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Automatic differentiation package - tensorplay.autograd

Automatic differentiation package - torch.autograd

`tensorplay._C._autograd.backward`
`tensorplay.autograd.grad`	Compute and return the sum of gradients of outputs with respect to the inputs.

Functional higher level API

`tensorplay.autograd.functional.jacobian`	Compute the Jacobian of a given function.
`tensorplay.autograd.functional.hessian`	Compute the Hessian of a given scalar function.
`tensorplay.autograd.functional.vjp`	Compute the dot product between a vector `v` and the Jacobian of the given function at the point given by the inputs.
`tensorplay.autograd.functional.jvp`	Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector `v`.
`tensorplay.autograd.functional.vhp`	Compute the dot product between vector `v` and Hessian of a given scalar function at a specified point.
`tensorplay.autograd.functional.hvp`	Compute the dot product between the scalar function's Hessian and a vector `v` at a specified point.

Function

`tensorplay.autograd.function.Function`	Records operation history and defines formulas for differentiating ops.
`tensorplay.autograd.backward`
`tensorplay.autograd.jvp`	Compute the dot product between the Jacobian of the given function at the point given by the inputs and a vector `v`.

Custom Function utilities

tensorplay.autograd.function.once_differentiable

Decorator to make a custom autograd Function's backward run once, with gradients detached and grad-mode disabled inside.

Numerical gradient checking

`tensorplay.autograd.gradcheck.gradcheck`	Check gradients computed via small finite differences against analytical gradients wrt tensors in `inputs` that are of floating point or complex type and with `requires_grad=True`.
`tensorplay.autograd.gradcheck.gradgradcheck`	Check gradients of gradients computed via small finite differences against analytical gradients wrt tensors in `inputs` and `grad_outputs` that are of floating point or complex type and with `requires_grad=True`.
`tensorplay.autograd.gradcheck.GradcheckError`	Error raised by `gradcheck()` and `gradgradcheck()`.
`tensorplay.autograd.gradcheck.get_analytical_jacobian`
`tensorplay.autograd.gradcheck.get_numerical_jacobian`	Compute the numerical Jacobian for a given fn and its inputs.
`tensorplay.autograd.gradcheck.get_numerical_jacobian_wrt_specific_input`

Debugging and anomaly detection

`tensorplay.autograd.anomaly_mode.detect_anomaly`	Context-manager that enables anomaly detection for the autograd engine.
`tensorplay.autograd.anomaly_mode.set_detect_anomaly`	Context-manager that sets the anomaly detection for the autograd engine on or off.

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TensorPlay documentation

TensorPlay is a PyTorch-compatible tensor library for deep learning using GPUs and CPUs.

Automatic Mixed Precision package - tensorplay.amp

tensorplay.amp.autocast_mode.is_autocast_available