well done sending a network over mpi, pat pat

now do a lot of nets in parallel and then we'll talk

library.pyx

@@ -2,25 +2,38 @@ cimport numpy as np
 import numpy as np
 import mynet as mn
 
+from libc.stdlib cimport malloc
+
 
 ctr = []
 X_train, y_train, X_test, y_test = mn.load_mnist()
 
 
+cdef extern from "numpy/arrayobject.h":
+    object PyArray_SimpleNewFromData(
+        int nd, long* dims, int typenum, void* data
+    )
+    void *PyArray_DATA(np.ndarray arr)
+
+
+ctypedef public struct Dense:
+    long[2] shape
+    int ownmem
+    float* W
+    float* b
+
+
+ctypedef public struct Network:
+    Py_ssize_t n_layers;
+    Dense* layers;
+
+
 cdef public char * greeting():
     return f'The value is {3**3**3}'.encode('utf-8')
 
 
 cdef public void debug_print(object o):
     print(o.flags)
-    # print(o)
-
-
-cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] dot(
-    np.ndarray[np.float32_t, ndim=2, mode='c'] x,
-    np.ndarray[np.float32_t, ndim=2, mode='c'] y
-):
-    return x @ y
 
 
 cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] predict(
@@ -66,7 +79,41 @@ cdef public np.ndarray[np.float32_t, ndim=2, mode='c'] mnist_batch(
     arr = np.concatenate([X_train[idx], y_train[idx]], axis=1)
     return arr
 
-cdef public float arrsum(
+
+cdef public void inspect_array(
     np.ndarray[np.float32_t, ndim=2, mode='c'] a
 ):
-    return np.sum(a)
+    print(a.flags)
+    print(a.dtype)
+    print(a.sum())
+
+
+cdef public void be_like_cified(
+    object net,
+    Network* c_net
+):
+   """WARNING this function makes an assumption that `net` and `c_net`
+   have the same shape and hopefully is going to crash horribly otherwise."""
+   for i, l in enumerate(net.layers):
+       w1, w2 = l.W.shape
+       l.W[:] = <float[:w1,:w2]>c_net.layers[i].W
+       l.b[:] = <float[:w2]>c_net.layers[i].b
+
+
+cdef public void cify_network(
+    object net, Network* c_net
+):
+    """WARNING `c_net` is valid as long as `net` is
+
+    Whoever has `c_net` is responsible for freeing c_net.layers list
+    Layers themselves don't need any de-init.
+    """
+    c_net.n_layers = len(net.layers)
+    c_net.layers = <Dense*>malloc(len(net.layers) * sizeof(Dense))
+    for i, l in enumerate(net.layers):
+        w1, w2 = l.W.shape
+        c_net.layers[i].shape[0] = w1
+        c_net.layers[i].shape[1] = w2
+        c_net.layers[i].W = <float*>PyArray_DATA(l.W)
+        c_net.layers[i].b = <float*>PyArray_DATA(l.b)
+        c_net.layers[i].ownmem = 0