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mcg1969/Pipeline proto.ipynb

Created September 17, 2016 18:11
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Pipeline proto.ipynb
{
"cells": [
{
"cell_type": "code",
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"metadata": {
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},
"outputs": [],
"source": [
"import inspect\n",
"import dask\n",
"from operator import getitem\n",
"from collections import namedtuple\n",
"from graphviz import Digraph\n",
"from dask import delayed\n",
"from dask.delayed import Delayed, to_task_dasks\n",
"from dask.dot import dot_graph, to_graphviz\n",
"from dask.async import get_sync\n",
"from dask.base import tokenize, visualize"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class SimpleDelayed(object):\n",
" def __init__(self, func, nout):\n",
" if nout == 0:\n",
" raise RuntimeError('SimpleDelayed functions must have at least one output')\n",
" self.nout = nout\n",
" self.func = func\n",
" self.dfunc = delayed(func)\n",
" if func.__doc__:\n",
" self.__doc__ = 'This is a SimpleDelayed wrapper around the following function:\\n--------\\n%s' % func.__doc__\n",
" \n",
" def __call__(self, *args, **kwargs):\n",
" delayed = kwargs.pop('delayed', None)\n",
" if (delayed or any(isinstance(arg, Delayed) for arg in args) or\n",
" kwargs and any(isinstance(arg, Delayed) for arg in kwargs.values())):\n",
" res = self.dfunc(*args, **kwargs)\n",
" if delayed is not None and not delayed:\n",
" res = res.compute()\n",
" elif kwargs:\n",
" res = self.func(*args, **kwargs)\n",
" else:\n",
" res = self.func(*args)\n",
" if self.nout == 1 or type(res) is tuple:\n",
" return res\n",
" else:\n",
" return tuple(res[k] for k in range(self.nout))\n",
" \n",
" def visualize(self):\n",
" graph = Digraph(graph_attr={'rankdir':'BT'})\n",
" fname = self.func.__name__\n",
" argspec = inspect.getargspec(self.func)\n",
" graph.node(fname, shape='circle')\n",
" args = list(argspec.args)\n",
" if argspec.varargs:\n",
" args.append('*' + argspec.varargs)\n",
" if argspec.keywords:\n",
" args.append('**' + argspec.keywords)\n",
" for k_name in args:\n",
" graph.node(k_name, shape='box')\n",
" graph.edge(k_name, fname)\n",
" for k in range(self.nout):\n",
" k_name = '%s_out[%d]' % (fname, k)\n",
" graph.node(k_name, shape='box')\n",
" graph.edge(fname, k_name)\n",
" return graph\n",
"\n",
" def __repr__(self):\n",
" return 'SimpleDelayed: name=%s, nout=%d' % (self.func.__name__, self.nout)\n",
"\n",
" def _repr_svg_(self):\n",
" return self.visualize()._repr_svg_()\n",
"\n",
"def simple_delayed(nout):\n",
" def decorator(function):\n",
" return SimpleDelayed(function, nout)\n",
" return decorator"
]
},
{
"cell_type": "code",
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"metadata": {
"collapsed": false
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"source": [
"@simple_delayed(2)\n",
"def f(x, y, z, *args, **kwargs):\n",
" \"\"\"Testing\"\"\"\n",
" return x + y, y + z"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
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}
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"source": [
"f.visualize()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Calling a SimpleDelayed function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling the function with non-delayed arguments produces an immediate result."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3, 5)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b = f(1,2,3)\n",
"(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calling the function with a `delayed` input produces `delayed` output. However, because the number of outputs is _known_, the function can return a tuple of each separate output."
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(Delayed('getitem-6d75232e95c9de2b282c1908c88d0e7a'),\n",
" Delayed('getitem-7de94c591141577a8a8d2392873273b4'))"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b = f(delayed(1), 2, 3)\n",
"(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding `delayed=True` forces delayed computation, even with constant inputs."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(Delayed('getitem-41867693f5e3b7907b6689055a15a460'),\n",
" Delayed('getitem-d48445505b3c5219bde203afdccf3e1f'))"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a, b = f(1, 2, 3, delayed=True)\n",
"(a, b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Adding `delayed=False` forces immediate computation even with delayed input."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(3, 5)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f(delayed(1), 2, 3, delayed=False)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"class PipelineFunction:\n",
" \n",
" def __init__(self, func, outputs):\n",
" self.nout = len(outputs)\n",
" self.func = func\n",
" self.__name__ = func.__name__\n",
" self.dfunc = delayed(func)\n",
" self.outputs = outputs\n",
" self.tuple = namedtuple(self.__name__ + '_out', outputs)._make\n",
" argspec = inspect.getargspec(func)\n",
" if argspec.varargs or argspec.keywords:\n",
" raise RuntimeError('PipelineFunctions cannot have *args or **kwargs')\n",
" self.inputs = argspec.args\n",
" \n",
" def __call__(self, *args, **kwargs):\n",
" delayed = kwargs.pop('delayed', None)\n",
" if delayed is False or delayed is None:\n",
" delay_comp = self.any_delayed(args)\n",
" delay_out = delay_comp and delayed is None\n",
" else:\n",
" delay_comp = delay_out = True\n",
" res = (self.dfunc if delay_comp else self.func)(*args)\n",
" if delay_comp != delay_out:\n",
" res = res.compute()\n",
" if self.nout <= 1:\n",
" return res\n",
" elif delay_out:\n",
" res = [res[k] for k in range(self.nout)]\n",
" return self.tuple(res)\n",
"\n",
" def to_dask(self, table=None):\n",
" if table is None:\n",
" table = {k: None for k in self.inputs}\n",
" if any(table.get(k) for k in self.outputs):\n",
" bad = [k for k in self.outputs if table.get(k)]\n",
" raise RuntimeError('The following output values are already written: %s' % (', '.join(bad),))\n",
" res = (self,) + tuple(self.inputs)\n",
" if len(self.outputs) == 1:\n",
" table[self.outputs[0]] = res\n",
" else:\n",
" nm = '@%s-%s' % (self.__name__,tokenize(res))\n",
" table[nm] = res\n",
" table.update({out: (getitem, nm, k) for k, out in enumerate(self.outputs)})\n",
" return table\n",
" \n",
" def visualize(self, graph=None):\n",
" if graph is None:\n",
" graph = Digraph(graph_attr={'rankdir':'BT'})\n",
" fname = self.__name__\n",
" graph.node(fname, shape='circle')\n",
" for k_name in self.inputs:\n",
" graph.node(k_name, shape='box')\n",
" graph.edge(k_name, fname)\n",
" for k_name in self.outputs:\n",
" graph.node(k_name, shape='box')\n",
" graph.edge(fname, k_name)\n",
" return graph\n",
"\n",
" def compute(self, table, delayed=None):\n",
" try:\n",
" args = [table[k] for k in self.inputs]\n",
" except KeyError:\n",
" bad = [k for k in self.inputs if table.get(k) is None]\n",
" raise RuntimeError('Missing inputs: %s' % (','.join(bad),))\n",
" res = self.__call__(*args, delayed=delayed)\n",
" if len(self.outputs) == 1:\n",
" table[self.outputs[0]] = res\n",
" elif isinstance(res, Delayed):\n",
" table.update({out: self.dextract(res, k) for k, out in enumerate(self.outputs)})\n",
" else:\n",
" table.update({out: val for out, val in zip(self.outputs, res)})\n",
" return table\n",
" \n",
" def __repr__(self):\n",
" return 'PipelineFunction: name=%s, inputs=(%s), outputs=(%s)' % (self.__name__, ','.join(self.inputs),','.join(self.outputs))\n",
" \n",
" def _repr_svg_(self):\n",
" return self.visualize()._repr_svg_()\n",
"\n",
"def pipe(*outputs):\n",
" def decorator(function):\n",
" return PipelineFunction(function, outputs)\n",
" return decorator"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"@pipe('a', 'b')\n",
"def f(x, y, z):\n",
" return x + y, y + z\n",
"@pipe('c')\n",
"def g(w):\n",
" return 2 * w"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examining the function in IPython produces a nice graph output."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
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