mirror of
https://github.com/Azure/MachineLearningNotebooks.git
synced 2025-12-22 02:25:12 -05:00
102 lines
4.6 KiB
Markdown
102 lines
4.6 KiB
Markdown
|
|
# Notebooks for Microsoft Azure Machine Learning Hardware Accelerated Models SDK
|
|
|
|
Easily create and train a model using various deep neural networks (DNNs) as a featurizer for deployment to Azure or a Data Box Edge device for ultra-low latency inferencing using FPGA's. These models are currently available:
|
|
|
|
* ResNet 50
|
|
* ResNet 152
|
|
* DenseNet-121
|
|
* VGG-16
|
|
* SSD-VGG
|
|
|
|
To learn more about the azureml-accel-model classes, see the section [Model Classes](#model-classes) below or the [Azure ML Accel Models SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel?view=azure-ml-py).
|
|
|
|
### Step 1: Create an Azure ML workspace
|
|
Follow [these instructions](https://docs.microsoft.com/en-us/azure/machine-learning/service/setup-create-workspace) to install the Azure ML SDK on your local machine, create an Azure ML workspace, and set up your notebook environment, which is required for the next step.
|
|
|
|
### Step 2: Check your FPGA quota
|
|
Use the Azure CLI to check whether you have quota.
|
|
|
|
```shell
|
|
az vm list-usage --location "eastus" -o table
|
|
```
|
|
|
|
The other locations are ``southeastasia``, ``westeurope``, and ``westus2``.
|
|
|
|
Under the "Name" column, look for "Standard PBS Family vCPUs" and ensure you have at least 6 vCPUs under "CurrentValue."
|
|
|
|
If you do not have quota, then submit a request form [here](https://aka.ms/accelerateAI).
|
|
|
|
### Step 3: Install the Azure ML Accelerated Models SDK
|
|
Once you have set up your environment, install the Azure ML Accel Models SDK. This package requires tensorflow >= 1.6,<2.0 to be installed.
|
|
|
|
If you already have tensorflow >= 1.6,<2.0 installed in your development environment, you can install the SDK package using:
|
|
|
|
```
|
|
pip install azureml-accel-models
|
|
```
|
|
|
|
If you do not have tensorflow >= 1.6,<2.0 and are using a CPU-only development environment, our SDK with tensorflow can be installed using:
|
|
|
|
```
|
|
pip install azureml-accel-models[cpu]
|
|
```
|
|
|
|
If your machine supports GPU (for example, on an [Azure DSVM](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview)), then you can leverage the tensorflow-gpu functionality using:
|
|
|
|
```
|
|
pip install azureml-accel-models[gpu]
|
|
```
|
|
|
|
### Step 4: Follow our notebooks
|
|
|
|
The notebooks in this repo walk through the following scenarios:
|
|
* [Quickstart](accelerated-models-quickstart.ipynb), deploy and inference a ResNet50 model trained on ImageNet
|
|
* [Object Detection](accelerated-models-object-detection.ipynb), deploy and inference an SSD-VGG model that can do object detection
|
|
* [Training models](accelerated-models-training.ipynb), train one of our accelerated models on the Kaggle Cats and Dogs dataset to see how to improve accuracy on custom datasets
|
|
|
|
<a name="model-classes"></a>
|
|
## Model Classes
|
|
As stated above, we support 5 Accelerated Models. Here's more information on their input and output tensors.
|
|
|
|
**Available models and output tensors**
|
|
|
|
The available models and the corresponding default classifier output tensors are below. This is the value that you would use during inferencing if you used the default classifier.
|
|
* Resnet50, QuantizedResnet50
|
|
``
|
|
output_tensors = "classifier_1/resnet_v1_50/predictions/Softmax:0"
|
|
``
|
|
* Resnet152, QuantizedResnet152
|
|
``
|
|
output_tensors = "classifier/resnet_v1_152/predictions/Softmax:0"
|
|
``
|
|
* Densenet121, QuantizedDensenet121
|
|
``
|
|
output_tensors = "classifier/densenet121/predictions/Softmax:0"
|
|
``
|
|
* Vgg16, QuantizedVgg16
|
|
``
|
|
output_tensors = "classifier/vgg_16/fc8/squeezed:0"
|
|
``
|
|
* SsdVgg, QuantizedSsdVgg
|
|
``
|
|
output_tensors = ['ssd_300_vgg/block4_box/Reshape_1:0', 'ssd_300_vgg/block7_box/Reshape_1:0', 'ssd_300_vgg/block8_box/Reshape_1:0', 'ssd_300_vgg/block9_box/Reshape_1:0', 'ssd_300_vgg/block10_box/Reshape_1:0', 'ssd_300_vgg/block11_box/Reshape_1:0', 'ssd_300_vgg/block4_box/Reshape:0', 'ssd_300_vgg/block7_box/Reshape:0', 'ssd_300_vgg/block8_box/Reshape:0', 'ssd_300_vgg/block9_box/Reshape:0', 'ssd_300_vgg/block10_box/Reshape:0', 'ssd_300_vgg/block11_box/Reshape:0']
|
|
``
|
|
|
|
For more information, please reference the azureml.accel.models package in the [Azure ML Python SDK documentation](https://docs.microsoft.com/en-us/python/api/azureml-accel-models/azureml.accel.models?view=azure-ml-py).
|
|
|
|
**Input tensors**
|
|
|
|
The input_tensors value defaults to "Placeholder:0" and is created in the [Image Preprocessing](#construct-model) step in the line:
|
|
``
|
|
in_images = tf.placeholder(tf.string)
|
|
``
|
|
|
|
You can change the input_tensors name by doing this:
|
|
``
|
|
in_images = tf.placeholder(tf.string, name="images")
|
|
``
|
|
|
|
|
|
## Resources
|
|
* [Read more about FPGAs](https://docs.microsoft.com/en-us/azure/machine-learning/service/concept-accelerate-with-fpgas) |