# Resource Manager

Sometimes we want to store data for different operations to share, or maintain a stateful kernel (data are shared across different invocations). One way to achieve this goal in the concurrency environment is to use ResourceMgr in C++ custom operators.

A typical usage of ResourceMgr is as follows

1. Define your own resource, which should inherent from ResourceBase and DebugString must be defined (it is an abstract method in ResourceBase).
#include "tensorflow/core/framework/resource_mgr.h"
struct MyVar: public ResourceBase{
string DebugString() const { return "MyVar"; };
mutex mu;
int32 val;
};
1. Access the system ResourceMgr through
auto rm = context->resource_manager();
1. Define your resource creation and manipulation method (make sure at any time there is only one single instance given the same container name and resource name).
MyVar* my_var;
Status s = rm->LookupOrCreate<MyVar>("my_container", "my_name", &my_var, [&](MyVar** ret){
printf("Create a new container\n");
*ret = new MyVar;
(*ret)->val = *u_tensor;
return Status::OK();
});
DCHECK_EQ(s, Status::OK());
my_var->val += 1;
my_var->Unref();

When using the ResourceMgr, keep in mind that whenever you execute a new path in the computational graph, the system will create a new ResourceMgr. Therefore, to run operators that manipulate ResourceMgr in parallel, the trigger operator (which is fed to run(sess, ...)) must be attached those manipulation dependencies.

See the following scripts for an example