Fixed documentation to get rid of AzureML logging
"metadata": {},
"source": [
"### 2.3.1 Prepare Training Script\n",
- "Now you will need to create your training script. In this tutorial, the script for distributed training of GENSEN is already provided for you at `train.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",
+ "Now you will need to create your training script. In this tutorial, the script for distributed training of GENSEN is already provided for you at `gensen_train.py`. \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",
+ "In this example, we use MLflow to log your metrics. We also use the [AzureML-Mlflow](https://pypi.org/project/azureml-mlflow/) package to further log these metrics to the Azure Portal. This is done with no change to the provided training script!\n",
"\n",
- "To do so, in `train.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",
- "```"
+ "In this example the script provided logs the loss for that minibatch to our Azure ML portal."
]
},
{
