def __init__(self, platform_util_obj=None):
self._common_arg_parser = None
self._define_args()
self.args, _ = self._common_arg_parser.parse_known_args()
# currently used for testing, need to be able to pass in mocked values
# TODO: but also, why is this class not inheriting PlatformUtil?
self._platform_util = platform_util_obj or platform_util.PlatformUtil(self.args)
self._validate_args()
def run_bare_metal(self, benchmark_scripts, intelai_models,
intelai_models_common, env_var_dict):
"""
Runs the model without a container
"""
# setup volume directories to be the local system directories, since we aren't
# mounting volumes when running bare metal, but start.sh expects these args
args = self.args
workspace = os.path.join(benchmark_scripts, "common", args.framework)
mount_benchmark = benchmark_scripts
in_graph_path = args.input_graph
checkpoint_path = args.checkpoint
backbone_model_path = args.backbone_model
dataset_path = args.data_location
mount_external_models_source = args.model_source_dir
mount_intelai_models = intelai_models
# To Launch Tensorflow Serving benchmark we need only --in-graph arg.
# It does not support checkpoint files.
if args.framework == "tensorflow_serving":
if checkpoint_path:
raise ValueError("--checkpoint-path arg is not supported with tensorflow serving benchmarking")
if args.mode != "inference":
raise ValueError("--mode arg should be set to inference")
if in_graph_path:
env_var_dict["IN_GRAPH"] = in_graph_path
else:
raise ValueError("--in-graph arg is required to run tensorflow serving benchmarking")
for env_var_name in env_var_dict:
os.environ[env_var_name] = str(env_var_dict[env_var_name])
# We need this env to be set for the platform util
os.environ["PYTHON_EXE"] = str(sys.executable if not args.docker_image else "python")
# Get Platformutil
platform_util_obj = None or platform_util.PlatformUtil(self.args)
# Configure num_inter_threads and num_intra_threads
base_obj = BaseModelInitializer(args=self.args, custom_args=[], platform_util=platform_util_obj)
base_obj.set_num_inter_intra_threads()
# Update num_inter_threads and num_intra_threads in env dictionary
env_var_dict["NUM_INTER_THREADS"] = self.args.num_inter_threads
env_var_dict["NUM_INTRA_THREADS"] = self.args.num_intra_threads
# Set OMP_NUM_THREADS
env_var_dict["OMP_NUM_THREADS"] = self.args.num_intra_threads
else:
mount_external_models_source = args.model_source_dir
mount_intelai_models = intelai_models
mount_intelai_models_common = intelai_models_common
# Add env vars with bare metal settings
env_var_dict["MOUNT_EXTERNAL_MODELS_SOURCE"] = mount_external_models_source
env_var_dict["MOUNT_INTELAI_MODELS_SOURCE"] = mount_intelai_models
env_var_dict["MOUNT_INTELAI_MODELS_COMMON_SOURCE"] = mount_intelai_models_common
if in_graph_path:
env_var_dict["IN_GRAPH"] = in_graph_path
if checkpoint_path:
env_var_dict["CHECKPOINT_DIRECTORY"] = checkpoint_path
if backbone_model_path:
env_var_dict["BACKBONE_MODEL_DIRECTORY"] = backbone_model_path
if dataset_path:
env_var_dict["DATASET_LOCATION"] = dataset_path
# if using the default output directory, get the full path
if args.output_dir == "/models/benchmarks/common/tensorflow/logs":
args.output_dir = os.path.join(workspace, "logs")
# Add env vars with bare metal settings
env_var_dict["WORKSPACE"] = workspace
env_var_dict["MOUNT_BENCHMARK"] = mount_benchmark
env_var_dict["OUTPUT_DIR"] = args.output_dir
# Set env vars for bare metal
for env_var_name in env_var_dict:
os.environ[env_var_name] = str(env_var_dict[env_var_name])
# Run the start script
start_script = os.path.join(workspace, "start.sh")
self._launch_command(["bash", start_script])
def __init__(self):
self._common_arg_parser = None
self._platform_util = platform_util.PlatformUtil()
