Table Of Contents
Table Of Contents


Tools for testing.


almost_equal(a, b[, rtol, atol, equal_nan])

Test if two numpy arrays are almost equal.

almost_equal_ignore_nan(a, b[, rtol, atol])

Test that two NumPy arrays are almost equal (ignoring NaN in either array).

assert_almost_equal(a, b[, rtol, atol, …])

Test that two numpy arrays are almost equal.

assert_almost_equal_ignore_nan(a, b[, rtol, …])

Test that two NumPy arrays are almost equal (ignoring NaN in either array).

assert_exception(f, exception_type, *args, …)

Test that function f will throw an exception of type given by exception_type

assign_each(the_input, function)

Return ndarray composed of passing each array value through some function

assign_each2(input1, input2, function)

Return ndarray composed of passing two array values through some function

check_consistency(sym, ctx_list[, scale, …])

Check symbol gives the same output for different running context

check_numeric_gradient(sym, location[, …])

Verify an operation by checking backward pass via finite difference method.

check_speed(sym[, location, ctx, N, …])

Check the running speed of a symbol.

check_symbolic_backward(sym, location, …)

Compares a symbol’s backward results with the expected ones.

check_symbolic_forward(sym, location, expected)

Compares a symbol’s forward results with the expected ones.

chi_square_check(generator, buckets, probs)

Run the chi-square test for the generator.

create_sparse_array(shape, stype[, …])

Create a sparse array, For Rsp, assure indices are in a canonical format

create_sparse_array_zd(shape, stype, density)

Create sparse array, using only rsp_indices to determine density


Get default context for regression test.


Get default data type for regression test.


Discards error output of a routine if invoked as:

download(url[, fname, dirname, overwrite, …])

Download an given URL

find_max_violation(a, b[, rtol, atol])

Finds and returns the location of maximum violation.

gen_buckets_probs_with_ppf(ppf, nbuckets)

Generate the buckets and probabilities for chi_square test when the ppf (Quantile function)


Get default numerical threshold for regression test.

get_bz2_data(data_dir, data_name, url, …)

Download and extract bz2 data.


Downloads CIFAR10 dataset into a directory in the current directory with the name data, and then extracts all files into the directory data/cifar.


Get path to the tool


Download and load the MNIST dataset

get_mnist_iterator(batch_size, input_shape)

Returns training and validation iterators for MNIST dataset


Downloads MNIST dataset as a pkl.gz into a directory in the current directory with the name data


Downloads ubyte version of the MNIST dataset into a directory in the current directory with the name data and extracts all files in the zip archive to this directory.


Get default numerical threshold for regression test.

get_zip_data(data_dir, url, data_origin_name)

Download and extract zip data.


Return a list of GPUs

mean_check(generator, mu, sigma[, nsamples])

Test the generator by matching the mean.

np_reduce(dat, axis, keepdims, numpy_reduce_func)

Compatible reduce for old version of NumPy.

numeric_grad(executor, location[, …])

Calculates a numeric gradient via finite difference method.

rand_ndarray(shape[, stype, density, dtype, …])

rand_shape_2d([dim0, dim1])

rand_shape_3d([dim0, dim1, dim2])

rand_shape_nd(num_dim[, dim])

rand_sparse_ndarray(shape, stype[, density, …])

Generate a random sparse ndarray.


Generate some random numpy arrays.

random_sample(population, k)

Return a k length list of the elements chosen from the population sequence.


Retry n times before failing for stochastic test cases.

same(a, b)

Test if two NumPy arrays are the same.

same_array(array1, array2)

Check whether two NDArrays sharing the same memory block


Set default context.

set_env_var(key, val[, default_val])

Set environment variable


Shuffle CSR column indices per row This allows validation of unordered column indices, which is not a requirement for a valid CSR matrix

simple_forward(sym[, ctx, is_train])

A simple forward function for a symbol.

var_check(generator, sigma[, nsamples])

Test the generator by matching the variance.

verify_generator(generator, buckets, probs)

Verify whether the generator is correct using chi-square testing.



A dummy iterator that always returns the same batch of data (the first data batch of the real data iter).