\n",
-        "print(aci_service.state)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "if aci_service.state != 'Healthy':\n",
-        "    # run this command for debugging.\n",
-        "    print(aci_service.get_logs())\n",
-        "    aci_service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Success!\n",
-        "\n",
-        "If you've made it this far, you've deployed a working web service that does handwritten digit classification using an ONNX model. You can get the URL for the webservice with the code below."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "print(aci_service.scoring_uri)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "When you are eventually done using the web service, remember to delete it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "aci_service.delete()"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "viswamy"
-      }
-    ],
-    "category": "deployment",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "MNIST"
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[14080/60000 (23%)]\tLoss: 0.673716\nTrain Epoch: 2 [14720/60000 (25%)]\tLoss: 0.388876\nTrain Epoch: 2 [15360/60000 (26%)]\tLoss: 0.462371\nTrain Epoch: 2 [16000/60000 (27%)]\tLoss: 0.530107\nTrain Epoch: 2 [16640/60000 (28%)]\tLoss: 0.448767\nTrain Epoch: 2 [17280/60000 (29%)]\tLoss: 0.412764\nTrain Epoch: 2 [17920/60000 (30%)]\tLoss: 0.301494\nTrain Epoch: 2 [18560/60000 (31%)]\tLoss: 0.465599\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.434249\nTrain Epoch: 2 [19840/60000 (33%)]\tLoss: 0.324006\nTrain Epoch: 2 [20480/60000 (34%)]\tLoss: 0.447446\nTrain Epoch: 2 [21120/60000 (35%)]\tLoss: 0.291222\nTrain Epoch: 2 [21760/60000 (36%)]\tLoss: 0.557065\nTrain Epoch: 2 [22400/60000 (37%)]\tLoss: 0.552659\nTrain Epoch: 2 [23040/60000 (38%)]\tLoss: 0.378901\nTrain Epoch: 2 [23680/60000 (39%)]\tLoss: 0.360550\nTrain Epoch: 2 [24320/60000 (41%)]\tLoss: 0.283795\nTrain Epoch: 2 [24960/60000 (42%)]\tLoss: 0.475816\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.283652\nTrain Epoch: 2 [26240/60000 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[26880/60000 (45%)]\tLoss: 0.229927\nTrain Epoch: 3 [27520/60000 (46%)]\tLoss: 0.236894\nTrain Epoch: 3 [28160/60000 (47%)]\tLoss: 0.486373\nTrain Epoch: 3 [28800/60000 (48%)]\tLoss: 0.453053\nTrain Epoch: 3 [29440/60000 (49%)]\tLoss: 0.283823\nTrain Epoch: 3 [30080/60000 (50%)]\tLoss: 0.185119\nTrain Epoch: 3 [30720/60000 (51%)]\tLoss: 0.381274\nTrain Epoch: 3 [31360/60000 (52%)]\tLoss: 0.394533\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.392791\nTrain Epoch: 3 [32640/60000 (54%)]\tLoss: 0.230672\nTrain Epoch: 3 [33280/60000 (55%)]\tLoss: 0.393846\nTrain Epoch: 3 [33920/60000 (57%)]\tLoss: 0.676802\nTrain Epoch: 3 [34560/60000 (58%)]\tLoss: 0.160434\nTrain Epoch: 3 [35200/60000 (59%)]\tLoss: 0.211318\nTrain Epoch: 3 [35840/60000 (60%)]\tLoss: 0.245763\nTrain Epoch: 3 [36480/60000 (61%)]\tLoss: 0.198454\nTrain Epoch: 3 [37120/60000 (62%)]\tLoss: 0.243536\nTrain Epoch: 3 [37760/60000 (63%)]\tLoss: 0.151804\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.176093\nTrain Epoch: 3 [39040/60000 (65%)]\tLoss: 0.237228\nTrain Epoch: 3 [39680/60000 (66%)]\tLoss: 0.146441\nTrain Epoch: 3 [40320/60000 (67%)]\tLoss: 0.345162\nTrain Epoch: 3 [40960/60000 (68%)]\tLoss: 0.400378\nTrain Epoch: 3 [41600/60000 (69%)]\tLoss: 0.259152\nTrain Epoch: 3 [42240/60000 (70%)]\tLoss: 0.569659\nTrain Epoch: 3 [42880/60000 (71%)]\tLoss: 0.166401\nTrain Epoch: 3 [43520/60000 (72%)]\tLoss: 0.220592\nTrain Epoch: 3 [44160/60000 (74%)]\tLoss: 0.303227\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.193691\nTrain Epoch: 3 [45440/60000 (76%)]\tLoss: 0.257408\nTrain Epoch: 3 [46080/60000 (77%)]\tLoss: 0.391211\nTrain Epoch: 3 [46720/60000 (78%)]\tLoss: 0.419841\nTrain Epoch: 3 [47360/60000 (79%)]\tLoss: 0.121861\nTrain Epoch: 3 [48000/60000 (80%)]\tLoss: 0.176442\nTrain Epoch: 3 [48640/60000 (81%)]\tLoss: 0.534631\nTrain Epoch: 3 [49280/60000 (82%)]\tLoss: 0.296596\nTrain Epoch: 3 [49920/60000 (83%)]\tLoss: 0.190096\nTrain Epoch: 3 [50560/60000 (84%)]\tLoss: 0.360826\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.427482\nTrain Epoch: 3 [51840/60000 (86%)]\tLoss: 0.251076\nTrain Epoch: 3 [52480/60000 (87%)]\tLoss: 0.319904\nTrain Epoch: 3 [53120/60000 (88%)]\tLoss: 0.228778\nTrain Epoch: 3 [53760/60000 (90%)]\tLoss: 0.180340\nTrain Epoch: 3 [54400/60000 (91%)]\tLoss: 0.236512\nTrain Epoch: 3 [55040/60000 (92%)]\tLoss: 0.206779\nTrain Epoch: 3 [55680/60000 (93%)]\tLoss: 0.323677\nTrain Epoch: 3 [56320/60000 (94%)]\tLoss: 0.406382\nTrain Epoch: 3 [56960/60000 (95%)]\tLoss: 0.426768\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.595419\nTrain Epoch: 3 [58240/60000 (97%)]\tLoss: 0.175457\nTrain Epoch: 3 [58880/60000 (98%)]\tLoss: 0.301019\nTrain Epoch: 3 [59520/60000 (99%)]\tLoss: 0.419139\n\nTest set: Average loss: 0.1049, Accuracy: 9686/10000 (97%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.352631\nTrain Epoch: 4 [640/60000 (1%)]\tLoss: 0.343671\nTrain Epoch: 4 [1280/60000 (2%)]\tLoss: 0.170439\nTrain Epoch: 4 [1920/60000 (3%)]\tLoss: 0.289486\nTrain Epoch: 4 [2560/60000 (4%)]\tLoss: 0.096597\nTrain Epoch: 4 [3200/60000 (5%)]\tLoss: 0.263759\nTrain Epoch: 4 [3840/60000 (6%)]\tLoss: 0.369941\nTrain Epoch: 4 [4480/60000 (7%)]\tLoss: 0.326594\nTrain Epoch: 4 [5120/60000 (9%)]\tLoss: 0.174094\nTrain Epoch: 4 [5760/60000 (10%)]\tLoss: 0.442069\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.179002\nTrain Epoch: 4 [7040/60000 (12%)]\tLoss: 0.292742\nTrain Epoch: 4 [7680/60000 (13%)]\tLoss: 0.209898\nTrain Epoch: 4 [8320/60000 (14%)]\tLoss: 0.401671\nTrain Epoch: 4 [8960/60000 (15%)]\tLoss: 0.205146\nTrain Epoch: 4 [9600/60000 (16%)]\tLoss: 0.250836\nTrain Epoch: 4 [10240/60000 (17%)]\tLoss: 0.156622\nTrain Epoch: 4 [10880/60000 (18%)]\tLoss: 0.214578\nTrain Epoch: 4 [11520/60000 (19%)]\tLoss: 0.155916\nTrain Epoch: 4 [12160/60000 (20%)]\tLoss: 0.416294\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.197429\nTrain Epoch: 4 [13440/60000 (22%)]\tLoss: 0.154103\nTrain Epoch: 4 [14080/60000 (23%)]\tLoss: 0.377950\nTrain Epoch: 4 [14720/60000 (25%)]\tLoss: 0.338084\nTrain Epoch: 4 [15360/60000 (26%)]\tLoss: 0.242834\nTrain Epoch: 4 [16000/60000 (27%)]\tLoss: 0.139219\nTrain Epoch: 4 [16640/60000 (28%)]\tLoss: 0.242067\nTrain Epoch: 4 [17280/60000 (29%)]\tLoss: 0.189929\nTrain Epoch: 4 [17920/60000 (30%)]\tLoss: 0.358215\nTrain Epoch: 4 [18560/60000 (31%)]\tLoss: 0.354969\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.303644\nTrain Epoch: 4 [19840/60000 (33%)]\tLoss: 0.322343\nTrain Epoch: 4 [20480/60000 (34%)]\tLoss: 0.225422\nTrain Epoch: 4 [21120/60000 (35%)]\tLoss: 0.614347\nTrain Epoch: 4 [21760/60000 (36%)]\tLoss: 0.448674\nTrain Epoch: 4 [22400/60000 (37%)]\tLoss: 0.362976\nTrain Epoch: 4 [23040/60000 (38%)]\tLoss: 0.100357\nTrain Epoch: 4 [23680/60000 (39%)]\tLoss: 0.289331\nTrain Epoch: 4 [24320/60000 (41%)]\tLoss: 0.405818\nTrain Epoch: 4 [24960/60000 (42%)]\tLoss: 0.212617\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.348597\nTrain Epoch: 4 [26240/60000 (44%)]\tLoss: 0.351009\nTrain Epoch: 4 [26880/60000 (45%)]\tLoss: 0.341456\nTrain Epoch: 4 [27520/60000 (46%)]\tLoss: 0.297527\nTrain Epoch: 4 [28160/60000 (47%)]\tLoss: 0.281190\nTrain Epoch: 4 [28800/60000 (48%)]\tLoss: 0.187359\nTrain Epoch: 4 [29440/60000 (49%)]\tLoss: 0.178844\nTrain Epoch: 4 [30080/60000 (50%)]\tLoss: 0.201243\nTrain Epoch: 4 [30720/60000 (51%)]\tLoss: 0.305701\nTrain Epoch: 4 [31360/60000 (52%)]\tLoss: 0.370592\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.241955\nTrain Epoch: 4 [32640/60000 (54%)]\tLoss: 0.278765\nTrain Epoch: 4 [33280/60000 (55%)]\tLoss: 0.284302\nTrain Epoch: 4 [33920/60000 (57%)]\tLoss: 0.337426\nTrain Epoch: 4 [34560/60000 (58%)]\tLoss: 0.277304\nTrain Epoch: 4 [35200/60000 (59%)]\tLoss: 0.221228\nTrain Epoch: 4 [35840/60000 (60%)]\tLoss: 0.150985\nTrain Epoch: 4 [36480/60000 (61%)]\tLoss: 0.312087\nTrain Epoch: 4 [37120/60000 (62%)]\tLoss: 0.170111\nTrain Epoch: 4 [37760/60000 (63%)]\tLoss: 0.291135\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.160971\nTrain Epoch: 4 [39040/60000 (65%)]\tLoss: 0.390679\nTrain Epoch: 4 [39680/60000 (66%)]\tLoss: 0.434802\nTrain Epoch: 4 [40320/60000 (67%)]\tLoss: 0.281539\nTrain Epoch: 4 [40960/60000 (68%)]\tLoss: 0.172577\nTrain Epoch: 4 [41600/60000 (69%)]\tLoss: 0.348624\nTrain Epoch: 4 [42240/60000 (70%)]\tLoss: 0.380416\nTrain Epoch: 4 [42880/60000 (71%)]\tLoss: 0.483520\nTrain Epoch: 4 [43520/60000 (72%)]\tLoss: 0.216825\nTrain Epoch: 4 [44160/60000 (74%)]\tLoss: 0.320874\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.213358\nTrain Epoch: 4 [45440/60000 (76%)]\tLoss: 0.218650\nTrain Epoch: 4 [46080/60000 (77%)]\tLoss: 0.221090\nTrain Epoch: 4 [46720/60000 (78%)]\tLoss: 0.325981\nTrain Epoch: 4 [47360/60000 (79%)]\tLoss: 0.283184\nTrain Epoch: 4 [48000/60000 (80%)]\tLoss: 0.072845\nTrain Epoch: 4 [48640/60000 (81%)]\tLoss: 0.206940\nTrain Epoch: 4 [49280/60000 (82%)]\tLoss: 0.423454\nTrain Epoch: 4 [49920/60000 (83%)]\tLoss: 0.475285\nTrain Epoch: 4 [50560/60000 (84%)]\tLoss: 0.128978\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.195609\nTrain Epoch: 4 [51840/60000 (86%)]\tLoss: 0.125730\nTrain Epoch: 4 [52480/60000 (87%)]\tLoss: 0.137783\nTrain Epoch: 4 [53120/60000 (88%)]\tLoss: 0.375247\nTrain Epoch: 4 [53760/60000 (90%)]\tLoss: 0.243497\nTrain Epoch: 4 [54400/60000 (91%)]\tLoss: 0.236100\nTrain Epoch: 4 [55040/60000 (92%)]\tLoss: 0.266795\nTrain Epoch: 4 [55680/60000 (93%)]\tLoss: 0.229095\nTrain Epoch: 4 [56320/60000 (94%)]\tLoss: 0.167610\nTrain Epoch: 4 [56960/60000 (95%)]\tLoss: 0.240640\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.153999\nTrain Epoch: 4 [58240/60000 (97%)]\tLoss: 0.753790\nTrain Epoch: 4 [58880/60000 (98%)]\tLoss: 0.143998\nTrain Epoch: 4 [59520/60000 (99%)]\tLoss: 0.310583\n\nTest set: Average loss: 0.0843, Accuracy: 9739/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.227892\nTrain Epoch: 5 [640/60000 (1%)]\tLoss: 0.162702\nTrain Epoch: 5 [1280/60000 (2%)]\tLoss: 0.227571\nTrain Epoch: 5 [1920/60000 (3%)]\tLoss: 0.148511\nTrain Epoch: 5 [2560/60000 (4%)]\tLoss: 0.187414\nTrain Epoch: 5 [3200/60000 (5%)]\tLoss: 0.194418\nTrain Epoch: 5 [3840/60000 (6%)]\tLoss: 0.276495\nTrain Epoch: 5 [4480/60000 (7%)]\tLoss: 0.268769\nTrain Epoch: 5 [5120/60000 (9%)]\tLoss: 0.163968\nTrain Epoch: 5 [5760/60000 (10%)]\tLoss: 0.349296\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.217248\nTrain Epoch: 5 [7040/60000 (12%)]\tLoss: 0.195263\nTrain Epoch: 5 [7680/60000 (13%)]\tLoss: 0.339447\nTrain Epoch: 5 [8320/60000 (14%)]\tLoss: 0.224461\nTrain Epoch: 5 [8960/60000 (15%)]\tLoss: 0.095605\nTrain Epoch: 5 [9600/60000 (16%)]\tLoss: 0.196891\nTrain Epoch: 5 [10240/60000 (17%)]\tLoss: 0.218742\nTrain Epoch: 5 [10880/60000 (18%)]\tLoss: 0.071347\nTrain Epoch: 5 [11520/60000 (19%)]\tLoss: 0.403286\nTrain Epoch: 5 [12160/60000 (20%)]\tLoss: 0.149740\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.160939\nTrain Epoch: 5 [13440/60000 (22%)]\tLoss: 0.236512\nTrain Epoch: 5 [14080/60000 (23%)]\tLoss: 0.348727\nTrain Epoch: 5 [14720/60000 (25%)]\tLoss: 0.190054\nTrain Epoch: 5 [15360/60000 (26%)]\tLoss: 0.272029\nTrain Epoch: 5 [16000/60000 (27%)]\tLoss: 0.427739\nTrain Epoch: 5 [16640/60000 (28%)]\tLoss: 0.322332\nTrain Epoch: 5 [17280/60000 (29%)]\tLoss: 0.141410\nTrain Epoch: 5 [17920/60000 (30%)]\tLoss: 0.098900\nTrain Epoch: 5 [18560/60000 (31%)]\tLoss: 0.252387\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.182150\nTrain Epoch: 5 [19840/60000 (33%)]\tLoss: 0.133239\nTrain Epoch: 5 [20480/60000 (34%)]\tLoss: 0.126683\nTrain Epoch: 5 [21120/60000 (35%)]\tLoss: 0.370189\nTrain Epoch: 5 [21760/60000 (36%)]\tLoss: 0.162514\nTrain Epoch: 5 [22400/60000 (37%)]\tLoss: 0.272352\nTrain Epoch: 5 [23040/60000 (38%)]\tLoss: 0.298543\nTrain Epoch: 5 [23680/60000 (39%)]\tLoss: 0.235891\nTrain Epoch: 5 [24320/60000 (41%)]\tLoss: 0.187710\nTrain Epoch: 5 [24960/60000 (42%)]\tLoss: 0.185363\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.193369\nTrain Epoch: 5 [26240/60000 (44%)]\tLoss: 0.155984\nTrain Epoch: 5 [26880/60000 (45%)]\tLoss: 0.388923\nTrain Epoch: 5 [27520/60000 (46%)]\tLoss: 0.192868\nTrain Epoch: 5 [28160/60000 (47%)]\tLoss: 0.535787\nTrain Epoch: 5 [28800/60000 (48%)]\tLoss: 0.161020\nTrain Epoch: 5 [29440/60000 (49%)]\tLoss: 0.242179\nTrain Epoch: 5 [30080/60000 (50%)]\tLoss: 0.136554\nTrain Epoch: 5 [30720/60000 (51%)]\tLoss: 0.190672\nTrain Epoch: 5 [31360/60000 (52%)]\tLoss: 0.118027\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.278750\nTrain Epoch: 5 [32640/60000 (54%)]\tLoss: 0.418058\nTrain Epoch: 5 [33280/60000 (55%)]\tLoss: 0.287063\nTrain Epoch: 5 [33920/60000 (57%)]\tLoss: 0.279596\nTrain Epoch: 5 [34560/60000 (58%)]\tLoss: 0.181579\nTrain Epoch: 5 [35200/60000 (59%)]\tLoss: 0.443592\nTrain Epoch: 5 [35840/60000 (60%)]\tLoss: 0.095470\nTrain Epoch: 5 [36480/60000 (61%)]\tLoss: 0.277385\nTrain Epoch: 5 [37120/60000 (62%)]\tLoss: 0.263358\nTrain Epoch: 5 [37760/60000 (63%)]\tLoss: 0.190867\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.176580\nTrain Epoch: 5 [39040/60000 (65%)]\tLoss: 0.360235\nTrain Epoch: 5 [39680/60000 (66%)]\tLoss: 0.172416\nTrain Epoch: 5 [40320/60000 (67%)]\tLoss: 0.174126\nTrain Epoch: 5 [40960/60000 (68%)]\tLoss: 0.202162\nTrain Epoch: 5 [41600/60000 (69%)]\tLoss: 0.196991\nTrain Epoch: 5 [42240/60000 (70%)]\tLoss: 0.224622\nTrain Epoch: 5 [42880/60000 (71%)]\tLoss: 0.180406\nTrain Epoch: 5 [43520/60000 (72%)]\tLoss: 0.060447\nTrain Epoch: 5 [44160/60000 (74%)]\tLoss: 0.322497\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.239324\nTrain Epoch: 5 [45440/60000 (76%)]\tLoss: 0.348920\nTrain Epoch: 5 [46080/60000 (77%)]\tLoss: 0.240017\nTrain Epoch: 5 [46720/60000 (78%)]\tLoss: 0.237575\nTrain Epoch: 5 [47360/60000 (79%)]\tLoss: 0.142648\nTrain Epoch: 5 [48000/60000 (80%)]\tLoss: 0.227562\nTrain Epoch: 5 [48640/60000 (81%)]\tLoss: 0.254358\nTrain Epoch: 5 [49280/60000 (82%)]\tLoss: 0.135818\nTrain Epoch: 5 [49920/60000 (83%)]\tLoss: 0.386120\nTrain Epoch: 5 [50560/60000 (84%)]\tLoss: 0.328150\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.276833\nTrain Epoch: 5 [51840/60000 (86%)]\tLoss: 0.308869\nTrain Epoch: 5 [52480/60000 (87%)]\tLoss: 0.246442\nTrain Epoch: 5 [53120/60000 (88%)]\tLoss: 0.240874\nTrain Epoch: 5 [53760/60000 (90%)]\tLoss: 0.114337\nTrain Epoch: 5 [54400/60000 (91%)]\tLoss: 0.217325\nTrain Epoch: 5 [55040/60000 (92%)]\tLoss: 0.223010\nTrain Epoch: 5 [55680/60000 (93%)]\tLoss: 0.138459\nTrain Epoch: 5 [56320/60000 (94%)]\tLoss: 0.283678\nTrain Epoch: 5 [56960/60000 (95%)]\tLoss: 0.158834\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.164267\nTrain Epoch: 5 [58240/60000 (97%)]\tLoss: 0.290795\nTrain Epoch: 5 [58880/60000 (98%)]\tLoss: 0.451639\nTrain Epoch: 5 [59520/60000 (99%)]\tLoss: 0.349018\n\nTest set: Average loss: 0.0797, Accuracy: 9758/10000 (98%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.311334\nTrain Epoch: 6 [640/60000 (1%)]\tLoss: 0.129143\nTrain Epoch: 6 [1280/60000 (2%)]\tLoss: 0.227222\nTrain Epoch: 6 [1920/60000 (3%)]\tLoss: 0.157591\nTrain Epoch: 6 [2560/60000 (4%)]\tLoss: 0.205490\nTrain Epoch: 6 [3200/60000 (5%)]\tLoss: 0.421089\nTrain Epoch: 6 [3840/60000 (6%)]\tLoss: 0.157544\nTrain Epoch: 6 [4480/60000 (7%)]\tLoss: 0.087023\nTrain Epoch: 6 [5120/60000 (9%)]\tLoss: 0.130669\nTrain Epoch: 6 [5760/60000 (10%)]\tLoss: 0.059450\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.121786\nTrain Epoch: 6 [7040/60000 (12%)]\tLoss: 0.177859\nTrain Epoch: 6 [7680/60000 (13%)]\tLoss: 0.217464\nTrain Epoch: 6 [8320/60000 (14%)]\tLoss: 0.183426\nTrain Epoch: 6 [8960/60000 (15%)]\tLoss: 0.237282\nTrain Epoch: 6 [9600/60000 (16%)]\tLoss: 0.210031\nTrain Epoch: 6 [10240/60000 (17%)]\tLoss: 0.256110\nTrain Epoch: 6 [10880/60000 (18%)]\tLoss: 0.155481\nTrain Epoch: 6 [11520/60000 (19%)]\tLoss: 0.166967\nTrain Epoch: 6 [12160/60000 (20%)]\tLoss: 0.144590\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.229593\nTrain Epoch: 6 [13440/60000 (22%)]\tLoss: 0.092102\nTrain Epoch: 6 [14080/60000 (23%)]\tLoss: 0.144247\nTrain Epoch: 6 [14720/60000 (25%)]\tLoss: 0.459083\nTrain Epoch: 6 [15360/60000 (26%)]\tLoss: 0.174974\nTrain Epoch: 6 [16000/60000 (27%)]\tLoss: 0.146433\nTrain Epoch: 6 [16640/60000 (28%)]\tLoss: 0.291392\nTrain Epoch: 6 [17280/60000 (29%)]\tLoss: 0.203127\nTrain Epoch: 6 [17920/60000 (30%)]\tLoss: 0.255063\nTrain Epoch: 6 [18560/60000 (31%)]\tLoss: 0.167576\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.171914\nTrain Epoch: 6 [19840/60000 (33%)]\tLoss: 0.215950\nTrain Epoch: 6 [20480/60000 (34%)]\tLoss: 0.246624\nTrain Epoch: 6 [21120/60000 (35%)]\tLoss: 0.242730\nTrain Epoch: 6 [21760/60000 (36%)]\tLoss: 0.345666\nTrain Epoch: 6 [22400/60000 (37%)]\tLoss: 0.229078\nTrain Epoch: 6 [23040/60000 (38%)]\tLoss: 0.283169\nTrain Epoch: 6 [23680/60000 (39%)]\tLoss: 0.246430\nTrain Epoch: 6 [24320/60000 (41%)]\tLoss: 0.217211\nTrain Epoch: 6 [24960/60000 (42%)]\tLoss: 0.168141\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.297715\nTrain Epoch: 6 [26240/60000 (44%)]\tLoss: 0.200130\nTrain Epoch: 6 [26880/60000 (45%)]\tLoss: 0.344390\nTrain Epoch: 6 [27520/60000 (46%)]\tLoss: 0.246202\nTrain Epoch: 6 [28160/60000 (47%)]\tLoss: 0.272422\nTrain Epoch: 6 [28800/60000 (48%)]\tLoss: 0.117001\nTrain Epoch: 6 [29440/60000 (49%)]\tLoss: 0.246031\nTrain Epoch: 6 [30080/60000 (50%)]\tLoss: 0.138119\nTrain Epoch: 6 [30720/60000 (51%)]\tLoss: 0.214345\nTrain Epoch: 6 [31360/60000 (52%)]\tLoss: 0.134483\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.201771\nTrain Epoch: 6 [32640/60000 (54%)]\tLoss: 0.201668\nTrain Epoch: 6 [33280/60000 (55%)]\tLoss: 0.111183\nTrain Epoch: 6 [33920/60000 (57%)]\tLoss: 0.093289\nTrain Epoch: 6 [34560/60000 (58%)]\tLoss: 0.171475\nTrain Epoch: 6 [35200/60000 (59%)]\tLoss: 0.178729\nTrain Epoch: 6 [35840/60000 (60%)]\tLoss: 0.144986\nTrain Epoch: 6 [36480/60000 (61%)]\tLoss: 0.302206\nTrain Epoch: 6 [37120/60000 (62%)]\tLoss: 0.389723\nTrain Epoch: 6 [37760/60000 (63%)]\tLoss: 0.268302\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.358240\nTrain Epoch: 6 [39040/60000 (65%)]\tLoss: 0.241359\nTrain Epoch: 6 [39680/60000 (66%)]\tLoss: 0.282464\nTrain Epoch: 6 [40320/60000 (67%)]\tLoss: 0.205064\nTrain Epoch: 6 [40960/60000 (68%)]\tLoss: 0.106739\nTrain Epoch: 6 [41600/60000 (69%)]\tLoss: 0.076333\nTrain Epoch: 6 [42240/60000 (70%)]\tLoss: 0.157558\nTrain Epoch: 6 [42880/60000 (71%)]\tLoss: 0.217494\nTrain Epoch: 6 [43520/60000 (72%)]\tLoss: 0.183687\nTrain Epoch: 6 [44160/60000 (74%)]\tLoss: 0.217155\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.108482\nTrain Epoch: 6 [45440/60000 (76%)]\tLoss: 0.324247\nTrain Epoch: 6 [46080/60000 (77%)]\tLoss: 0.352494\nTrain Epoch: 6 [46720/60000 (78%)]\tLoss: 0.163462\nTrain Epoch: 6 [47360/60000 (79%)]\tLoss: 0.154820\nTrain Epoch: 6 [48000/60000 (80%)]\tLoss: 0.174164\nTrain Epoch: 6 [48640/60000 (81%)]\tLoss: 0.196258\nTrain Epoch: 6 [49280/60000 (82%)]\tLoss: 0.226030\nTrain Epoch: 6 [49920/60000 (83%)]\tLoss: 0.306971\nTrain Epoch: 6 [50560/60000 (84%)]\tLoss: 0.387282\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.213550\nTrain Epoch: 6 [51840/60000 (86%)]\tLoss: 0.133755\nTrain Epoch: 6 [52480/60000 (87%)]\tLoss: 0.176044\nTrain Epoch: 6 [53120/60000 (88%)]\tLoss: 0.282900\nTrain Epoch: 6 [53760/60000 (90%)]\tLoss: 0.154157\nTrain Epoch: 6 [54400/60000 (91%)]\tLoss: 0.138895\nTrain Epoch: 6 [55040/60000 (92%)]\tLoss: 0.254137\nTrain Epoch: 6 [55680/60000 (93%)]\tLoss: 0.107765\nTrain Epoch: 6 [56320/60000 (94%)]\tLoss: 0.118788\nTrain Epoch: 6 [56960/60000 (95%)]\tLoss: 0.142051\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.176375\nTrain Epoch: 6 [58240/60000 (97%)]\tLoss: 0.131573\nTrain Epoch: 6 [58880/60000 (98%)]\tLoss: 0.347166\nTrain Epoch: 6 [59520/60000 (99%)]\tLoss: 0.217951\n\nTest set: Average loss: 0.0690, Accuracy: 9776/10000 (98%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.142441\nTrain Epoch: 7 [640/60000 (1%)]\tLoss: 0.078599\nTrain Epoch: 7 [1280/60000 (2%)]\tLoss: 0.121731\nTrain Epoch: 7 [1920/60000 (3%)]\tLoss: 0.070044\nTrain Epoch: 7 [2560/60000 (4%)]\tLoss: 0.224216\nTrain Epoch: 7 [3200/60000 (5%)]\tLoss: 0.104122\nTrain Epoch: 7 [3840/60000 (6%)]\tLoss: 0.228575\nTrain Epoch: 7 [4480/60000 (7%)]\tLoss: 0.377044\nTrain Epoch: 7 [5120/60000 (9%)]\tLoss: 0.296184\nTrain Epoch: 7 [5760/60000 (10%)]\tLoss: 0.099891\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.269691\nTrain Epoch: 7 [7040/60000 (12%)]\tLoss: 0.240640\nTrain Epoch: 7 [7680/60000 (13%)]\tLoss: 0.171192\nTrain Epoch: 7 [8320/60000 (14%)]\tLoss: 0.306889\nTrain Epoch: 7 [8960/60000 (15%)]\tLoss: 0.238503\nTrain Epoch: 7 [9600/60000 (16%)]\tLoss: 0.286252\nTrain Epoch: 7 [10240/60000 (17%)]\tLoss: 0.171058\nTrain Epoch: 7 [10880/60000 (18%)]\tLoss: 0.208866\nTrain Epoch: 7 [11520/60000 (19%)]\tLoss: 0.418091\nTrain Epoch: 7 [12160/60000 (20%)]\tLoss: 0.115058\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.159557\nTrain Epoch: 7 [13440/60000 (22%)]\tLoss: 0.085076\nTrain Epoch: 7 [14080/60000 (23%)]\tLoss: 0.244673\nTrain Epoch: 7 [14720/60000 (25%)]\tLoss: 0.316326\nTrain Epoch: 7 [15360/60000 (26%)]\tLoss: 0.370775\nTrain Epoch: 7 [16000/60000 (27%)]\tLoss: 0.235262\nTrain Epoch: 7 [16640/60000 (28%)]\tLoss: 0.296188\nTrain Epoch: 7 [17280/60000 (29%)]\tLoss: 0.224960\nTrain Epoch: 7 [17920/60000 (30%)]\tLoss: 0.162341\nTrain Epoch: 7 [18560/60000 (31%)]\tLoss: 0.136551\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.111435\nTrain Epoch: 7 [19840/60000 (33%)]\tLoss: 0.173483\nTrain Epoch: 7 [20480/60000 (34%)]\tLoss: 0.170351\nTrain Epoch: 7 [21120/60000 (35%)]\tLoss: 0.109828\nTrain Epoch: 7 [21760/60000 (36%)]\tLoss: 0.219692\nTrain Epoch: 7 [22400/60000 (37%)]\tLoss: 0.085780\nTrain Epoch: 7 [23040/60000 (38%)]\tLoss: 0.076800\nTrain Epoch: 7 [23680/60000 (39%)]\tLoss: 0.163377\nTrain Epoch: 7 [24320/60000 (41%)]\tLoss: 0.178391\nTrain Epoch: 7 [24960/60000 (42%)]\tLoss: 0.311988\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.215559\nTrain Epoch: 7 [26240/60000 (44%)]\tLoss: 0.199207\nTrain Epoch: 7 [26880/60000 (45%)]\tLoss: 0.201917\nTrain Epoch: 7 [27520/60000 (46%)]\tLoss: 0.163283\nTrain Epoch: 7 [28160/60000 (47%)]\tLoss: 0.107533\nTrain Epoch: 7 [28800/60000 (48%)]\tLoss: 0.046209\nTrain Epoch: 7 [29440/60000 (49%)]\tLoss: 0.173062\nTrain Epoch: 7 [30080/60000 (50%)]\tLoss: 0.088925\nTrain Epoch: 7 [30720/60000 (51%)]\tLoss: 0.068962\nTrain Epoch: 7 [31360/60000 (52%)]\tLoss: 0.223214\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.096083\nTrain Epoch: 7 [32640/60000 (54%)]\tLoss: 0.327635\nTrain Epoch: 7 [33280/60000 (55%)]\tLoss: 0.278620\nTrain Epoch: 7 [33920/60000 (57%)]\tLoss: 0.223806\nTrain Epoch: 7 [34560/60000 (58%)]\tLoss: 0.121638\nTrain Epoch: 7 [35200/60000 (59%)]\tLoss: 0.182739\nTrain Epoch: 7 [35840/60000 (60%)]\tLoss: 0.172866\nTrain Epoch: 7 [36480/60000 (61%)]\tLoss: 0.180873\nTrain Epoch: 7 [37120/60000 (62%)]\tLoss: 0.298984\nTrain Epoch: 7 [37760/60000 (63%)]\tLoss: 0.251939\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.105321\nTrain Epoch: 7 [39040/60000 (65%)]\tLoss: 0.200500\nTrain Epoch: 7 [39680/60000 (66%)]\tLoss: 0.309791\nTrain Epoch: 7 [40320/60000 (67%)]\tLoss: 0.114949\nTrain Epoch: 7 [40960/60000 (68%)]\tLoss: 0.066153\nTrain Epoch: 7 [41600/60000 (69%)]\tLoss: 0.327437\nTrain Epoch: 7 [42240/60000 (70%)]\tLoss: 0.179023\nTrain Epoch: 7 [42880/60000 (71%)]\tLoss: 0.089861\nTrain Epoch: 7 [43520/60000 (72%)]\tLoss: 0.111230\nTrain Epoch: 7 [44160/60000 (74%)]\tLoss: 0.108233\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.145669\nTrain Epoch: 7 [45440/60000 (76%)]\tLoss: 0.122024\nTrain Epoch: 7 [46080/60000 (77%)]\tLoss: 0.083490\nTrain Epoch: 7 [46720/60000 (78%)]\tLoss: 0.116002\nTrain Epoch: 7 [47360/60000 (79%)]\tLoss: 0.200240\nTrain Epoch: 7 [48000/60000 (80%)]\tLoss: 0.363707\nTrain Epoch: 7 [48640/60000 (81%)]\tLoss: 0.294594\nTrain Epoch: 7 [49280/60000 (82%)]\tLoss: 0.127643\nTrain Epoch: 7 [49920/60000 (83%)]\tLoss: 0.202008\nTrain Epoch: 7 [50560/60000 (84%)]\tLoss: 0.159551\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.221197\nTrain Epoch: 7 [51840/60000 (86%)]\tLoss: 0.266463\nTrain Epoch: 7 [52480/60000 (87%)]\tLoss: 0.073967\nTrain Epoch: 7 [53120/60000 (88%)]\tLoss: 0.350092\nTrain Epoch: 7 [53760/60000 (90%)]\tLoss: 0.106500\nTrain Epoch: 7 [54400/60000 (91%)]\tLoss: 0.208859\nTrain Epoch: 7 [55040/60000 (92%)]\tLoss: 0.209937\nTrain Epoch: 7 [55680/60000 (93%)]\tLoss: 0.215286\nTrain Epoch: 7 [56320/60000 (94%)]\tLoss: 0.117026\nTrain Epoch: 7 [56960/60000 (95%)]\tLoss: 0.132321\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.286004\nTrain Epoch: 7 [58240/60000 (97%)]\tLoss: 0.170485\nTrain Epoch: 7 [58880/60000 (98%)]\tLoss: 0.196613\nTrain Epoch: 7 [59520/60000 (99%)]\tLoss: 0.293870\n\nTest set: Average loss: 0.0657, Accuracy: 9801/10000 (98%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.315451\nTrain Epoch: 8 [640/60000 (1%)]\tLoss: 0.114413\nTrain Epoch: 8 [1280/60000 (2%)]\tLoss: 0.129036\nTrain Epoch: 8 [1920/60000 (3%)]\tLoss: 0.141999\nTrain Epoch: 8 [2560/60000 (4%)]\tLoss: 0.118697\nTrain Epoch: 8 [3200/60000 (5%)]\tLoss: 0.126823\nTrain Epoch: 8 [3840/60000 (6%)]\tLoss: 0.053924\nTrain Epoch: 8 [4480/60000 (7%)]\tLoss: 0.296224\nTrain Epoch: 8 [5120/60000 (9%)]\tLoss: 0.121338\nTrain Epoch: 8 [5760/60000 (10%)]\tLoss: 0.255161\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.170684\nTrain Epoch: 8 [7040/60000 (12%)]\tLoss: 0.092008\nTrain Epoch: 8 [7680/60000 (13%)]\tLoss: 0.283091\nTrain Epoch: 8 [8320/60000 (14%)]\tLoss: 0.027133\nTrain Epoch: 8 [8960/60000 (15%)]\tLoss: 0.195686\nTrain Epoch: 8 [9600/60000 (16%)]\tLoss: 0.343612\nTrain Epoch: 8 [10240/60000 (17%)]\tLoss: 0.108563\nTrain Epoch: 8 [10880/60000 (18%)]\tLoss: 0.223832\nTrain Epoch: 8 [11520/60000 (19%)]\tLoss: 0.175617\nTrain Epoch: 8 [12160/60000 (20%)]\tLoss: 0.145828\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.178722\nTrain Epoch: 8 [13440/60000 (22%)]\tLoss: 0.151158\nTrain Epoch: 8 [14080/60000 (23%)]\tLoss: 0.183155\nTrain Epoch: 8 [14720/60000 (25%)]\tLoss: 0.110281\nTrain Epoch: 8 [15360/60000 (26%)]\tLoss: 0.282224\nTrain Epoch: 8 [16000/60000 (27%)]\tLoss: 0.097411\nTrain Epoch: 8 [16640/60000 (28%)]\tLoss: 0.264533\nTrain Epoch: 8 [17280/60000 (29%)]\tLoss: 0.194778\nTrain Epoch: 8 [17920/60000 (30%)]\tLoss: 0.235924\nTrain Epoch: 8 [18560/60000 (31%)]\tLoss: 0.236801\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.178174\nTrain Epoch: 8 [19840/60000 (33%)]\tLoss: 0.218752\nTrain Epoch: 8 [20480/60000 (34%)]\tLoss: 0.208353\nTrain Epoch: 8 [21120/60000 (35%)]\tLoss: 0.193034\nTrain Epoch: 8 [21760/60000 (36%)]\tLoss: 0.138453\nTrain Epoch: 8 [22400/60000 (37%)]\tLoss: 0.175271\nTrain Epoch: 8 [23040/60000 (38%)]\tLoss: 0.157295\nTrain Epoch: 8 [23680/60000 (39%)]\tLoss: 0.156248\nTrain Epoch: 8 [24320/60000 (41%)]\tLoss: 0.153413\nTrain Epoch: 8 [24960/60000 (42%)]\tLoss: 0.084870\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.150966\nTrain Epoch: 8 [26240/60000 (44%)]\tLoss: 0.160973\nTrain Epoch: 8 [26880/60000 (45%)]\tLoss: 0.231433\nTrain Epoch: 8 [27520/60000 (46%)]\tLoss: 0.144396\nTrain Epoch: 8 [28160/60000 (47%)]\tLoss: 0.200417\nTrain Epoch: 8 [28800/60000 (48%)]\tLoss: 0.152939\nTrain Epoch: 8 [29440/60000 (49%)]\tLoss: 0.109962\nTrain Epoch: 8 [30080/60000 (50%)]\tLoss: 0.134907\nTrain Epoch: 8 [30720/60000 (51%)]\tLoss: 0.088782\nTrain Epoch: 8 [31360/60000 (52%)]\tLoss: 0.129031\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.184744\nTrain Epoch: 8 [32640/60000 (54%)]\tLoss: 0.155463\nTrain Epoch: 8 [33280/60000 (55%)]\tLoss: 0.174192\nTrain Epoch: 8 [33920/60000 (57%)]\tLoss: 0.172103\nTrain Epoch: 8 [34560/60000 (58%)]\tLoss: 0.201503\nTrain Epoch: 8 [35200/60000 (59%)]\tLoss: 0.287885\nTrain Epoch: 8 [35840/60000 (60%)]\tLoss: 0.133675\nTrain Epoch: 8 [36480/60000 (61%)]\tLoss: 0.243534\nTrain Epoch: 8 [37120/60000 (62%)]\tLoss: 0.196020\nTrain Epoch: 8 [37760/60000 (63%)]\tLoss: 0.101380\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.108299\nTrain Epoch: 8 [39040/60000 (65%)]\tLoss: 0.159048\nTrain Epoch: 8 [39680/60000 (66%)]\tLoss: 0.204734\nTrain Epoch: 8 [40320/60000 (67%)]\tLoss: 0.238383\nTrain Epoch: 8 [40960/60000 (68%)]\tLoss: 0.592663\nTrain Epoch: 8 [41600/60000 (69%)]\tLoss: 0.116080\nTrain Epoch: 8 [42240/60000 (70%)]\tLoss: 0.039719\nTrain Epoch: 8 [42880/60000 (71%)]\tLoss: 0.148190\nTrain Epoch: 8 [43520/60000 (72%)]\tLoss: 0.241765\nTrain Epoch: 8 [44160/60000 (74%)]\tLoss: 0.235942\nTrain Epoch: 8 [44800/60000 (75%)]\tLoss: 0.175277\nTrain Epoch: 8 [45440/60000 (76%)]\tLoss: 0.143608\nTrain Epoch: 8 [46080/60000 (77%)]\tLoss: 0.114853\nTrain Epoch: 8 [46720/60000 (78%)]\tLoss: 0.232284\nTrain Epoch: 8 [47360/60000 (79%)]\tLoss: 0.321072\nTrain Epoch: 8 [48000/60000 (80%)]\tLoss: 0.310765\nTrain Epoch: 8 [48640/60000 (81%)]\tLoss: 0.102070\nTrain Epoch: 8 [49280/60000 (82%)]\tLoss: 0.372137\nTrain Epoch: 8 [49920/60000 (83%)]\tLoss: 0.109344\nTrain Epoch: 8 [50560/60000 (84%)]\tLoss: 0.382866\nTrain Epoch: 8 [51200/60000 (85%)]\tLoss: 0.270467\nTrain Epoch: 8 [51840/60000 (86%)]\tLoss: 0.061211\nTrain Epoch: 8 [52480/60000 (87%)]\tLoss: 0.233812\nTrain Epoch: 8 [53120/60000 (88%)]\tLoss: 0.176510\nTrain Epoch: 8 [53760/60000 (90%)]\tLoss: 0.120536\nTrain Epoch: 8 [54400/60000 (91%)]\tLoss: 0.241959\nTrain Epoch: 8 [55040/60000 (92%)]\tLoss: 0.183966\nTrain Epoch: 8 [55680/60000 (93%)]\tLoss: 0.125279\nTrain Epoch: 8 [56320/60000 (94%)]\tLoss: 0.152849\nTrain Epoch: 8 [56960/60000 (95%)]\tLoss: 0.219788\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.077843\nTrain Epoch: 8 [58240/60000 (97%)]\tLoss: 0.304191\nTrain Epoch: 8 [58880/60000 (98%)]\tLoss: 0.363550\nTrain Epoch: 8 [59520/60000 (99%)]\tLoss: 0.326421\n\nTest set: Average loss: 0.0632, Accuracy: 9807/10000 (98%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.140965\nTrain Epoch: 9 [640/60000 (1%)]\tLoss: 0.206063\nTrain Epoch: 9 [1280/60000 (2%)]\tLoss: 0.189364\nTrain Epoch: 9 [1920/60000 (3%)]\tLoss: 0.367962\nTrain Epoch: 9 [2560/60000 (4%)]\tLoss: 0.108362\nTrain Epoch: 9 [3200/60000 (5%)]\tLoss: 0.109142\nTrain Epoch: 9 [3840/60000 (6%)]\tLoss: 0.270022\nTrain Epoch: 9 [4480/60000 (7%)]\tLoss: 0.200647\nTrain Epoch: 9 [5120/60000 (9%)]\tLoss: 0.162118\nTrain Epoch: 9 [5760/60000 (10%)]\tLoss: 0.167245\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.188903\nTrain Epoch: 9 [7040/60000 (12%)]\tLoss: 0.280550\nTrain Epoch: 9 [7680/60000 (13%)]\tLoss: 0.116265\nTrain Epoch: 9 [8320/60000 (14%)]\tLoss: 0.602693\nTrain Epoch: 9 [8960/60000 (15%)]\tLoss: 0.148682\nTrain Epoch: 9 [9600/60000 (16%)]\tLoss: 0.225477\nTrain Epoch: 9 [10240/60000 (17%)]\tLoss: 0.133642\nTrain Epoch: 9 [10880/60000 (18%)]\tLoss: 0.116083\nTrain Epoch: 9 [11520/60000 (19%)]\tLoss: 0.348113\nTrain Epoch: 9 [12160/60000 (20%)]\tLoss: 0.219562\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.117716\nTrain Epoch: 9 [13440/60000 (22%)]\tLoss: 0.218508\nTrain Epoch: 9 [14080/60000 (23%)]\tLoss: 0.323755\nTrain Epoch: 9 [14720/60000 (25%)]\tLoss: 0.211174\nTrain Epoch: 9 [15360/60000 (26%)]\tLoss: 0.451853\nTrain Epoch: 9 [16000/60000 (27%)]\tLoss: 0.155174\nTrain Epoch: 9 [16640/60000 (28%)]\tLoss: 0.134905\nTrain Epoch: 9 [17280/60000 (29%)]\tLoss: 0.172428\nTrain Epoch: 9 [17920/60000 (30%)]\tLoss: 0.306172\nTrain Epoch: 9 [18560/60000 (31%)]\tLoss: 0.133085\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.449040\nTrain Epoch: 9 [19840/60000 (33%)]\tLoss: 0.084722\nTrain Epoch: 9 [20480/60000 (34%)]\tLoss: 0.188086\nTrain Epoch: 9 [21120/60000 (35%)]\tLoss: 0.222472\nTrain Epoch: 9 [21760/60000 (36%)]\tLoss: 0.275132\nTrain Epoch: 9 [22400/60000 (37%)]\tLoss: 0.287421\nTrain Epoch: 9 [23040/60000 (38%)]\tLoss: 0.105733\nTrain Epoch: 9 [23680/60000 (39%)]\tLoss: 0.157949\nTrain Epoch: 9 [24320/60000 (41%)]\tLoss: 0.073462\nTrain Epoch: 9 [24960/60000 (42%)]\tLoss: 0.240201\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.060848\nTrain Epoch: 9 [26240/60000 (44%)]\tLoss: 0.173801\nTrain Epoch: 9 [26880/60000 (45%)]\tLoss: 0.148143\nTrain Epoch: 9 [27520/60000 (46%)]\tLoss: 0.180779\nTrain Epoch: 9 [28160/60000 (47%)]\tLoss: 0.393192\nTrain Epoch: 9 [28800/60000 (48%)]\tLoss: 0.239243\nTrain Epoch: 9 [29440/60000 (49%)]\tLoss: 0.064345\nTrain Epoch: 9 [30080/60000 (50%)]\tLoss: 0.315658\nTrain Epoch: 9 [30720/60000 (51%)]\tLoss: 0.105739\nTrain Epoch: 9 [31360/60000 (52%)]\tLoss: 0.246439\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.145221\nTrain Epoch: 9 [32640/60000 (54%)]\tLoss: 0.287615\nTrain Epoch: 9 [33280/60000 (55%)]\tLoss: 0.310717\nTrain Epoch: 9 [33920/60000 (57%)]\tLoss: 0.322760\nTrain Epoch: 9 [34560/60000 (58%)]\tLoss: 0.294462\nTrain Epoch: 9 [35200/60000 (59%)]\tLoss: 0.168697\nTrain Epoch: 9 [35840/60000 (60%)]\tLoss: 0.153495\nTrain Epoch: 9 [36480/60000 (61%)]\tLoss: 0.146843\nTrain Epoch: 9 [37120/60000 (62%)]\tLoss: 0.176622\nTrain Epoch: 9 [37760/60000 (63%)]\tLoss: 0.400825\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.197533\nTrain Epoch: 9 [39040/60000 (65%)]\tLoss: 0.109741\nTrain Epoch: 9 [39680/60000 (66%)]\tLoss: 0.049689\nTrain Epoch: 9 [40320/60000 (67%)]\tLoss: 0.253087\nTrain Epoch: 9 [40960/60000 (68%)]\tLoss: 0.222971\nTrain Epoch: 9 [41600/60000 (69%)]\tLoss: 0.095467\nTrain Epoch: 9 [42240/60000 (70%)]\tLoss: 0.043052\nTrain Epoch: 9 [42880/60000 (71%)]\tLoss: 0.105347\nTrain Epoch: 9 [43520/60000 (72%)]\tLoss: 0.133342\nTrain Epoch: 9 [44160/60000 (74%)]\tLoss: 0.266375\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.156081\nTrain Epoch: 9 [45440/60000 (76%)]\tLoss: 0.206747\nTrain Epoch: 9 [46080/60000 (77%)]\tLoss: 0.158561\nTrain Epoch: 9 [46720/60000 (78%)]\tLoss: 0.416148\nTrain Epoch: 9 [47360/60000 (79%)]\tLoss: 0.147991\nTrain Epoch: 9 [48000/60000 (80%)]\tLoss: 0.112567\nTrain Epoch: 9 [48640/60000 (81%)]\tLoss: 0.100846\nTrain Epoch: 9 [49280/60000 (82%)]\tLoss: 0.103345\nTrain Epoch: 9 [49920/60000 (83%)]\tLoss: 0.205922\nTrain Epoch: 9 [50560/60000 (84%)]\tLoss: 0.097610\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.071967\nTrain Epoch: 9 [51840/60000 (86%)]\tLoss: 0.068125\nTrain Epoch: 9 [52480/60000 (87%)]\tLoss: 0.057313\nTrain Epoch: 9 [53120/60000 (88%)]\tLoss: 0.162428\nTrain Epoch: 9 [53760/60000 (90%)]\tLoss: 0.097614\nTrain Epoch: 9 [54400/60000 (91%)]\tLoss: 0.075174\nTrain Epoch: 9 [55040/60000 (92%)]\tLoss: 0.095530\nTrain Epoch: 9 [55680/60000 (93%)]\tLoss: 0.142529\nTrain Epoch: 9 [56320/60000 (94%)]\tLoss: 0.132163\nTrain Epoch: 9 [56960/60000 (95%)]\tLoss: 0.201932\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.238939\nTrain Epoch: 9 [58240/60000 (97%)]\tLoss: 0.037396\nTrain Epoch: 9 [58880/60000 (98%)]\tLoss: 0.077772\nTrain Epoch: 9 [59520/60000 (99%)]\tLoss: 0.177759\n\nTest set: Average loss: 0.0559, Accuracy: 9813/10000 (98%)\n\nTrain Epoch: 10 [0/60000 (0%)]\tLoss: 0.112115\nTrain Epoch: 10 [640/60000 (1%)]\tLoss: 0.089035\nTrain Epoch: 10 [1280/60000 (2%)]\tLoss: 0.177925\nTrain Epoch: 10 [1920/60000 (3%)]\tLoss: 0.147350\nTrain Epoch: 10 [2560/60000 (4%)]\tLoss: 0.170561\nTrain Epoch: 10 [3200/60000 (5%)]\tLoss: 0.207891\nTrain Epoch: 10 [3840/60000 (6%)]\tLoss: 0.340160\nTrain Epoch: 10 [4480/60000 (7%)]\tLoss: 0.229032\nTrain Epoch: 10 [5120/60000 (9%)]\tLoss: 0.335419\nTrain Epoch: 10 [5760/60000 (10%)]\tLoss: 0.101219\nTrain Epoch: 10 [6400/60000 (11%)]\tLoss: 0.085085\nTrain Epoch: 10 [7040/60000 (12%)]\tLoss: 0.053658\nTrain Epoch: 10 [7680/60000 (13%)]\tLoss: 0.106224\nTrain Epoch: 10 [8320/60000 (14%)]\tLoss: 0.146947\nTrain Epoch: 10 [8960/60000 (15%)]\tLoss: 0.210157\nTrain Epoch: 10 [9600/60000 (16%)]\tLoss: 0.167598\nTrain Epoch: 10 [10240/60000 (17%)]\tLoss: 0.184822\nTrain Epoch: 10 [10880/60000 (18%)]\tLoss: 0.149518\nTrain Epoch: 10 [11520/60000 (19%)]\tLoss: 0.091374\nTrain Epoch: 10 [12160/60000 (20%)]\tLoss: 0.331635\nTrain Epoch: 10 [12800/60000 (21%)]\tLoss: 0.345818\nTrain Epoch: 10 [13440/60000 (22%)]\tLoss: 0.057789\nTrain Epoch: 10 [14080/60000 (23%)]\tLoss: 0.189208\nTrain Epoch: 10 [14720/60000 (25%)]\tLoss: 0.116747\nTrain Epoch: 10 [15360/60000 (26%)]\tLoss: 0.101344\nTrain Epoch: 10 [16000/60000 (27%)]\tLoss: 0.116675\nTrain Epoch: 10 [16640/60000 (28%)]\tLoss: 0.158562\nTrain Epoch: 10 [17280/60000 (29%)]\tLoss: 0.173697\nTrain Epoch: 10 [17920/60000 (30%)]\tLoss: 0.167972\nTrain Epoch: 10 [18560/60000 (31%)]\tLoss: 0.125186\nTrain Epoch: 10 [19200/60000 (32%)]\tLoss: 0.116458\nTrain Epoch: 10 [19840/60000 (33%)]\tLoss: 0.107688\nTrain Epoch: 10 [20480/60000 (34%)]\tLoss: 0.131942\nTrain Epoch: 10 [21120/60000 (35%)]\tLoss: 0.189690\nTrain Epoch: 10 [21760/60000 (36%)]\tLoss: 0.106075\nTrain Epoch: 10 [22400/60000 (37%)]\tLoss: 0.100791\nTrain Epoch: 10 [23040/60000 (38%)]\tLoss: 0.151750\nTrain Epoch: 10 [23680/60000 (39%)]\tLoss: 0.242852\nTrain Epoch: 10 [24320/60000 (41%)]\tLoss: 0.367772\nTrain Epoch: 10 [24960/60000 (42%)]\tLoss: 0.160668\nTrain Epoch: 10 [25600/60000 (43%)]\tLoss: 0.209858\nTrain Epoch: 10 [26240/60000 (44%)]\tLoss: 0.267443\nTrain Epoch: 10 [26880/60000 (45%)]\tLoss: 0.134159\nTrain Epoch: 10 [27520/60000 (46%)]\tLoss: 0.176844\nTrain Epoch: 10 [28160/60000 (47%)]\tLoss: 0.083609\nTrain Epoch: 10 [28800/60000 (48%)]\tLoss: 0.093472\nTrain Epoch: 10 [29440/60000 (49%)]\tLoss: 0.133502\nTrain Epoch: 10 [30080/60000 (50%)]\tLoss: 0.207314\nTrain Epoch: 10 [30720/60000 (51%)]\tLoss: 0.095819\nTrain Epoch: 10 [31360/60000 (52%)]\tLoss: 0.165338\nTrain Epoch: 10 [32000/60000 (53%)]\tLoss: 0.172792\nTrain Epoch: 10 [32640/60000 (54%)]\tLoss: 0.200346\nTrain Epoch: 10 [33280/60000 (55%)]\tLoss: 0.188566\nTrain Epoch: 10 [33920/60000 (57%)]\tLoss: 0.063107\nTrain Epoch: 10 [34560/60000 (58%)]\tLoss: 0.208076\nTrain Epoch: 10 [35200/60000 (59%)]\tLoss: 0.336500\nTrain Epoch: 10 [35840/60000 (60%)]\tLoss: 0.098523\nTrain Epoch: 10 [36480/60000 (61%)]\tLoss: 0.239501\nTrain Epoch: 10 [37120/60000 (62%)]\tLoss: 0.108441\nTrain Epoch: 10 [37760/60000 (63%)]\tLoss: 0.161891\nTrain Epoch: 10 [38400/60000 (64%)]\tLoss: 0.232178\nTrain Epoch: 10 [39040/60000 (65%)]\tLoss: 0.281599\nTrain Epoch: 10 [39680/60000 (66%)]\tLoss: 0.202701\nTrain Epoch: 10 [40320/60000 (67%)]\tLoss: 0.313276\nTrain Epoch: 10 [40960/60000 (68%)]\tLoss: 0.149932\nTrain Epoch: 10 [41600/60000 (69%)]\tLoss: 0.078690\nTrain Epoch: 10 [42240/60000 (70%)]\tLoss: 0.068174\nTrain Epoch: 10 [42880/60000 (71%)]\tLoss: 0.114682\nTrain Epoch: 10 [43520/60000 (72%)]\tLoss: 0.278032\nTrain Epoch: 10 [44160/60000 (74%)]\tLoss: 0.207701\nTrain Epoch: 10 [44800/60000 (75%)]\tLoss: 0.149129\nTrain Epoch: 10 [45440/60000 (76%)]\tLoss: 0.209997\nTrain Epoch: 10 [46080/60000 (77%)]\tLoss: 0.181944\nTrain Epoch: 10 [46720/60000 (78%)]\tLoss: 0.071149\nTrain Epoch: 10 [47360/60000 (79%)]\tLoss: 0.088598\nTrain Epoch: 10 [48000/60000 (80%)]\tLoss: 0.196593\nTrain Epoch: 10 [48640/60000 (81%)]\tLoss: 0.195960\nTrain Epoch: 10 [49280/60000 (82%)]\tLoss: 0.227564\nTrain Epoch: 10 [49920/60000 (83%)]\tLoss: 0.051203\nTrain Epoch: 10 [50560/60000 (84%)]\tLoss: 0.105916\nTrain Epoch: 10 [51200/60000 (85%)]\tLoss: 0.176384\nTrain Epoch: 10 [51840/60000 (86%)]\tLoss: 0.054657\nTrain Epoch: 10 [52480/60000 (87%)]\tLoss: 0.107465\nTrain Epoch: 10 [53120/60000 (88%)]\tLoss: 0.072626\nTrain Epoch: 10 [53760/60000 (90%)]\tLoss: 0.187904\nTrain Epoch: 10 [54400/60000 (91%)]\tLoss: 0.104509\nTrain Epoch: 10 [55040/60000 (92%)]\tLoss: 0.174006\nTrain Epoch: 10 [55680/60000 (93%)]\tLoss: 0.122760\nTrain Epoch: 10 [56320/60000 (94%)]\tLoss: 0.150131\nTrain Epoch: 10 [56960/60000 (95%)]\tLoss: 0.076365\nTrain Epoch: 10 [57600/60000 (96%)]\tLoss: 0.127536\nTrain Epoch: 10 [58240/60000 (97%)]\tLoss: 0.233154\nTrain Epoch: 10 [58880/60000 (98%)]\tLoss: 0.113188\nTrain Epoch: 10 [59520/60000 (99%)]\tLoss: 0.282389\n\nTest set: Average loss: 0.0531, Accuracy: 9837/10000 (98%)\n\n\n\nThe experiment completed successfully. Finalizing run...\nLogging experiment finalizing status in history service\n\n\nRun is completed.",
-              "run_properties": {
-                "SendToClient": "1",
-                "arguments": "--output-dir ./outputs",
-                "created_utc": "2018-09-25T11:56:04.832205Z",
-                "distributed_processes": [],
-                "end_time_utc": "2018-09-25T12:15:57.841467Z",
-                "log_files": {
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-                  "azureml-logs/60_control_log.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/60_control_log.txt?sv=2017-04-17&sr=b&sig=i2mtPt6w5xHkEjpkyfl%2BSD1GPpIdpzIbY6sVUQ62QMo%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r",
-                  "azureml-logs/80_driver_log.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/80_driver_log.txt?sv=2017-04-17&sr=b&sig=CvqNHP18huWuXWdi%2BeiPcnztgJfI1iQQ6fV6Li25z1Y%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r",
-                  "azureml-logs/azureml.log": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/azureml.log?sv=2017-04-17&sr=b&sig=UTaxvUU4Ua%2FpsXPwQnSIV%2FbKK1zERtclIIjcTfbcSzQ%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r"
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-                "run_duration": "0:19:53",
-                "run_id": "pytorch1-mnist_1537876563990",
-                "script_name": "mnist.py",
-                "status": "Completed",
-                "tags": {}
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-              "widget_settings": {},
-              "workbench_uri": "https://mlworkspace.azure.ai/portal/subscriptions/75f78a03-482f-4fd8-8c71-5ddc08f92726/resourceGroups/onnxdemos/providers/Microsoft.MachineLearningServices/workspaces/onnx-aml-ignite-demo/experiment/pytorch1-mnist/run/pytorch1-mnist_1537876563990"
-            }
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-        },
-        "version_major": 2,
-        "version_minor": 0
-      }
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.  \n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/deployment/onnx/onnx-train-pytorch-aml-deploy-mnist.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MNIST Handwritten Digit Classification using ONNX and AzureML\n",
+    "\n",
+    "This example shows how to train a model on the MNIST data using PyTorch, save it as an ONNX model, and deploy it as a web service using Azure Machine Learning services and the ONNX Runtime.\n",
+    "\n",
+    "## What is ONNX\n",
+    "ONNX is an open format for representing machine learning and deep learning models. ONNX enables open and interoperable AI by enabling data scientists and developers to use the tools of their choice without worrying about lock-in and flexibility to deploy to a variety of platforms. ONNX is developed and supported by a community of partners including Microsoft, Facebook, and Amazon. For more information, explore the [ONNX website](http://onnx.ai).\n",
+    "\n",
+    "## MNIST Details\n",
+    "The Modified National Institute of Standards and Technology (MNIST) dataset consists of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing numbers from 0 to 9. For more information about the MNIST dataset, please visit [Yan LeCun's website](http://yann.lecun.com/exdb/mnist/). For more information about the MNIST model and how it was created can be found on the [ONNX Model Zoo github](https://github.com/onnx/models/tree/master/vision/classification/mnist). "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* Understand the [architecture and terms](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture) introduced by Azure Machine Learning\n",
+    "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) to:\n",
+    "    * install the AML SDK\n",
+    "    * create a workspace and its configuration file (`config.json`)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Resource group: ' + ws.resource_group, sep = '\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model\n",
+    "\n",
+    "### Create a remote compute target\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) to execute your training script on. In this tutorial, you create an [Azure Batch AI](https://docs.microsoft.com/azure/batch-ai/overview) cluster as your training compute resource. This code creates a cluster for you if it does not already exist in your workspace.\n",
+    "\n",
+    "**Creation of the cluster takes approximately 5 minutes.** If the cluster is already in your workspace this code will skip the cluster creation process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpu-cluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target.')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3', \n",
+    "                                                                max_nodes=6)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# Use the 'status' property to get a detailed status for the current cluster. \n",
+    "print(compute_target.status.serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code creates a GPU cluster. If you instead want to create a CPU cluster, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './pytorch-mnist'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copy the training script [`mnist.py`](mnist.py) into your project directory. Make sure the training script has the following code to create an ONNX file:\n",
+    "```python\n",
+    "dummy_input = torch.randn(1, 1, 28, 28, device=device)\n",
+    "model_path = os.path.join(output_dir, 'mnist.onnx')\n",
+    "torch.onnx.export(model, dummy_input, model_path)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "shutil.copy('mnist.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this transfer learning PyTorch tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'pytorch1-mnist'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a PyTorch estimator\n",
+    "The AML SDK's PyTorch estimator enables you to easily submit PyTorch training jobs for both single-node and distributed runs. For more information on the PyTorch estimator, refer [here](https://docs.microsoft.com/azure/machine-learning/service/how-to-train-pytorch). The following code will define a single-node PyTorch job."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Environment, ScriptRunConfig\n",
+    "\n",
+    "pytorch_env = Environment.get(ws, name='AzureML-acpt-pytorch-1.13-cuda11.7')\n",
+    "\n",
+    "estimator = ScriptRunConfig(source_directory=project_folder,\n",
+    "                            compute_target=compute_target,\n",
+    "                            script='mnist.py',\n",
+    "                            environment=pytorch_env)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `script_params` parameter is a dictionary containing the command-line arguments to your training script `entry_script`. Please note the following:\n",
+    "- We specified the output directory as `./outputs`. The `outputs` directory is specially treated by AML in that all the content in this directory gets uploaded to your workspace as part of your run history. The files written to this directory are therefore accessible even once your remote run is over. In this tutorial, we will save our trained model to this output directory.\n",
+    "\n",
+    "To leverage the Azure VM's GPU for training, we set `use_gpu=True`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your estimator object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(estimator)\n",
+    "print(run.get_details())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "run.wait_for_completion(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Download the model (optional)\n",
+    "\n",
+    "Once the run completes, you can choose to download the ONNX model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# list all the files from the run\n",
+    "run.get_file_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_path = os.path.join('outputs', 'mnist.onnx')\n",
+    "run.download_file(model_path, output_file_path=model_path)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Register the model\n",
+    "You can also register the model from your run to your workspace. The `model_path` parameter takes in the relative path on the remote VM to the model file in your `outputs` directory. You can then deploy this registered model as a web service through the AML SDK."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = run.register_model(model_name='mnist', model_path=model_path)\n",
+    "print(model.name, model.id, model.version, sep = '\\t')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Displaying your registered models (optional)\n",
+    "\n",
+    "You can optionally list out all the models that you have registered in this workspace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "models = ws.models\n",
+    "for name, m in models.items():\n",
+    "    print(\"Name:\", name,\"\\tVersion:\", m.version, \"\\tDescription:\", m.description, m.tags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploying as a web service\n",
+    "\n",
+    "### Write scoring file\n",
+    "\n",
+    "We are now going to deploy our ONNX model on Azure ML using the ONNX Runtime. We begin by writing a score.py file that will be invoked by the web service call. The `init()` function is called once when the container is started so we load the model using the ONNX Runtime into a global session object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%writefile score.py\n",
+    "import json\n",
+    "import time\n",
+    "import sys\n",
+    "import os\n",
+    "from azureml.core.model import Model\n",
+    "import numpy as np    # we're going to use numpy to process input and output data\n",
+    "import onnxruntime    # to inference ONNX models, we use the ONNX Runtime\n",
+    "\n",
+    "def init():\n",
+    "    global session\n",
+    "    # AZUREML_MODEL_DIR is an environment variable created during deployment.\n",
+    "    # It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)\n",
+    "    # For multiple models, it points to the folder containing all deployed models (./azureml-models)\n",
+    "    model = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'mnist.onnx')\n",
+    "    session = onnxruntime.InferenceSession(model)\n",
+    "\n",
+    "def preprocess(input_data_json):\n",
+    "    # convert the JSON data into the tensor input\n",
+    "    return np.array(json.loads(input_data_json)['data']).astype('float32')\n",
+    "\n",
+    "def postprocess(result):\n",
+    "    # We use argmax to pick the highest confidence label\n",
+    "    return int(np.argmax(np.array(result).squeeze(), axis=0))\n",
+    "\n",
+    "def run(input_data_json):\n",
+    "    try:\n",
+    "        start = time.time()   # start timer\n",
+    "        input_data = preprocess(input_data_json)\n",
+    "        input_name = session.get_inputs()[0].name  # get the id of the first input of the model   \n",
+    "        result = session.run([], {input_name: input_data})\n",
+    "        end = time.time()     # stop timer\n",
+    "        return {\"result\": postprocess(result),\n",
+    "                \"time\": end - start}\n",
+    "    except Exception as e:\n",
+    "        result = str(e)\n",
+    "        return {\"error\": result}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create inference configuration\n",
+    "First we create a YAML file that specifies which dependencies we would like to see in our container. Please note that you must indicate azureml-defaults with verion >= 1.0.45 as a pip dependency, because it contains the functionality needed to host the model as a web service."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.conda_dependencies import CondaDependencies \n",
+    "\n",
+    "myenv = CondaDependencies.create(pip_packages=[\"numpy\",\"onnxruntime==1.15.1\",\"azureml-core\", \"azureml-defaults\"])\n",
+    "\n",
+    "with open(\"myenv.yml\",\"w\") as f:\n",
+    "    f.write(myenv.serialize_to_string())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then we setup the inference configuration "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
+    "\n",
+    "\n",
+    "myenv = Environment.from_conda_specification(name=\"myenv\", file_path=\"myenv.yml\")\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=myenv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Deploy the model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1, \n",
+    "                                               memory_gb = 1, \n",
+    "                                               tags = {'demo': 'onnx'}, \n",
+    "                                               description = 'web service for MNIST ONNX model')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The following cell will likely take a few minutes to run as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.model import Model\n",
+    "from random import randint\n",
+    "\n",
+    "aci_service_name = 'onnx-demo-mnist'+str(randint(0,100))\n",
+    "print(\"Service\", aci_service_name)\n",
+    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
+    "aci_service.wait_for_deployment(True)\n",
+    "print(aci_service.state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In case the deployment fails, you can check the logs. Make sure to delete your aci_service before trying again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if aci_service.state != 'Healthy':\n",
+    "    # run this command for debugging.\n",
+    "    print(aci_service.get_logs())\n",
+    "    aci_service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Success!\n",
+    "\n",
+    "If you've made it this far, you've deployed a working web service that does handwritten digit classification using an ONNX model. You can get the URL for the webservice with the code below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(aci_service.scoring_uri)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you are eventually done using the web service, remember to delete it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "aci_service.delete()"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "viswamy"
+   }
+  ],
+  "category": "deployment",
+  "compute": [
+   "AML Compute"
+  ],
+  "datasets": [
+   "MNIST"
+  ],
+  "deployment": [
+   "Azure Container Instance"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "ONNX"
+  ],
+  "friendly_name": "Train MNIST in PyTorch, convert, and deploy with ONNX Runtime",
+  "index_order": 3,
+  "kernelspec": {
+   "display_name": "Python 3.8 - AzureML",
+   "language": "python",
+   "name": "python38-azureml"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "star_tag": [],
+  "tags": [
+   "ONNX Converter"
+  ],
+  "task": "Image Classification",
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {
+     "c899ddfc2b134ca9b89a4f278ac7c997": {
+      "model_module": "@jupyter-widgets/base",
+      "model_module_version": "1.1.0",
+      "model_name": "LayoutModel",
+      "state": {}
+     },
+     "d146cbdbd4e04710b3eebc15a66957ce": {
+      "model_module": "azureml_widgets",
+      "model_module_version": "1.0.0",
+      "model_name": "ShowRunDetailsModel",
+      "state": {
+       "child_runs_metrics": {},
+       "compute_target_status": {
+        "current_node_count": 1,
+        "node_state_counts": {
+         "idleNodeCount": 1,
+         "leavingNodeCount": 0,
+         "preparingNodeCount": 0,
+         "runningNodeCount": 0,
+         "unusableNodeCount": 0
+        },
+        "provisioning_errors": null,
+        "provisioning_state": "Succeeded",
+        "requested_node_count": 1,
+        "scale_settings": {
+         "autoScale": {
+          "initialNodeCount": 0,
+          "maximumNodeCount": 4,
+          "minimumNodeCount": 0
+         },
+         "manual": null
+        },
+        "vm_size": "Standard_NC6s_v3"
+       },
+       "error": "",
+       "layout": "IPY_MODEL_c899ddfc2b134ca9b89a4f278ac7c997",
+       "run_id": "pytorch1-mnist_1537876563990",
+       "run_logs": "Uploading experiment status to history service.\nAdding run profile attachment azureml-logs/60_control_log.txt\nUploading experiment status to history service.\nAdding run profile attachment azureml-logs/80_driver_log.txt\nScript process exited with code 0\nUploading driver log...\nFinalizing run...\n\nDownloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\nDownloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\nProcessing...\nDone!\nTrain Epoch: 1 [0/60000 (0%)]\tLoss: 2.365850\nTrain Epoch: 1 [640/60000 (1%)]\tLoss: 2.305295\nTrain Epoch: 1 [1280/60000 (2%)]\tLoss: 2.301407\nTrain Epoch: 1 [1920/60000 (3%)]\tLoss: 2.316538\nTrain Epoch: 1 [2560/60000 (4%)]\tLoss: 2.255810\nTrain Epoch: 1 [3200/60000 (5%)]\tLoss: 2.224511\nTrain Epoch: 1 [3840/60000 (6%)]\tLoss: 2.216569\nTrain Epoch: 1 [4480/60000 (7%)]\tLoss: 2.181396\nTrain Epoch: 1 [5120/60000 (9%)]\tLoss: 2.116898\nTrain Epoch: 1 [5760/60000 (10%)]\tLoss: 2.045963\nTrain Epoch: 1 [6400/60000 (11%)]\tLoss: 1.973494\nTrain Epoch: 1 [7040/60000 (12%)]\tLoss: 1.968609\nTrain Epoch: 1 [7680/60000 (13%)]\tLoss: 1.787280\nTrain Epoch: 1 [8320/60000 (14%)]\tLoss: 1.735044\nTrain Epoch: 1 [8960/60000 (15%)]\tLoss: 1.680426\nTrain Epoch: 1 [9600/60000 (16%)]\tLoss: 1.486279\nTrain Epoch: 1 [10240/60000 (17%)]\tLoss: 1.545747\nTrain Epoch: 1 [10880/60000 (18%)]\tLoss: 1.193543\nTrain Epoch: 1 [11520/60000 (19%)]\tLoss: 1.652350\nTrain Epoch: 1 [12160/60000 (20%)]\tLoss: 0.982182\nTrain Epoch: 1 [12800/60000 (21%)]\tLoss: 1.331902\nTrain Epoch: 1 [13440/60000 (22%)]\tLoss: 1.089598\nTrain Epoch: 1 [14080/60000 (23%)]\tLoss: 0.998703\nTrain Epoch: 1 [14720/60000 (25%)]\tLoss: 0.992036\nTrain Epoch: 1 [15360/60000 (26%)]\tLoss: 0.979473\nTrain Epoch: 1 [16000/60000 (27%)]\tLoss: 1.141276\nTrain Epoch: 1 [16640/60000 (28%)]\tLoss: 0.836921\nTrain Epoch: 1 [17280/60000 (29%)]\tLoss: 0.764657\nTrain Epoch: 1 [17920/60000 (30%)]\tLoss: 0.826818\nTrain Epoch: 1 [18560/60000 (31%)]\tLoss: 0.837834\nTrain Epoch: 1 [19200/60000 (32%)]\tLoss: 0.899033\nTrain Epoch: 1 [19840/60000 (33%)]\tLoss: 0.868245\nTrain Epoch: 1 [20480/60000 (34%)]\tLoss: 0.930491\nTrain Epoch: 1 [21120/60000 (35%)]\tLoss: 0.795202\nTrain Epoch: 1 [21760/60000 (36%)]\tLoss: 0.575117\nTrain Epoch: 1 [22400/60000 (37%)]\tLoss: 0.577884\nTrain Epoch: 1 [23040/60000 (38%)]\tLoss: 0.708801\nTrain Epoch: 1 [23680/60000 (39%)]\tLoss: 0.927512\nTrain Epoch: 1 [24320/60000 (41%)]\tLoss: 0.598836\nTrain Epoch: 1 [24960/60000 (42%)]\tLoss: 0.944021\nTrain Epoch: 1 [25600/60000 (43%)]\tLoss: 0.811654\nTrain Epoch: 1 [26240/60000 (44%)]\tLoss: 0.590322\nTrain Epoch: 1 [26880/60000 (45%)]\tLoss: 0.555104\nTrain Epoch: 1 [27520/60000 (46%)]\tLoss: 0.795565\nTrain Epoch: 1 [28160/60000 (47%)]\tLoss: 0.603378\nTrain Epoch: 1 [28800/60000 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(68%)]\tLoss: 0.514364\nTrain Epoch: 1 [41600/60000 (69%)]\tLoss: 0.400510\nTrain Epoch: 1 [42240/60000 (70%)]\tLoss: 0.526099\nTrain Epoch: 1 [42880/60000 (71%)]\tLoss: 0.387087\nTrain Epoch: 1 [43520/60000 (72%)]\tLoss: 0.730123\nTrain Epoch: 1 [44160/60000 (74%)]\tLoss: 0.678924\nTrain Epoch: 1 [44800/60000 (75%)]\tLoss: 0.425195\nTrain Epoch: 1 [45440/60000 (76%)]\tLoss: 0.656437\nTrain Epoch: 1 [46080/60000 (77%)]\tLoss: 0.348130\nTrain Epoch: 1 [46720/60000 (78%)]\tLoss: 0.487442\nTrain Epoch: 1 [47360/60000 (79%)]\tLoss: 0.649533\nTrain Epoch: 1 [48000/60000 (80%)]\tLoss: 0.541395\nTrain Epoch: 1 [48640/60000 (81%)]\tLoss: 0.464202\nTrain Epoch: 1 [49280/60000 (82%)]\tLoss: 0.750336\nTrain Epoch: 1 [49920/60000 (83%)]\tLoss: 0.548484\nTrain Epoch: 1 [50560/60000 (84%)]\tLoss: 0.421382\nTrain Epoch: 1 [51200/60000 (85%)]\tLoss: 0.680766\nTrain Epoch: 1 [51840/60000 (86%)]\tLoss: 0.483003\nTrain Epoch: 1 [52480/60000 (87%)]\tLoss: 0.610840\nTrain Epoch: 1 [53120/60000 (88%)]\tLoss: 0.483278\nTrain Epoch: 1 [53760/60000 (90%)]\tLoss: 0.553161\nTrain Epoch: 1 [54400/60000 (91%)]\tLoss: 0.465237\nTrain Epoch: 1 [55040/60000 (92%)]\tLoss: 0.558884\nTrain Epoch: 1 [55680/60000 (93%)]\tLoss: 0.528969\nTrain Epoch: 1 [56320/60000 (94%)]\tLoss: 0.370189\nTrain Epoch: 1 [56960/60000 (95%)]\tLoss: 0.379404\nTrain Epoch: 1 [57600/60000 (96%)]\tLoss: 0.263894\nTrain Epoch: 1 [58240/60000 (97%)]\tLoss: 0.432745\nTrain Epoch: 1 [58880/60000 (98%)]\tLoss: 0.455681\nTrain Epoch: 1 [59520/60000 (99%)]\tLoss: 0.483901\n/azureml-envs/azureml_de892a6d0f01a442356c3959dd42e13b/lib/python3.6/site-packages/torch/nn/functional.py:54: UserWarning: size_average and reduce args will be deprecated, please use reduction='sum' instead.\n  warnings.warn(warning.format(ret))\n\nTest set: Average loss: 0.2073, Accuracy: 9384/10000 (94%)\n\nTrain Epoch: 2 [0/60000 (0%)]\tLoss: 0.390797\nTrain Epoch: 2 [640/60000 (1%)]\tLoss: 0.214512\nTrain Epoch: 2 [1280/60000 (2%)]\tLoss: 0.226415\nTrain Epoch: 2 [1920/60000 (3%)]\tLoss: 0.491764\nTrain Epoch: 2 [2560/60000 (4%)]\tLoss: 0.333604\nTrain Epoch: 2 [3200/60000 (5%)]\tLoss: 0.514239\nTrain Epoch: 2 [3840/60000 (6%)]\tLoss: 0.430618\nTrain Epoch: 2 [4480/60000 (7%)]\tLoss: 0.579474\nTrain Epoch: 2 [5120/60000 (9%)]\tLoss: 0.259456\nTrain Epoch: 2 [5760/60000 (10%)]\tLoss: 0.651198\nTrain Epoch: 2 [6400/60000 (11%)]\tLoss: 0.338269\nTrain Epoch: 2 [7040/60000 (12%)]\tLoss: 0.335233\nTrain Epoch: 2 [7680/60000 (13%)]\tLoss: 0.518132\nTrain Epoch: 2 [8320/60000 (14%)]\tLoss: 0.363488\nTrain Epoch: 2 [8960/60000 (15%)]\tLoss: 0.437092\nTrain Epoch: 2 [9600/60000 (16%)]\tLoss: 0.362660\nTrain Epoch: 2 [10240/60000 (17%)]\tLoss: 0.432337\nTrain Epoch: 2 [10880/60000 (18%)]\tLoss: 0.360611\nTrain Epoch: 2 [11520/60000 (19%)]\tLoss: 0.305427\nTrain Epoch: 2 [12160/60000 (20%)]\tLoss: 0.347859\nTrain Epoch: 2 [12800/60000 (21%)]\tLoss: 0.408770\nTrain Epoch: 2 [13440/60000 (22%)]\tLoss: 0.469975\nTrain Epoch: 2 [14080/60000 (23%)]\tLoss: 0.673716\nTrain Epoch: 2 [14720/60000 (25%)]\tLoss: 0.388876\nTrain Epoch: 2 [15360/60000 (26%)]\tLoss: 0.462371\nTrain Epoch: 2 [16000/60000 (27%)]\tLoss: 0.530107\nTrain Epoch: 2 [16640/60000 (28%)]\tLoss: 0.448767\nTrain Epoch: 2 [17280/60000 (29%)]\tLoss: 0.412764\nTrain Epoch: 2 [17920/60000 (30%)]\tLoss: 0.301494\nTrain Epoch: 2 [18560/60000 (31%)]\tLoss: 0.465599\nTrain Epoch: 2 [19200/60000 (32%)]\tLoss: 0.434249\nTrain Epoch: 2 [19840/60000 (33%)]\tLoss: 0.324006\nTrain Epoch: 2 [20480/60000 (34%)]\tLoss: 0.447446\nTrain Epoch: 2 [21120/60000 (35%)]\tLoss: 0.291222\nTrain Epoch: 2 [21760/60000 (36%)]\tLoss: 0.557065\nTrain Epoch: 2 [22400/60000 (37%)]\tLoss: 0.552659\nTrain Epoch: 2 [23040/60000 (38%)]\tLoss: 0.378901\nTrain Epoch: 2 [23680/60000 (39%)]\tLoss: 0.360550\nTrain Epoch: 2 [24320/60000 (41%)]\tLoss: 0.283795\nTrain Epoch: 2 [24960/60000 (42%)]\tLoss: 0.475816\nTrain Epoch: 2 [25600/60000 (43%)]\tLoss: 0.283652\nTrain Epoch: 2 [26240/60000 (44%)]\tLoss: 0.276265\nTrain Epoch: 2 [26880/60000 (45%)]\tLoss: 0.527902\nTrain Epoch: 2 [27520/60000 (46%)]\tLoss: 0.437130\nTrain Epoch: 2 [28160/60000 (47%)]\tLoss: 0.277132\nTrain Epoch: 2 [28800/60000 (48%)]\tLoss: 0.471580\nTrain Epoch: 2 [29440/60000 (49%)]\tLoss: 0.380154\nTrain Epoch: 2 [30080/60000 (50%)]\tLoss: 0.232072\nTrain Epoch: 2 [30720/60000 (51%)]\tLoss: 0.366567\nTrain Epoch: 2 [31360/60000 (52%)]\tLoss: 0.469628\nTrain Epoch: 2 [32000/60000 (53%)]\tLoss: 0.440017\nTrain Epoch: 2 [32640/60000 (54%)]\tLoss: 0.421814\nTrain Epoch: 2 [33280/60000 (55%)]\tLoss: 0.367687\nTrain Epoch: 2 [33920/60000 (57%)]\tLoss: 0.448384\nTrain Epoch: 2 [34560/60000 (58%)]\tLoss: 0.550283\nTrain Epoch: 2 [35200/60000 (59%)]\tLoss: 0.609798\nTrain Epoch: 2 [35840/60000 (60%)]\tLoss: 0.461334\nTrain Epoch: 2 [36480/60000 (61%)]\tLoss: 0.443838\nTrain Epoch: 2 [37120/60000 (62%)]\tLoss: 0.306666\nTrain Epoch: 2 [37760/60000 (63%)]\tLoss: 0.432083\nTrain Epoch: 2 [38400/60000 (64%)]\tLoss: 0.277025\nTrain Epoch: 2 [39040/60000 (65%)]\tLoss: 0.298752\nTrain Epoch: 2 [39680/60000 (66%)]\tLoss: 0.427435\nTrain Epoch: 2 [40320/60000 (67%)]\tLoss: 0.374736\nTrain Epoch: 2 [40960/60000 (68%)]\tLoss: 0.246496\nTrain Epoch: 2 [41600/60000 (69%)]\tLoss: 0.662259\nTrain Epoch: 2 [42240/60000 (70%)]\tLoss: 0.497635\nTrain Epoch: 2 [42880/60000 (71%)]\tLoss: 0.237556\nTrain Epoch: 2 [43520/60000 (72%)]\tLoss: 0.194535\nTrain Epoch: 2 [44160/60000 (74%)]\tLoss: 0.258943\nTrain Epoch: 2 [44800/60000 (75%)]\tLoss: 0.437360\nTrain Epoch: 2 [45440/60000 (76%)]\tLoss: 0.355489\nTrain Epoch: 2 [46080/60000 (77%)]\tLoss: 0.335020\nTrain Epoch: 2 [46720/60000 (78%)]\tLoss: 0.565189\nTrain Epoch: 2 [47360/60000 (79%)]\tLoss: 0.430366\nTrain Epoch: 2 [48000/60000 (80%)]\tLoss: 0.266303\nTrain Epoch: 2 [48640/60000 (81%)]\tLoss: 0.172954\nTrain Epoch: 2 [49280/60000 (82%)]\tLoss: 0.245803\nTrain Epoch: 2 [49920/60000 (83%)]\tLoss: 0.426530\nTrain Epoch: 2 [50560/60000 (84%)]\tLoss: 0.468984\nTrain Epoch: 2 [51200/60000 (85%)]\tLoss: 0.370892\nTrain Epoch: 2 [51840/60000 (86%)]\tLoss: 0.300021\nTrain Epoch: 2 [52480/60000 (87%)]\tLoss: 0.392199\nTrain Epoch: 2 [53120/60000 (88%)]\tLoss: 0.510658\nTrain Epoch: 2 [53760/60000 (90%)]\tLoss: 0.376290\nTrain Epoch: 2 [54400/60000 (91%)]\tLoss: 0.273752\nTrain Epoch: 2 [55040/60000 (92%)]\tLoss: 0.234505\nTrain Epoch: 2 [55680/60000 (93%)]\tLoss: 0.610978\nTrain Epoch: 2 [56320/60000 (94%)]\tLoss: 0.154850\nTrain Epoch: 2 [56960/60000 (95%)]\tLoss: 0.374254\nTrain Epoch: 2 [57600/60000 (96%)]\tLoss: 0.292167\nTrain Epoch: 2 [58240/60000 (97%)]\tLoss: 0.478376\nTrain Epoch: 2 [58880/60000 (98%)]\tLoss: 0.303128\nTrain Epoch: 2 [59520/60000 (99%)]\tLoss: 0.376779\n\nTest set: Average loss: 0.1297, Accuracy: 9597/10000 (96%)\n\nTrain Epoch: 3 [0/60000 (0%)]\tLoss: 0.450588\nTrain Epoch: 3 [640/60000 (1%)]\tLoss: 0.361118\nTrain Epoch: 3 [1280/60000 (2%)]\tLoss: 0.374497\nTrain Epoch: 3 [1920/60000 (3%)]\tLoss: 0.312127\nTrain Epoch: 3 [2560/60000 (4%)]\tLoss: 0.353896\nTrain Epoch: 3 [3200/60000 (5%)]\tLoss: 0.320840\nTrain Epoch: 3 [3840/60000 (6%)]\tLoss: 0.218477\nTrain Epoch: 3 [4480/60000 (7%)]\tLoss: 0.295629\nTrain Epoch: 3 [5120/60000 (9%)]\tLoss: 0.339400\nTrain Epoch: 3 [5760/60000 (10%)]\tLoss: 0.170357\nTrain Epoch: 3 [6400/60000 (11%)]\tLoss: 0.416447\nTrain Epoch: 3 [7040/60000 (12%)]\tLoss: 0.320326\nTrain Epoch: 3 [7680/60000 (13%)]\tLoss: 0.318410\nTrain Epoch: 3 [8320/60000 (14%)]\tLoss: 0.384793\nTrain Epoch: 3 [8960/60000 (15%)]\tLoss: 0.343415\nTrain Epoch: 3 [9600/60000 (16%)]\tLoss: 0.284627\nTrain Epoch: 3 [10240/60000 (17%)]\tLoss: 0.151805\nTrain Epoch: 3 [10880/60000 (18%)]\tLoss: 0.401332\nTrain Epoch: 3 [11520/60000 (19%)]\tLoss: 0.253159\nTrain Epoch: 3 [12160/60000 (20%)]\tLoss: 0.339563\nTrain Epoch: 3 [12800/60000 (21%)]\tLoss: 0.237430\nTrain Epoch: 3 [13440/60000 (22%)]\tLoss: 0.311402\nTrain Epoch: 3 [14080/60000 (23%)]\tLoss: 0.241667\nTrain Epoch: 3 [14720/60000 (25%)]\tLoss: 0.265347\nTrain Epoch: 3 [15360/60000 (26%)]\tLoss: 0.367453\nTrain Epoch: 3 [16000/60000 (27%)]\tLoss: 0.190671\nTrain Epoch: 3 [16640/60000 (28%)]\tLoss: 0.313052\nTrain Epoch: 3 [17280/60000 (29%)]\tLoss: 0.368028\nTrain Epoch: 3 [17920/60000 (30%)]\tLoss: 0.268639\nTrain Epoch: 3 [18560/60000 (31%)]\tLoss: 0.341066\nTrain Epoch: 3 [19200/60000 (32%)]\tLoss: 0.457961\nTrain Epoch: 3 [19840/60000 (33%)]\tLoss: 0.732400\nTrain Epoch: 3 [20480/60000 (34%)]\tLoss: 0.330679\nTrain Epoch: 3 [21120/60000 (35%)]\tLoss: 0.279778\nTrain Epoch: 3 [21760/60000 (36%)]\tLoss: 0.305972\nTrain Epoch: 3 [22400/60000 (37%)]\tLoss: 0.402131\nTrain Epoch: 3 [23040/60000 (38%)]\tLoss: 0.345302\nTrain Epoch: 3 [23680/60000 (39%)]\tLoss: 0.251726\nTrain Epoch: 3 [24320/60000 (41%)]\tLoss: 0.152062\nTrain Epoch: 3 [24960/60000 (42%)]\tLoss: 0.149305\nTrain Epoch: 3 [25600/60000 (43%)]\tLoss: 0.364678\nTrain Epoch: 3 [26240/60000 (44%)]\tLoss: 0.067165\nTrain Epoch: 3 [26880/60000 (45%)]\tLoss: 0.229927\nTrain Epoch: 3 [27520/60000 (46%)]\tLoss: 0.236894\nTrain Epoch: 3 [28160/60000 (47%)]\tLoss: 0.486373\nTrain Epoch: 3 [28800/60000 (48%)]\tLoss: 0.453053\nTrain Epoch: 3 [29440/60000 (49%)]\tLoss: 0.283823\nTrain Epoch: 3 [30080/60000 (50%)]\tLoss: 0.185119\nTrain Epoch: 3 [30720/60000 (51%)]\tLoss: 0.381274\nTrain Epoch: 3 [31360/60000 (52%)]\tLoss: 0.394533\nTrain Epoch: 3 [32000/60000 (53%)]\tLoss: 0.392791\nTrain Epoch: 3 [32640/60000 (54%)]\tLoss: 0.230672\nTrain Epoch: 3 [33280/60000 (55%)]\tLoss: 0.393846\nTrain Epoch: 3 [33920/60000 (57%)]\tLoss: 0.676802\nTrain Epoch: 3 [34560/60000 (58%)]\tLoss: 0.160434\nTrain Epoch: 3 [35200/60000 (59%)]\tLoss: 0.211318\nTrain Epoch: 3 [35840/60000 (60%)]\tLoss: 0.245763\nTrain Epoch: 3 [36480/60000 (61%)]\tLoss: 0.198454\nTrain Epoch: 3 [37120/60000 (62%)]\tLoss: 0.243536\nTrain Epoch: 3 [37760/60000 (63%)]\tLoss: 0.151804\nTrain Epoch: 3 [38400/60000 (64%)]\tLoss: 0.176093\nTrain Epoch: 3 [39040/60000 (65%)]\tLoss: 0.237228\nTrain Epoch: 3 [39680/60000 (66%)]\tLoss: 0.146441\nTrain Epoch: 3 [40320/60000 (67%)]\tLoss: 0.345162\nTrain Epoch: 3 [40960/60000 (68%)]\tLoss: 0.400378\nTrain Epoch: 3 [41600/60000 (69%)]\tLoss: 0.259152\nTrain Epoch: 3 [42240/60000 (70%)]\tLoss: 0.569659\nTrain Epoch: 3 [42880/60000 (71%)]\tLoss: 0.166401\nTrain Epoch: 3 [43520/60000 (72%)]\tLoss: 0.220592\nTrain Epoch: 3 [44160/60000 (74%)]\tLoss: 0.303227\nTrain Epoch: 3 [44800/60000 (75%)]\tLoss: 0.193691\nTrain Epoch: 3 [45440/60000 (76%)]\tLoss: 0.257408\nTrain Epoch: 3 [46080/60000 (77%)]\tLoss: 0.391211\nTrain Epoch: 3 [46720/60000 (78%)]\tLoss: 0.419841\nTrain Epoch: 3 [47360/60000 (79%)]\tLoss: 0.121861\nTrain Epoch: 3 [48000/60000 (80%)]\tLoss: 0.176442\nTrain Epoch: 3 [48640/60000 (81%)]\tLoss: 0.534631\nTrain Epoch: 3 [49280/60000 (82%)]\tLoss: 0.296596\nTrain Epoch: 3 [49920/60000 (83%)]\tLoss: 0.190096\nTrain Epoch: 3 [50560/60000 (84%)]\tLoss: 0.360826\nTrain Epoch: 3 [51200/60000 (85%)]\tLoss: 0.427482\nTrain Epoch: 3 [51840/60000 (86%)]\tLoss: 0.251076\nTrain Epoch: 3 [52480/60000 (87%)]\tLoss: 0.319904\nTrain Epoch: 3 [53120/60000 (88%)]\tLoss: 0.228778\nTrain Epoch: 3 [53760/60000 (90%)]\tLoss: 0.180340\nTrain Epoch: 3 [54400/60000 (91%)]\tLoss: 0.236512\nTrain Epoch: 3 [55040/60000 (92%)]\tLoss: 0.206779\nTrain Epoch: 3 [55680/60000 (93%)]\tLoss: 0.323677\nTrain Epoch: 3 [56320/60000 (94%)]\tLoss: 0.406382\nTrain Epoch: 3 [56960/60000 (95%)]\tLoss: 0.426768\nTrain Epoch: 3 [57600/60000 (96%)]\tLoss: 0.595419\nTrain Epoch: 3 [58240/60000 (97%)]\tLoss: 0.175457\nTrain Epoch: 3 [58880/60000 (98%)]\tLoss: 0.301019\nTrain Epoch: 3 [59520/60000 (99%)]\tLoss: 0.419139\n\nTest set: Average loss: 0.1049, Accuracy: 9686/10000 (97%)\n\nTrain Epoch: 4 [0/60000 (0%)]\tLoss: 0.352631\nTrain Epoch: 4 [640/60000 (1%)]\tLoss: 0.343671\nTrain Epoch: 4 [1280/60000 (2%)]\tLoss: 0.170439\nTrain Epoch: 4 [1920/60000 (3%)]\tLoss: 0.289486\nTrain Epoch: 4 [2560/60000 (4%)]\tLoss: 0.096597\nTrain Epoch: 4 [3200/60000 (5%)]\tLoss: 0.263759\nTrain Epoch: 4 [3840/60000 (6%)]\tLoss: 0.369941\nTrain Epoch: 4 [4480/60000 (7%)]\tLoss: 0.326594\nTrain Epoch: 4 [5120/60000 (9%)]\tLoss: 0.174094\nTrain Epoch: 4 [5760/60000 (10%)]\tLoss: 0.442069\nTrain Epoch: 4 [6400/60000 (11%)]\tLoss: 0.179002\nTrain Epoch: 4 [7040/60000 (12%)]\tLoss: 0.292742\nTrain Epoch: 4 [7680/60000 (13%)]\tLoss: 0.209898\nTrain Epoch: 4 [8320/60000 (14%)]\tLoss: 0.401671\nTrain Epoch: 4 [8960/60000 (15%)]\tLoss: 0.205146\nTrain Epoch: 4 [9600/60000 (16%)]\tLoss: 0.250836\nTrain Epoch: 4 [10240/60000 (17%)]\tLoss: 0.156622\nTrain Epoch: 4 [10880/60000 (18%)]\tLoss: 0.214578\nTrain Epoch: 4 [11520/60000 (19%)]\tLoss: 0.155916\nTrain Epoch: 4 [12160/60000 (20%)]\tLoss: 0.416294\nTrain Epoch: 4 [12800/60000 (21%)]\tLoss: 0.197429\nTrain Epoch: 4 [13440/60000 (22%)]\tLoss: 0.154103\nTrain Epoch: 4 [14080/60000 (23%)]\tLoss: 0.377950\nTrain Epoch: 4 [14720/60000 (25%)]\tLoss: 0.338084\nTrain Epoch: 4 [15360/60000 (26%)]\tLoss: 0.242834\nTrain Epoch: 4 [16000/60000 (27%)]\tLoss: 0.139219\nTrain Epoch: 4 [16640/60000 (28%)]\tLoss: 0.242067\nTrain Epoch: 4 [17280/60000 (29%)]\tLoss: 0.189929\nTrain Epoch: 4 [17920/60000 (30%)]\tLoss: 0.358215\nTrain Epoch: 4 [18560/60000 (31%)]\tLoss: 0.354969\nTrain Epoch: 4 [19200/60000 (32%)]\tLoss: 0.303644\nTrain Epoch: 4 [19840/60000 (33%)]\tLoss: 0.322343\nTrain Epoch: 4 [20480/60000 (34%)]\tLoss: 0.225422\nTrain Epoch: 4 [21120/60000 (35%)]\tLoss: 0.614347\nTrain Epoch: 4 [21760/60000 (36%)]\tLoss: 0.448674\nTrain Epoch: 4 [22400/60000 (37%)]\tLoss: 0.362976\nTrain Epoch: 4 [23040/60000 (38%)]\tLoss: 0.100357\nTrain Epoch: 4 [23680/60000 (39%)]\tLoss: 0.289331\nTrain Epoch: 4 [24320/60000 (41%)]\tLoss: 0.405818\nTrain Epoch: 4 [24960/60000 (42%)]\tLoss: 0.212617\nTrain Epoch: 4 [25600/60000 (43%)]\tLoss: 0.348597\nTrain Epoch: 4 [26240/60000 (44%)]\tLoss: 0.351009\nTrain Epoch: 4 [26880/60000 (45%)]\tLoss: 0.341456\nTrain Epoch: 4 [27520/60000 (46%)]\tLoss: 0.297527\nTrain Epoch: 4 [28160/60000 (47%)]\tLoss: 0.281190\nTrain Epoch: 4 [28800/60000 (48%)]\tLoss: 0.187359\nTrain Epoch: 4 [29440/60000 (49%)]\tLoss: 0.178844\nTrain Epoch: 4 [30080/60000 (50%)]\tLoss: 0.201243\nTrain Epoch: 4 [30720/60000 (51%)]\tLoss: 0.305701\nTrain Epoch: 4 [31360/60000 (52%)]\tLoss: 0.370592\nTrain Epoch: 4 [32000/60000 (53%)]\tLoss: 0.241955\nTrain Epoch: 4 [32640/60000 (54%)]\tLoss: 0.278765\nTrain Epoch: 4 [33280/60000 (55%)]\tLoss: 0.284302\nTrain Epoch: 4 [33920/60000 (57%)]\tLoss: 0.337426\nTrain Epoch: 4 [34560/60000 (58%)]\tLoss: 0.277304\nTrain Epoch: 4 [35200/60000 (59%)]\tLoss: 0.221228\nTrain Epoch: 4 [35840/60000 (60%)]\tLoss: 0.150985\nTrain Epoch: 4 [36480/60000 (61%)]\tLoss: 0.312087\nTrain Epoch: 4 [37120/60000 (62%)]\tLoss: 0.170111\nTrain Epoch: 4 [37760/60000 (63%)]\tLoss: 0.291135\nTrain Epoch: 4 [38400/60000 (64%)]\tLoss: 0.160971\nTrain Epoch: 4 [39040/60000 (65%)]\tLoss: 0.390679\nTrain Epoch: 4 [39680/60000 (66%)]\tLoss: 0.434802\nTrain Epoch: 4 [40320/60000 (67%)]\tLoss: 0.281539\nTrain Epoch: 4 [40960/60000 (68%)]\tLoss: 0.172577\nTrain Epoch: 4 [41600/60000 (69%)]\tLoss: 0.348624\nTrain Epoch: 4 [42240/60000 (70%)]\tLoss: 0.380416\nTrain Epoch: 4 [42880/60000 (71%)]\tLoss: 0.483520\nTrain Epoch: 4 [43520/60000 (72%)]\tLoss: 0.216825\nTrain Epoch: 4 [44160/60000 (74%)]\tLoss: 0.320874\nTrain Epoch: 4 [44800/60000 (75%)]\tLoss: 0.213358\nTrain Epoch: 4 [45440/60000 (76%)]\tLoss: 0.218650\nTrain Epoch: 4 [46080/60000 (77%)]\tLoss: 0.221090\nTrain Epoch: 4 [46720/60000 (78%)]\tLoss: 0.325981\nTrain Epoch: 4 [47360/60000 (79%)]\tLoss: 0.283184\nTrain Epoch: 4 [48000/60000 (80%)]\tLoss: 0.072845\nTrain Epoch: 4 [48640/60000 (81%)]\tLoss: 0.206940\nTrain Epoch: 4 [49280/60000 (82%)]\tLoss: 0.423454\nTrain Epoch: 4 [49920/60000 (83%)]\tLoss: 0.475285\nTrain Epoch: 4 [50560/60000 (84%)]\tLoss: 0.128978\nTrain Epoch: 4 [51200/60000 (85%)]\tLoss: 0.195609\nTrain Epoch: 4 [51840/60000 (86%)]\tLoss: 0.125730\nTrain Epoch: 4 [52480/60000 (87%)]\tLoss: 0.137783\nTrain Epoch: 4 [53120/60000 (88%)]\tLoss: 0.375247\nTrain Epoch: 4 [53760/60000 (90%)]\tLoss: 0.243497\nTrain Epoch: 4 [54400/60000 (91%)]\tLoss: 0.236100\nTrain Epoch: 4 [55040/60000 (92%)]\tLoss: 0.266795\nTrain Epoch: 4 [55680/60000 (93%)]\tLoss: 0.229095\nTrain Epoch: 4 [56320/60000 (94%)]\tLoss: 0.167610\nTrain Epoch: 4 [56960/60000 (95%)]\tLoss: 0.240640\nTrain Epoch: 4 [57600/60000 (96%)]\tLoss: 0.153999\nTrain Epoch: 4 [58240/60000 (97%)]\tLoss: 0.753790\nTrain Epoch: 4 [58880/60000 (98%)]\tLoss: 0.143998\nTrain Epoch: 4 [59520/60000 (99%)]\tLoss: 0.310583\n\nTest set: Average loss: 0.0843, Accuracy: 9739/10000 (97%)\n\nTrain Epoch: 5 [0/60000 (0%)]\tLoss: 0.227892\nTrain Epoch: 5 [640/60000 (1%)]\tLoss: 0.162702\nTrain Epoch: 5 [1280/60000 (2%)]\tLoss: 0.227571\nTrain Epoch: 5 [1920/60000 (3%)]\tLoss: 0.148511\nTrain Epoch: 5 [2560/60000 (4%)]\tLoss: 0.187414\nTrain Epoch: 5 [3200/60000 (5%)]\tLoss: 0.194418\nTrain Epoch: 5 [3840/60000 (6%)]\tLoss: 0.276495\nTrain Epoch: 5 [4480/60000 (7%)]\tLoss: 0.268769\nTrain Epoch: 5 [5120/60000 (9%)]\tLoss: 0.163968\nTrain Epoch: 5 [5760/60000 (10%)]\tLoss: 0.349296\nTrain Epoch: 5 [6400/60000 (11%)]\tLoss: 0.217248\nTrain Epoch: 5 [7040/60000 (12%)]\tLoss: 0.195263\nTrain Epoch: 5 [7680/60000 (13%)]\tLoss: 0.339447\nTrain Epoch: 5 [8320/60000 (14%)]\tLoss: 0.224461\nTrain Epoch: 5 [8960/60000 (15%)]\tLoss: 0.095605\nTrain Epoch: 5 [9600/60000 (16%)]\tLoss: 0.196891\nTrain Epoch: 5 [10240/60000 (17%)]\tLoss: 0.218742\nTrain Epoch: 5 [10880/60000 (18%)]\tLoss: 0.071347\nTrain Epoch: 5 [11520/60000 (19%)]\tLoss: 0.403286\nTrain Epoch: 5 [12160/60000 (20%)]\tLoss: 0.149740\nTrain Epoch: 5 [12800/60000 (21%)]\tLoss: 0.160939\nTrain Epoch: 5 [13440/60000 (22%)]\tLoss: 0.236512\nTrain Epoch: 5 [14080/60000 (23%)]\tLoss: 0.348727\nTrain Epoch: 5 [14720/60000 (25%)]\tLoss: 0.190054\nTrain Epoch: 5 [15360/60000 (26%)]\tLoss: 0.272029\nTrain Epoch: 5 [16000/60000 (27%)]\tLoss: 0.427739\nTrain Epoch: 5 [16640/60000 (28%)]\tLoss: 0.322332\nTrain Epoch: 5 [17280/60000 (29%)]\tLoss: 0.141410\nTrain Epoch: 5 [17920/60000 (30%)]\tLoss: 0.098900\nTrain Epoch: 5 [18560/60000 (31%)]\tLoss: 0.252387\nTrain Epoch: 5 [19200/60000 (32%)]\tLoss: 0.182150\nTrain Epoch: 5 [19840/60000 (33%)]\tLoss: 0.133239\nTrain Epoch: 5 [20480/60000 (34%)]\tLoss: 0.126683\nTrain Epoch: 5 [21120/60000 (35%)]\tLoss: 0.370189\nTrain Epoch: 5 [21760/60000 (36%)]\tLoss: 0.162514\nTrain Epoch: 5 [22400/60000 (37%)]\tLoss: 0.272352\nTrain Epoch: 5 [23040/60000 (38%)]\tLoss: 0.298543\nTrain Epoch: 5 [23680/60000 (39%)]\tLoss: 0.235891\nTrain Epoch: 5 [24320/60000 (41%)]\tLoss: 0.187710\nTrain Epoch: 5 [24960/60000 (42%)]\tLoss: 0.185363\nTrain Epoch: 5 [25600/60000 (43%)]\tLoss: 0.193369\nTrain Epoch: 5 [26240/60000 (44%)]\tLoss: 0.155984\nTrain Epoch: 5 [26880/60000 (45%)]\tLoss: 0.388923\nTrain Epoch: 5 [27520/60000 (46%)]\tLoss: 0.192868\nTrain Epoch: 5 [28160/60000 (47%)]\tLoss: 0.535787\nTrain Epoch: 5 [28800/60000 (48%)]\tLoss: 0.161020\nTrain Epoch: 5 [29440/60000 (49%)]\tLoss: 0.242179\nTrain Epoch: 5 [30080/60000 (50%)]\tLoss: 0.136554\nTrain Epoch: 5 [30720/60000 (51%)]\tLoss: 0.190672\nTrain Epoch: 5 [31360/60000 (52%)]\tLoss: 0.118027\nTrain Epoch: 5 [32000/60000 (53%)]\tLoss: 0.278750\nTrain Epoch: 5 [32640/60000 (54%)]\tLoss: 0.418058\nTrain Epoch: 5 [33280/60000 (55%)]\tLoss: 0.287063\nTrain Epoch: 5 [33920/60000 (57%)]\tLoss: 0.279596\nTrain Epoch: 5 [34560/60000 (58%)]\tLoss: 0.181579\nTrain Epoch: 5 [35200/60000 (59%)]\tLoss: 0.443592\nTrain Epoch: 5 [35840/60000 (60%)]\tLoss: 0.095470\nTrain Epoch: 5 [36480/60000 (61%)]\tLoss: 0.277385\nTrain Epoch: 5 [37120/60000 (62%)]\tLoss: 0.263358\nTrain Epoch: 5 [37760/60000 (63%)]\tLoss: 0.190867\nTrain Epoch: 5 [38400/60000 (64%)]\tLoss: 0.176580\nTrain Epoch: 5 [39040/60000 (65%)]\tLoss: 0.360235\nTrain Epoch: 5 [39680/60000 (66%)]\tLoss: 0.172416\nTrain Epoch: 5 [40320/60000 (67%)]\tLoss: 0.174126\nTrain Epoch: 5 [40960/60000 (68%)]\tLoss: 0.202162\nTrain Epoch: 5 [41600/60000 (69%)]\tLoss: 0.196991\nTrain Epoch: 5 [42240/60000 (70%)]\tLoss: 0.224622\nTrain Epoch: 5 [42880/60000 (71%)]\tLoss: 0.180406\nTrain Epoch: 5 [43520/60000 (72%)]\tLoss: 0.060447\nTrain Epoch: 5 [44160/60000 (74%)]\tLoss: 0.322497\nTrain Epoch: 5 [44800/60000 (75%)]\tLoss: 0.239324\nTrain Epoch: 5 [45440/60000 (76%)]\tLoss: 0.348920\nTrain Epoch: 5 [46080/60000 (77%)]\tLoss: 0.240017\nTrain Epoch: 5 [46720/60000 (78%)]\tLoss: 0.237575\nTrain Epoch: 5 [47360/60000 (79%)]\tLoss: 0.142648\nTrain Epoch: 5 [48000/60000 (80%)]\tLoss: 0.227562\nTrain Epoch: 5 [48640/60000 (81%)]\tLoss: 0.254358\nTrain Epoch: 5 [49280/60000 (82%)]\tLoss: 0.135818\nTrain Epoch: 5 [49920/60000 (83%)]\tLoss: 0.386120\nTrain Epoch: 5 [50560/60000 (84%)]\tLoss: 0.328150\nTrain Epoch: 5 [51200/60000 (85%)]\tLoss: 0.276833\nTrain Epoch: 5 [51840/60000 (86%)]\tLoss: 0.308869\nTrain Epoch: 5 [52480/60000 (87%)]\tLoss: 0.246442\nTrain Epoch: 5 [53120/60000 (88%)]\tLoss: 0.240874\nTrain Epoch: 5 [53760/60000 (90%)]\tLoss: 0.114337\nTrain Epoch: 5 [54400/60000 (91%)]\tLoss: 0.217325\nTrain Epoch: 5 [55040/60000 (92%)]\tLoss: 0.223010\nTrain Epoch: 5 [55680/60000 (93%)]\tLoss: 0.138459\nTrain Epoch: 5 [56320/60000 (94%)]\tLoss: 0.283678\nTrain Epoch: 5 [56960/60000 (95%)]\tLoss: 0.158834\nTrain Epoch: 5 [57600/60000 (96%)]\tLoss: 0.164267\nTrain Epoch: 5 [58240/60000 (97%)]\tLoss: 0.290795\nTrain Epoch: 5 [58880/60000 (98%)]\tLoss: 0.451639\nTrain Epoch: 5 [59520/60000 (99%)]\tLoss: 0.349018\n\nTest set: Average loss: 0.0797, Accuracy: 9758/10000 (98%)\n\nTrain Epoch: 6 [0/60000 (0%)]\tLoss: 0.311334\nTrain Epoch: 6 [640/60000 (1%)]\tLoss: 0.129143\nTrain Epoch: 6 [1280/60000 (2%)]\tLoss: 0.227222\nTrain Epoch: 6 [1920/60000 (3%)]\tLoss: 0.157591\nTrain Epoch: 6 [2560/60000 (4%)]\tLoss: 0.205490\nTrain Epoch: 6 [3200/60000 (5%)]\tLoss: 0.421089\nTrain Epoch: 6 [3840/60000 (6%)]\tLoss: 0.157544\nTrain Epoch: 6 [4480/60000 (7%)]\tLoss: 0.087023\nTrain Epoch: 6 [5120/60000 (9%)]\tLoss: 0.130669\nTrain Epoch: 6 [5760/60000 (10%)]\tLoss: 0.059450\nTrain Epoch: 6 [6400/60000 (11%)]\tLoss: 0.121786\nTrain Epoch: 6 [7040/60000 (12%)]\tLoss: 0.177859\nTrain Epoch: 6 [7680/60000 (13%)]\tLoss: 0.217464\nTrain Epoch: 6 [8320/60000 (14%)]\tLoss: 0.183426\nTrain Epoch: 6 [8960/60000 (15%)]\tLoss: 0.237282\nTrain Epoch: 6 [9600/60000 (16%)]\tLoss: 0.210031\nTrain Epoch: 6 [10240/60000 (17%)]\tLoss: 0.256110\nTrain Epoch: 6 [10880/60000 (18%)]\tLoss: 0.155481\nTrain Epoch: 6 [11520/60000 (19%)]\tLoss: 0.166967\nTrain Epoch: 6 [12160/60000 (20%)]\tLoss: 0.144590\nTrain Epoch: 6 [12800/60000 (21%)]\tLoss: 0.229593\nTrain Epoch: 6 [13440/60000 (22%)]\tLoss: 0.092102\nTrain Epoch: 6 [14080/60000 (23%)]\tLoss: 0.144247\nTrain Epoch: 6 [14720/60000 (25%)]\tLoss: 0.459083\nTrain Epoch: 6 [15360/60000 (26%)]\tLoss: 0.174974\nTrain Epoch: 6 [16000/60000 (27%)]\tLoss: 0.146433\nTrain Epoch: 6 [16640/60000 (28%)]\tLoss: 0.291392\nTrain Epoch: 6 [17280/60000 (29%)]\tLoss: 0.203127\nTrain Epoch: 6 [17920/60000 (30%)]\tLoss: 0.255063\nTrain Epoch: 6 [18560/60000 (31%)]\tLoss: 0.167576\nTrain Epoch: 6 [19200/60000 (32%)]\tLoss: 0.171914\nTrain Epoch: 6 [19840/60000 (33%)]\tLoss: 0.215950\nTrain Epoch: 6 [20480/60000 (34%)]\tLoss: 0.246624\nTrain Epoch: 6 [21120/60000 (35%)]\tLoss: 0.242730\nTrain Epoch: 6 [21760/60000 (36%)]\tLoss: 0.345666\nTrain Epoch: 6 [22400/60000 (37%)]\tLoss: 0.229078\nTrain Epoch: 6 [23040/60000 (38%)]\tLoss: 0.283169\nTrain Epoch: 6 [23680/60000 (39%)]\tLoss: 0.246430\nTrain Epoch: 6 [24320/60000 (41%)]\tLoss: 0.217211\nTrain Epoch: 6 [24960/60000 (42%)]\tLoss: 0.168141\nTrain Epoch: 6 [25600/60000 (43%)]\tLoss: 0.297715\nTrain Epoch: 6 [26240/60000 (44%)]\tLoss: 0.200130\nTrain Epoch: 6 [26880/60000 (45%)]\tLoss: 0.344390\nTrain Epoch: 6 [27520/60000 (46%)]\tLoss: 0.246202\nTrain Epoch: 6 [28160/60000 (47%)]\tLoss: 0.272422\nTrain Epoch: 6 [28800/60000 (48%)]\tLoss: 0.117001\nTrain Epoch: 6 [29440/60000 (49%)]\tLoss: 0.246031\nTrain Epoch: 6 [30080/60000 (50%)]\tLoss: 0.138119\nTrain Epoch: 6 [30720/60000 (51%)]\tLoss: 0.214345\nTrain Epoch: 6 [31360/60000 (52%)]\tLoss: 0.134483\nTrain Epoch: 6 [32000/60000 (53%)]\tLoss: 0.201771\nTrain Epoch: 6 [32640/60000 (54%)]\tLoss: 0.201668\nTrain Epoch: 6 [33280/60000 (55%)]\tLoss: 0.111183\nTrain Epoch: 6 [33920/60000 (57%)]\tLoss: 0.093289\nTrain Epoch: 6 [34560/60000 (58%)]\tLoss: 0.171475\nTrain Epoch: 6 [35200/60000 (59%)]\tLoss: 0.178729\nTrain Epoch: 6 [35840/60000 (60%)]\tLoss: 0.144986\nTrain Epoch: 6 [36480/60000 (61%)]\tLoss: 0.302206\nTrain Epoch: 6 [37120/60000 (62%)]\tLoss: 0.389723\nTrain Epoch: 6 [37760/60000 (63%)]\tLoss: 0.268302\nTrain Epoch: 6 [38400/60000 (64%)]\tLoss: 0.358240\nTrain Epoch: 6 [39040/60000 (65%)]\tLoss: 0.241359\nTrain Epoch: 6 [39680/60000 (66%)]\tLoss: 0.282464\nTrain Epoch: 6 [40320/60000 (67%)]\tLoss: 0.205064\nTrain Epoch: 6 [40960/60000 (68%)]\tLoss: 0.106739\nTrain Epoch: 6 [41600/60000 (69%)]\tLoss: 0.076333\nTrain Epoch: 6 [42240/60000 (70%)]\tLoss: 0.157558\nTrain Epoch: 6 [42880/60000 (71%)]\tLoss: 0.217494\nTrain Epoch: 6 [43520/60000 (72%)]\tLoss: 0.183687\nTrain Epoch: 6 [44160/60000 (74%)]\tLoss: 0.217155\nTrain Epoch: 6 [44800/60000 (75%)]\tLoss: 0.108482\nTrain Epoch: 6 [45440/60000 (76%)]\tLoss: 0.324247\nTrain Epoch: 6 [46080/60000 (77%)]\tLoss: 0.352494\nTrain Epoch: 6 [46720/60000 (78%)]\tLoss: 0.163462\nTrain Epoch: 6 [47360/60000 (79%)]\tLoss: 0.154820\nTrain Epoch: 6 [48000/60000 (80%)]\tLoss: 0.174164\nTrain Epoch: 6 [48640/60000 (81%)]\tLoss: 0.196258\nTrain Epoch: 6 [49280/60000 (82%)]\tLoss: 0.226030\nTrain Epoch: 6 [49920/60000 (83%)]\tLoss: 0.306971\nTrain Epoch: 6 [50560/60000 (84%)]\tLoss: 0.387282\nTrain Epoch: 6 [51200/60000 (85%)]\tLoss: 0.213550\nTrain Epoch: 6 [51840/60000 (86%)]\tLoss: 0.133755\nTrain Epoch: 6 [52480/60000 (87%)]\tLoss: 0.176044\nTrain Epoch: 6 [53120/60000 (88%)]\tLoss: 0.282900\nTrain Epoch: 6 [53760/60000 (90%)]\tLoss: 0.154157\nTrain Epoch: 6 [54400/60000 (91%)]\tLoss: 0.138895\nTrain Epoch: 6 [55040/60000 (92%)]\tLoss: 0.254137\nTrain Epoch: 6 [55680/60000 (93%)]\tLoss: 0.107765\nTrain Epoch: 6 [56320/60000 (94%)]\tLoss: 0.118788\nTrain Epoch: 6 [56960/60000 (95%)]\tLoss: 0.142051\nTrain Epoch: 6 [57600/60000 (96%)]\tLoss: 0.176375\nTrain Epoch: 6 [58240/60000 (97%)]\tLoss: 0.131573\nTrain Epoch: 6 [58880/60000 (98%)]\tLoss: 0.347166\nTrain Epoch: 6 [59520/60000 (99%)]\tLoss: 0.217951\n\nTest set: Average loss: 0.0690, Accuracy: 9776/10000 (98%)\n\nTrain Epoch: 7 [0/60000 (0%)]\tLoss: 0.142441\nTrain Epoch: 7 [640/60000 (1%)]\tLoss: 0.078599\nTrain Epoch: 7 [1280/60000 (2%)]\tLoss: 0.121731\nTrain Epoch: 7 [1920/60000 (3%)]\tLoss: 0.070044\nTrain Epoch: 7 [2560/60000 (4%)]\tLoss: 0.224216\nTrain Epoch: 7 [3200/60000 (5%)]\tLoss: 0.104122\nTrain Epoch: 7 [3840/60000 (6%)]\tLoss: 0.228575\nTrain Epoch: 7 [4480/60000 (7%)]\tLoss: 0.377044\nTrain Epoch: 7 [5120/60000 (9%)]\tLoss: 0.296184\nTrain Epoch: 7 [5760/60000 (10%)]\tLoss: 0.099891\nTrain Epoch: 7 [6400/60000 (11%)]\tLoss: 0.269691\nTrain Epoch: 7 [7040/60000 (12%)]\tLoss: 0.240640\nTrain Epoch: 7 [7680/60000 (13%)]\tLoss: 0.171192\nTrain Epoch: 7 [8320/60000 (14%)]\tLoss: 0.306889\nTrain Epoch: 7 [8960/60000 (15%)]\tLoss: 0.238503\nTrain Epoch: 7 [9600/60000 (16%)]\tLoss: 0.286252\nTrain Epoch: 7 [10240/60000 (17%)]\tLoss: 0.171058\nTrain Epoch: 7 [10880/60000 (18%)]\tLoss: 0.208866\nTrain Epoch: 7 [11520/60000 (19%)]\tLoss: 0.418091\nTrain Epoch: 7 [12160/60000 (20%)]\tLoss: 0.115058\nTrain Epoch: 7 [12800/60000 (21%)]\tLoss: 0.159557\nTrain Epoch: 7 [13440/60000 (22%)]\tLoss: 0.085076\nTrain Epoch: 7 [14080/60000 (23%)]\tLoss: 0.244673\nTrain Epoch: 7 [14720/60000 (25%)]\tLoss: 0.316326\nTrain Epoch: 7 [15360/60000 (26%)]\tLoss: 0.370775\nTrain Epoch: 7 [16000/60000 (27%)]\tLoss: 0.235262\nTrain Epoch: 7 [16640/60000 (28%)]\tLoss: 0.296188\nTrain Epoch: 7 [17280/60000 (29%)]\tLoss: 0.224960\nTrain Epoch: 7 [17920/60000 (30%)]\tLoss: 0.162341\nTrain Epoch: 7 [18560/60000 (31%)]\tLoss: 0.136551\nTrain Epoch: 7 [19200/60000 (32%)]\tLoss: 0.111435\nTrain Epoch: 7 [19840/60000 (33%)]\tLoss: 0.173483\nTrain Epoch: 7 [20480/60000 (34%)]\tLoss: 0.170351\nTrain Epoch: 7 [21120/60000 (35%)]\tLoss: 0.109828\nTrain Epoch: 7 [21760/60000 (36%)]\tLoss: 0.219692\nTrain Epoch: 7 [22400/60000 (37%)]\tLoss: 0.085780\nTrain Epoch: 7 [23040/60000 (38%)]\tLoss: 0.076800\nTrain Epoch: 7 [23680/60000 (39%)]\tLoss: 0.163377\nTrain Epoch: 7 [24320/60000 (41%)]\tLoss: 0.178391\nTrain Epoch: 7 [24960/60000 (42%)]\tLoss: 0.311988\nTrain Epoch: 7 [25600/60000 (43%)]\tLoss: 0.215559\nTrain Epoch: 7 [26240/60000 (44%)]\tLoss: 0.199207\nTrain Epoch: 7 [26880/60000 (45%)]\tLoss: 0.201917\nTrain Epoch: 7 [27520/60000 (46%)]\tLoss: 0.163283\nTrain Epoch: 7 [28160/60000 (47%)]\tLoss: 0.107533\nTrain Epoch: 7 [28800/60000 (48%)]\tLoss: 0.046209\nTrain Epoch: 7 [29440/60000 (49%)]\tLoss: 0.173062\nTrain Epoch: 7 [30080/60000 (50%)]\tLoss: 0.088925\nTrain Epoch: 7 [30720/60000 (51%)]\tLoss: 0.068962\nTrain Epoch: 7 [31360/60000 (52%)]\tLoss: 0.223214\nTrain Epoch: 7 [32000/60000 (53%)]\tLoss: 0.096083\nTrain Epoch: 7 [32640/60000 (54%)]\tLoss: 0.327635\nTrain Epoch: 7 [33280/60000 (55%)]\tLoss: 0.278620\nTrain Epoch: 7 [33920/60000 (57%)]\tLoss: 0.223806\nTrain Epoch: 7 [34560/60000 (58%)]\tLoss: 0.121638\nTrain Epoch: 7 [35200/60000 (59%)]\tLoss: 0.182739\nTrain Epoch: 7 [35840/60000 (60%)]\tLoss: 0.172866\nTrain Epoch: 7 [36480/60000 (61%)]\tLoss: 0.180873\nTrain Epoch: 7 [37120/60000 (62%)]\tLoss: 0.298984\nTrain Epoch: 7 [37760/60000 (63%)]\tLoss: 0.251939\nTrain Epoch: 7 [38400/60000 (64%)]\tLoss: 0.105321\nTrain Epoch: 7 [39040/60000 (65%)]\tLoss: 0.200500\nTrain Epoch: 7 [39680/60000 (66%)]\tLoss: 0.309791\nTrain Epoch: 7 [40320/60000 (67%)]\tLoss: 0.114949\nTrain Epoch: 7 [40960/60000 (68%)]\tLoss: 0.066153\nTrain Epoch: 7 [41600/60000 (69%)]\tLoss: 0.327437\nTrain Epoch: 7 [42240/60000 (70%)]\tLoss: 0.179023\nTrain Epoch: 7 [42880/60000 (71%)]\tLoss: 0.089861\nTrain Epoch: 7 [43520/60000 (72%)]\tLoss: 0.111230\nTrain Epoch: 7 [44160/60000 (74%)]\tLoss: 0.108233\nTrain Epoch: 7 [44800/60000 (75%)]\tLoss: 0.145669\nTrain Epoch: 7 [45440/60000 (76%)]\tLoss: 0.122024\nTrain Epoch: 7 [46080/60000 (77%)]\tLoss: 0.083490\nTrain Epoch: 7 [46720/60000 (78%)]\tLoss: 0.116002\nTrain Epoch: 7 [47360/60000 (79%)]\tLoss: 0.200240\nTrain Epoch: 7 [48000/60000 (80%)]\tLoss: 0.363707\nTrain Epoch: 7 [48640/60000 (81%)]\tLoss: 0.294594\nTrain Epoch: 7 [49280/60000 (82%)]\tLoss: 0.127643\nTrain Epoch: 7 [49920/60000 (83%)]\tLoss: 0.202008\nTrain Epoch: 7 [50560/60000 (84%)]\tLoss: 0.159551\nTrain Epoch: 7 [51200/60000 (85%)]\tLoss: 0.221197\nTrain Epoch: 7 [51840/60000 (86%)]\tLoss: 0.266463\nTrain Epoch: 7 [52480/60000 (87%)]\tLoss: 0.073967\nTrain Epoch: 7 [53120/60000 (88%)]\tLoss: 0.350092\nTrain Epoch: 7 [53760/60000 (90%)]\tLoss: 0.106500\nTrain Epoch: 7 [54400/60000 (91%)]\tLoss: 0.208859\nTrain Epoch: 7 [55040/60000 (92%)]\tLoss: 0.209937\nTrain Epoch: 7 [55680/60000 (93%)]\tLoss: 0.215286\nTrain Epoch: 7 [56320/60000 (94%)]\tLoss: 0.117026\nTrain Epoch: 7 [56960/60000 (95%)]\tLoss: 0.132321\nTrain Epoch: 7 [57600/60000 (96%)]\tLoss: 0.286004\nTrain Epoch: 7 [58240/60000 (97%)]\tLoss: 0.170485\nTrain Epoch: 7 [58880/60000 (98%)]\tLoss: 0.196613\nTrain Epoch: 7 [59520/60000 (99%)]\tLoss: 0.293870\n\nTest set: Average loss: 0.0657, Accuracy: 9801/10000 (98%)\n\nTrain Epoch: 8 [0/60000 (0%)]\tLoss: 0.315451\nTrain Epoch: 8 [640/60000 (1%)]\tLoss: 0.114413\nTrain Epoch: 8 [1280/60000 (2%)]\tLoss: 0.129036\nTrain Epoch: 8 [1920/60000 (3%)]\tLoss: 0.141999\nTrain Epoch: 8 [2560/60000 (4%)]\tLoss: 0.118697\nTrain Epoch: 8 [3200/60000 (5%)]\tLoss: 0.126823\nTrain Epoch: 8 [3840/60000 (6%)]\tLoss: 0.053924\nTrain Epoch: 8 [4480/60000 (7%)]\tLoss: 0.296224\nTrain Epoch: 8 [5120/60000 (9%)]\tLoss: 0.121338\nTrain Epoch: 8 [5760/60000 (10%)]\tLoss: 0.255161\nTrain Epoch: 8 [6400/60000 (11%)]\tLoss: 0.170684\nTrain Epoch: 8 [7040/60000 (12%)]\tLoss: 0.092008\nTrain Epoch: 8 [7680/60000 (13%)]\tLoss: 0.283091\nTrain Epoch: 8 [8320/60000 (14%)]\tLoss: 0.027133\nTrain Epoch: 8 [8960/60000 (15%)]\tLoss: 0.195686\nTrain Epoch: 8 [9600/60000 (16%)]\tLoss: 0.343612\nTrain Epoch: 8 [10240/60000 (17%)]\tLoss: 0.108563\nTrain Epoch: 8 [10880/60000 (18%)]\tLoss: 0.223832\nTrain Epoch: 8 [11520/60000 (19%)]\tLoss: 0.175617\nTrain Epoch: 8 [12160/60000 (20%)]\tLoss: 0.145828\nTrain Epoch: 8 [12800/60000 (21%)]\tLoss: 0.178722\nTrain Epoch: 8 [13440/60000 (22%)]\tLoss: 0.151158\nTrain Epoch: 8 [14080/60000 (23%)]\tLoss: 0.183155\nTrain Epoch: 8 [14720/60000 (25%)]\tLoss: 0.110281\nTrain Epoch: 8 [15360/60000 (26%)]\tLoss: 0.282224\nTrain Epoch: 8 [16000/60000 (27%)]\tLoss: 0.097411\nTrain Epoch: 8 [16640/60000 (28%)]\tLoss: 0.264533\nTrain Epoch: 8 [17280/60000 (29%)]\tLoss: 0.194778\nTrain Epoch: 8 [17920/60000 (30%)]\tLoss: 0.235924\nTrain Epoch: 8 [18560/60000 (31%)]\tLoss: 0.236801\nTrain Epoch: 8 [19200/60000 (32%)]\tLoss: 0.178174\nTrain Epoch: 8 [19840/60000 (33%)]\tLoss: 0.218752\nTrain Epoch: 8 [20480/60000 (34%)]\tLoss: 0.208353\nTrain Epoch: 8 [21120/60000 (35%)]\tLoss: 0.193034\nTrain Epoch: 8 [21760/60000 (36%)]\tLoss: 0.138453\nTrain Epoch: 8 [22400/60000 (37%)]\tLoss: 0.175271\nTrain Epoch: 8 [23040/60000 (38%)]\tLoss: 0.157295\nTrain Epoch: 8 [23680/60000 (39%)]\tLoss: 0.156248\nTrain Epoch: 8 [24320/60000 (41%)]\tLoss: 0.153413\nTrain Epoch: 8 [24960/60000 (42%)]\tLoss: 0.084870\nTrain Epoch: 8 [25600/60000 (43%)]\tLoss: 0.150966\nTrain Epoch: 8 [26240/60000 (44%)]\tLoss: 0.160973\nTrain Epoch: 8 [26880/60000 (45%)]\tLoss: 0.231433\nTrain Epoch: 8 [27520/60000 (46%)]\tLoss: 0.144396\nTrain Epoch: 8 [28160/60000 (47%)]\tLoss: 0.200417\nTrain Epoch: 8 [28800/60000 (48%)]\tLoss: 0.152939\nTrain Epoch: 8 [29440/60000 (49%)]\tLoss: 0.109962\nTrain Epoch: 8 [30080/60000 (50%)]\tLoss: 0.134907\nTrain Epoch: 8 [30720/60000 (51%)]\tLoss: 0.088782\nTrain Epoch: 8 [31360/60000 (52%)]\tLoss: 0.129031\nTrain Epoch: 8 [32000/60000 (53%)]\tLoss: 0.184744\nTrain Epoch: 8 [32640/60000 (54%)]\tLoss: 0.155463\nTrain Epoch: 8 [33280/60000 (55%)]\tLoss: 0.174192\nTrain Epoch: 8 [33920/60000 (57%)]\tLoss: 0.172103\nTrain Epoch: 8 [34560/60000 (58%)]\tLoss: 0.201503\nTrain Epoch: 8 [35200/60000 (59%)]\tLoss: 0.287885\nTrain Epoch: 8 [35840/60000 (60%)]\tLoss: 0.133675\nTrain Epoch: 8 [36480/60000 (61%)]\tLoss: 0.243534\nTrain Epoch: 8 [37120/60000 (62%)]\tLoss: 0.196020\nTrain Epoch: 8 [37760/60000 (63%)]\tLoss: 0.101380\nTrain Epoch: 8 [38400/60000 (64%)]\tLoss: 0.108299\nTrain Epoch: 8 [39040/60000 (65%)]\tLoss: 0.159048\nTrain Epoch: 8 [39680/60000 (66%)]\tLoss: 0.204734\nTrain Epoch: 8 [40320/60000 (67%)]\tLoss: 0.238383\nTrain Epoch: 8 [40960/60000 (68%)]\tLoss: 0.592663\nTrain Epoch: 8 [41600/60000 (69%)]\tLoss: 0.116080\nTrain Epoch: 8 [42240/60000 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(91%)]\tLoss: 0.241959\nTrain Epoch: 8 [55040/60000 (92%)]\tLoss: 0.183966\nTrain Epoch: 8 [55680/60000 (93%)]\tLoss: 0.125279\nTrain Epoch: 8 [56320/60000 (94%)]\tLoss: 0.152849\nTrain Epoch: 8 [56960/60000 (95%)]\tLoss: 0.219788\nTrain Epoch: 8 [57600/60000 (96%)]\tLoss: 0.077843\nTrain Epoch: 8 [58240/60000 (97%)]\tLoss: 0.304191\nTrain Epoch: 8 [58880/60000 (98%)]\tLoss: 0.363550\nTrain Epoch: 8 [59520/60000 (99%)]\tLoss: 0.326421\n\nTest set: Average loss: 0.0632, Accuracy: 9807/10000 (98%)\n\nTrain Epoch: 9 [0/60000 (0%)]\tLoss: 0.140965\nTrain Epoch: 9 [640/60000 (1%)]\tLoss: 0.206063\nTrain Epoch: 9 [1280/60000 (2%)]\tLoss: 0.189364\nTrain Epoch: 9 [1920/60000 (3%)]\tLoss: 0.367962\nTrain Epoch: 9 [2560/60000 (4%)]\tLoss: 0.108362\nTrain Epoch: 9 [3200/60000 (5%)]\tLoss: 0.109142\nTrain Epoch: 9 [3840/60000 (6%)]\tLoss: 0.270022\nTrain Epoch: 9 [4480/60000 (7%)]\tLoss: 0.200647\nTrain Epoch: 9 [5120/60000 (9%)]\tLoss: 0.162118\nTrain Epoch: 9 [5760/60000 (10%)]\tLoss: 0.167245\nTrain Epoch: 9 [6400/60000 (11%)]\tLoss: 0.188903\nTrain Epoch: 9 [7040/60000 (12%)]\tLoss: 0.280550\nTrain Epoch: 9 [7680/60000 (13%)]\tLoss: 0.116265\nTrain Epoch: 9 [8320/60000 (14%)]\tLoss: 0.602693\nTrain Epoch: 9 [8960/60000 (15%)]\tLoss: 0.148682\nTrain Epoch: 9 [9600/60000 (16%)]\tLoss: 0.225477\nTrain Epoch: 9 [10240/60000 (17%)]\tLoss: 0.133642\nTrain Epoch: 9 [10880/60000 (18%)]\tLoss: 0.116083\nTrain Epoch: 9 [11520/60000 (19%)]\tLoss: 0.348113\nTrain Epoch: 9 [12160/60000 (20%)]\tLoss: 0.219562\nTrain Epoch: 9 [12800/60000 (21%)]\tLoss: 0.117716\nTrain Epoch: 9 [13440/60000 (22%)]\tLoss: 0.218508\nTrain Epoch: 9 [14080/60000 (23%)]\tLoss: 0.323755\nTrain Epoch: 9 [14720/60000 (25%)]\tLoss: 0.211174\nTrain Epoch: 9 [15360/60000 (26%)]\tLoss: 0.451853\nTrain Epoch: 9 [16000/60000 (27%)]\tLoss: 0.155174\nTrain Epoch: 9 [16640/60000 (28%)]\tLoss: 0.134905\nTrain Epoch: 9 [17280/60000 (29%)]\tLoss: 0.172428\nTrain Epoch: 9 [17920/60000 (30%)]\tLoss: 0.306172\nTrain Epoch: 9 [18560/60000 (31%)]\tLoss: 0.133085\nTrain Epoch: 9 [19200/60000 (32%)]\tLoss: 0.449040\nTrain Epoch: 9 [19840/60000 (33%)]\tLoss: 0.084722\nTrain Epoch: 9 [20480/60000 (34%)]\tLoss: 0.188086\nTrain Epoch: 9 [21120/60000 (35%)]\tLoss: 0.222472\nTrain Epoch: 9 [21760/60000 (36%)]\tLoss: 0.275132\nTrain Epoch: 9 [22400/60000 (37%)]\tLoss: 0.287421\nTrain Epoch: 9 [23040/60000 (38%)]\tLoss: 0.105733\nTrain Epoch: 9 [23680/60000 (39%)]\tLoss: 0.157949\nTrain Epoch: 9 [24320/60000 (41%)]\tLoss: 0.073462\nTrain Epoch: 9 [24960/60000 (42%)]\tLoss: 0.240201\nTrain Epoch: 9 [25600/60000 (43%)]\tLoss: 0.060848\nTrain Epoch: 9 [26240/60000 (44%)]\tLoss: 0.173801\nTrain Epoch: 9 [26880/60000 (45%)]\tLoss: 0.148143\nTrain Epoch: 9 [27520/60000 (46%)]\tLoss: 0.180779\nTrain Epoch: 9 [28160/60000 (47%)]\tLoss: 0.393192\nTrain Epoch: 9 [28800/60000 (48%)]\tLoss: 0.239243\nTrain Epoch: 9 [29440/60000 (49%)]\tLoss: 0.064345\nTrain Epoch: 9 [30080/60000 (50%)]\tLoss: 0.315658\nTrain Epoch: 9 [30720/60000 (51%)]\tLoss: 0.105739\nTrain Epoch: 9 [31360/60000 (52%)]\tLoss: 0.246439\nTrain Epoch: 9 [32000/60000 (53%)]\tLoss: 0.145221\nTrain Epoch: 9 [32640/60000 (54%)]\tLoss: 0.287615\nTrain Epoch: 9 [33280/60000 (55%)]\tLoss: 0.310717\nTrain Epoch: 9 [33920/60000 (57%)]\tLoss: 0.322760\nTrain Epoch: 9 [34560/60000 (58%)]\tLoss: 0.294462\nTrain Epoch: 9 [35200/60000 (59%)]\tLoss: 0.168697\nTrain Epoch: 9 [35840/60000 (60%)]\tLoss: 0.153495\nTrain Epoch: 9 [36480/60000 (61%)]\tLoss: 0.146843\nTrain Epoch: 9 [37120/60000 (62%)]\tLoss: 0.176622\nTrain Epoch: 9 [37760/60000 (63%)]\tLoss: 0.400825\nTrain Epoch: 9 [38400/60000 (64%)]\tLoss: 0.197533\nTrain Epoch: 9 [39040/60000 (65%)]\tLoss: 0.109741\nTrain Epoch: 9 [39680/60000 (66%)]\tLoss: 0.049689\nTrain Epoch: 9 [40320/60000 (67%)]\tLoss: 0.253087\nTrain Epoch: 9 [40960/60000 (68%)]\tLoss: 0.222971\nTrain Epoch: 9 [41600/60000 (69%)]\tLoss: 0.095467\nTrain Epoch: 9 [42240/60000 (70%)]\tLoss: 0.043052\nTrain Epoch: 9 [42880/60000 (71%)]\tLoss: 0.105347\nTrain Epoch: 9 [43520/60000 (72%)]\tLoss: 0.133342\nTrain Epoch: 9 [44160/60000 (74%)]\tLoss: 0.266375\nTrain Epoch: 9 [44800/60000 (75%)]\tLoss: 0.156081\nTrain Epoch: 9 [45440/60000 (76%)]\tLoss: 0.206747\nTrain Epoch: 9 [46080/60000 (77%)]\tLoss: 0.158561\nTrain Epoch: 9 [46720/60000 (78%)]\tLoss: 0.416148\nTrain Epoch: 9 [47360/60000 (79%)]\tLoss: 0.147991\nTrain Epoch: 9 [48000/60000 (80%)]\tLoss: 0.112567\nTrain Epoch: 9 [48640/60000 (81%)]\tLoss: 0.100846\nTrain Epoch: 9 [49280/60000 (82%)]\tLoss: 0.103345\nTrain Epoch: 9 [49920/60000 (83%)]\tLoss: 0.205922\nTrain Epoch: 9 [50560/60000 (84%)]\tLoss: 0.097610\nTrain Epoch: 9 [51200/60000 (85%)]\tLoss: 0.071967\nTrain Epoch: 9 [51840/60000 (86%)]\tLoss: 0.068125\nTrain Epoch: 9 [52480/60000 (87%)]\tLoss: 0.057313\nTrain Epoch: 9 [53120/60000 (88%)]\tLoss: 0.162428\nTrain Epoch: 9 [53760/60000 (90%)]\tLoss: 0.097614\nTrain Epoch: 9 [54400/60000 (91%)]\tLoss: 0.075174\nTrain Epoch: 9 [55040/60000 (92%)]\tLoss: 0.095530\nTrain Epoch: 9 [55680/60000 (93%)]\tLoss: 0.142529\nTrain Epoch: 9 [56320/60000 (94%)]\tLoss: 0.132163\nTrain Epoch: 9 [56960/60000 (95%)]\tLoss: 0.201932\nTrain Epoch: 9 [57600/60000 (96%)]\tLoss: 0.238939\nTrain Epoch: 9 [58240/60000 (97%)]\tLoss: 0.037396\nTrain Epoch: 9 [58880/60000 (98%)]\tLoss: 0.077772\nTrain Epoch: 9 [59520/60000 (99%)]\tLoss: 0.177759\n\nTest set: Average loss: 0.0559, Accuracy: 9813/10000 (98%)\n\nTrain Epoch: 10 [0/60000 (0%)]\tLoss: 0.112115\nTrain Epoch: 10 [640/60000 (1%)]\tLoss: 0.089035\nTrain Epoch: 10 [1280/60000 (2%)]\tLoss: 0.177925\nTrain Epoch: 10 [1920/60000 (3%)]\tLoss: 0.147350\nTrain Epoch: 10 [2560/60000 (4%)]\tLoss: 0.170561\nTrain Epoch: 10 [3200/60000 (5%)]\tLoss: 0.207891\nTrain Epoch: 10 [3840/60000 (6%)]\tLoss: 0.340160\nTrain Epoch: 10 [4480/60000 (7%)]\tLoss: 0.229032\nTrain Epoch: 10 [5120/60000 (9%)]\tLoss: 0.335419\nTrain Epoch: 10 [5760/60000 (10%)]\tLoss: 0.101219\nTrain Epoch: 10 [6400/60000 (11%)]\tLoss: 0.085085\nTrain Epoch: 10 [7040/60000 (12%)]\tLoss: 0.053658\nTrain Epoch: 10 [7680/60000 (13%)]\tLoss: 0.106224\nTrain Epoch: 10 [8320/60000 (14%)]\tLoss: 0.146947\nTrain Epoch: 10 [8960/60000 (15%)]\tLoss: 0.210157\nTrain Epoch: 10 [9600/60000 (16%)]\tLoss: 0.167598\nTrain Epoch: 10 [10240/60000 (17%)]\tLoss: 0.184822\nTrain Epoch: 10 [10880/60000 (18%)]\tLoss: 0.149518\nTrain Epoch: 10 [11520/60000 (19%)]\tLoss: 0.091374\nTrain Epoch: 10 [12160/60000 (20%)]\tLoss: 0.331635\nTrain Epoch: 10 [12800/60000 (21%)]\tLoss: 0.345818\nTrain Epoch: 10 [13440/60000 (22%)]\tLoss: 0.057789\nTrain Epoch: 10 [14080/60000 (23%)]\tLoss: 0.189208\nTrain Epoch: 10 [14720/60000 (25%)]\tLoss: 0.116747\nTrain Epoch: 10 [15360/60000 (26%)]\tLoss: 0.101344\nTrain Epoch: 10 [16000/60000 (27%)]\tLoss: 0.116675\nTrain Epoch: 10 [16640/60000 (28%)]\tLoss: 0.158562\nTrain Epoch: 10 [17280/60000 (29%)]\tLoss: 0.173697\nTrain Epoch: 10 [17920/60000 (30%)]\tLoss: 0.167972\nTrain Epoch: 10 [18560/60000 (31%)]\tLoss: 0.125186\nTrain Epoch: 10 [19200/60000 (32%)]\tLoss: 0.116458\nTrain Epoch: 10 [19840/60000 (33%)]\tLoss: 0.107688\nTrain Epoch: 10 [20480/60000 (34%)]\tLoss: 0.131942\nTrain Epoch: 10 [21120/60000 (35%)]\tLoss: 0.189690\nTrain Epoch: 10 [21760/60000 (36%)]\tLoss: 0.106075\nTrain Epoch: 10 [22400/60000 (37%)]\tLoss: 0.100791\nTrain Epoch: 10 [23040/60000 (38%)]\tLoss: 0.151750\nTrain Epoch: 10 [23680/60000 (39%)]\tLoss: 0.242852\nTrain Epoch: 10 [24320/60000 (41%)]\tLoss: 0.367772\nTrain Epoch: 10 [24960/60000 (42%)]\tLoss: 0.160668\nTrain Epoch: 10 [25600/60000 (43%)]\tLoss: 0.209858\nTrain Epoch: 10 [26240/60000 (44%)]\tLoss: 0.267443\nTrain Epoch: 10 [26880/60000 (45%)]\tLoss: 0.134159\nTrain Epoch: 10 [27520/60000 (46%)]\tLoss: 0.176844\nTrain Epoch: 10 [28160/60000 (47%)]\tLoss: 0.083609\nTrain Epoch: 10 [28800/60000 (48%)]\tLoss: 0.093472\nTrain Epoch: 10 [29440/60000 (49%)]\tLoss: 0.133502\nTrain Epoch: 10 [30080/60000 (50%)]\tLoss: 0.207314\nTrain Epoch: 10 [30720/60000 (51%)]\tLoss: 0.095819\nTrain Epoch: 10 [31360/60000 (52%)]\tLoss: 0.165338\nTrain Epoch: 10 [32000/60000 (53%)]\tLoss: 0.172792\nTrain Epoch: 10 [32640/60000 (54%)]\tLoss: 0.200346\nTrain Epoch: 10 [33280/60000 (55%)]\tLoss: 0.188566\nTrain Epoch: 10 [33920/60000 (57%)]\tLoss: 0.063107\nTrain Epoch: 10 [34560/60000 (58%)]\tLoss: 0.208076\nTrain Epoch: 10 [35200/60000 (59%)]\tLoss: 0.336500\nTrain Epoch: 10 [35840/60000 (60%)]\tLoss: 0.098523\nTrain Epoch: 10 [36480/60000 (61%)]\tLoss: 0.239501\nTrain Epoch: 10 [37120/60000 (62%)]\tLoss: 0.108441\nTrain Epoch: 10 [37760/60000 (63%)]\tLoss: 0.161891\nTrain Epoch: 10 [38400/60000 (64%)]\tLoss: 0.232178\nTrain Epoch: 10 [39040/60000 (65%)]\tLoss: 0.281599\nTrain Epoch: 10 [39680/60000 (66%)]\tLoss: 0.202701\nTrain Epoch: 10 [40320/60000 (67%)]\tLoss: 0.313276\nTrain Epoch: 10 [40960/60000 (68%)]\tLoss: 0.149932\nTrain Epoch: 10 [41600/60000 (69%)]\tLoss: 0.078690\nTrain Epoch: 10 [42240/60000 (70%)]\tLoss: 0.068174\nTrain Epoch: 10 [42880/60000 (71%)]\tLoss: 0.114682\nTrain Epoch: 10 [43520/60000 (72%)]\tLoss: 0.278032\nTrain Epoch: 10 [44160/60000 (74%)]\tLoss: 0.207701\nTrain Epoch: 10 [44800/60000 (75%)]\tLoss: 0.149129\nTrain Epoch: 10 [45440/60000 (76%)]\tLoss: 0.209997\nTrain Epoch: 10 [46080/60000 (77%)]\tLoss: 0.181944\nTrain Epoch: 10 [46720/60000 (78%)]\tLoss: 0.071149\nTrain Epoch: 10 [47360/60000 (79%)]\tLoss: 0.088598\nTrain Epoch: 10 [48000/60000 (80%)]\tLoss: 0.196593\nTrain Epoch: 10 [48640/60000 (81%)]\tLoss: 0.195960\nTrain Epoch: 10 [49280/60000 (82%)]\tLoss: 0.227564\nTrain Epoch: 10 [49920/60000 (83%)]\tLoss: 0.051203\nTrain Epoch: 10 [50560/60000 (84%)]\tLoss: 0.105916\nTrain Epoch: 10 [51200/60000 (85%)]\tLoss: 0.176384\nTrain Epoch: 10 [51840/60000 (86%)]\tLoss: 0.054657\nTrain Epoch: 10 [52480/60000 (87%)]\tLoss: 0.107465\nTrain Epoch: 10 [53120/60000 (88%)]\tLoss: 0.072626\nTrain Epoch: 10 [53760/60000 (90%)]\tLoss: 0.187904\nTrain Epoch: 10 [54400/60000 (91%)]\tLoss: 0.104509\nTrain Epoch: 10 [55040/60000 (92%)]\tLoss: 0.174006\nTrain Epoch: 10 [55680/60000 (93%)]\tLoss: 0.122760\nTrain Epoch: 10 [56320/60000 (94%)]\tLoss: 0.150131\nTrain Epoch: 10 [56960/60000 (95%)]\tLoss: 0.076365\nTrain Epoch: 10 [57600/60000 (96%)]\tLoss: 0.127536\nTrain Epoch: 10 [58240/60000 (97%)]\tLoss: 0.233154\nTrain Epoch: 10 [58880/60000 (98%)]\tLoss: 0.113188\nTrain Epoch: 10 [59520/60000 (99%)]\tLoss: 0.282389\n\nTest set: Average loss: 0.0531, Accuracy: 9837/10000 (98%)\n\n\n\nThe experiment completed successfully. Finalizing run...\nLogging experiment finalizing status in history service\n\n\nRun is completed.",
+       "run_properties": {
+        "SendToClient": "1",
+        "arguments": "--output-dir ./outputs",
+        "created_utc": "2018-09-25T11:56:04.832205Z",
+        "distributed_processes": [],
+        "end_time_utc": "2018-09-25T12:15:57.841467Z",
+        "log_files": {
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+         "azureml-logs/80_driver_log.txt": "https://onnxamlistorageekgyifen.blob.core.windows.net/azureml/ExperimentRun/pytorch1-mnist_1537876563990/azureml-logs/80_driver_log.txt?sv=2017-04-17&sr=b&sig=CvqNHP18huWuXWdi%2BeiPcnztgJfI1iQQ6fV6Li25z1Y%3D&st=2018-09-25T12%3A06%3A00Z&se=2018-09-25T20%3A16%3A00Z&sp=r",
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+        "properties": {
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+        "run_duration": "0:19:53",
+        "run_id": "pytorch1-mnist_1537876563990",
+        "script_name": "mnist.py",
+        "status": "Completed",
+        "tags": {}
+       },
+       "widget_settings": {},
+       "workbench_uri": "https://mlworkspace.azure.ai/portal/subscriptions/75f78a03-482f-4fd8-8c71-5ddc08f92726/resourceGroups/onnxdemos/providers/Microsoft.MachineLearningServices/workspaces/onnx-aml-ignite-demo/experiment/pytorch1-mnist/run/pytorch1-mnist_1537876563990"
+      }
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+    },
+    "version_major": 2,
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+ },
+ "nbformat": 4,
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+}
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
index 7d019039..d3e1b8c2 100644
--- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
+++ b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.ipynb
@@ -1,378 +1,378 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Copyright (c) Microsoft Corporation. All rights reserved.\n",
-        "\n",
-        "Licensed under the MIT License."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Distributed PyTorch with Horovod\n",
-        "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Prerequisites\n",
-        "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
-        "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Check core SDK version number\n",
-        "import azureml.core\n",
-        "\n",
-        "print(\"SDK version:\", azureml.core.VERSION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Diagnostics\n",
-        "Opt-in diagnostics for better experience, quality, and security of future releases."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "tags": [
-          "Diagnostics"
-        ]
-      },
-      "outputs": [],
-      "source": [
-        "from azureml.telemetry import set_diagnostics_collection\n",
-        "\n",
-        "set_diagnostics_collection(send_diagnostics=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Initialize workspace\n",
-        "\n",
-        "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.workspace import Workspace\n",
-        "\n",
-        "ws = Workspace.from_config()\n",
-        "print('Workspace name: ' + ws.name, \n",
-        "      'Azure region: ' + ws.location, \n",
-        "      'Subscription id: ' + ws.subscription_id, \n",
-        "      'Resource group: ' + ws.resource_group, sep='\\n')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Create or attach existing AmlCompute\n",
-        "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `Standard_NC6s_v3` GPU cluster that autoscales from `0` to `4` nodes.\n",
-        "\n",
-        "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
-        "\n",
-        "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
-        "\n",
-        "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core.compute import ComputeTarget, AmlCompute\n",
-        "from azureml.core.compute_target import ComputeTargetException\n",
-        "\n",
-        "# choose a name for your cluster\n",
-        "cluster_name = \"gpu-cluster\"\n",
-        "\n",
-        "try:\n",
-        "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
-        "    print('Found existing compute target.')\n",
-        "except ComputeTargetException:\n",
-        "    print('Creating a new compute target...')\n",
-        "    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3',\n",
-        "                                                           max_nodes=4)\n",
-        "\n",
-        "    # create the cluster\n",
-        "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
-        "\n",
-        "    compute_target.wait_for_completion(show_output=True)\n",
-        "\n",
-        "# use get_status() to get a detailed status for the current AmlCompute. \n",
-        "print(compute_target.get_status().serialize())"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Train model on the remote compute\n",
-        "Now that we have the AmlCompute ready to go, let's run our distributed training job."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create a project directory\n",
-        "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\n",
-        "project_folder = './pytorch-distr-hvd'\n",
-        "os.makedirs(project_folder, exist_ok=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Prepare training script\n",
-        "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
-        "\n",
-        "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
-        "\n",
-        "To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
-        "```Python\n",
-        "from azureml.core.run import Run\n",
-        "run = Run.get_context()\n",
-        "```\n",
-        "Later within the script, we log the loss metric to our run:\n",
-        "```Python\n",
-        "run.log('loss', loss.item())\n",
-        "```"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import shutil\n",
-        "\n",
-        "shutil.copy('pytorch_horovod_mnist.py', project_folder)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an experiment\n",
-        "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Experiment\n",
-        "\n",
-        "experiment_name = 'pytorch-distr-hvd'\n",
-        "experiment = Experiment(ws, name=experiment_name)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Create an environment\n",
-        "\n",
-        "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment. The curated environment includes the `torch`, `torchvision` and `horovod` packages required by the training script."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import Environment\n",
-        "\n",
-        "pytorch_env = Environment.get(ws, name='AzureML-PyTorch-1.6-GPU')"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Configure the training job\n",
-        "\n",
-        "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n",
-        "\n",
-        "In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.core import ScriptRunConfig\n",
-        "from azureml.core.runconfig import MpiConfiguration\n",
-        "\n",
-        "src = ScriptRunConfig(source_directory=project_folder,\n",
-        "                      script='pytorch_horovod_mnist.py',\n",
-        "                      compute_target=compute_target,\n",
-        "                      environment=pytorch_env,\n",
-        "                      distributed_job_config=MpiConfiguration(node_count=2))"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Submit job\n",
-        "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run = experiment.submit(src)\n",
-        "print(run)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Monitor your run\n",
-        "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from azureml.widgets import RunDetails\n",
-        "\n",
-        "RunDetails(run).show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Alternatively, you can block until the script has completed training before running more code."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "run.wait_for_completion(show_output=True) # this provides a verbose log"
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "ninhu"
-      }
-    ],
-    "category": "training",
-    "compute": [
-      "AML Compute"
-    ],
-    "datasets": [
-      "MNIST"
-    ],
-    "deployment": [
-      "None"
-    ],
-    "exclude_from_index": false,
-    "framework": [
-      "PyTorch"
-    ],
-    "friendly_name": "Distributed PyTorch",
-    "index_order": 1,
-    "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
-      "language": "python",
-      "name": "python38-azureml"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
-    },
-    "tags": [
-      "None"
-    ],
-    "task": "Train a model using the distributed training via Horovod"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Copyright (c) Microsoft Corporation. All rights reserved.\n",
+    "\n",
+    "Licensed under the MIT License."
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 2
-}
\ No newline at end of file
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/distributed-pytorch-with-horovod.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distributed PyTorch with Horovod\n",
+    "In this tutorial, you will train a PyTorch model on the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset using distributed training via [Horovod](https://github.com/uber/horovod) across a GPU cluster."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prerequisites\n",
+    "* If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [Configuration](../../../../configuration.ipynb) notebook to install the Azure Machine Learning Python SDK and create an Azure ML `Workspace`\n",
+    "* Review the [tutorial](../train-hyperparameter-tune-deploy-with-pytorch/train-hyperparameter-tune-deploy-with-pytorch.ipynb) on single-node PyTorch training using Azure Machine Learning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Check core SDK version number\n",
+    "import azureml.core\n",
+    "\n",
+    "print(\"SDK version:\", azureml.core.VERSION)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Diagnostics\n",
+    "Opt-in diagnostics for better experience, quality, and security of future releases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "tags": [
+     "Diagnostics"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "from azureml.telemetry import set_diagnostics_collection\n",
+    "\n",
+    "set_diagnostics_collection(send_diagnostics=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Initialize workspace\n",
+    "\n",
+    "Initialize a [Workspace](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#workspace) object from the existing workspace you created in the Prerequisites step. `Workspace.from_config()` creates a workspace object from the details stored in `config.json`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.workspace import Workspace\n",
+    "\n",
+    "ws = Workspace.from_config()\n",
+    "print('Workspace name: ' + ws.name, \n",
+    "      'Azure region: ' + ws.location, \n",
+    "      'Subscription id: ' + ws.subscription_id, \n",
+    "      'Resource group: ' + ws.resource_group, sep='\\n')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create or attach existing AmlCompute\n",
+    "You will need to create a [compute target](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#compute-target) for training your model. In this tutorial, we use Azure ML managed compute ([AmlCompute](https://docs.microsoft.com/azure/machine-learning/service/how-to-set-up-training-targets#amlcompute)) for our remote training compute resource. Specifically, the below code creates an `Standard_NC6s_v3` GPU cluster that autoscales from `0` to `4` nodes.\n",
+    "\n",
+    "> Note that if you have an AzureML Data Scientist role, you will not have permission to create compute resources. Talk to your workspace or IT admin to create the compute targets described in this section, if they do not already exist.\n",
+    "\n",
+    "**Creation of AmlCompute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace, this code will skip the creation process.\n",
+    "\n",
+    "As with other Azure services, there are limits on certain resources (e.g. AmlCompute) associated with the Azure Machine Learning service. Please read [this article](https://docs.microsoft.com/en-us/azure/machine-learning/service/how-to-manage-quotas) on the default limits and how to request more quota."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core.compute import ComputeTarget, AmlCompute\n",
+    "from azureml.core.compute_target import ComputeTargetException\n",
+    "\n",
+    "# choose a name for your cluster\n",
+    "cluster_name = \"gpu-cluster\"\n",
+    "\n",
+    "try:\n",
+    "    compute_target = ComputeTarget(workspace=ws, name=cluster_name)\n",
+    "    print('Found existing compute target.')\n",
+    "except ComputeTargetException:\n",
+    "    print('Creating a new compute target...')\n",
+    "    compute_config = AmlCompute.provisioning_configuration(vm_size='Standard_NC6s_v3',\n",
+    "                                                           max_nodes=4)\n",
+    "\n",
+    "    # create the cluster\n",
+    "    compute_target = ComputeTarget.create(ws, cluster_name, compute_config)\n",
+    "\n",
+    "    compute_target.wait_for_completion(show_output=True)\n",
+    "\n",
+    "# use get_status() to get a detailed status for the current AmlCompute. \n",
+    "print(compute_target.get_status().serialize())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above code creates GPU compute. If you instead want to create CPU compute, provide a different VM size to the `vm_size` parameter, such as `STANDARD_D2_V2`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train model on the remote compute\n",
+    "Now that we have the AmlCompute ready to go, let's run our distributed training job."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create a project directory\n",
+    "Create a directory that will contain all the necessary code from your local machine that you will need access to on the remote resource. This includes the training script and any additional files your training script depends on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "project_folder = './pytorch-distr-hvd'\n",
+    "os.makedirs(project_folder, exist_ok=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Prepare training script\n",
+    "Now you will need to create your training script. In this tutorial, the script for distributed training of MNIST is already provided for you at `pytorch_horovod_mnist.py`. In practice, you should be able to take any custom PyTorch training script as is and run it with Azure ML without having to modify your code.\n",
+    "\n",
+    "However, if you would like to use Azure ML's [metric logging](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#logging) capabilities, you will have to add a small amount of Azure ML logic inside your training script. In this example, at each logging interval, we will log the loss for that minibatch to our Azure ML run.\n",
+    "\n",
+    "To do so, in `pytorch_horovod_mnist.py`, we will first access the Azure ML `Run` object within the script:\n",
+    "```Python\n",
+    "from azureml.core.run import Run\n",
+    "run = Run.get_context()\n",
+    "```\n",
+    "Later within the script, we log the loss metric to our run:\n",
+    "```Python\n",
+    "run.log('loss', loss.item())\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once your script is ready, copy the training script `pytorch_horovod_mnist.py` into the project directory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import shutil\n",
+    "\n",
+    "shutil.copy('pytorch_horovod_mnist.py', project_folder)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an experiment\n",
+    "Create an [Experiment](https://docs.microsoft.com/azure/machine-learning/service/concept-azure-machine-learning-architecture#experiment) to track all the runs in your workspace for this distributed PyTorch tutorial. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Experiment\n",
+    "\n",
+    "experiment_name = 'pytorch-distr-hvd'\n",
+    "experiment = Experiment(ws, name=experiment_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create an environment\n",
+    "\n",
+    "In this tutorial, we will use one of Azure ML's curated PyTorch environments for training. [Curated environments](https://docs.microsoft.com/azure/machine-learning/how-to-use-environments#use-a-curated-environment) are available in your workspace by default. Specifically, we will use the PyTorch 1.6 GPU curated environment. The curated environment includes the `torch`, `torchvision` and `horovod` packages required by the training script."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import Environment\n",
+    "\n",
+    "pytorch_env = Environment.get(ws, name='AzureML-acpt-pytorch-1.13-cuda11.7')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure the training job\n",
+    "\n",
+    "Create a ScriptRunConfig object to specify the configuration details of your training job, including your training script, environment to use, and the compute target to run on.\n",
+    "\n",
+    "In order to execute a distributed run using MPI/Horovod, you must create an `MpiConfiguration` object and pass it to the `distributed_job_config` parameter of the ScriptRunConfig constructor. The below code will configure a 2-node distributed job running one process per node. If you would also like to run multiple processes per node (i.e. if your cluster SKU has multiple GPUs), additionally specify the `process_count_per_node` parameter in `MpiConfiguration` (the default is `1`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.core import ScriptRunConfig\n",
+    "from azureml.core.runconfig import MpiConfiguration\n",
+    "\n",
+    "src = ScriptRunConfig(source_directory=project_folder,\n",
+    "                      script='pytorch_horovod_mnist.py',\n",
+    "                      compute_target=compute_target,\n",
+    "                      environment=pytorch_env,\n",
+    "                      distributed_job_config=MpiConfiguration(node_count=2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Submit job\n",
+    "Run your experiment by submitting your ScriptRunConfig object. Note that this call is asynchronous."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run = experiment.submit(src)\n",
+    "print(run)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Monitor your run\n",
+    "You can monitor the progress of the run with a Jupyter widget. Like the run submission, the widget is asynchronous and provides live updates every 10-15 seconds until the job completes. You can see that the widget automatically plots and visualizes the loss metric that we logged to the Azure ML run."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from azureml.widgets import RunDetails\n",
+    "\n",
+    "RunDetails(run).show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Alternatively, you can block until the script has completed training before running more code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "run.wait_for_completion(show_output=True) # this provides a verbose log"
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "ninhu"
+   }
+  ],
+  "category": "training",
+  "compute": [
+   "AML Compute"
+  ],
+  "datasets": [
+   "MNIST"
+  ],
+  "deployment": [
+   "None"
+  ],
+  "exclude_from_index": false,
+  "framework": [
+   "PyTorch"
+  ],
+  "friendly_name": "Distributed PyTorch",
+  "index_order": 1,
+  "kernelspec": {
+   "display_name": "Python 3.8 - AzureML",
+   "language": "python",
+   "name": "python38-azureml"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "tags": [
+   "None"
+  ],
+  "task": "Train a model using the distributed training via Horovod"
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
index 617ebef4..4b504d43 100644
--- a/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
+++ b/how-to-use-azureml/ml-frameworks/pytorch/distributed-pytorch-with-horovod/pytorch_horovod_mnist.py
@@ -51,15 +51,15 @@ if args.cuda:
 
 
 kwargs = {}
-# Use Azure Open Datasets for MNIST dataset
+# MNIST dataset
 datasets.MNIST.resources = [
-    ("https://azureopendatastorage.azurefd.net/mnist/train-images-idx3-ubyte.gz",
+    ("train-images-idx3-ubyte.gz",
      "f68b3c2dcbeaaa9fbdd348bbdeb94873"),
-    ("https://azureopendatastorage.azurefd.net/mnist/train-labels-idx1-ubyte.gz",
+    ("train-labels-idx1-ubyte.gz",
      "d53e105ee54ea40749a09fcbcd1e9432"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-images-idx3-ubyte.gz",
+    ("t10k-images-idx3-ubyte.gz",
      "9fb629c4189551a2d022fa330f9573f3"),
-    ("https://azureopendatastorage.azurefd.net/mnist/t10k-labels-idx1-ubyte.gz",
+    ("t10k-labels-idx1-ubyte.gz",
      "ec29112dd5afa0611ce80d1b7f02629c")
 ]
 train_dataset = \
diff --git a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb
index 54c1554f..6b45eadc 100644
--- a/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb
+++ b/tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb
@@ -1,502 +1,501 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/quickstart-ci/AzureMLin10mins.png)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "# Quickstart: Train and deploy a model in Azure Machine Learning in 10 minutes\n",
-        "\n",
-        "In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset.\n",
-        "\n",
-        "You'll learn how to:\n",
-        "\n",
-        "* Download a dataset and look at the data\n",
-        "* Train an image classification model and log metrics using MLflow\n",
-        "* Deploy the model to do real-time inference"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "## Import Data\n",
-        "\n",
-        "Before you train a model, you need to understand the data you're using to train it. In this section, learn how to:\n",
-        "\n",
-        "* Download the MNIST dataset\n",
-        "* Display some sample images\n",
-        "\n",
-        "You'll use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for better models. Each dataset has a corresponding class, `MNIST` in this case, to retrieve the data in different ways."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "from azureml.opendatasets import MNIST\n",
-        "\n",
-        "data_folder = os.path.join(os.getcwd(), \"/tmp/qs_data\")\n",
-        "os.makedirs(data_folder, exist_ok=True)\n",
-        "\n",
-        "mnist_file_dataset = MNIST.get_file_dataset()\n",
-        "mnist_file_dataset.download(data_folder, overwrite=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "### Take a look at the data\n",
-        "\n",
-        "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. \n",
-        "\n",
-        "Note this step requires a `load_data` function that's included in an `utils.py` file. This file is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from utils import load_data\n",
-        "import matplotlib.pyplot as plt\n",
-        "import numpy as np\n",
-        "import glob\n",
-        "\n",
-        "\n",
-        "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
-        "X_train = (\n",
-        "    load_data(\n",
-        "        glob.glob(\n",
-        "            os.path.join(data_folder, \"**/train-images-idx3-ubyte.gz\"), recursive=True\n",
-        "        )[0],\n",
-        "        False,\n",
-        "    )\n",
-        "    / 255.0\n",
-        ")\n",
-        "X_test = (\n",
-        "    load_data(\n",
-        "        glob.glob(\n",
-        "            os.path.join(data_folder, \"**/t10k-images-idx3-ubyte.gz\"), recursive=True\n",
-        "        )[0],\n",
-        "        False,\n",
-        "    )\n",
-        "    / 255.0\n",
-        ")\n",
-        "y_train = load_data(\n",
-        "    glob.glob(\n",
-        "        os.path.join(data_folder, \"**/train-labels-idx1-ubyte.gz\"), recursive=True\n",
-        "    )[0],\n",
-        "    True,\n",
-        ").reshape(-1)\n",
-        "y_test = load_data(\n",
-        "    glob.glob(\n",
-        "        os.path.join(data_folder, \"**/t10k-labels-idx1-ubyte.gz\"), recursive=True\n",
-        "    )[0],\n",
-        "    True,\n",
-        ").reshape(-1)\n",
-        "\n",
-        "\n",
-        "# now let's show some randomly chosen images from the traininng set.\n",
-        "count = 0\n",
-        "sample_size = 30\n",
-        "plt.figure(figsize=(16, 6))\n",
-        "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
-        "    count = count + 1\n",
-        "    plt.subplot(1, sample_size, count)\n",
-        "    plt.axhline(\"\")\n",
-        "    plt.axvline(\"\")\n",
-        "    plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
-        "    plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
-        "plt.show()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "## Train model and log metrics with MLflow\n",
-        "\n",
-        "You'll train the model using the code below. Note that you are using MLflow autologging to track metrics and log model artefacts.\n",
-        "\n",
-        "You'll be using the [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) classifier from the [SciKit Learn framework](https://scikit-learn.org/) to classify the data.\n",
-        "\n",
-        "**Note: The model training takes approximately 2 minutes to complete.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1612966046970
-        },
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# create the model\n",
-        "import mlflow\n",
-        "import numpy as np\n",
-        "from sklearn.linear_model import LogisticRegression\n",
-        "from azureml.core import Workspace\n",
-        "\n",
-        "# connect to your workspace\n",
-        "ws = Workspace.from_config()\n",
-        "\n",
-        "# create experiment and start logging to a new run in the experiment\n",
-        "experiment_name = \"azure-ml-in10-mins-tutorial\"\n",
-        "\n",
-        "# set up MLflow to track the metrics\n",
-        "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())\n",
-        "mlflow.set_experiment(experiment_name)\n",
-        "mlflow.autolog()\n",
-        "\n",
-        "# set up the Logistic regression model\n",
-        "reg = 0.5\n",
-        "clf = LogisticRegression(\n",
-        "    C=1.0 / reg, solver=\"liblinear\", multi_class=\"auto\", random_state=42\n",
-        ")\n",
-        "\n",
-        "# train the model\n",
-        "with mlflow.start_run() as run:\n",
-        "    clf.fit(X_train, y_train)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## View Experiment\n",
-        "In the left-hand menu in Azure Machine Learning Studio, select __Jobs__ and then select your experiment (azure-ml-in10-mins-tutorial). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.\n",
-        "\n",
-        "## Version control your models with the model registry\n",
-        "\n",
-        "You can use model registration to store and version your models in your workspace. Registered models are identified by name and version. Each time you register a model with the same name as an existing one, the registry increments the version. The code below registers and versions the model you trained above. Once you have executed the code cell below you will be able to see the model in the registry by selecting __Models__ in the left-hand menu in Azure Machine Learning Studio."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1612881042710
-        },
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# register the model\n",
-        "model_uri = \"runs:/{}/model\".format(run.info.run_id)\n",
-        "model = mlflow.register_model(model_uri, \"sklearn_mnist_model\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Deploy the model for real-time inference\n",
-        "In this section you learn how to deploy a model so that an application can consume (inference) the model over REST.\n",
-        "\n",
-        "### Create deployment configuration\n",
-        "The code cell gets a _curated environment_, which specifies all the dependencies required to host the model (for example, the packages like scikit-learn). Also, you create a _deployment configuration_, which specifies the amount of compute required to host the model. In this case, the compute will have 1CPU and 1GB memory."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1612881061728
-        },
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# create environment for the deploy\n",
-        "from azureml.core.environment import Environment\n",
-        "from azureml.core.conda_dependencies import CondaDependencies\n",
-        "from azureml.core.webservice import AciWebservice\n",
-        "\n",
-        "# get a curated environment\n",
-        "env = Environment.get(\n",
-        "    workspace=ws, \n",
-        "    name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\",\n",
-        "    version=1\n",
-        ")\n",
-        "env.inferencing_stack_version='latest'\n",
-        "\n",
-        "# create deployment config i.e. compute resources\n",
-        "aciconfig = AciWebservice.deploy_configuration(\n",
-        "    cpu_cores=1,\n",
-        "    memory_gb=1,\n",
-        "    tags={\"data\": \"MNIST\", \"method\": \"sklearn\"},\n",
-        "    description=\"Predict MNIST with sklearn\",\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "### Deploy model\n",
-        "\n",
-        "This next code cell deploys the model to Azure Container Instance (ACI).\n",
-        "\n",
-        "**Note: The deployment takes approximately 3 minutes to complete.**"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "%%time\n",
-        "import uuid\n",
-        "from azureml.core.model import InferenceConfig\n",
-        "from azureml.core.environment import Environment\n",
-        "from azureml.core.model import Model\n",
-        "\n",
-        "# get the registered model\n",
-        "model = Model(ws, \"sklearn_mnist_model\")\n",
-        "\n",
-        "# create an inference config i.e. the scoring script and environment\n",
-        "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
-        "\n",
-        "# deploy the service\n",
-        "service_name = \"sklearn-mnist-svc-\" + str(uuid.uuid4())[:4]\n",
-        "service = Model.deploy(\n",
-        "    workspace=ws,\n",
-        "    name=service_name,\n",
-        "    models=[model],\n",
-        "    inference_config=inference_config,\n",
-        "    deployment_config=aciconfig,\n",
-        ")\n",
-        "\n",
-        "service.wait_for_deployment(show_output=True)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The [*scoring script*](score.py) file referenced in the code above can be found in the same folder as this notebook, and has two functions:\n",
-        "\n",
-        "1. an `init` function that executes once when the service starts - in this function you normally get the model from the registry and set global variables\n",
-        "1. a `run(data)` function that executes each time a call is made to the service. In this function, you normally format the input data, run a prediction, and output the predicted result.\n",
-        "\n",
-        "### View Endpoint\n",
-        "Once the model has been successfully deployed, you can view the endpoint by navigating to __Endpoints__ in the left-hand menu in Azure Machine Learning Studio. You will be able to see the state of the endpoint (healthy/unhealthy), logs, and consume (how applications can consume the model)."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "source": [
-        "## Test the model service\n",
-        "\n",
-        "You can test the model by sending a raw HTTP request to test the web service. "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1612881538381
-        },
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# send raw HTTP request to test the web service.\n",
-        "import requests\n",
-        "\n",
-        "# send a random row from the test set to score\n",
-        "random_index = np.random.randint(0, len(X_test) - 1)\n",
-        "input_data = '{\"data\": [' + str(list(X_test[random_index])) + \"]}\"\n",
-        "\n",
-        "headers = {\"Content-Type\": \"application/json\"}\n",
-        "\n",
-        "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
-        "\n",
-        "print(\"POST to url\", service.scoring_uri)\n",
-        "print(\"label:\", y_test[random_index])\n",
-        "print(\"prediction:\", resp.text)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Clean up resources\n",
-        "\n",
-        "If you're not going to continue to use this model, delete the Model service using:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "gather": {
-          "logged": 1612881556520
-        },
-        "jupyter": {
-          "outputs_hidden": false,
-          "source_hidden": false
-        },
-        "nteract": {
-          "transient": {
-            "deleting": false
-          }
-        }
-      },
-      "outputs": [],
-      "source": [
-        "# if you want to keep workspace and only delete endpoint (it will incur cost while running)\n",
-        "service.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "If you want to control cost further, stop the compute instance by selecting the \"Stop compute\" button next to the **Compute** dropdown.  Then start the compute instance again the next time you need it."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "\n",
-        "## Next Steps\n",
-        "\n",
-        "In this quickstart, you learned how to run machine learning code in Azure Machine Learning.\n",
-        "\n",
-        "Now that you have working code in a development environment, learn how to submit a **_job_** - ideally on a schedule or trigger (for example, arrival of new data).\n",
-        "\n",
-        " [**Learn how to get started with Azure ML Job Submission**](../quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) "
-      ]
-    }
-  ],
-  "metadata": {
-    "authors": [
-      {
-        "name": "cewidste"
-      }
-    ],
-    "kernelspec": {
-      "display_name": "Python 3.8 - AzureML",
-      "language": "python",
-      "name": "python38-azureml"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.6.9"
-    },
-    "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
-    "nteract": {
-      "version": "nteract-front-end@1.0.0"
-    }
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Impressions](https://PixelServer20190423114238.azurewebsites.net/api/impressions/MachineLearningNotebooks/tutorials/quickstart-ci/AzureMLin10mins.png)"
+   ]
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
-}
\ No newline at end of file
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "# Quickstart: Train and deploy a model in Azure Machine Learning in 10 minutes\n",
+    "\n",
+    "In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset.\n",
+    "\n",
+    "You'll learn how to:\n",
+    "\n",
+    "* Download a dataset and look at the data\n",
+    "* Train an image classification model and log metrics using MLflow\n",
+    "* Deploy the model to do real-time inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "## Import Data\n",
+    "\n",
+    "Before you train a model, you need to understand the data you're using to train it. In this section, learn how to:\n",
+    "\n",
+    "* Download the MNIST dataset\n",
+    "* Display some sample images\n",
+    "\n",
+    "You'll use Azure Open Datasets to get the raw MNIST data files. [Azure Open Datasets](https://docs.microsoft.com/azure/open-datasets/overview-what-are-open-datasets) are curated public datasets that you can use to add scenario-specific features to machine learning solutions for better models. Each dataset has a corresponding class, `MNIST` in this case, to retrieve the data in different ways."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from azureml.opendatasets import MNIST\n",
+    "\n",
+    "data_folder = os.path.join(os.getcwd(), \"/tmp/qs_data\")\n",
+    "os.makedirs(data_folder, exist_ok=True)\n",
+    "\n",
+    "mnist_file_dataset = MNIST.get_file_dataset()\n",
+    "mnist_file_dataset.download(data_folder, overwrite=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "### Take a look at the data\n",
+    "\n",
+    "Load the compressed files into `numpy` arrays. Then use `matplotlib` to plot 30 random images from the dataset with their labels above them. \n",
+    "\n",
+    "Note this step requires a `load_data` function that's included in an `utils.py` file. This file is placed in the same folder as this notebook. The `load_data` function simply parses the compressed files into numpy arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from utils import load_data\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import glob\n",
+    "\n",
+    "\n",
+    "# note we also shrink the intensity values (X) from 0-255 to 0-1. This helps the model converge faster.\n",
+    "X_train = (\n",
+    "    load_data(\n",
+    "        glob.glob(\n",
+    "            os.path.join(data_folder, \"**/train-images-idx3-ubyte.gz\"), recursive=True\n",
+    "        )[0],\n",
+    "        False,\n",
+    "    )\n",
+    "    / 255.0\n",
+    ")\n",
+    "X_test = (\n",
+    "    load_data(\n",
+    "        glob.glob(\n",
+    "            os.path.join(data_folder, \"**/t10k-images-idx3-ubyte.gz\"), recursive=True\n",
+    "        )[0],\n",
+    "        False,\n",
+    "    )\n",
+    "    / 255.0\n",
+    ")\n",
+    "y_train = load_data(\n",
+    "    glob.glob(\n",
+    "        os.path.join(data_folder, \"**/train-labels-idx1-ubyte.gz\"), recursive=True\n",
+    "    )[0],\n",
+    "    True,\n",
+    ").reshape(-1)\n",
+    "y_test = load_data(\n",
+    "    glob.glob(\n",
+    "        os.path.join(data_folder, \"**/t10k-labels-idx1-ubyte.gz\"), recursive=True\n",
+    "    )[0],\n",
+    "    True,\n",
+    ").reshape(-1)\n",
+    "\n",
+    "\n",
+    "# now let's show some randomly chosen images from the traininng set.\n",
+    "count = 0\n",
+    "sample_size = 30\n",
+    "plt.figure(figsize=(16, 6))\n",
+    "for i in np.random.permutation(X_train.shape[0])[:sample_size]:\n",
+    "    count = count + 1\n",
+    "    plt.subplot(1, sample_size, count)\n",
+    "    plt.axhline(\"\")\n",
+    "    plt.axvline(\"\")\n",
+    "    plt.text(x=10, y=-10, s=y_train[i], fontsize=18)\n",
+    "    plt.imshow(X_train[i].reshape(28, 28), cmap=plt.cm.Greys)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "## Train model and log metrics with MLflow\n",
+    "\n",
+    "You'll train the model using the code below. Note that you are using MLflow autologging to track metrics and log model artefacts.\n",
+    "\n",
+    "You'll be using the [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) classifier from the [SciKit Learn framework](https://scikit-learn.org/) to classify the data.\n",
+    "\n",
+    "**Note: The model training takes approximately 2 minutes to complete.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1612966046970
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create the model\n",
+    "import mlflow\n",
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from azureml.core import Workspace\n",
+    "\n",
+    "# connect to your workspace\n",
+    "ws = Workspace.from_config()\n",
+    "\n",
+    "# create experiment and start logging to a new run in the experiment\n",
+    "experiment_name = \"azure-ml-in10-mins-tutorial\"\n",
+    "\n",
+    "# set up MLflow to track the metrics\n",
+    "mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())\n",
+    "mlflow.set_experiment(experiment_name)\n",
+    "mlflow.autolog()\n",
+    "\n",
+    "# set up the Logistic regression model\n",
+    "reg = 0.5\n",
+    "clf = LogisticRegression(\n",
+    "    C=1.0 / reg, solver=\"liblinear\", multi_class=\"auto\", random_state=42\n",
+    ")\n",
+    "\n",
+    "# train the model\n",
+    "with mlflow.start_run() as run:\n",
+    "    clf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## View Experiment\n",
+    "In the left-hand menu in Azure Machine Learning Studio, select __Jobs__ and then select your experiment (azure-ml-in10-mins-tutorial). An experiment is a grouping of many runs from a specified script or piece of code. Information for the run is stored under that experiment. If the name doesn't exist when you submit an experiment, if you select your run you will see various tabs containing metrics, logs, explanations, etc.\n",
+    "\n",
+    "## Version control your models with the model registry\n",
+    "\n",
+    "You can use model registration to store and version your models in your workspace. Registered models are identified by name and version. Each time you register a model with the same name as an existing one, the registry increments the version. The code below registers and versions the model you trained above. Once you have executed the code cell below you will be able to see the model in the registry by selecting __Models__ in the left-hand menu in Azure Machine Learning Studio."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1612881042710
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# register the model\n",
+    "model_uri = \"runs:/{}/model\".format(run.info.run_id)\n",
+    "model = mlflow.register_model(model_uri, \"sklearn_mnist_model\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Deploy the model for real-time inference\n",
+    "In this section you learn how to deploy a model so that an application can consume (inference) the model over REST.\n",
+    "\n",
+    "### Create deployment configuration\n",
+    "The code cell gets a _curated environment_, which specifies all the dependencies required to host the model (for example, the packages like scikit-learn). Also, you create a _deployment configuration_, which specifies the amount of compute required to host the model. In this case, the compute will have 1CPU and 1GB memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1612881061728
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# create environment for the deploy\n",
+    "from azureml.core.environment import Environment\n",
+    "from azureml.core.conda_dependencies import CondaDependencies\n",
+    "from azureml.core.webservice import AciWebservice\n",
+    "\n",
+    "# get a curated environment\n",
+    "env = Environment.get(\n",
+    "    workspace=ws, \n",
+    "    name=\"AzureML-sklearn-1.0\"\n",
+    ")\n",
+    "env.inferencing_stack_version='latest'\n",
+    "\n",
+    "# create deployment config i.e. compute resources\n",
+    "aciconfig = AciWebservice.deploy_configuration(\n",
+    "    cpu_cores=1,\n",
+    "    memory_gb=1,\n",
+    "    tags={\"data\": \"MNIST\", \"method\": \"sklearn\"},\n",
+    "    description=\"Predict MNIST with sklearn\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "### Deploy model\n",
+    "\n",
+    "This next code cell deploys the model to Azure Container Instance (ACI).\n",
+    "\n",
+    "**Note: The deployment takes approximately 3 minutes to complete.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "import uuid\n",
+    "from azureml.core.model import InferenceConfig\n",
+    "from azureml.core.environment import Environment\n",
+    "from azureml.core.model import Model\n",
+    "\n",
+    "# get the registered model\n",
+    "model = Model(ws, \"sklearn_mnist_model\")\n",
+    "\n",
+    "# create an inference config i.e. the scoring script and environment\n",
+    "inference_config = InferenceConfig(entry_script=\"score.py\", environment=env)\n",
+    "\n",
+    "# deploy the service\n",
+    "service_name = \"sklearn-mnist-svc-\" + str(uuid.uuid4())[:4]\n",
+    "service = Model.deploy(\n",
+    "    workspace=ws,\n",
+    "    name=service_name,\n",
+    "    models=[model],\n",
+    "    inference_config=inference_config,\n",
+    "    deployment_config=aciconfig,\n",
+    ")\n",
+    "\n",
+    "service.wait_for_deployment(show_output=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [*scoring script*](score.py) file referenced in the code above can be found in the same folder as this notebook, and has two functions:\n",
+    "\n",
+    "1. an `init` function that executes once when the service starts - in this function you normally get the model from the registry and set global variables\n",
+    "1. a `run(data)` function that executes each time a call is made to the service. In this function, you normally format the input data, run a prediction, and output the predicted result.\n",
+    "\n",
+    "### View Endpoint\n",
+    "Once the model has been successfully deployed, you can view the endpoint by navigating to __Endpoints__ in the left-hand menu in Azure Machine Learning Studio. You will be able to see the state of the endpoint (healthy/unhealthy), logs, and consume (how applications can consume the model)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "source": [
+    "## Test the model service\n",
+    "\n",
+    "You can test the model by sending a raw HTTP request to test the web service. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1612881538381
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# send raw HTTP request to test the web service.\n",
+    "import requests\n",
+    "\n",
+    "# send a random row from the test set to score\n",
+    "random_index = np.random.randint(0, len(X_test) - 1)\n",
+    "input_data = '{\"data\": [' + str(list(X_test[random_index])) + \"]}\"\n",
+    "\n",
+    "headers = {\"Content-Type\": \"application/json\"}\n",
+    "\n",
+    "resp = requests.post(service.scoring_uri, input_data, headers=headers)\n",
+    "\n",
+    "print(\"POST to url\", service.scoring_uri)\n",
+    "print(\"label:\", y_test[random_index])\n",
+    "print(\"prediction:\", resp.text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Clean up resources\n",
+    "\n",
+    "If you're not going to continue to use this model, delete the Model service using:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "gather": {
+     "logged": 1612881556520
+    },
+    "jupyter": {
+     "outputs_hidden": false,
+     "source_hidden": false
+    },
+    "nteract": {
+     "transient": {
+      "deleting": false
+     }
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# if you want to keep workspace and only delete endpoint (it will incur cost while running)\n",
+    "service.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you want to control cost further, stop the compute instance by selecting the \"Stop compute\" button next to the **Compute** dropdown.  Then start the compute instance again the next time you need it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "## Next Steps\n",
+    "\n",
+    "In this quickstart, you learned how to run machine learning code in Azure Machine Learning.\n",
+    "\n",
+    "Now that you have working code in a development environment, learn how to submit a **_job_** - ideally on a schedule or trigger (for example, arrival of new data).\n",
+    "\n",
+    " [**Learn how to get started with Azure ML Job Submission**](../quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb) "
+   ]
+  }
+ ],
+ "metadata": {
+  "authors": [
+   {
+    "name": "cewidste"
+   }
+  ],
+  "kernelspec": {
+   "display_name": "Python 3.8 - AzureML",
+   "language": "python",
+   "name": "python38-azureml"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "notice": "Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the MIT License.",
+  "nteract": {
+   "version": "nteract-front-end@1.0.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}