Utilities & Extras
Helpers and utility functions

fix_random_seed function

Set the random seed for random, numpy.random and cupy.random (if available). Should be called at the top of a file or function.

Examplefrom thinc.api import fix_random_seed
fix_random_seed(0)
ArgumentTypeDescription
seedintThe seed. Defaults to 0.

prefer_gpu function

Allocate data and perform operations on GPU, if available. If data has already been allocated on CPU, it will not be moved. Ideally, this function should be called right after importing Thinc.

Examplefrom thinc.api import prefer_gpu
is_gpu = prefer_gpu()
ArgumentTypeDescription
gpu_idintDevice index to select. Defaults to 0.
RETURNSboolWhether the GPU was activated.

require_gpu function

Allocate data and perform operations on GPU. Will raise an error if no GPU is available. If data has already been allocated on CPU, it will not be moved. Ideally, this function should be called right after importing Thinc.

Examplefrom thinc.api import require_gpu
require_gpu()
ArgumentTypeDescription
RETURNSboolTrue.

set_active_gpu function

Set the current GPU device for cupy and torch (if available).

Examplefrom thinc.api import set_active_gpu
set_active_gpu(0)
ArgumentTypeDescription
gpu_idintDevice index to select.
RETURNScupy.cuda.DeviceThe device.

use_pytorch_for_gpu_memory function

Route GPU memory allocation via PyTorch. This is recommended for using PyTorch and cupy together, as otherwise OOM errors can occur when there’s available memory sitting in the other library’s pool. We’d like to support routing TensorFlow memory allocation via PyTorch as well (or vice versa), but do not currently have an implementation for it.

Examplefrom thinc.api import prefer_gpu, use_pytorch_for_gpu_memory

if prefer_gpu():
    use_pytorch_for_gpu_memory()

use_tensorflow_for_gpu_memory function

Route GPU memory allocation via TensorFlow. This is recommended for using TensorFlow and cupy together, as otherwise OOM errors can occur when there’s available memory sitting in the other library’s pool. We’d like to support routing PyTorch memory allocation via TensorFlow as well (or vice versa), but do not currently have an implementation for it.

Examplefrom thinc.api import prefer_gpu, use_tensorflow_for_gpu_memory

if prefer_gpu():
    use_tensorflow_for_gpu_memory()

get_width function

Infer the width of a batch of data, which could be any of: an n-dimensional array (use the shape) or a sequence of arrays (use the shape of the first element).

ArgumentTypeDescription
XUnion[ArrayXd, Ragged, Padded, Sequence[ArrayXd]]The array(s).
keyword-only
dimintWhich dimension to get the size for. Defaults to -1.
RETURNSintThe array’s inferred width.

to_categorical function

Converts a class vector (integers) to binary class matrix. Based on keras.utils.to_categorical.

ArgumentTypeDescription
YIntsXdClass vector to be converted into a matrix (integers from 0 to n_classes).
n_classesOptional[int]Total number of classes.
RETURNS Floats2dA binary matrix representation of the input. The axis representing the classes is placed last.

xp2torch function

Convert a numpy or cupy tensor to a PyTorch tensor.

ArgumentTypeDescription
xp_tensorArrayXdThe tensor to convert.
requires_gradboolWhether to backpropagate through the variable.
RETURNStorch.TensorThe converted tensor.

torch2xp function

Convert a PyTorch tensor to a numpy or cupy tensor.

ArgumentTypeDescription
torch_tensortorch.TensorThe tensor to convert.
RETURNSArrayXdThe converted tensor.

xp2tensorflow function

Convert a numpy or cupy tensor to a TensorFlow tensor.

ArgumentTypeDescription
xp_tensorArrayXdThe tensor to convert.
requires_gradboolWhether to backpropagate through the variable.
as_variableboolConvert the result to a tensorflow.Variable object.
RETURNStensorflow.TensorThe converted tensor.

tensorflow2xp function

Convert a TensorFlow tensor to a numpy or cupy tensor.

ArgumentTypeDescription
tensorflow_tensortensorflow.TensorThe tensor to convert.
RETURNSArrayXdThe converted tensor.

xp2mxnet function

Convert a numpy or cupy tensor to an MXNet tensor.

ArgumentTypeDescription
xp_tensorArrayXdThe tensor to convert.
requires_gradboolWhether to backpropagate through the variable.
RETURNSmx.nd.NDArrayThe converted tensor.

mxnet2xp function

Convert an MXNet tensor to a numpy or cupy tensor.

ArgumentTypeDescription
mx_tensormx.nd.NDArrayThe tensor to convert.
RETURNSArrayXdThe converted tensor.

Errors

Thinc uses the following custom errors:

NameDescription
ConfigValidationErrorRaised if invalid config settings are encountered by Config or the registry, or if resolving and validating the referenced functions fails.
DataValidationErrorRaised if Model.initialize is called with sample input or output data that doesn’t match the expected input or output of the network, or leads to mismatched input or output in any of its layer.