Document updated release process (#150)

CHANGELOG.md

@@ -5,12 +5,13 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres
 to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-For each Pull Request, the affected code parts should be briefly described and added here in the "Upcoming" section.
-Once a release is prepared, the "Upcoming" section becomes the release changelog, and a new empty "Upcoming" should be
-created.
+This file has sections for all previous releases, and the next one. 
+For each Pull Request, the affected code parts should be briefly described and added in the section for the upcoming
+release. In the first PR after a release has been made, a section for the upcoming release should be added, by copying
+the section headers (Added/Changed/...) and incrementing the package version.
 
 
-## Upcoming
+## 0.1.10
 
 ### Added
 - ([#142](https://github.com/microsoft/hi-ml/pull/142)) Adding AzureML progress bar and diagnostics for batch loading

docs/source/developers.md

@@ -93,15 +93,13 @@ When running the tests locally, they can either be run against the source direct
 ## Creating a New Release
 
 To create a new package release, follow these steps:
-* Modify `CHANGELOG.md` as follows:
-  * Copy the whole section called "Upcoming", including its subsections for "Added/Removed/..." to a new section,
-  that has the desired package version and the current date as the title. For example, to release package version 
-  `0.12.17` on Oct 10th, that section would be called "0.12.17 (2021-10-21)". 
-  * In the section for the new release, remove any empty subsections if needed.
-  * Clean up all PR links from the "Upcoming" section, to effectively create an empty template for the next release.
-* Create a PR for this change. While creating the PR, add the "no changelog needed" label that exists on the repo.
-  *Important*: This label needs to be added right when the PR is created, not afterwards - the github workflows will
-  not pick it up if added afterwards. In the worst case, you can added the label afterwards, and push a whitespace
-  change to the PR.
-* Once the PR with the updated `CHANGELOG.md` is in, create a tag that has the desired version number, plus a "v" 
-  prefix. For example, to create package version 0.12.17, create a tag `v0.12.17`
+* Double-check that `CHANGELOG.md` is up-to-date: It should contain a section for the next package version with
+  subsections Added/Changed/...
+* On the repository's github page, click on "Releases", then "Draft a new release"
+* In the "Draft a new release" page, click "Choose a tag". In the text box, enter a (new) tag name that has 
+  the desired version number, plus a "v" prefix. For example, to create package version 0.12.17, create a 
+  tag `v0.12.17`. Then choose "+ Create new tag" below the text box.
+* Enter a "Release title" that highlights the main feature(s) of this new package version.
+* Click "Auto-generate  release notes" to pull in the titles of the Pull Requests since the last release.
+* Before the auto-generated "What's changed" section, add a few sentences that summarize what's new.
+* Click "Publish release"

hi-ml/src/health_ml/utils/logging.py

@@ -171,24 +171,20 @@ class AzureMLProgressBar(ProgressBarBase):
         self.total_num_batches = total_num_batches
         self.stage_start_time = time.time()
 
-    def on_train_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any,
-                           batch_idx: int, dataloader_idx: int) -> None:
-        super().on_train_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+    def on_train_batch_end(self, *args: Any, **kwargs: Any) -> None:
+        super().on_train_batch_end(*args, **kwargs)
         self.update_progress(batches_processed=self.train_batch_idx)
 
-    def on_validation_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any,
-                                batch_idx: int, dataloader_idx: int) -> None:
-        super().on_validation_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+    def on_validation_batch_end(self, *args: Any, **kwargs: Any) -> None:
+        super().on_validation_batch_end(*args, **kwargs)
         self.update_progress(batches_processed=self.val_batch_idx)
 
-    def on_test_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any,
-                          batch_idx: int, dataloader_idx: int) -> None:
-        super().on_test_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+    def on_test_batch_end(self, *args: Any, **kwargs: Any) -> None:
+        super().on_test_batch_end(*args, **kwargs)
         self.update_progress(batches_processed=self.test_batch_idx)
 
-    def on_predict_batch_end(self, trainer: Trainer, pl_module: LightningModule, outputs: Any, batch: Any,
-                             batch_idx: int, dataloader_idx: int) -> None:
-        super().on_predict_batch_end(trainer, pl_module, outputs, batch, batch_idx, dataloader_idx)
+    def on_predict_batch_end(self, *args: Any, **kwargs: Any) -> None:
+        super().on_predict_batch_end(*args, **kwargs)
         self.update_progress(batches_processed=self.predict_batch_idx)
 
     def update_progress(self, batches_processed: int) -> None:

hi-ml/testhiml/testhiml/utils/test_logging.py

@@ -13,6 +13,7 @@ import pytest
 import torch
 from _pytest.capture import SysCapture
 from _pytest.logging import LogCaptureFixture
+from pytorch_lightning import Trainer
 
 from health_ml.utils import AzureMLLogger, AzureMLProgressBar, log_learning_rate, log_on_epoch
 
@@ -253,22 +254,23 @@ def test_progress_bar(capsys: SysCapture) -> None:
         return capsys.readouterr().out.splitlines()[-1]  # type: ignore
 
     # Messages in training
-    bar.on_train_epoch_start(None, None)  # type: ignore
+    trainer = Trainer()
+    bar.on_train_epoch_start(trainer, None)  # type: ignore
     assert bar.stage == AzureMLProgressBar.PROGRESS_STAGE_TRAIN
     assert bar.train_batch_idx == 0
     assert bar.val_batch_idx == 0
     assert bar.test_batch_idx == 0
     assert bar.predict_batch_idx == 0
-    bar.on_train_batch_end(None, None, None, None, None, None)  # type: ignore
+    bar.on_train_batch_end(None, None, None, None, None)  # type: ignore
     assert bar.train_batch_idx == 1
     latest = latest_message()
     assert "Training epoch 12 (step 34)" in latest
     assert "1/10 ( 10%) completed" in latest
     # When starting the next training epoch, the counters should be reset
-    bar.on_train_epoch_start(None, None)  # type: ignore
+    bar.on_train_epoch_start(trainer, None)  # type: ignore
     assert bar.train_batch_idx == 0
     # Messages in validation
-    bar.on_validation_start(None, None)  # type: ignore
+    bar.on_validation_start(trainer, None)  # type: ignore
     assert bar.stage == AzureMLProgressBar.PROGRESS_STAGE_VAL
     assert bar.total_num_batches == 0
     assert bar.val_batch_idx == 0
@@ -280,7 +282,7 @@ def test_progress_bar(capsys: SysCapture) -> None:
     assert "Validation epoch 12: " in latest
     assert "1/5 ( 20%) completed" in latest
     # Messages in testing
-    bar.on_test_epoch_start(None, None)  # type: ignore
+    bar.on_test_epoch_start(trainer, None)  # type: ignore
     assert bar.stage == AzureMLProgressBar.PROGRESS_STAGE_TEST
     test_count = 2
     for _ in range(test_count):
@@ -290,7 +292,7 @@ def test_progress_bar(capsys: SysCapture) -> None:
     assert "Testing:" in latest
     assert f"{test_count}/20 ( 10%)" in latest
     # Messages in prediction
-    bar.on_predict_epoch_start(None, None)  # type: ignore
+    bar.on_predict_epoch_start(trainer, None)  # type: ignore
     assert bar.stage == AzureMLProgressBar.PROGRESS_STAGE_PREDICT
     predict_count = 3
     for _ in range(predict_count):

pyrightconfig.json

@@ -14,6 +14,6 @@
       "extraPaths": [
         "hi-ml/testhiml/testhiml/utils/slide_image_loading/src"
       ]
-    },
-  ],
+    }
+  ]
 }