pytorch · soumith · Nov 7, 2019 · Nov 6, 2019 · Nov 6, 2019 · Nov 6, 2019
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diff --git a/docs/1.3.0/_modules/torchvision/utils.html b/docs/1.3.0/_modules/torchvision/utils.html
diff --git a/docs/1.3.0/_sources/__config__.rst.txt b/docs/1.3.0/_sources/__config__.rst.txt
@@ -0,0 +1,7 @@
+torch.__config__
+===================================
+
+.. automodule:: torch.__config__
+
+.. autofunction:: show
+.. autofunction:: parallel_info
diff --git a/docs/1.3.0/_sources/autograd.rst.txt b/docs/1.3.0/_sources/autograd.rst.txt
@@ -0,0 +1,110 @@
+.. role:: hidden
+    :class: hidden-section
+
+Automatic differentiation package - torch.autograd
+==================================================
+
+.. automodule:: torch.autograd
+.. currentmodule:: torch.autograd
+
+.. autofunction:: backward
+
+.. autofunction:: grad
+
+.. _locally-disable-grad:
+
+Locally disabling gradient computation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: no_grad
+
+.. autoclass:: enable_grad
+
+.. autoclass:: set_grad_enabled
+
+In-place operations on Tensors
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Supporting in-place operations in autograd is a hard matter, and we discourage
+their use in most cases. Autograd's aggressive buffer freeing and reuse makes
+it very efficient and there are very few occasions when in-place operations
+actually lower memory usage by any significant amount. Unless you're operating
+under heavy memory pressure, you might never need to use them.
+
+In-place correctness checks
+---------------------------
+
+All :class:`Tensor` s keep track of in-place operations applied to them, and
+if the implementation detects that a tensor was saved for backward in one of
+the functions, but it was modified in-place afterwards, an error will be raised
+once backward pass is started. This ensures that if you're using in-place
+functions and not seeing any errors, you can be sure that the computed
+gradients are correct.
+
+Variable (deprecated)
+^^^^^^^^^^^^^^^^^^^^^
+
+.. warning::
+    The Variable API has been deprecated: Variables are no longer necessary to
+    use autograd with tensors. Autograd automatically supports Tensors with
+    ``requires_grad`` set to ``True``. Below please find a quick guide on what
+    has changed:
+
+    - ``Variable(tensor)`` and ``Variable(tensor, requires_grad)`` still work as expected,
+      but they return Tensors instead of Variables.
+    - ``var.data`` is the same thing as ``tensor.data``.
+    - Methods such as ``var.backward(), var.detach(), var.register_hook()`` now work on tensors
+      with the same method names.
+
+    In addition, one can now create tensors with ``requires_grad=True`` using factory
+    methods such as :func:`torch.randn`, :func:`torch.zeros`, :func:`torch.ones`, and others
+    like the following:
+
+    ``autograd_tensor = torch.randn((2, 3, 4), requires_grad=True)``
+
+Tensor autograd functions
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: torch.Tensor
+   :members: grad, requires_grad, is_leaf, backward, detach, detach_, register_hook, retain_grad
+
+:hidden:`Function`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: Function
+    :members:
+
+.. _grad-check:
+
+Numerical gradient checking
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autofunction:: gradcheck
+
+.. autofunction:: gradgradcheck
+
+Profiler
+^^^^^^^^
+
+Autograd includes a profiler that lets you inspect the cost of different
+operators inside your model - both on the CPU and GPU. There are two modes
+implemented at the moment - CPU-only using :class:`~torch.autograd.profiler.profile`.
+and nvprof based (registers both CPU and GPU activity) using
+:class:`~torch.autograd.profiler.emit_nvtx`.
+
+.. autoclass:: torch.autograd.profiler.profile
+    :members:
+
+.. autoclass:: torch.autograd.profiler.record_function
+    :members:
+
+.. autoclass:: torch.autograd.profiler.emit_nvtx
+    :members:
+
+.. autofunction:: torch.autograd.profiler.load_nvprof
+
+Anomaly detection
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+.. autoclass:: detect_anomaly
+
+.. autoclass:: set_detect_anomaly
diff --git a/docs/1.3.0/_sources/bottleneck.rst.txt b/docs/1.3.0/_sources/bottleneck.rst.txt
@@ -0,0 +1,59 @@
+torch.utils.bottleneck
+======================
+
+.. currentmodule:: torch.utils.bottleneck
+
+`torch.utils.bottleneck` is a tool that can be used as an initial step for
+debugging bottlenecks in your program. It summarizes runs of your script with
+the Python profiler and PyTorch's autograd profiler.
+
+Run it on the command line with
+
+::
+
+    python -m torch.utils.bottleneck /path/to/source/script.py [args]
+
+where [args] are any number of arguments to `script.py`, or run
+``python -m torch.utils.bottleneck -h`` for more usage instructions.
+
+.. warning::
+    Because your script will be profiled, please ensure that it exits in a
+    finite amount of time.
+
+.. warning::
+    Due to the asynchronous nature of CUDA kernels, when running against
+    CUDA code, the cProfile output and CPU-mode autograd profilers may
+    not show correct timings: the reported CPU time reports the amount of time
+    used to launch the kernels but does not include the time the kernel
+    spent executing on a GPU unless the operation does a synchronize.
+    Ops that do synchronize appear to be extremely expensive under regular
+    CPU-mode profilers.
+    In these case where timings are incorrect, the CUDA-mode autograd profiler
+    may be helpful.
+
+.. note::
+    To decide which (CPU-only-mode or CUDA-mode) autograd profiler output to
+    look at, you should first check if your script is CPU-bound
+    ("CPU total time is much greater than CUDA total time").
+    If it is CPU-bound, looking at the results of the CPU-mode autograd
+    profiler will help. If on the other hand your script spends most of its
+    time executing on the GPU, then it makes sense to start
+    looking for responsible CUDA operators in the output of the CUDA-mode
+    autograd profiler.
+
+    Of course the reality is much more complicated and your script might not be
+    in one of those two extremes depending on the part of the model you're
+    evaluating. If the profiler outputs don't help, you could try looking at
+    the result of :func:`torch.autograd.profiler.emit_nvtx()` with ``nvprof``.
+    However, please take into account that the NVTX overhead is very high and
+    often gives a heavily skewed timeline.
+
+.. warning::
+    If you are profiling CUDA code, the first profiler that ``bottleneck`` runs
+    (cProfile) will include the CUDA startup time (CUDA buffer allocation cost)
+    in its time reporting. This should not matter if your bottlenecks result
+    in code much slower than the CUDA startup time.
+
+For more complicated uses of the profilers (like in a multi-GPU case),
+please see https://docs.python.org/3/library/profile.html
+or :func:`torch.autograd.profiler.profile()` for more information.
diff --git a/docs/1.3.0/_sources/checkpoint.rst.txt b/docs/1.3.0/_sources/checkpoint.rst.txt
@@ -0,0 +1,28 @@
+torch.utils.checkpoint
+======================
+
+.. note::
+    Checkpointing is implemented by rerunning a forward-pass segment for
+    each checkpointed segment during backward.  This can cause persistent
+    states like the RNG state to be advanced than they would without
+    checkpointing.  By default, checkpointing includes logic to juggle
+    the RNG state such that checkpointed passes making use of RNG
+    (through dropout for example) have deterministic output as
+    compared to non-checkpointed passes.  The logic to stash and restore
+    RNG states can incur a moderate performance hit depending on the runtime
+    of checkpointed operations.  If deterministic output compared to
+    non-checkpointed passes is not required, supply ``preserve_rng_state=False``
+    to ``checkpoint`` or ``checkpoint_sequential`` to omit stashing and
+    restoring the RNG state during each checkpoint.
+
+    The stashing logic saves and restores the RNG state for the current device
+    and the device of all cuda Tensor arguments to the ``run_fn``.
+    However, the logic has no way to anticipate if the user will move
+    Tensors to a new device within the ``run_fn`` itself.  Therefore, if you move
+    Tensors to a new device ("new" meaning not belonging to the set of
+    [current device + devices of Tensor arguments]) within ``run_fn``, deterministic
+    output compared to non-checkpointed passes is never guaranteed.
+
+.. currentmodule:: torch.utils.checkpoint
+.. autofunction:: checkpoint
+.. autofunction:: checkpoint_sequential