update templates

.github/workflows/tests_unit.yml

@@ -50,6 +50,20 @@ jobs:
           #
           poetry install --with dev
 
+      - name: Simple Template
+        run: |
+          source $(poetry env info -p)/bin/activate
+          milabench new --name simplebench --template simple
+          cd benchmarks/simplebench
+          make tests
+
+      - name: Voir Template
+        run: |
+          source $(poetry env info -p)/bin/activate
+          milabench new --name  voirbench --template voir
+          cd benchmarks/voirbench
+          make tests
+        
       - name: tests
         run: |
           source $(poetry env info -p)/bin/activate

.gitignore

@@ -47,3 +47,8 @@ dry/
 
 stderr.txt
 stdout.txt
+
+base/
+
+benchmarks/simple
+benchmarks/voir

benchmarks/_templates/simple/Makefile

@@ -0,0 +1,4 @@
+tests:
+	milabench install --config dev.yaml --base base
+	milabench prepare --config dev.yaml --base base
+	milabench run --config dev.yaml --base base

benchmarks/_template/README.md → benchmarks/_templates/simple/README.md

@@ -1,4 +1,4 @@
 
-# {{STEM}}
+# Template
 
 Rewrite this README to explain what the benchmark is!

benchmarks/_template/benchfile.py → benchmarks/_templates/simple/benchfile.py

@@ -1,7 +1,7 @@
 from milabench.pack import Package
 
 
-class TheBenchmark(Package):
+class Template(Package):
     # Requirements file installed by install(). It can be empty or absent.
     base_requirements = "requirements.in"
 
@@ -26,8 +26,6 @@ async def install(self):
     async def prepare(self):
         await super().prepare()  # super() call executes prepare_script
 
-    async def run(self):
-        return await super().run()
 
 
-__pack__ = TheBenchmark
+__pack__ = Template

benchmarks/_template/dev.yaml → benchmarks/_templates/simple/dev.yaml

@@ -1,5 +1,5 @@
 
-{{STEM}}:
+template:
   inherits: _defaults
   definition: .
   install-variant: unpinned

benchmarks/_template/main.py → benchmarks/_templates/simple/main.py

@@ -5,33 +5,45 @@
 # be deleted.
 
 import time
+import random
 
 import torchcompat.core as accelerator
 from benchmate.observer import BenchObserver
 
 
+def criterion(*args, **kwargs):
+    return random.normalvariate()
+
+
 def main():
     device = accelerator.fetch_device(0) # <= This is your cuda device
-
-    observer = BenchObserver(batch_size_fn=lambda batch: 1)
+
+    observer = BenchObserver(
+        batch_size_fn=lambda batch: 1
+    )
     # optimizer = observer.optimizer(optimizer)
     # criterion = observer.criterion(criterion)
 
-    dataloader = [1, 2, 3, 4]
+    dataloader = list(range(6000))
 
-    for epoch in range(10):
+    for epoch in range(10000):
         for i in observer.iterate(dataloader):
             # avoid .item()
             # avoid torch.cuda; use accelerator from torchcompat instead
             # avoid torch.cuda.synchronize or accelerator.synchronize
 
             # y = model(i)
-            # loss = criterion(y)
+            loss = criterion()
             # loss.backward()
             # optimizer.step()
+
+            observer.record_loss(loss)
 
             time.sleep(0.1)
 
+    assert epoch < 2, "milabench stopped the train script before the end of training"
+    assert i < 72, "milabench stopped the train script before the end of training"
+
 
 if __name__ == "__main__":
     main()

benchmarks/_template/requirements.in → benchmarks/_templates/simple/requirements.in

@@ -1 +1,2 @@
 voir>=0.2.9,<0.3
+torch

benchmarks/_template/voirfile.py → benchmarks/_templates/simple/voirfile.py

@@ -26,25 +26,13 @@ class Config:
 
 @configurable
 def instrument_main(ov, options: Config):
-    try:
-        import torch
-    except ImportError:
-        torch = None
-
     yield ov.phases.init
 
     if options.dash:
         ov.require(dash)
 
     ov.require(
         log("value", "progress", "rate", "units", "loss", "gpudata", context="task"),
-        rate(
-            interval=options.interval,
-            skip=options.skip,
-            sync=torch.cuda.synchronize
-            if torch and torch.cuda.is_available()
-            else None,
-        ),
         early_stop(n=options.stop, key="rate", task="train"),
         monitor_monogpu(poll_interval=options.gpu_poll),
     )

benchmarks/_templates/voir/Makefile

@@ -0,0 +1,4 @@
+tests:
+	milabench install --config dev.yaml --base base
+	milabench prepare --config dev.yaml --base base
+	milabench run --config dev.yaml --base base

benchmarks/_templates/voir/README.md

@@ -0,0 +1,4 @@
+
+# Template
+
+Rewrite this README to explain what the benchmark is!

benchmarks/_templates/voir/benchfile.py

@@ -0,0 +1,42 @@
+from milabench.pack import Package
+
+
+SOURCE_DIR = "src"
+REPO_URL = "https://github.com/Delaunay/extern_example.git"
+BRANCH = "a524286ab6364bca6729dd6ef4936e175a87c7e4"
+
+
+class Template(Package):
+    # Requirements file installed by install(). It can be empty or absent.
+    base_requirements = "requirements.in"
+
+    # The preparation script called by prepare(). It must be executable,
+    # but it can be any type of script. It can be empty or absent.
+    prepare_script = "prepare.py"
+
+    # The main script called by run(). It must be a Python file. It has to
+    # be present.
+    main_script = f"{SOURCE_DIR}/main.py"
+
+    # You can remove the functions below if you don't need to modify them.
+
+    def make_env(self):
+        # Return a dict of environment variables for prepare_script and
+        # main_script.
+        return super().make_env()
+
+    async def install(self):
+        await super().install()
+
+        source_destination = self.dirs.code / SOURCE_DIR
+        if not source_destination.exists():
+            source_destination.clone_subtree(
+                REPO_URL, BRANCH
+            )
+
+    async def prepare(self):
+        await super().prepare()  # super() call executes prepare_script
+
+
+
+__pack__ = Template

benchmarks/_templates/voir/dev.yaml

@@ -0,0 +1,7 @@
+
+template:
+  inherits: _defaults
+  definition: .
+  install-variant: unpinned
+  plan:
+    method: per_gpu

benchmarks/_templates/voir/prepare.py

@@ -0,0 +1,16 @@
+#!/usr/bin/env python
+
+import os
+
+if __name__ == "__main__":
+    # If you need the whole configuration:
+    # config = json.loads(os.environ["MILABENCH_CONFIG"])
+
+    data_directory = os.environ["MILABENCH_DIR_DATA"]
+
+    # Download (or generate) the needed dataset(s). You are responsible
+    # to check if it has already been properly downloaded or not, and to
+    # do nothing if it has been.
+    print("Hello I am doing some data stuff!")
+
+    # If there is nothing to download or generate, just delete this file.

benchmarks/_templates/voir/requirements.in

@@ -0,0 +1,2 @@
+voir>=0.2.9,<0.3
+torch

benchmarks/_templates/voir/voirfile.py

@@ -0,0 +1,69 @@
+from dataclasses import dataclass
+
+from voir.phase import StopProgram
+from voir import configurable
+from voir.instruments import dash, early_stop, log
+from benchmate.monitor import monitor_monogpu
+from benchmate.observer import BenchObserver
+
+
+@dataclass
+class Config:
+    """voir configuration"""
+
+    # Whether to display the dash or not
+    dash: bool = False
+
+    # How often to log the rates
+    interval: str = "1s"
+
+    # Number of rates to skip before logging
+    skip: int = 5
+
+    # Number of rates to log before stopping
+    stop: int = 20
+
+    # Number of seconds between each gpu poll
+    gpu_poll: int = 3
+
+
+@configurable
+def instrument_main(ov, options: Config):
+    yield ov.phases.init
+
+
+    yield ov.phases.load_script
+
+    if options.dash:
+        ov.require(dash)
+
+    ov.require(
+        log("value", "progress", "rate", "units", "loss", "gpudata", context="task"),
+        early_stop(n=options.stop, key="rate", task="train"),
+        monitor_monogpu(poll_interval=options.gpu_poll),
+    )
+
+    #
+    # Insert milabench tools
+    #
+    observer = BenchObserver(
+        earlystop=options.stop + options.skip,
+        batch_size_fn=lambda x: 1
+    )
+
+    probe = ov.probe("//my_dataloader_creator() as loader", overridable=True)
+    probe['loader'].override(observer.loader)
+
+    probe = ov.probe("//my_criterion_creator() as criterion", overridable=True)
+    probe['criterion'].override(observer.criterion)
+
+    probe = ov.probe("//my_optimizer_creator() as optimizer", overridable=True)
+    probe['optimizer'].override(observer.optimizer)
+
+    #
+    # Run the benchmark
+    #
+    try:
+        yield ov.phases.run_script
+    except StopProgram:
+        print("early stopped")

benchmarks/torchvision_ddp/benchfile.py

@@ -8,7 +8,6 @@ class TorchvisionBenchmarkDDP(Package):
 
     def build_run_plan(self) -> "Command":
         import milabench.commands as cmd
-
         pack = cmd.PackCommand(self, *self.argv, lazy=True)
         pack = cmd.ActivatorCommand(pack, use_stdout=True)
         return pack

benchmate/benchmate/observer.py

@@ -68,7 +68,7 @@ def override_return_value(self, function, override):
             raise RuntimeError("Not running through voir")
 
     def iterate(self, iterator):
-        return self.loader(loader)
+        return self.loader(iterator)
 
     def loader(self, loader):
         """Wrap a dataloader or an iterable which enable accurate measuring of time spent in the loop's body"""
@@ -94,7 +94,7 @@ def new_backward(*args, **kwargs):
 
                 loss.backward = new_backward
 
-            self.record_loss(loss.detach())
+            self.record_loss(loss)
             return loss
 
         return wrapped

docs/index.rst

@@ -7,12 +7,15 @@ Welcome to milabench's documentation!
    :caption: Contents:
 
    usage.rst
+   recipes.rst
+   new_benchmarks.rst
+
    docker.rst
    dev-usage.rst
-   new_benchmarks.rst
    reference.rst
    sizer.rst
 
+
 Indices and tables
 ==================