Update DEVCATALOG.md

.gitignore

@@ -16,4 +16,5 @@ output/
 tools/docker/models*
 .ipynb_checkpoints
 nc_workspace
-
+benchmarks/horovod
+data_connector/credentials.json

.gitmodules

@@ -0,0 +1,3 @@
+[submodule "models/aidd/pytorch/alphafold2/inference/alphafold"]
+	path = models/aidd/pytorch/alphafold2/inference/alphafold
+	url = https://github.com/deepmind/alphafold

CODEOWNERS

@@ -3,12 +3,14 @@
 
 # These owners will be the default owners for everything in the repo,
 # but PR owner should be able to assign other contributors when appropriate
-*    @ashahba @claynerobison @dmsuehir
+* [email protected] @ashahba @claynerobison 
 datasets @ashahba @claynerobison @dzungductran
 docs @claynerobison @mhbuehler
-k8s  @ashahba @dzungductran @kkasravi
-models @agramesh1 @ashraf-bhuiyan @riverliuintel @wei-v-wang
+k8s  @ashahba @dzungductran
+models @ashraf-bhuiyan @riverliuintel
+models @riverliuintel
 models/**/pytorch/ @leslie-fang-intel @jiayisunx @zhuhaozhe
+quickstart [email protected]
 quickstart/**/pytorch/ @leslie-fang-intel @jiayisunx @zhuhaozhe
 
 # Order is important. The last matching pattern has the most precedence.

SECURITY.md

@@ -7,6 +7,6 @@ Please report security issues or vulnerabilities to the [Intel® Security Center
 For more information on how Intel® works to resolve security issues, see
 [Vulnerability Handling Guidelines].
 
-[Intel® Security Center]:https://www.intel.com/security
+[Intel® Security Center]:https://www.intel.com/content/www/us/en/security-center/default.html
 
 [Vulnerability Handling Guidelines]:https://www.intel.com/content/www/us/en/security-center/vulnerability-handling-guidelines.html

benchmarks/common/base_benchmark_util.py

@@ -1,7 +1,7 @@
 #
 # -*- coding: utf-8 -*-
 #
-# Copyright (c) 2018-2019 Intel Corporation
+# Copyright (c) 2018-2023 Intel Corporation
 #
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
@@ -68,8 +68,8 @@ def _define_args(self):
 
         self._common_arg_parser.add_argument(
             "-p", "--precision",
-            help="Specify the model precision to use: fp32, int8, or bfloat16",
-            required=required_arg, choices=["fp32", "int8", "bfloat16"],
+            help="Specify the model precision to use: fp32, int8, bfloat16 or fp16",
+            required=required_arg, choices=["fp32", "int8", "bfloat16", "fp16"],
             dest="precision")
 
         self._common_arg_parser.add_argument(
@@ -170,6 +170,10 @@ def _define_args(self):
             "--weight-sharing",
             help="Enables experimental weight-sharing feature for RN50 int8/bf16 inference only",
             dest="weight_sharing", action="store_true")
+        self._common_arg_parser.add_argument(
+            "--synthetic-data",
+            help="Enables synthetic data layer for some models like SSD-ResNet34 where support exists",
+            dest="synthetic_data", action="store_true")
 
         self._common_arg_parser.add_argument(
             "-c", "--checkpoint",
@@ -224,6 +228,13 @@ def _define_args(self):
             dest="experimental_gelu", choices=["True", "False"],
             default=False)
 
+        self._common_arg_parser.add_argument(
+            "--amp",
+            help="use grappler auto-mixed precision as opposed to \
+            keras mixed precision",
+            dest="amp", choices=["True", "False"],
+            default=False)
+
         # Note this can't be a normal boolean flag, because we need to know when the user
         # does not explicitly set the arg value so that we can apply the appropriate
         # default value, depending on the the precision.
@@ -270,6 +281,12 @@ def _define_args(self):
             help="Additional command line arguments (prefix flag start with"
                  " '--').")
 
+        # Check if GPU is enabled.
+        self._common_arg_parser.add_argument(
+            "--gpu",
+            help="Run the benchmark script using GPU",
+            dest="gpu", action="store_true")
+
     def _validate_args(self):
         """validate the args and initializes platform_util"""
         # check if socket id is in socket number range
@@ -300,8 +317,9 @@ def _validate_args(self):
                              format(system_num_cores))
 
         if args.output_results and ((args.model_name != "resnet50" and
-                                    args.model_name != "resnet50v1_5") or args.precision != "fp32"):
-            raise ValueError("--output-results is currently only supported for resnet50 FP32 inference.")
+                                    args.model_name != "resnet50v1_5") or
+                                    (args.precision != "fp32" and args.precision != "fp16")):
+            raise ValueError("--output-results is currently only supported for resnet50 FP32 or FP16 inference.")
         elif args.output_results and (args.mode != "inference" or not args.data_location):
             raise ValueError("--output-results can only be used when running inference with a dataset.")
 
@@ -344,6 +362,14 @@ def _validate_args(self):
                       "This is less than the number of cores per socket on the system ({})".
                       format(args.socket_id, cpuset_len_for_socket, self._platform_util.num_cores_per_socket))
 
+        if args.gpu:
+            if args.socket_id != -1:
+                raise ValueError("--socket-id cannot be used with --gpu parameter.")
+            if args.num_intra_threads is not None:
+                raise ValueError("--num-intra-threads cannot be used with --gpu parameter.")
+            if args.num_inter_threads is not None:
+                raise ValueError("--num-inter-threads cannot be used with --gpu parameter.")
+
     def initialize_model(self, args, unknown_args):
         """Create model initializer for the specified model"""
         model_initializer = None

benchmarks/common/platform_util.py

@@ -229,7 +229,7 @@ def _get_list_from_string_ranges(self, str_ranges):
                 start, end = section.split("-")
                 section_list = range(int(start), int(end) + 1)
                 result_list += section_list
-            elif(len(section)):
+            elif len(section):
                 # This section is either empty or just a single number and not a range
                 result_list.append(int(section))