from azureml.core import Workspace
from azureml.core import Environment
from azureml.core.model import InferenceConfig, Model
from azureml.core.webservice import AciWebservice, Webservice
ws = Workspace(subscription_id="19f6dcec-26c8-4916-89b0-ac775bc5e6b8",
resource_group="dev",
workspace_name="climbing-hold-detection")
env = Environment.from_dockerfile(name="yolov5s", dockerfile="Dockerfile")
env.inferencing_stack_version = "latest"
env.python.user_managed_dependencies = True
env.python.interpreter_path = "/usr/bin/python3.7"
inference_config = InferenceConfig(
environment=env,
source_directory="./source_dir",
entry_script="./entry_script.py",
)
deployment_config = AciWebservice.deploy_configuration(cpu_cores=1,
memory_gb=1)
deployment_config.auth_enabled = True
model = Model(workspace=ws, name="yolov5s", id="yolov5s_onnx:1")
service = Model.deploy(
ws,
"yolov5s-service",
[model],
import time
from azureml.core import Workspace, Experiment, ScriptRunConfig, Environment
from azureml.core.runconfig import RunConfiguration, DockerConfiguration
ws = Workspace.from_config()
ray_environment_name = "aml-ray-cpu"
ray_environment_dockerfile_path = "./Docker/Dockerfile-cpu"
# Build CPU image for Ray
ray_cpu_env = Environment.from_dockerfile(
name=ray_environment_name, dockerfile=ray_environment_dockerfile_path)
ray_cpu_env.register(workspace=ws)
ray_cpu_build_details = ray_cpu_env.build(workspace=ws)
while ray_cpu_build_details.status not in ["Succeeded", "Failed"]:
print(
f"Awaiting completion of ray CPU environment build. Current status is: {ray_cpu_build_details.status}"
)
time.sleep(10)
command = ["python distribute_automl.py"]
env = Environment.get(workspace=ws, name=ray_environment_name)
compute_target = ws.compute_targets["cpucluster"]
aml_run_config = RunConfiguration(communicator="OpenMpi")
aml_run_config.target = compute_target
aml_run_config.docker = DockerConfiguration(use_docker=True)
aml_run_config.environment = env
aml_run_config.node_count = 2
config = ScriptRunConfig(
source_directory="ray/",
command=command,
else:
model_batchsize = MODEL_BATCHSIZE_DICT[args.hf_model]
base_args_dict = {
"bert-large" : ['--model_name_or_path', 'bert-large-uncased', '--dataset_name', 'wikitext', '--dataset_config_name', 'wikitext-2-raw-v1', '--do_train', '--max_steps', args.max_steps, '--logging_steps', 200, '--output_dir', '/tmp/test-mlm-bbu', '--overwrite_output_dir', '--per_device_train_batch_size', model_batchsize, '--fp16'],
"distilbert-base" : ['--model_name_or_path', 'distilbert-base-uncased', '--dataset_name', 'wikitext', '--dataset_config_name', 'wikitext-2-raw-v1', '--do_train', '--max_steps', args.max_steps, '--logging_steps', 200, '--output_dir', '/tmp/test-mlm-bbu', '--overwrite_output_dir', '--per_device_train_batch_size', model_batchsize, '--fp16'],
"gpt2" : ['--model_name_or_path', 'gpt2', '--dataset_name', 'wikitext', '--dataset_config_name', 'wikitext-2-raw-v1', '--do_train', '--label_smoothing', 0.1, '--max_steps', args.max_steps, '--logging_steps', 200, '--overwrite_output_dir', '--output_dir', '/tmp/test-clm', '--per_device_train_batch_size', model_batchsize, '--fp16'],
"bart-large" : ['--dataset_name', 'wmt16', '--dataset_config', 'ro-en', '--model_name_or_path', 'facebook/bart-large', '--output_dir', '/tmp/tst-translation', '--do_train', '--label_smoothing', 0.1, '--logging_steps', 200, '--overwrite_output_dir', '--per_device_train_batch_size', model_batchsize, '--predict_with_generate', '--source_lang', 'en', '--target_lang', 'ro', '--warmup_steps', 5, '--fp16', '--max_steps', args.max_steps],
"t5-large" : ['--source_prefix', 'translate English to Romanian:', '--dataset_name', 'wmt16', '--dataset_config', 'ro-en', '--model_name_or_path', 't5-large', '--output_dir', '/tmp/tst-translation', '--do_train', '--label_smoothing', 0.1, '--logging_steps', 200, '--overwrite_output_dir', '--per_device_train_batch_size', model_batchsize, '--predict_with_generate', '--source_lang', 'en', '--target_lang', 'ro', '--warmup_steps', 5, '--fp16', '--max_steps', args.max_steps],
"deberta-v2-xxlarge" : ['--model_name_or_path', 'microsoft/deberta-v2-xxlarge', '--task_name', 'MRPC', '--do_train', '--max_seq_length', 128, '--per_device_train_batch_size', model_batchsize, '--learning_rate', '3e-6', '--max_steps', args.max_steps, '--output_dir', '/tmp/deberta_res', '--overwrite_output_dir', '--logging_steps', 200, '--fp16'],
"roberta-large" : ['--model_name_or_path', 'roberta-large', '--dataset_name', 'squad', '--do_train', '--per_device_train_batch_size', model_batchsize, '--learning_rate', '3e-5', '--max_steps', args.max_steps, '--max_seq_length', 384, '--doc_stride', 128, '--output_dir', '/tmp/roberta_res', '--overwrite_output_dir', '--logging_steps', 200, '--fp16']
}
if not args.local_run:
if args.use_cu102:
hf_ort_env = Environment.from_dockerfile(name='hf-ort-dockerfile-10.2', dockerfile='../docker/Dockerfile-10.2')
else:
hf_ort_env = Environment.from_dockerfile(name='hf-ort-dockerfile', dockerfile='../docker/Dockerfile')
# This step builds a new docker image from dockerfile
if not args.skip_docker_build:
hf_ort_env.register(ws).build(ws).wait_for_completion()
model_experiment_name = 'hf-ortmodule-recipe-' + args.hf_model
model_run_args_base = base_args_dict[args.hf_model]
model_run_scripts = RUN_SCRIPT_DICT[args.hf_model]
# copy dependent run script to current folder
for script_file in model_run_scripts:
model_run_script_path = os.path.normcase(os.path.join(TRAINER_DIR, RUN_SCRIPT_DIR_DICT[args.hf_model], script_file))
shutil.copy(model_run_script_path, '.')
