# 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. This section discusses the inner mechanics of intercell seeding, 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. There are two methods of seeding: the use of a seed variable, and seeding the libraries themselves.

The examples in this section are all in Python, but they also work the same in R.

### Seed Variables¶

The use of seed variables is configured by setting both the seed and seed_variable configurations in your otter_config.json:

{
"seed": 42,
"seed_variable": "rng_seed"
}


#### Notebooks¶

When seeding in notebooks, an assignment of the variable name indicated by seed_variable is added at the top of every cell. In this way, cells that use the seed variable to create an RNG or seed libraries can have their seed changed from the default at runtime.

For example, let’s say we have the configuration above and the two cells below:

x = 2 ** np.arange(5)


and

rng = np.random.default_rng(rng_seed)
y = rng.normal(100)


They would be sent to the autograder as:

rng_seed = 42
x = 2 ** np.arange(5)


and

rng_seed = 42
rng = np.random.default_rng(rng_seed)
y = rng.normal(100)


Note that the initial value of the seed variable must be set in a separate cell from any of its uses, or the original value will override autograder value.

#### Python Scripts¶

Seed variables currently do not support Python scripts.

### Seeding Libraries¶

Seeding libraries is configured by setting the seed configuration in your otter_config.json:

{
"seed": 42
}


#### 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 configuration above and 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(42)
random.seed(42)
x = 2 ** np.arange(5)


and

np.random.seed(42)
random.seed(42)
y = np.random.normal(100)


#### 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(42)
random.seed(42)
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 environment that the script is executed against.

## Caveats¶

Remekber, 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. Also, 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.