Pyomo Debug No Solution - pyomo

Question

I have a very strange problem. I made a notebook containing my model, a very simple linear problem with some constraints. And it works, i.e. the solution is found. The env is made with conda.
In this case I get:
{'Problem':
[{'Name': 'unknown',
'Lower bound': 23.02222576,
'Upper bound': 23.02222576,
'Number of objectives': 1,
'Number of constraints': 395,
'Number of variables': 961,
'Number of nonzeros': 200,
'Sense': 'minimize'}],
'Solver': [{'Status': 'ok', 'User time': -1.0, 'System time': 0.02, 'Wallclock time': 0.01, 'Termination condition': 'optimal', 'Termination message': 'Model was solved to optimality (subject to tolerances), and an optimal solution is available.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': None, 'Number of created subproblems': None}, 'Black box': {'Number of iterations': 448}}, 'Error rc': 0, 'Time': 0.020696401596069336}], 'Solution': [OrderedDict([('number of solutions', 0), ('number of solutions displayed', 0)])]}
Now, I copied the notebook code inside my big python service. Same code, more or less.
The solver is cbc installed system wide.
The solution is never found.
Before posting my code, I would understand how to debug .
In the second case, I get always:
{'Problem':
[{'Name': 'unknown',
'Lower bound': None,
'Upper bound': inf, 'Number of objectives': 1,
'Number of constraints': 395,
'Number of variables': 961,
'Number of nonzeros': 202, 'Sense': 'minimize'}],
'Solver': [{'Status': 'warning', 'User time': -1.0, 'System time': 0.01, 'Wallclock time': 0.01, 'Termination condition': 'infeasible', 'Termination message': 'Model was proven to be infeasible.', 'Statistics': {'Branch and bound': {'Number of bounded subproblems': 0, 'Number of created subproblems': 0}, 'Black box': {'Number of iterations': 0}}, 'Error rc': 0, 'Time': 0.024977684020996094}], 'Solution': [OrderedDict([('number of solutions', 1), ('number of solutions displayed', 1)]), {'Status': 'unknown', 'Problem': {}, 'Objective': {}, 'Variable': {}, 'Constraint': {}}]}
The number of constraints and variable is the same in both cases, and I could swear that the code is the same. The only difference is the 'cbc' solver that in the first case is from conda.
I checked the variables with model.pprint(), and in the second case, I saw that some of them never get increased or changed.
Another strange thing, is that the number of iterations is zero in the second case....
[EDIT]
My fault. The configuration parameters were different between the 2 env.