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2019-07-22 10:31:22 -07:00
parent b025816c92
commit d9fbe4cd87
61 changed files with 3248 additions and 234 deletions
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165
how-to-use-azureml/deployment/accelerated-models/finetune-ssd-vgg/notebooks/Deploy Accelerated.ipynb
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@@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deploy Vgg Ssd Model"
"## Deploy SSD-VGG Model as Web Service on FPGA"
]
},
{
@@ -18,6 +18,7 @@
"\n",
"import os, sys\n",
"import tensorflow as tf\n",
"import azureml\n",
"\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
@@ -31,7 +32,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Restore the AzureML workspace"
"### Restore AzureML workspace & register Model"
]
},
{
@@ -42,17 +43,10 @@
"source": [
"from azureml.core import Workspace\n",
"\n",
"ws = Workspace.from_config(path=\"./config.json\")\n",
"ws = Workspace.from_config()\n",
"print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Accellerated Container Image"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -63,9 +57,11 @@
"from azureml.core.image import Image\n",
"from azureml.accel import AccelOnnxConverter\n",
"from azureml.accel import AccelContainerImage\n",
"from os.path import expanduser\n",
"\n",
"model_ckpt_dir = r'/datadrive/Dropbox/neal/kroger/azml_ssd_vgg/'\n",
"model_name = r'ssdvgg'\n",
"\n",
"model_ckpt_dir = expanduser(\"~/azml_ssd_vgg\")\n",
"model_name = r'ssdvgg-fpga'\n",
"model_save_path = os.path.join(model_ckpt_dir, model_name)\n",
"\n",
"# model_save_path should NOT exist prior to saving the model\n",
@@ -86,43 +82,74 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"# Register model\n",
"registered_model = Model.register(workspace = ws,\n",
" model_path = model_save_path,\n",
" model_name = model_name)\n",
"print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')\n",
"\n",
"print(\"Successfully registered: \", registered_model.name, registered_model.description, registered_model.version, '\\n', sep = '\\t')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Convert inference model to ONNX format"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"#Convert the TensorFlow graph to the Open Neural Network Exchange format (ONNX). \n",
"\n",
"input_tensor = saver.input_name_str\n",
"output_tensors_str = \",\".join(saver.output_names)\n",
"\n",
"# Convert model\n",
"convert_request = AccelOnnxConverter.convert_tf_model(ws, registered_model, input_tensor, output_tensors_str)\n",
"\n",
"# If it fails, you can run wait_for_completion again with show_output=True.\n",
"convert_request.wait_for_completion(show_output=True)\n",
"converted_model = convert_request.result\n",
"\n",
"print(\"\\nSuccessfully converted: \", converted_model.name, converted_model.url, converted_model.version, \n",
" converted_model.id, converted_model.created_time, '\\n')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create Docker Image"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Package into AccelContainerImage\n",
"\n",
"image_config = AccelContainerImage.image_configuration()\n",
"\n",
"# Image name must be lowercase\n",
"image_name = \"{}-image\".format(model_name)\n",
"\n",
"image = Image.create(name = image_name,\n",
" models = [converted_model],\n",
" image_config = image_config, \n",
" workspace = ws)\n",
"image.wait_for_creation(show_output=True)\n",
"\n",
"# List the images by tag and get the detailed logs for any debugging.\n",
"print(\"Created AccelContainerImage: {} {} {}\\n\".format(image.name, image.creation_state, image.image_location))"
]
},
@@ -130,7 +157,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy to AKS Cluster"
"### Deploy to the cloud"
]
},
{
@@ -139,6 +166,8 @@
"metadata": {},
"outputs": [],
"source": [
"#Create a new Azure Kubernetes Service\n",
"\n",
"from azureml.core.compute import AksCompute, ComputeTarget\n",
"\n",
"# Uses the specific FPGA enabled VM (sku: Standard_PB6s)\n",
@@ -147,7 +176,8 @@
" agent_count = 1, \n",
" location = \"westus2\")\n",
"\n",
"aks_name = 'aks-pb6-obj2'\n",
"aks_name = 'aks-pb6-obj'\n",
"\n",
"# Create the cluster\n",
"aks_target = ComputeTarget.create(workspace = ws, \n",
" name = aks_name, \n",
@@ -160,18 +190,12 @@
"metadata": {},
"outputs": [],
"source": [
"# Monitor deployment\n",
"aks_target.wait_for_completion(show_output=True)\n",
"print(aks_target.provisioning_state)\n",
"print(aks_target.provisioning_errors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Deploy AccelContainerImage to AKS ComputeTarget"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -186,14 +210,21 @@
" num_replicas=1,\n",
" auth_enabled = False)\n",
"\n",
"aks_service_name ='my-aks-service'\n",
"aks_service_name ='fpga-aks-service'\n",
"\n",
"aks_service = Webservice.deploy_from_image(workspace = ws,\n",
" name = aks_service_name,\n",
" image = image,\n",
" deployment_config = aks_config,\n",
" deployment_target = aks_target)\n",
"aks_service.wait_for_deployment(show_output = True)"
"aks_service.wait_for_deployment(show_output = True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Test the cloud service "
]
},
{
@@ -203,15 +234,8 @@
"outputs": [],
"source": [
"# Using the grpc client in AzureML Accelerated Models SDK\n",
"from azureml.accel import PredictionClient"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from azureml.accel import PredictionClient\n",
"\n",
"address = aks_service.scoring_uri\n",
"ssl_enabled = address.startswith(\"https\")\n",
"address = address[address.find('/')+2:].strip('/')\n",
@@ -225,7 +249,6 @@
"metadata": {},
"outputs": [],
"source": [
"\n",
"# Initialize AzureML Accelerated Models client\n",
"client = PredictionClient(address=address,\n",
" port=port,\n",
@@ -243,6 +266,13 @@
"output_tensors = saver.output_names"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Visualize prediction using the deployed model"
]
},
{
"cell_type": "code",
"execution_count": null,
@@ -250,39 +280,76 @@
"outputs": [],
"source": [
"import glob\n",
"import matplotlib as plt\n",
"import cv2\n",
"\n",
"# Select an example image to test. \n",
"# Default directory is the image_dir created in the Finetune VGG SSD notebook.\n",
"\n",
"image_dir = expanduser(\"~/azml_ssd_vgg/JPEGImages\")\n",
"\n",
"image_dir = r'/datadrive/Dropbox/neal/data/kroger/dataset/test/JPEGImages/'\n",
"im_files = glob.glob(os.path.join(image_dir, '*.jpg'))\n",
"im_file = im_files[-21]\n",
"\n",
"im_file = im_files[0]\n",
"\n",
"\n",
"import azureml.accel._external.ssdvgg_utils as ssdvgg_utils\n",
"\n",
"result = client.score_file(path=im_file, input_name=saver.input_name_str, outputs=output_tensors)\n",
"result = client.score_file(path=im_file, \n",
" input_name=saver.input_name_str, \n",
" outputs=output_tensors)\n",
"\n",
"classes, scores, bboxes = ssdvgg_utils.postprocess(result, select_threshold=0.5)\n",
"\n",
"plt.rcParams['figure.figsize'] = 15, 15\n",
"img = cv2.imread(im_file)\n",
"img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
"visualization.plt_bboxes(img, classes, scores, bboxes)\n"
"visualization.plt_bboxes(img, classes, scores, bboxes)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Clean up image (optional)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"metadata": {},
"outputs": [],
"source": [
"result"
"#Delete your web service, image, and model (must be done in this order since there are dependencies).\n",
"\n",
"#aks_service.delete()\n",
"#aks_target.delete()\n",
"#image.delete()\n",
"#registered_model.delete()\n",
"#converted_model.delete()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"authors": [
{
"name": "v-borisk"
},
{
"name": "v-zaper"
}
],
"kernelspec": {
"display_name": "Python 3",
"display_name": "python 3.6 brain",
"language": "python",
"name": "python3"
"name": "brain"
},
"language_info": {
"codemirror_mode": {
@@ -294,7 +361,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
"version": "3.6.8"
}
},
"nbformat": 4,

274
how-to-use-azureml/deployment/accelerated-models/finetune-ssd-vgg/notebooks/Finetune VGG SSD.ipynb
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9
how-to-use-azureml/deployment/accelerated-models/finetune-ssd-vgg/tfssd/dataprep/dataset_utils.py
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@@ -46,14 +46,19 @@ def create_dir(path):
try:
path_annotations = path + '/Annotations'
path_images = path + '/JPEGImages'
path_tfrec = path + '/TFreccords'
os.makedirs(path_annotations)
os.makedirs(path_images)
os.makedirs(path_tfrec)
except OSError:
print("Creation of folders in directory %s failed. Folder may already exist." % path)
print("Creation of folders in directory %s failed. Folders may already exist." % path)
else:
print("Successfully created images and annotations folders at %s" % path)
print("Successfully created Annotations, JPEGImages, and TFreccords folders at %s" % path)
print('Please copy your annotation and image files to the Annotations and JPEGImages folders before moving to the next step')
def move_images(data_dir, train_images, train_annotations,
test_images, test_annotations):