add beautiful_mnist_multigpu example

examples/beautiful_mnist_multigpu.py

@@ -0,0 +1,57 @@
+# model based off https://towardsdatascience.com/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
+from typing import List, Callable
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Device
+from tinygrad.helpers import getenv, colored
+from extra.datasets import fetch_mnist
+from tqdm import trange
+
+GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 2))]
+
+class Model:
+  def __init__(self):
+    self.layers: List[Callable[[Tensor], Tensor]] = [
+      nn.Conv2d(1, 32, 5), Tensor.relu,
+      nn.Conv2d(32, 32, 5), Tensor.relu,
+      nn.BatchNorm2d(32), Tensor.max_pool2d,
+      nn.Conv2d(32, 64, 3), Tensor.relu,
+      nn.Conv2d(64, 64, 3), Tensor.relu,
+      nn.BatchNorm2d(64), Tensor.max_pool2d,
+      lambda x: x.flatten(1), nn.Linear(576, 10)]
+
+  def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
+
+if __name__ == "__main__":
+  X_train, Y_train, X_test, Y_test = fetch_mnist(tensors=True)
+  # we shard the test data on axis 0
+  X_test.shard_(GPUS, axis=0)
+  Y_test.shard_(GPUS, axis=0)
+
+  model = Model()
+  for k, x in nn.state.get_state_dict(model).items(): x.to_(GPUS)  # we put a copy of the model on every GPU
+  opt = nn.optim.Adam(nn.state.get_parameters(model))
+
+  @TinyJit
+  def train_step() -> Tensor:
+    with Tensor.train():
+      opt.zero_grad()
+      samples = Tensor.randint(512, high=X_train.shape[0])
+      Xt, Yt = X_train[samples].shard_(GPUS, axis=0), Y_train[samples].shard_(GPUS, axis=0)  # we shard the data on axis 0
+      # TODO: this "gather" of samples is very slow. will be under 5s when this is fixed
+      loss = model(Xt).sparse_categorical_crossentropy(Yt).backward()
+      opt.step()
+      return loss
+
+  @TinyJit
+  def get_test_acc() -> Tensor: return (model(X_test).argmax(axis=1) == Y_test).mean()*100
+
+  test_acc = float('nan')
+  for i in (t:=trange(70)):
+    GlobalCounters.reset()   # NOTE: this makes it nice for DEBUG=2 timing
+    loss = train_step()
+    if i%10 == 9: test_acc = get_test_acc().item()
+    t.set_description(f"loss: {loss.item():6.2f} test_accuracy: {test_acc:5.2f}%")
+
+  # verify eval acc
+  if target := getenv("TARGET_EVAL_ACC_PCT", 0.0):
+    if test_acc >= target: print(colored(f"{test_acc=} >= {target}", "green"))
+    else: raise ValueError(colored(f"{test_acc=} < {target}", "red"))