Release v1.5.3 (ashleve#543)

.pre-commit-config.yaml

@@ -71,6 +71,7 @@ repos:
     hooks:
       - id: prettier
         types: [yaml]
+        exclude: "environment.yaml"
 
   # shell scripts linter
   - repo: https://github.com/shellcheck-py/shellcheck-py

README.md

@@ -124,6 +124,7 @@ The directory structure of new project looks like this:
 ├── .gitignore                <- List of files ignored by git
 ├── .pre-commit-config.yaml   <- Configuration of pre-commit hooks for code formatting
 ├── .project-root             <- File for inferring the position of project root directory
+├── environment.yaml          <- File for installing conda environment
 ├── Makefile                  <- Makefile with commands like `make train` or `make test`
 ├── pyproject.toml            <- Configuration options for testing and linting
 ├── requirements.txt          <- File for installing python dependencies
@@ -1216,9 +1217,9 @@ ______________________________________________________________________
 
 What it does
 
-## How to run
+## Installation
 
-Install dependencies
+#### Pip
 
 ```bash
 # clone project
@@ -1236,6 +1237,22 @@ conda activate myenv
 pip install -r requirements.txt
 ```
 
+#### Conda
+
+```bash
+# clone project
+git clone https://github.com/YourGithubName/your-repo-name
+cd your-repo-name
+
+# create conda environment and install dependencies
+conda env create -f environment.yaml
+
+# activate conda environment
+conda activate myenv
+```
+
+## How to run
+
 Train model with default configuration
 
 ```bash

configs/__init__.py

@@ -0,0 +1 @@
+# this file is needed here to include configs when building project as a package

environment.yaml

@@ -0,0 +1,43 @@
+# reasons you might want to use `environment.yaml` instead of `requirements.txt`:
+# - pip installs packages in a loop, without ensuring dependencies across all packages
+#   are fulfilled simultaneously, but conda achieves proper dependency control across
+#   all packages
+# - conda allows for installing packages without requiring certain compilers or
+#   libraries to be available in the system, since it installs precompiled binaries
+
+name: myenv
+
+channels:
+  - pytorch
+  - conda-forge
+  - defaults
+
+# it is strongly recommended to specify versions of packages installed through conda
+# to avoid situation when version-unspecified packages install their latest major
+# versions which can sometimes break things
+
+# current approach below keeps the dependencies in the same major versions across all
+# users, but allows for different minor and patch versions of packages where backwards
+# compatibility is usually guaranteed
+
+dependencies:
+  - pytorch>=1.10
+  - torchvision>=0.11
+  - pytorch-lightning=1.*
+  - torchmetrics=0.*
+  - hydra-core=1.*
+  - rich=13.*
+  - pre-commit=3.*
+  - pytest=7.*
+
+  # --------- loggers --------- #
+  # - wandb
+  # - neptune-client
+  # - mlflow
+  # - comet-ml
+
+  - pip>=23
+  - pip:
+      - hydra-optuna-sweeper
+      - hydra-colorlog
+      - pyrootutils

requirements.txt

@@ -5,7 +5,7 @@ pytorch-lightning==1.9.1
 torchmetrics==0.11.0
 
 # --------- hydra --------- #
-hydra-core==1.3.1
+hydra-core==1.3.2
 hydra-colorlog==1.2.0
 hydra-optuna-sweeper==1.2.0
 

setup.py

@@ -8,7 +8,14 @@
     description="Describe Your Cool Project",
     author="",
     author_email="",
-    url="https://github.com/user/project",  # REPLACE WITH YOUR OWN GITHUB PROJECT LINK
+    url="https://github.com/user/project",
     install_requires=["pytorch-lightning", "hydra-core"],
     packages=find_packages(),
+    # use this to customize global commands available in the terminal after installing the package
+    entry_points={
+        "console_scripts": [
+            "train_command = src.train:main",
+            "eval_command = src.eval:main",
+        ]
+    },
 )

src/models/mnist_module.py

@@ -56,7 +56,9 @@ def forward(self, x: torch.Tensor):
 
     def on_train_start(self):
         # by default lightning executes validation step sanity checks before training starts,
-        # so we need to make sure val_acc_best doesn't store accuracy from these checks
+        # so it's worth to make sure validation metrics don't store results from these checks
+        self.val_loss.reset()
+        self.val_acc.reset()
         self.val_acc_best.reset()
 
     def model_step(self, batch: Any):

tests/conftest.py

@@ -9,7 +9,7 @@
 
 @pytest.fixture(scope="package")
 def cfg_train_global() -> DictConfig:
-    with initialize(version_base="1.2", config_path="../configs"):
+    with initialize(version_base="1.3", config_path="../configs"):
         cfg = compose(config_name="train.yaml", return_hydra_config=True, overrides=[])
 
         # set defaults for all tests
@@ -32,7 +32,7 @@ def cfg_train_global() -> DictConfig:
 
 @pytest.fixture(scope="package")
 def cfg_eval_global() -> DictConfig:
-    with initialize(version_base="1.2", config_path="../configs"):
+    with initialize(version_base="1.3", config_path="../configs"):
         cfg = compose(config_name="eval.yaml", return_hydra_config=True, overrides=["ckpt_path=."])
 
         # set defaults for all tests