Add Python integration

Project.toml

@@ -32,13 +32,15 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GraphViz = "f526b714-d49f-11e8-06ff-31ed36ee7ee0"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 
 [extensions]
 DistributionsExt = "Distributions"
 GraphVizExt = "GraphViz"
 GraphVizSimpleExt = "Colors"
 JSON3Ext = "JSON3"
 PlotsExt = ["DataFrames", "Plots"]
+PythonExt = "PythonCall"
 
 [compat]
 Adapt = "4.0.4"
@@ -69,3 +71,4 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 GraphViz = "f526b714-d49f-11e8-06ff-31ed36ee7ee0"
 JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"

docs/make.jl

@@ -32,6 +32,9 @@ makedocs(;
             "Logging: Visualization" => "logging-visualization.md",
             "Logging: Advanced" => "logging-advanced.md",
         ],
+        "External Languages" => [
+            "Python" => "external-languages/python.md",
+        ],
         "Checkpointing" => "checkpointing.md",
         "Benchmarking" => "benchmarking.md",
         "Dynamic Scheduler Control" => "dynamic.md",

docs/src/external-languages/python.md

@@ -0,0 +1,49 @@
+# Python Integration
+
+If you're using Python as your main programming language, Dagger can be easily
+integrated into your workflow. Dagger has built-in support for Python, which
+you can easily access through the `pydaggerjl` library (accessible through
+`pip`). This library provides a Pythonic interface to Dagger, allowing you to
+spawn Dagger tasks that run Python functions on Python arguments.
+
+Here's a simple example of interfacing between Python's `numpy` library and
+Dagger:
+
+```python
+import numpy as np
+from pydaggerjl import daggerjl
+
+# Create a Dagger DTask to sum the elements of an array
+task = daggerjl.spawn(np.sum, np.array([1, 2, 3]))
+
+# Wait on task to finish
+# This is purely educational, as fetch will wait for the task to finish
+daggerjl.wait(task)
+
+# Fetch the result
+result = daggerjl.fetch(task)
+
+print(f"The sum is: {result}")
+
+# Create two numpy arrays
+a = np.array([1, 2, 3])
+b = np.array([4, 5, 6])
+
+# Element-wise sum of the two arrays
+task = daggerjl.spawn(np.add, a, b)
+
+# Fetch the result
+result = daggerjl.fetch(task)
+
+print(f"The element-wise sum is: {result}")
+
+# Element-wise sum of last result with itself
+task2 = daggerjl.spawn(np.add, task, task)
+
+# Fetch the result
+result2 = daggerjl.fetch(task2)
+
+print(f"The element-wise sum of the last result with itself is: {result2}")
+```
+
+Keep an eye on Dagger and pydaggerjl - new features are soon to come!

ext/PythonExt.jl

@@ -0,0 +1,46 @@
+module PythonExt
+
+if isdefined(Base, :get_extension)
+    using PythonCall
+else
+    using ..PythonCall
+end
+
+import Dagger
+import Dagger: Processor, OSProc, ThreadProc, Chunk
+import Distributed: myid
+
+const CPUProc = Union{OSProc, ThreadProc}
+
+struct PythonProcessor <: Processor
+    owner::Int
+end
+
+Dagger.root_worker_id(proc::PythonProcessor) = proc.owner
+Dagger.get_parent(proc::PythonProcessor) = OSProc(proc.owner)
+Dagger.default_enabled(::PythonProcessor) = true
+
+Dagger.iscompatible_func(::ThreadProc, opts, ::Type{Py}) = false
+Dagger.iscompatible_func(::PythonProcessor, opts, ::Type{Py}) = true
+Dagger.iscompatible_arg(::PythonProcessor, opts, ::Type{Py}) = true
+Dagger.iscompatible_arg(::PythonProcessor, opts, ::Type{<:PyArray}) = true
+
+Dagger.move(from_proc::CPUProc, to_proc::PythonProcessor, x::Chunk) =
+    Dagger.move(from_proc, to_proc, Dagger.move(from_proc, Dagger.get_parent(to_proc), x))
+Dagger.move(::CPUProc, ::PythonProcessor, x) = Py(x)
+Dagger.move(::CPUProc, ::PythonProcessor, x::Py) = x
+Dagger.move(::CPUProc, ::PythonProcessor, x::PyArray) = x
+# FIXME: Conversion from Python to Julia
+
+function Dagger.execute!(::PythonProcessor, f, args...; kwargs...)
+    @assert f isa Py "Function must be a Python object"
+    return f(args...; kwargs...)
+end
+
+function __init__()
+    Dagger.add_processor_callback!(:pythonproc) do
+        return PythonProcessor(myid())
+    end
+end
+
+end # module PythonExt

test/Project.toml

@@ -1,6 +1,7 @@
 [deps]
 ArgParse = "c7e460c6-2fb9-53a9-8c5b-16f535851c63"
 Colors = "5ae59095-9a9b-59fe-a467-6f913c188581"
+CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
@@ -12,6 +13,7 @@ MemPool = "f9f48841-c794-520a-933b-121f7ba6ed94"
 OnlineStats = "a15396b6-48d5-5d58-9928-6d29437db91e"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 SharedArrays = "1a1011a3-84de-559e-8e89-a11a2f7dc383"

test/extlang/python.jl

@@ -0,0 +1,30 @@
+using PythonCall
+using CondaPkg
+
+CondaPkg.add("numpy")
+
+np = pyimport("numpy")
+
+# Restart scheduler to see new methods
+Dagger.cancel!(;halt_sch=true)
+
+@testset "spawn" begin
+    a = np.array([1, 2, 3])
+
+    t = Dagger.@spawn np.sum(a)
+    result = fetch(t)
+    @test result isa Py
+    @test pyconvert(Int, result) == 6
+
+    b = np.array([4, 5, 6])
+
+    t = Dagger.@spawn np.add(a, b)
+    result = fetch(t)
+    @test result isa Py
+    @test pyconvert(Array, result) == [5, 7, 9]
+
+    t2 = Dagger.@spawn np.add(t, b)
+    result = fetch(t2)
+    @test result isa Py
+    @test pyconvert(Array, result) == [9, 12, 15]
+end

test/runtests.jl

@@ -23,6 +23,7 @@ tests = [
     ("Caching", "cache.jl"),
     ("Disk Caching", "diskcaching.jl"),
     ("File IO", "file-io.jl"),
+    ("External Languages - Python", "extlang/python.jl"),
     #("Fault Tolerance", "fault-tolerance.jl"),
 ]
 all_test_names = map(test -> replace(last(test), ".jl"=>""), tests)