Let be a natural number $ n\ge 3. $ Find
$$ \inf_{\stackrel{ x_1,x_2,\ldots ,x_n\in\mathbb{R}_{>0}}{1=P\left( x_1,x_2,\ldots ,x_n\right)}}\sum_{i=1}^n\left( \frac{1}{x_i} -x_i \right) , $$
where $ P\left( x_1,x_2,\ldots ,x_n\right) :=\sum_{i=1}^n \frac{1}{x_i+n-1} , $ and find in which circumstances this infimum is attained.
To solve the problem, we need to find the infimum of the function 
\[ f(x_1, x_2, \ldots, x_n) = \sum_{i=1}^n \left( \frac{1}{x_i} - x_i \right) \]
subject to the constraint 
\[ P(x_1, x_2, \ldots, x_n) = \sum_{i=1}^n \frac{1}{x_i + n - 1} = 1. \]

1. **Evaluate the function at a specific point:**
   Let's first evaluate \( f \) at the point \( (1, 1, \ldots, 1) \):
   \[
   f(1, 1, \ldots, 1) = \sum_{i=1}^n \left( \frac{1}{1} - 1 \right) = \sum_{i=1}^n (1 - 1) = \sum_{i=1}^n 0 = 0.
   \]
   Therefore, \( f(1, 1, \ldots, 1) = 0 \).

2. **Behavior of \( f \) for large values of \( x_i \):**
   When some \( x_i \) is large, \( \frac{1}{x_i} \) becomes small and \( -x_i \) becomes large negative. Thus, \( f \) becomes large negative. However, since \( x_i \) are positive, we need to consider the constraint \( P(x_1, x_2, \ldots, x_n) = 1 \).

3. **Convexity and boundedness:**
   The function \( f(x_1, x_2, \ldots, x_n) \) is convex because each term \( \frac{1}{x_i} - x_i \) is convex. We consider the set \( D = \{(x_1, x_2, \ldots, x_n) : 0 < x_i \leq A\} \) for some large \( A \). This set is bounded.

4. **Compact subset and constraint:**
   Define \( F = \{(x_1, x_2, \ldots, x_n) : P(x_1, x_2, \ldots, x_n) = 1\} \). Since \( P \) is continuous, \( F \) is closed. The intersection \( K' = K \cap F \) is compact and non-empty (since \( (1, 1, \ldots, 1) \in K' \)).

5. **Minimization on compact set:**
   Since \( f \) is continuous and \( K' \) is compact, \( f \) attains its minimum on \( K' \). Suppose the minimum is attained at \( (x_1^0, x_2^0, \ldots, x_n^0) \).

6. **Symmetry and contradiction:**
   Assume for contradiction that not all \( x_i^0 \) are equal. Without loss of generality, let \( x_1^0 \neq x_2^0 \). Vary \( x_1 \) and \( x_2 \) such that 
   \[
   \frac{1}{x_1 + n - 1} + \frac{1}{x_2 + n - 1} = \frac{1}{x_1^0 + n - 1} + \frac{1}{x_2^0 + n - 1} = \text{const}.
   \]
   When \( x_1 = x_2 \), the value of \( f \) is less than \( f(x_1^0, x_2^0, \ldots) \), leading to a contradiction. Hence, all \( x_i \) must be equal.

7. **Conclusion:**
   The minimum value of \( f \) under the given constraint is \( f(1, 1, \ldots, 1) = 0 \).

The final answer is \(\boxed{0}\).