# Intercell Seeding¶

The Otter suite of tools supports intercell seeding, a process by which notebooks and scripts can be seeded for the generation of pseudorandom numbers, which is very advantageous for writing deterministic hidden tests. Otter implements this through the use of a --seed flag, which can be set to any integer. We discuss the inner mechanics of intercell seeding in this section, while the flags and other UI aspects are discussed in the sections corresponding to the Otter tool you’re using.

## Seeding Mechanics¶

This section describes at a high-level how seeding is implemented in the autograder at the layer of code execution.

### Notebooks¶

When seeding in notebooks, both NumPy and random are seeded using an integer provided by the instructor. The seeding code is added to each cell’s source before running it through the executor, meaning that the results of every cell are seeded with the same seed. For example, let’s say we have the two cells below:

x = 2 ** np.arange(5)


and

y = np.random.normal(100)


they would be sent to the autograder as:

np.random.seed(SEED)
random.seed(SEED)
x = 2 ** np.arange(5)


and

np.random.seed(SEED)
random.seed(SEED)
y = np.random.normal(100)


where SEED is the seed you passed to Otter. This has two important consequences:

1. When writing assignments or using assignment generation tools like Otter Assign, the instructor must seed the solutions themselves before writing hidden tests in order to ensure they are grading the correct values.

2. Students will not have access to the random seed, so any values they compute in the notebook may be different from the results of their submission when it is run through the autograder.

With respect to (1), Otter Assign implements this behavior through the use of seeding cells that are discarded in the output. This has a natural consequence of (2), which highlights the important of writing public tests that do not rely on the use of seeds unless they are provided in the distribution notebooks themselves (but I guess that renders the use of behind-the-scenes seeding useless, doesn’t it?).

### Python Scripts¶

Seeding Python files is relatively more simple. The implementation is similar to that of notebooks, but the script is only seeded once, at the beginning. Thus, the Python file below:

import numpy as np

def sigmoid(t):
return 1 / (1 + np.exp(-1 * t))


would be sent to the autograder as

np.random.seed(SEED)
random.seed(SEED)
import numpy as np

def sigmoid(t):
return 1 / (1 + np.exp(-1 * t))


You don’t need to worry about importing NumPy and random before seeding as these modules are loaded by the autograder and provided in the global env that the script is executed against.

## Cautions¶

In this section, we highlight a few important things that bear repeating.

• Make sure to use the same seed when creating assignments. Also make sure that you pass this seed to the --seed flag of any Otter tool you use.

• Write public tests agnostic to the seed. Students won’t have access to it, remember!