Revised according to the comment

Author: cehongwang

core/runtime/runtime.h

@@ -16,7 +16,7 @@ namespace core {
 namespace runtime {
 
 using EngineID = int64_t;
-const std::string ABI_VERSION = "7";
+const std::string ABI_VERSION = "8";
 extern bool MULTI_DEVICE_SAFE_MODE;
 
 typedef enum {
@@ -46,9 +46,6 @@ std::string base64_encode(const std::string& in);
 std::string base64_decode(const std::string& in);
 std::string serialize_bindings(const std::vector<std::string>& bindings);
 
-std::string resource_allocation_strategy_to_string(TRTEngine::ResourceAllocationStrategy strategy);
-TRTEngine::ResourceAllocationStrategy resource_allocation_strategy_from_string(const std::string& str);
-
 c10::optional<RTDevice> get_most_compatible_device(
     const RTDevice& target_device,
     const RTDevice& curr_device = RTDevice(),

examples/dynamo/dynamic_memory_allocation.py

@@ -1,10 +1,11 @@
 # %%
+import gc
+import time
+
 import numpy as np
 import torch
 import torch_tensorrt as torch_trt
 import torchvision.models as models
-import time
-import gc
 
 np.random.seed(5)
 torch.manual_seed(5)
@@ -16,17 +17,19 @@
     "enabled_precisions": {torch.float32},
     "immutable_weights": False,
     "lazy_engine_init": True,
-    "dynamically_allocate_resources": True
-
+    "dynamically_allocate_resources": True,
 }
 
 model = models.resnet152(pretrained=True).eval().to("cuda")
 compiled_module = torch_trt.compile(model, inputs=inputs, **settings)
 print((torch.cuda.mem_get_info()[1] - torch.cuda.mem_get_info()[0]) / 1024**3)
 compiled_module(*inputs)
 
+
 time.sleep(30)
-with torch_trt.dynamo.runtime.ResourceAllocationStrategy(compiled_module, dynamically_allocate_resources=False):
+with torch_trt.dynamo.runtime.ResourceAllocationStrategy(
+    compiled_module, dynamically_allocate_resources=False
+):
     print(
         "Memory used (GB):",
         (torch.cuda.mem_get_info()[1] - torch.cuda.mem_get_info()[0]) / 1024**3,