add training and tuning results in the notebook
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"2. Train on the training dataset for each batch epoch (batch size = 48 updates)\n",
"3. Evaluate on the validation dataset for every 10 epochs\n",
"4. Find the local minimum point on validation loss\n",
- "5. Save the best model and stop the training process"
+ "5. Save the best model and stop the training process\n",
+ "\n",
+ "The following chart is the results with different nodes with AmlCompute:\n",
+ "\n",
+ "| Standard_NC6 | AML_1node | AML_2nodes | AML_4nodes | AML_8nodes |\n",
+ "| --- | --- | --- | --- | --- |\n",
+ "| Best_val_loss | 4.81 | 4.78 | 4.77 | 4.58 |\n"
]
},
{
"**Interpret the Tuning Results**\n",
"\n",
"The chart shows 4 different threads running in parallel with different learning rate, and the number of total runs is 8. By comparing the 'Best Metric' which is `best_val_loss` in our case, we can pick the best learning rate. The HyperDrive run automatically shows the tracking charts (example in the following) to let users understand the tuning process.\n",
- "![Tuning](https://nlpbp.blob.core.windows.net/images/tuning.PNG)**HyperParameter Tuning with HyperDrive**"
+ "![Tuning](https://nlpbp.blob.core.windows.net/images/tuning.PNG)**HyperParameter Tuning with HyperDrive**\n",
+ "\n",
+ "From the results in section [2.3.5 Monitor your run](#2.3.5-Monitor-your-run), the `best_val_loss` for 1 nodes is 4.81, but with tuning we can easily achieve better performance around "
]
},
{
