Improve CIFAR10 example

computer_vision/cifar10_pytorch/CIFAR10-PyTorch.ipynb

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Test importing Determined.  In Determined is properly installed, you should see no output.\n",
+    "# Test importing Determined. If Determined is properly installed, you should see no output.\n",
     "import determined as det"
    ]
   },
@@ -80,11 +80,17 @@
    "metadata": {},
    "source": [
     "### const.yaml\n",
-    "For our first Determined experiment, we'll run this model training job with fixed hyperparameters.  Note the following sections:\n",
-    "- `description`: A short description of the experiment\n",
-    "- `data`: A section for user to provide custom key value pairs.  Here we specify where the data resides.  \n",
-    "- `hyperparameters`: area for user to define hyperparameters that will be injected into the trial class at runtime. There are constant values for this configuration\n",
-    "- `searcher`: hyperparameter search algorithm for the experiment"
+    "For our first Determined experiment, we'll run this model training job with fixed hyperparameters. Note the following sections (<u>keywords are clickable</u> and bring you to the [official API docs](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html)):\n",
+    "\n",
+    "- [`name`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#name): A short human-readable name for the experiment.\n",
+    "- [`description`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#description): A short description of the experiment (ideally <255 chars).\n",
+    "- [`hyperparameters`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#hyperparameters): area for user to define hyperparameters that will be injected into the trial class at runtime. There are constant values for this configuration\n",
+    "- [`records_per_epoch`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#records-per-epoch): The number of records in the training data set. Mandatory since we're also setting `min_validation_period`.\n",
+    "- [`searcher`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#searcher): hyperparameter search algorithm for the experiment.\n",
+    "- [`entrypoint`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#experiment-config-entrypoint): A model definition trial class specification or Python launcher script, which is the model processing entrypoint.\n",
+    "- [`min_validation_period`](https://hpe-mlde.determined.ai/latest/reference/training/experiment-config-reference.html#min-validation-period): Specifies the minimum frequency at which validation should be run for each trial.\n",
+    "\n",
+    "Not all of these settings are always mandatory. See the references API documentation for details."
    ]
   },
   {
@@ -163,17 +169,111 @@
     "When the experiment finishes, note that your best performing model achieves a lower validation error than our first experiment that ran with constant hyperparameter values.  From the Determined experiment detail page, you can drill in to a particular trial and view the hyperparameter values used.  You can also access the saved checkpoint of your best-performing model and load it for real-time or batch inference as described in the PyTorch documentation [here](https://pytorch.org/tutorials/beginner/saving_loading_models.html#saving-loading-a-general-checkpoint-for-inference-and-or-resuming-training)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distributed training on multiple GPUs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "See also the introduction to implementing distributed training, which you can find [here](https://docs.determined.ai/latest/model-dev-guide/dtrain/dtrain-implement.html#multi-gpu-training-implement)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### distributed.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you have a multi-GPU cluster set up that's running Determined AI, you can distribute your training on multiple GPUs by changing a few settings in your experiment configuration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cat -n distributed.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<b>Note the slight difference to `const.yaml`:</b>\n",
+    "- We added `slots_per_trial` and set it to the number of GPUs we're training on (here: 16).\n",
+    "- Since we're training on 16 GPUs and we want a per-GPU batch size of 32, we're setting `global_batch_size` to (32*16=)512."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!det -m {determined_master} experiment create distributed.yaml ."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Distributed Batch Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using PyTorch, you can use the distributed training workflow with PyTorchTrial to accelerate inference workloads. This workflow is not yet officially supported, therefore, users must specify certain training-specific artifacts that are not used for inference. This is covered below. Also, you can find further documentation [here](https://docs.determined.ai/latest/model-dev-guide/dtrain/dtrain-implement.html#distributed-inference)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### distributed_inference.yaml"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!cat -n distributed_inference.yaml"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, launch the batch inference the same way as you would launch a training job."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "!det -m {determined_master} experiment create distributed_inference.yaml ."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -187,7 +287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,

computer_vision/cifar10_pytorch/README.md

@@ -9,8 +9,9 @@ example](https://github.com/keras-team/keras/blob/keras-2/examples/cifar10_cnn.p
 
 ### Configuration Files
 * **const.yaml**: Train the model with constant hyperparameter values.
-* **distributed.yaml**: Same as `const.yaml`, but trains the model with multiple GPUs (distributed training).
 * **adaptive.yaml**: Perform a hyperparameter search using Determined's state-of-the-art adaptive hyperparameter tuning algorithm.
+* **distributed.yaml**: Same as `const.yaml`, but trains the model with multiple GPUs (distributed training).
+* **distributed_inference.yaml**: Use the distributed training workflow with PyTorchTrial to accelerate batch inference workloads.
 
 ## Data
 The CIFAR-10 dataset is downloaded from https://www.cs.toronto.edu/~kriz/cifar.html.
@@ -19,10 +20,9 @@ The CIFAR-10 dataset is downloaded from https://www.cs.toronto.edu/~kriz/cifar.h
 If you have not yet installed Determined, installation instructions can be found
 under `docs/install-admin.html` or at https://docs.determined.ai/latest/index.html
 
-Run the following command: `det -m <master host:port> experiment create -f
+Run the following command: `det -m <master-host:port> experiment create -f
 const.yaml .`. The other configurations can be run by specifying the appropriate
 configuration file in place of `const.yaml`.
 
 ## Results
-Training the model with the hyperparameter settings in `const.yaml` should yield
-a validation accuracy of ~74%.
+Training the model with the hyperparameter settings in `const.yaml` should yield a validation accuracy of ~74%.

computer_vision/cifar10_pytorch/adaptive.yaml

@@ -1,4 +1,5 @@
 name: cifar10_pytorch_adaptive_search
+description: An example experiment of hyperparameter tuning using Determined AI with CIFAR10 and PyTorch.
 hyperparameters:
   learning_rate:
     type: log

computer_vision/cifar10_pytorch/const.yaml

@@ -1,4 +1,5 @@
 name: cifar10_pytorch_const
+description: An example experiment using Determined AI with CIFAR10 and PyTorch.
 hyperparameters:
   learning_rate: 1.0e-4
   learning_rate_decay: 1.0e-6
@@ -14,4 +15,4 @@ searcher:
     epochs: 32
 entrypoint: model_def:CIFARTrial
 min_validation_period:
-  epochs: 1
+  epochs: 1

computer_vision/cifar10_pytorch/distributed.yaml

@@ -1,4 +1,5 @@
 name: cifar10_pytorch_distributed
+description: An example experiment using Determined AI with CIFAR10, PyTorch and distributed multi-GPU training.
 hyperparameters:
   learning_rate: 1.0e-4
   learning_rate_decay: 1.0e-6

computer_vision/cifar10_pytorch/distributed_inference_example.yaml → computer_vision/cifar10_pytorch/distributed_inference.yaml

@@ -1,11 +1,10 @@
-name: distributed_inference_example
+name: cifar10_pytorch_distributed_inference
+description: An example using Determined AI with CIFAR10, PyTorch and distributed batch inference.
 entrypoint: >-
   python3 -m determined.launch.torch_distributed
   python3 inference_example.py
-
 resources:
   slots_per_trial: 2
-
 searcher:
   name: grid
   metric: x
@@ -31,7 +30,6 @@ hyperparameters:
       - 12
       - 13
       - 14
-
 max_restarts: 0
 bind_mounts:
   - host_path: /tmp