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`mypy`](https://thinc.ai/docs/usage-type-checking#install) with the +custom Thinc plugin and get live feedback about mismatched types as you write +code. + +[![](../images/thinc_mypy.jpg)](https://thinc.ai/docs/usage-type-checking#linting) + + + ## Defining sublayers {#sublayers} ​ Model architecture functions often accept **sublayers as arguments**, so that @@ -111,8 +122,48 @@ you'll be able to try it out in any of spaCy components. ​ ## Wrapping PyTorch, TensorFlow and other frameworks {#frameworks} + + +Thinc allows you to wrap models written in other machine learning frameworks +like PyTorch, TensorFlow and MXNet using a unified +[`Model`](https://thinc.ai/docs/api-model) API. As well as **wrapping whole +models**, Thinc lets you call into an external framework for just **part of your +model**: you can have a model where you use PyTorch just for the transformer +layers, using "native" Thinc layers to do fiddly input and output +transformations and add on task-specific "heads", as efficiency is less of a +consideration for those parts of the network. + +Thinc uses a special class, [`Shim`](https://thinc.ai/docs/api-model#shim), to +hold references to external objects. This allows each wrapper space to define a +custom type, with whatever attributes and methods are helpful, to assist in +managing the communication between Thinc and the external library. The +[`Model`](/docs/api-model#model) class holds `shim` instances in a separate +list, and communicates with the shims about updates, serialization, changes of +device, etc. + +The wrapper will receive each batch of inputs, convert them into a suitable form +for the underlying model instance, and pass them over to the shim, which will +**manage the actual communication** with the model. The output is then passed +back into the wrapper, and converted for use in the rest of the network. The +equivalent procedure happens during backpropagation. Array conversion is handled +via the [DLPack](https://github.com/dmlc/dlpack) standard wherever possible, so +that data can be passed between the frameworks **without copying the data back** +to the host device unnecessarily. + +| Framework | Wrapper layer | Shim | DLPack | +| -------------- | ------------------------------------------------------------------------- | --------------------------------------------------------- | --------------- | +| **PyTorch** | [`PyTorchWrapper`](https://thinc.ai/docs/api-layers#pytorchwrapper) | [`PyTorchShim`](https://thinc.ai/docs/api-model#shims) | ✅ | +| **TensorFlow** | [`TensorFlowWrapper`](https://thinc.ai/docs/api-layers#tensorflowwrapper) | [`TensorFlowShim`](https://thinc.ai/docs/api-model#shims) | ❌ 1 | +| **MXNet** | [`MXNetWrapper`](https://thinc.ai/docs/api-layers#mxnetwrapper) | [`MXNetShim`](https://thinc.ai/docs/api-model#shims) | ✅ | + +1. DLPack support in TensorFlow is now + [available](<(https://github.com/tensorflow/tensorflow/issues/24453)>) but + still experimental. + + ## Models for trainable components {#components}