dmlc--dgl
John Andrilla
43 行
1.6 KiB
ReStructuredText
43 行
1.6 KiB
ReStructuredText
FAQ
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===
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Troubleshooting
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----------------
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Deep Graph Library (DGL) is still in its alpha stage, so expect some trial and error. Keep in mind that
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DGL is a framework atop other frameworks, e.g., PyTorch, MXNet, so it is important
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to figure out whether a bug is due to DGL or the backend framework. For example,
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DGL will usually complain and throw a ``DGLError`` if anything goes wrong. If you
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are pretty confident that it is a bug, feel free to raise an issue.
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Out-of-memory
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-------------
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Graph can be very large and training on graph may cause out of memory (OOM) errors. There are several
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tips to check when you get an OOM error.
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* Try to avoid propagating node features to edges. Number of edges are usually
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much larger than number of nodes. Try to use out built-in functions whenever
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it is possible.
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* Look out for cyclic references due to user-defined functions. Usually we recommend
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using global function or module class for the user-defined functions. Pay
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attention to the variables in function closure. Also, it is usually better to
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directly provide the UDFs in the message passing APIs rather than register them:
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::
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# define a message function
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def mfunc(edges): return edges.data['x']
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# better as the graph `mfunc` does not hold a reference to `mfunc`
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g.send(some_edges, mfunc)
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# the graph hold a reference to `mfunc` so as all the variables in its closure
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g.register(mfunc)
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g.send(some_edges)
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* If your scenario does not require autograd, you can use ``inplace=True`` flag
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in the message passing APIs. This will update features inplacely that might
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save memory.
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