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align=\"center\"\u003e\n\n# Lightning-Hydra-Template\n\n[![python](https://img.shields.io/badge/-Python_3.8_%7C_3.9_%7C_3.10-blue?logo=python\u0026logoColor=white)](https://github.com/pre-commit/pre-commit)\n[![pytorch](https://img.shields.io/badge/PyTorch_2.0+-ee4c2c?logo=pytorch\u0026logoColor=white)](https://pytorch.org/get-started/locally/)\n[![lightning](https://img.shields.io/badge/-Lightning_2.0+-792ee5?logo=pytorchlightning\u0026logoColor=white)](https://pytorchlightning.ai/)\n[![hydra](https://img.shields.io/badge/Config-Hydra_1.3-89b8cd)](https://hydra.cc/)\n[![black](https://img.shields.io/badge/Code%20Style-Black-black.svg?labelColor=gray)](https://black.readthedocs.io/en/stable/)\n[![isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat\u0026labelColor=ef8336)](https://pycqa.github.io/isort/) \u003cbr\u003e\n[![tests](https://github.com/ashleve/lightning-hydra-template/actions/workflows/test.yml/badge.svg)](https://github.com/ashleve/lightning-hydra-template/actions/workflows/test.yml)\n[![code-quality](https://github.com/ashleve/lightning-hydra-template/actions/workflows/code-quality-main.yaml/badge.svg)](https://github.com/ashleve/lightning-hydra-template/actions/workflows/code-quality-main.yaml)\n[![codecov](https://codecov.io/gh/ashleve/lightning-hydra-template/branch/main/graph/badge.svg)](https://codecov.io/gh/ashleve/lightning-hydra-template) \u003cbr\u003e\n[![license](https://img.shields.io/badge/License-MIT-green.svg?labelColor=gray)](https://github.com/ashleve/lightning-hydra-template#license)\n[![PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/ashleve/lightning-hydra-template/pulls)\n[![contributors](https://img.shields.io/github/contributors/ashleve/lightning-hydra-template.svg)](https://github.com/ashleve/lightning-hydra-template/graphs/contributors)\n\nA clean template to kickstart your deep learning project 🚀⚡🔥\u003cbr\u003e\nClick on [\u003ckbd\u003eUse this template\u003c/kbd\u003e](https://github.com/ashleve/lightning-hydra-template/generate) to initialize new repository.\n\n_Suggestions are always welcome!_\n\n\u003c/div\u003e\n\n\u003cbr\u003e\n\n## 📌  Introduction\n\n**Why you might want to use it:**\n\n✅ Save on boilerplate \u003cbr\u003e\nEasily add new models, datasets, tasks, experiments, and train on different accelerators, like multi-GPU, TPU or SLURM clusters.\n\n✅ Education \u003cbr\u003e\nThoroughly commented. You can use this repo as a learning resource.\n\n✅ Reusability \u003cbr\u003e\nCollection of useful MLOps tools, configs, and code snippets. You can use this repo as a reference for various utilities.\n\n**Why you might not want to use it:**\n\n❌ Things break from time to time \u003cbr\u003e\nLightning and Hydra are still evolving and integrate many libraries, which means sometimes things break. For the list of currently known problems visit [this page](https://github.com/ashleve/lightning-hydra-template/labels/bug).\n\n❌ Not adjusted for data engineering \u003cbr\u003e\nTemplate is not really adjusted for building data pipelines that depend on each other. It's more efficient to use it for model prototyping on ready-to-use data.\n\n❌ Overfitted to simple use case \u003cbr\u003e\nThe configuration setup is built with simple lightning training in mind. You might need to put some effort to adjust it for different use cases, e.g. lightning fabric.\n\n❌ Might not support your workflow \u003cbr\u003e\nFor example, you can't resume hydra-based multirun or hyperparameter search.\n\n\u003e **Note**: _Keep in mind this is unofficial community project._\n\n\u003cbr\u003e\n\n## Main Technologies\n\n[PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) - a lightweight PyTorch wrapper for high-performance AI research. Think of it as a framework for organizing your PyTorch code.\n\n[Hydra](https://github.com/facebookresearch/hydra) - a framework for elegantly configuring complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line.\n\n\u003cbr\u003e\n\n## Main Ideas\n\n- [**Rapid Experimentation**](#your-superpowers): thanks to hydra command line superpowers\n- [**Minimal Boilerplate**](#how-it-works): thanks to automating pipelines with config instantiation\n- [**Main Configs**](#main-config): allow you to specify default training configuration\n- [**Experiment Configs**](#experiment-config): allow you to override chosen hyperparameters and version control experiments\n- [**Workflow**](#workflow): comes down to 4 simple steps\n- [**Experiment Tracking**](#experiment-tracking): Tensorboard, W\u0026B, Neptune, Comet, MLFlow and CSVLogger\n- [**Logs**](#logs): all logs (checkpoints, configs, etc.) are stored in a dynamically generated folder structure\n- [**Hyperparameter Search**](#hyperparameter-search): simple search is effortless with Hydra plugins like Optuna Sweeper\n- [**Tests**](#tests): generic, easy-to-adapt smoke tests for speeding up the development\n- [**Continuous Integration**](#continuous-integration): automatically test and lint your repo with Github Actions\n- [**Best Practices**](#best-practices): a couple of recommended tools, practices and standards\n\n\u003cbr\u003e\n\n## Project Structure\n\nThe directory structure of new project looks like this:\n\n```\n├── .github                   \u003c- Github Actions workflows\n│\n├── configs                   \u003c- Hydra configs\n│   ├── callbacks                \u003c- Callbacks configs\n│   ├── data                     \u003c- Data configs\n│   ├── debug                    \u003c- Debugging configs\n│   ├── experiment               \u003c- Experiment configs\n│   ├── extras                   \u003c- Extra utilities configs\n│   ├── hparams_search           \u003c- Hyperparameter search configs\n│   ├── hydra                    \u003c- Hydra configs\n│   ├── local                    \u003c- Local configs\n│   ├── logger                   \u003c- Logger configs\n│   ├── model                    \u003c- Model configs\n│   ├── paths                    \u003c- Project paths configs\n│   ├── trainer                  \u003c- Trainer configs\n│   │\n│   ├── eval.yaml             \u003c- Main config for evaluation\n│   └── train.yaml            \u003c- Main config for training\n│\n├── data                   \u003c- Project data\n│\n├── logs                   \u003c- Logs generated by hydra and lightning loggers\n│\n├── notebooks              \u003c- Jupyter notebooks. Naming convention is a number (for ordering),\n│                             the creator's initials, and a short `-` delimited description,\n│                             e.g. `1.0-jqp-initial-data-exploration.ipynb`.\n│\n├── scripts                \u003c- Shell scripts\n│\n├── src                    \u003c- Source code\n│   ├── data                     \u003c- Data scripts\n│   ├── models                   \u003c- Model scripts\n│   ├── utils                    \u003c- Utility scripts\n│   │\n│   ├── eval.py                  \u003c- Run evaluation\n│   └── train.py                 \u003c- Run training\n│\n├── tests                  \u003c- Tests of any kind\n│\n├── .env.example              \u003c- Example of file for storing private environment variables\n├── .gitignore                \u003c- List of files ignored by git\n├── .pre-commit-config.yaml   \u003c- Configuration of pre-commit hooks for code formatting\n├── .project-root             \u003c- File for inferring the position of project root directory\n├── environment.yaml          \u003c- File for installing conda environment\n├── Makefile                  \u003c- Makefile with commands like `make train` or `make test`\n├── pyproject.toml            \u003c- Configuration options for testing and linting\n├── requirements.txt          \u003c- File for installing python dependencies\n├── setup.py                  \u003c- File for installing project as a package\n└── README.md\n```\n\n\u003cbr\u003e\n\n## 🚀  Quickstart\n\n```bash\n# clone project\ngit clone https://github.com/ashleve/lightning-hydra-template\ncd lightning-hydra-template\n\n# [OPTIONAL] create conda environment\nconda create -n myenv python=3.9\nconda activate myenv\n\n# install pytorch according to instructions\n# https://pytorch.org/get-started/\n\n# install requirements\npip install -r requirements.txt\n```\n\nTemplate contains example with MNIST classification.\u003cbr\u003e\nWhen running `python src/train.py` you should see something like this:\n\n\u003cdiv align=\"center\"\u003e\n\n![](https://github.com/ashleve/lightning-hydra-template/blob/resources/terminal.png)\n\n\u003c/div\u003e\n\n## ⚡  Your Superpowers\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eOverride any config parameter from command line\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py trainer.max_epochs=20 model.optimizer.lr=1e-4\n```\n\n\u003e **Note**: You can also add new parameters with `+` sign.\n\n```bash\npython train.py +model.new_param=\"owo\"\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eTrain on CPU, GPU, multi-GPU and TPU\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# train on CPU\npython train.py trainer=cpu\n\n# train on 1 GPU\npython train.py trainer=gpu\n\n# train on TPU\npython train.py +trainer.tpu_cores=8\n\n# train with DDP (Distributed Data Parallel) (4 GPUs)\npython train.py trainer=ddp trainer.devices=4\n\n# train with DDP (Distributed Data Parallel) (8 GPUs, 2 nodes)\npython train.py trainer=ddp trainer.devices=4 trainer.num_nodes=2\n\n# simulate DDP on CPU processes\npython train.py trainer=ddp_sim trainer.devices=2\n\n# accelerate training on mac\npython train.py trainer=mps\n```\n\n\u003e **Warning**: Currently there are problems with DDP mode, read [this issue](https://github.com/ashleve/lightning-hydra-template/issues/393) to learn more.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eTrain with mixed precision\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# train with pytorch native automatic mixed precision (AMP)\npython train.py trainer=gpu +trainer.precision=16\n```\n\n\u003c/details\u003e\n\n\u003c!-- deepspeed support still in beta\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eOptimize large scale models on multiple GPUs with Deepspeed\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py +trainer.\n```\n\n\u003c/details\u003e\n --\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eTrain model with any logger available in PyTorch Lightning, like W\u0026B or Tensorboard\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\n# set project and entity names in `configs/logger/wandb`\nwandb:\n  project: \"your_project_name\"\n  entity: \"your_wandb_team_name\"\n```\n\n```bash\n# train model with Weights\u0026Biases (link to wandb dashboard should appear in the terminal)\npython train.py logger=wandb\n```\n\n\u003e **Note**: Lightning provides convenient integrations with most popular logging frameworks. Learn more [here](#experiment-tracking).\n\n\u003e **Note**: Using wandb requires you to [setup account](https://www.wandb.com/) first. After that just complete the config as below.\n\n\u003e **Note**: Click [here](https://wandb.ai/hobglob/template-dashboard/) to see example wandb dashboard generated with this template.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eTrain model with chosen experiment config\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py experiment=example\n```\n\n\u003e **Note**: Experiment configs are placed in [configs/experiment/](configs/experiment/).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eAttach some callbacks to run\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py callbacks=default\n```\n\n\u003e **Note**: Callbacks can be used for things such as as model checkpointing, early stopping and [many more](https://pytorch-lightning.readthedocs.io/en/latest/extensions/callbacks.html#built-in-callbacks).\n\n\u003e **Note**: Callbacks configs are placed in [configs/callbacks/](configs/callbacks/).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse different tricks available in Pytorch Lightning\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\n# gradient clipping may be enabled to avoid exploding gradients\npython train.py +trainer.gradient_clip_val=0.5\n\n# run validation loop 4 times during a training epoch\npython train.py +trainer.val_check_interval=0.25\n\n# accumulate gradients\npython train.py +trainer.accumulate_grad_batches=10\n\n# terminate training after 12 hours\npython train.py +trainer.max_time=\"00:12:00:00\"\n```\n\n\u003e **Note**: PyTorch Lightning provides about [40+ useful trainer flags](https://pytorch-lightning.readthedocs.io/en/latest/common/trainer.html#trainer-flags).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eEasily debug\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# runs 1 epoch in default debugging mode\n# changes logging directory to `logs/debugs/...`\n# sets level of all command line loggers to 'DEBUG'\n# enforces debug-friendly configuration\npython train.py debug=default\n\n# run 1 train, val and test loop, using only 1 batch\npython train.py debug=fdr\n\n# print execution time profiling\npython train.py debug=profiler\n\n# try overfitting to 1 batch\npython train.py debug=overfit\n\n# raise exception if there are any numerical anomalies in tensors, like NaN or +/-inf\npython train.py +trainer.detect_anomaly=true\n\n# use only 20% of the data\npython train.py +trainer.limit_train_batches=0.2 \\\n+trainer.limit_val_batches=0.2 +trainer.limit_test_batches=0.2\n```\n\n\u003e **Note**: Visit [configs/debug/](configs/debug/) for different debugging configs.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eResume training from checkpoint\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\npython train.py ckpt_path=\"/path/to/ckpt/name.ckpt\"\n```\n\n\u003e **Note**: Checkpoint can be either path or URL.\n\n\u003e **Note**: Currently loading ckpt doesn't resume logger experiment, but it will be supported in future Lightning release.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eEvaluate checkpoint on test dataset\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\npython eval.py ckpt_path=\"/path/to/ckpt/name.ckpt\"\n```\n\n\u003e **Note**: Checkpoint can be either path or URL.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eCreate a sweep over hyperparameters\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# this will run 6 experiments one after the other,\n# each with different combination of batch_size and learning rate\npython train.py -m data.batch_size=32,64,128 model.lr=0.001,0.0005\n```\n\n\u003e **Note**: Hydra composes configs lazily at job launch time. If you change code or configs after launching a job/sweep, the final composed configs might be impacted.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eCreate a sweep over hyperparameters with Optuna\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# this will run hyperparameter search defined in `configs/hparams_search/mnist_optuna.yaml`\n# over chosen experiment config\npython train.py -m hparams_search=mnist_optuna experiment=example\n```\n\n\u003e **Note**: Using [Optuna Sweeper](https://hydra.cc/docs/next/plugins/optuna_sweeper) doesn't require you to add any boilerplate to your code, everything is defined in a [single config file](configs/hparams_search/mnist_optuna.yaml).\n\n\u003e **Warning**: Optuna sweeps are not failure-resistant (if one job crashes then the whole sweep crashes).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eExecute all experiments from folder\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py -m 'experiment=glob(*)'\n```\n\n\u003e **Note**: Hydra provides special syntax for controlling behavior of multiruns. Learn more [here](https://hydra.cc/docs/next/tutorials/basic/running_your_app/multi-run). The command above executes all experiments from [configs/experiment/](configs/experiment/).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eExecute run for multiple different seeds\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npython train.py -m seed=1,2,3,4,5 trainer.deterministic=True logger=csv tags=[\"benchmark\"]\n```\n\n\u003e **Note**: `trainer.deterministic=True` makes pytorch more deterministic but impacts the performance.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eExecute sweep on a remote AWS cluster\u003c/b\u003e\u003c/summary\u003e\n\n\u003e **Note**: This should be achievable with simple config using [Ray AWS launcher for Hydra](https://hydra.cc/docs/next/plugins/ray_launcher). Example is not implemented in this template.\n\n\u003c/details\u003e\n\n\u003c!-- \u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eExecute sweep on a SLURM cluster\u003c/b\u003e\u003c/summary\u003e\n\n\u003e This should be achievable with either [the right lightning trainer flags](https://pytorch-lightning.readthedocs.io/en/latest/clouds/cluster.html?highlight=SLURM#slurm-managed-cluster) or simple config using [Submitit launcher for Hydra](https://hydra.cc/docs/plugins/submitit_launcher). Example is not yet implemented in this template.\n\n\u003c/details\u003e --\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse Hydra tab completion\u003c/b\u003e\u003c/summary\u003e\n\n\u003e **Note**: Hydra allows you to autocomplete config argument overrides in shell as you write them, by pressing `tab` key. Read the [docs](https://hydra.cc/docs/tutorials/basic/running_your_app/tab_completion).\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eApply pre-commit hooks\u003c/b\u003e\u003c/summary\u003e\n\n```bash\npre-commit run -a\n```\n\n\u003e **Note**: Apply pre-commit hooks to do things like auto-formatting code and configs, performing code analysis or removing output from jupyter notebooks. See [# Best Practices](#best-practices) for more.\n\nUpdate pre-commit hook versions in `.pre-commit-config.yaml` with:\n\n```bash\npre-commit autoupdate\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eRun tests\u003c/b\u003e\u003c/summary\u003e\n\n```bash\n# run all tests\npytest\n\n# run tests from specific file\npytest tests/test_train.py\n\n# run all tests except the ones marked as slow\npytest -k \"not slow\"\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse tags\u003c/b\u003e\u003c/summary\u003e\n\nEach experiment should be tagged in order to easily filter them across files or in logger UI:\n\n```bash\npython train.py tags=[\"mnist\",\"experiment_X\"]\n```\n\n\u003e **Note**: You might need to escape the bracket characters in your shell with `python train.py tags=\\[\"mnist\",\"experiment_X\"\\]`.\n\nIf no tags are provided, you will be asked to input them from command line:\n\n```bash\n\u003e\u003e\u003e python train.py tags=[]\n[2022-07-11 15:40:09,358][src.utils.utils][INFO] - Enforcing tags! \u003ccfg.extras.enforce_tags=True\u003e\n[2022-07-11 15:40:09,359][src.utils.rich_utils][WARNING] - No tags provided in config. Prompting user to input tags...\nEnter a list of comma separated tags (dev):\n```\n\nIf no tags are provided for multirun, an error will be raised:\n\n```bash\n\u003e\u003e\u003e python train.py -m +x=1,2,3 tags=[]\nValueError: Specify tags before launching a multirun!\n```\n\n\u003e **Note**: Appending lists from command line is currently not supported in hydra :(\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n## ❤️  Contributions\n\nThis project exists thanks to all the people who contribute.\n\n![Contributors](https://readme-contributors.now.sh/ashleve/lightning-hydra-template?extension=jpg\u0026width=400\u0026aspectRatio=1)\n\nHave a question? Found a bug? Missing a specific feature? Feel free to file a new issue, discussion or PR with respective title and description.\n\nBefore making an issue, please verify that:\n\n- The problem still exists on the current `main` branch.\n- Your python dependencies are updated to recent versions.\n\nSuggestions for improvements are always welcome!\n\n\u003cbr\u003e\n\n## How It Works\n\nAll PyTorch Lightning modules are dynamically instantiated from module paths specified in config. Example model config:\n\n```yaml\n_target_: src.models.mnist_model.MNISTLitModule\nlr: 0.001\nnet:\n  _target_: src.models.components.simple_dense_net.SimpleDenseNet\n  input_size: 784\n  lin1_size: 256\n  lin2_size: 256\n  lin3_size: 256\n  output_size: 10\n```\n\nUsing this config we can instantiate the object with the following line:\n\n```python\nmodel = hydra.utils.instantiate(config.model)\n```\n\nThis allows you to easily iterate over new models! Every time you create a new one, just specify its module path and parameters in appropriate config file. \u003cbr\u003e\n\nSwitch between models and datamodules with command line arguments:\n\n```bash\npython train.py model=mnist\n```\n\nExample pipeline managing the instantiation logic: [src/train.py](src/train.py).\n\n\u003cbr\u003e\n\n## Main Config\n\nLocation: [configs/train.yaml](configs/train.yaml) \u003cbr\u003e\nMain project config contains default training configuration.\u003cbr\u003e\nIt determines how config is composed when simply executing command `python train.py`.\u003cbr\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eShow main project config\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\n# order of defaults determines the order in which configs override each other\ndefaults:\n  - _self_\n  - data: mnist.yaml\n  - model: mnist.yaml\n  - callbacks: default.yaml\n  - logger: null # set logger here or use command line (e.g. `python train.py logger=csv`)\n  - trainer: default.yaml\n  - paths: default.yaml\n  - extras: default.yaml\n  - hydra: default.yaml\n\n  # experiment configs allow for version control of specific hyperparameters\n  # e.g. best hyperparameters for given model and datamodule\n  - experiment: null\n\n  # config for hyperparameter optimization\n  - hparams_search: null\n\n  # optional local config for machine/user specific settings\n  # it's optional since it doesn't need to exist and is excluded from version control\n  - optional local: default.yaml\n\n  # debugging config (enable through command line, e.g. `python train.py debug=default)\n  - debug: null\n\n# task name, determines output directory path\ntask_name: \"train\"\n\n# tags to help you identify your experiments\n# you can overwrite this in experiment configs\n# overwrite from command line with `python train.py tags=\"[first_tag, second_tag]\"`\n# appending lists from command line is currently not supported :(\n# https://github.com/facebookresearch/hydra/issues/1547\ntags: [\"dev\"]\n\n# set False to skip model training\ntrain: True\n\n# evaluate on test set, using best model weights achieved during training\n# lightning chooses best weights based on the metric specified in checkpoint callback\ntest: True\n\n# simply provide checkpoint path to resume training\nckpt_path: null\n\n# seed for random number generators in pytorch, numpy and python.random\nseed: null\n```\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n## Experiment Config\n\nLocation: [configs/experiment](configs/experiment)\u003cbr\u003e\nExperiment configs allow you to overwrite parameters from main config.\u003cbr\u003e\nFor example, you can use them to version control best hyperparameters for each combination of model and dataset.\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eShow example experiment config\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\n# @package _global_\n\n# to execute this experiment run:\n# python train.py experiment=example\n\ndefaults:\n  - override /data: mnist.yaml\n  - override /model: mnist.yaml\n  - override /callbacks: default.yaml\n  - override /trainer: default.yaml\n\n# all parameters below will be merged with parameters from default configurations set above\n# this allows you to overwrite only specified parameters\n\ntags: [\"mnist\", \"simple_dense_net\"]\n\nseed: 12345\n\ntrainer:\n  min_epochs: 10\n  max_epochs: 10\n  gradient_clip_val: 0.5\n\nmodel:\n  optimizer:\n    lr: 0.002\n  net:\n    lin1_size: 128\n    lin2_size: 256\n    lin3_size: 64\n\ndata:\n  batch_size: 64\n\nlogger:\n  wandb:\n    tags: ${tags}\n    group: \"mnist\"\n```\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n## Workflow\n\n**Basic workflow**\n\n1. Write your PyTorch Lightning module (see [models/mnist_module.py](src/models/mnist_module.py) for example)\n2. Write your PyTorch Lightning datamodule (see [data/mnist_datamodule.py](src/data/mnist_datamodule.py) for example)\n3. Write your experiment config, containing paths to model and datamodule\n4. Run training with chosen experiment config:\n   ```bash\n   python src/train.py experiment=experiment_name.yaml\n   ```\n\n**Experiment design**\n\n_Say you want to execute many runs to plot how accuracy changes in respect to batch size._\n\n1. Execute the runs with some config parameter that allows you to identify them easily, like tags:\n\n   ```bash\n   python train.py -m logger=csv data.batch_size=16,32,64,128 tags=[\"batch_size_exp\"]\n   ```\n\n2. Write a script or notebook that searches over the `logs/` folder and retrieves csv logs from runs containing given tags in config. Plot the results.\n\n\u003cbr\u003e\n\n## Logs\n\nHydra creates new output directory for every executed run.\n\nDefault logging structure:\n\n```\n├── logs\n│   ├── task_name\n│   │   ├── runs                        # Logs generated by single runs\n│   │   │   ├── YYYY-MM-DD_HH-MM-SS       # Datetime of the run\n│   │   │   │   ├── .hydra                  # Hydra logs\n│   │   │   │   ├── csv                     # Csv logs\n│   │   │   │   ├── wandb                   # Weights\u0026Biases logs\n│   │   │   │   ├── checkpoints             # Training checkpoints\n│   │   │   │   └── ...                     # Any other thing saved during training\n│   │   │   └── ...\n│   │   │\n│   │   └── multiruns                   # Logs generated by multiruns\n│   │       ├── YYYY-MM-DD_HH-MM-SS       # Datetime of the multirun\n│   │       │   ├──1                        # Multirun job number\n│   │       │   ├──2\n│   │       │   └── ...\n│   │       └── ...\n│   │\n│   └── debugs                          # Logs generated when debugging config is attached\n│       └── ...\n```\n\n\u003c/details\u003e\n\nYou can change this structure by modifying paths in [hydra configuration](configs/hydra).\n\n\u003cbr\u003e\n\n## Experiment Tracking\n\nPyTorch Lightning supports many popular logging frameworks: [Weights\u0026Biases](https://www.wandb.com/), [Neptune](https://neptune.ai/), [Comet](https://www.comet.ml/), [MLFlow](https://mlflow.org), [Tensorboard](https://www.tensorflow.org/tensorboard/).\n\nThese tools help you keep track of hyperparameters and output metrics and allow you to compare and visualize results. To use one of them simply complete its configuration in [configs/logger](configs/logger) and run:\n\n```bash\npython train.py logger=logger_name\n```\n\nYou can use many of them at once (see [configs/logger/many_loggers.yaml](configs/logger/many_loggers.yaml) for example).\n\nYou can also write your own logger.\n\nLightning provides convenient method for logging custom metrics from inside LightningModule. Read the [docs](https://pytorch-lightning.readthedocs.io/en/latest/extensions/logging.html#automatic-logging) or take a look at [MNIST example](src/models/mnist_module.py).\n\n\u003cbr\u003e\n\n## Tests\n\nTemplate comes with generic tests implemented with `pytest`.\n\n```bash\n# run all tests\npytest\n\n# run tests from specific file\npytest tests/test_train.py\n\n# run all tests except the ones marked as slow\npytest -k \"not slow\"\n```\n\nMost of the implemented tests don't check for any specific output - they exist to simply verify that executing some commands doesn't end up in throwing exceptions. You can execute them once in a while to speed up the development.\n\nCurrently, the tests cover cases like:\n\n- running 1 train, val and test step\n- running 1 epoch on 1% of data, saving ckpt and resuming for the second epoch\n- running 2 epochs on 1% of data, with DDP simulated on CPU\n\nAnd many others. You should be able to modify them easily for your use case.\n\nThere is also `@RunIf` decorator implemented, that allows you to run tests only if certain conditions are met, e.g. GPU is available or system is not windows. See the [examples](tests/test_train.py).\n\n\u003cbr\u003e\n\n## Hyperparameter Search\n\nYou can define hyperparameter search by adding new config file to [configs/hparams_search](configs/hparams_search).\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eShow example hyperparameter search config\u003c/b\u003e\u003c/summary\u003e\n\n```yaml\n# @package _global_\n\ndefaults:\n  - override /hydra/sweeper: optuna\n\n# choose metric which will be optimized by Optuna\n# make sure this is the correct name of some metric logged in lightning module!\noptimized_metric: \"val/acc_best\"\n\n# here we define Optuna hyperparameter search\n# it optimizes for value returned from function with @hydra.main decorator\nhydra:\n  sweeper:\n    _target_: hydra_plugins.hydra_optuna_sweeper.optuna_sweeper.OptunaSweeper\n\n    # 'minimize' or 'maximize' the objective\n    direction: maximize\n\n    # total number of runs that will be executed\n    n_trials: 20\n\n    # choose Optuna hyperparameter sampler\n    # docs: https://optuna.readthedocs.io/en/stable/reference/samplers.html\n    sampler:\n      _target_: optuna.samplers.TPESampler\n      seed: 1234\n      n_startup_trials: 10 # number of random sampling runs before optimization starts\n\n    # define hyperparameter search space\n    params:\n      model.optimizer.lr: interval(0.0001, 0.1)\n      data.batch_size: choice(32, 64, 128, 256)\n      model.net.lin1_size: choice(64, 128, 256)\n      model.net.lin2_size: choice(64, 128, 256)\n      model.net.lin3_size: choice(32, 64, 128, 256)\n```\n\n\u003c/details\u003e\n\nNext, execute it with: `python train.py -m hparams_search=mnist_optuna`\n\nUsing this approach doesn't require adding any boilerplate to code, everything is defined in a single config file. The only necessary thing is to return the optimized metric value from the launch file.\n\nYou can use different optimization frameworks integrated with Hydra, like [Optuna, Ax or Nevergrad](https://hydra.cc/docs/plugins/optuna_sweeper/).\n\nThe `optimization_results.yaml` will be available under `logs/task_name/multirun` folder.\n\nThis approach doesn't support resuming interrupted search and advanced techniques like prunning - for more sophisticated search and workflows, you should probably write a dedicated optimization task (without multirun feature).\n\n\u003cbr\u003e\n\n## Continuous Integration\n\nTemplate comes with CI workflows implemented in Github Actions:\n\n- `.github/workflows/test.yaml`: running all tests with pytest\n- `.github/workflows/code-quality-main.yaml`: running pre-commits on main branch for all files\n- `.github/workflows/code-quality-pr.yaml`: running pre-commits on pull requests for modified files only\n\n\u003cbr\u003e\n\n## Distributed Training\n\nLightning supports multiple ways of doing distributed training. The most common one is DDP, which spawns separate process for each GPU and averages gradients between them. To learn about other approaches read the [lightning docs](https://lightning.ai/docs/pytorch/latest/advanced/speed.html).\n\nYou can run DDP on mnist example with 4 GPUs like this:\n\n```bash\npython train.py trainer=ddp\n```\n\n\u003e **Note**: When using DDP you have to be careful how you write your models - read the [docs](https://lightning.ai/docs/pytorch/latest/advanced/speed.html).\n\n\u003cbr\u003e\n\n## Accessing Datamodule Attributes In Model\n\nThe simplest way is to pass datamodule attribute directly to model on initialization:\n\n```python\n# ./src/train.py\ndatamodule = hydra.utils.instantiate(config.data)\nmodel = hydra.utils.instantiate(config.model, some_param=datamodule.some_param)\n```\n\n\u003e **Note**: Not a very robust solution, since it assumes all your datamodules have `some_param` attribute available.\n\nSimilarly, you can pass a whole datamodule config as an init parameter:\n\n```python\n# ./src/train.py\nmodel = hydra.utils.instantiate(config.model, dm_conf=config.data, _recursive_=False)\n```\n\nYou can also pass a datamodule config parameter to your model through variable interpolation:\n\n```yaml\n# ./configs/model/my_model.yaml\n_target_: src.models.my_module.MyLitModule\nlr: 0.01\nsome_param: ${data.some_param}\n```\n\nAnother approach is to access datamodule in LightningModule directly through Trainer:\n\n```python\n# ./src/models/mnist_module.py\ndef on_train_start(self):\n  self.some_param = self.trainer.datamodule.some_param\n```\n\n\u003e **Note**: This only works after the training starts since otherwise trainer won't be yet available in LightningModule.\n\n\u003cbr\u003e\n\n## Best Practices\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse Miniconda\u003c/b\u003e\u003c/summary\u003e\n\nIt's usually unnecessary to install full anaconda environment, miniconda should be enough (weights around 80MB).\n\nBig advantage of conda is that it allows for installing packages without requiring certain compilers or libraries to be available in the system (since it installs precompiled binaries), so it often makes it easier to install some dependencies e.g. cudatoolkit for GPU support.\n\nIt also allows you to access your environments globally which might be more convenient than creating new local environment for every project.\n\nExample installation:\n\n```bash\nwget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\nbash Miniconda3-latest-Linux-x86_64.sh\n```\n\nUpdate conda:\n\n```bash\nconda update -n base -c defaults conda\n```\n\nCreate new conda environment:\n\n```bash\nconda create -n myenv python=3.10\nconda activate myenv\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse automatic code formatting\u003c/b\u003e\u003c/summary\u003e\n\nUse pre-commit hooks to standardize code formatting of your project and save mental energy.\u003cbr\u003e\nSimply install pre-commit package with:\n\n```bash\npip install pre-commit\n```\n\nNext, install hooks from [.pre-commit-config.yaml](.pre-commit-config.yaml):\n\n```bash\npre-commit install\n```\n\nAfter that your code will be automatically reformatted on every new commit.\n\nTo reformat all files in the project use command:\n\n```bash\npre-commit run -a\n```\n\nTo update hook versions in [.pre-commit-config.yaml](.pre-commit-config.yaml) use:\n\n```bash\npre-commit autoupdate\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eSet private environment variables in .env file\u003c/b\u003e\u003c/summary\u003e\n\nSystem specific variables (e.g. absolute paths to datasets) should not be under version control or it will result in conflict between different users. Your private keys also shouldn't be versioned since you don't want them to be leaked.\u003cbr\u003e\n\nTemplate contains `.env.example` file, which serves as an example. Create a new file called `.env` (this name is excluded from version control in .gitignore).\nYou should use it for storing environment variables like this:\n\n```\nMY_VAR=/home/user/my_system_path\n```\n\nAll variables from `.env` are loaded in `train.py` automatically.\n\nHydra allows you to reference any env variable in `.yaml` configs like this:\n\n```yaml\npath_to_data: ${oc.env:MY_VAR}\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eName metrics using '/' character\u003c/b\u003e\u003c/summary\u003e\n\nDepending on which logger you're using, it's often useful to define metric name with `/` character:\n\n```python\nself.log(\"train/loss\", loss)\n```\n\nThis way loggers will treat your metrics as belonging to different sections, which helps to get them organised in UI.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eUse torchmetrics\u003c/b\u003e\u003c/summary\u003e\n\nUse official [torchmetrics](https://github.com/PytorchLightning/metrics) library to ensure proper calculation of metrics. This is especially important for multi-GPU training!\n\nFor example, instead of calculating accuracy by yourself, you should use the provided `Accuracy` class like this:\n\n```python\nfrom torchmetrics.classification.accuracy import Accuracy\n\n\nclass LitModel(LightningModule):\n    def __init__(self)\n        self.train_acc = Accuracy()\n        self.val_acc = Accuracy()\n\n    def training_step(self, batch, batch_idx):\n        ...\n        acc = self.train_acc(predictions, targets)\n        self.log(\"train/acc\", acc)\n        ...\n\n    def validation_step(self, batch, batch_idx):\n        ...\n        acc = self.val_acc(predictions, targets)\n        self.log(\"val/acc\", acc)\n        ...\n```\n\nMake sure to use different metric instance for each step to ensure proper value reduction over all GPU processes.\n\nTorchmetrics provides metrics for most use cases, like F1 score or confusion matrix. Read [documentation](https://torchmetrics.readthedocs.io/en/latest/#more-reading) for more.\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eFollow PyTorch Lightning style guide\u003c/b\u003e\u003c/summary\u003e\n\nThe style guide is available [here](https://pytorch-lightning.readthedocs.io/en/latest/starter/style_guide.html).\u003cbr\u003e\n\n1. Be explicit in your init. Try to define all the relevant defaults so that the user doesn’t have to guess. Provide type hints. This way your module is reusable across projects!\n\n   ```python\n   class LitModel(LightningModule):\n       def __init__(self, layer_size: int = 256, lr: float = 0.001):\n   ```\n\n2. Preserve the recommended method order.\n\n   ```python\n   class LitModel(LightningModule):\n\n       def __init__():\n           ...\n\n       def forward():\n           ...\n\n       def training_step():\n           ...\n\n       def training_step_end():\n           ...\n\n       def on_train_epoch_end():\n           ...\n\n       def validation_step():\n           ...\n\n       def validation_step_end():\n           ...\n\n       def on_validation_epoch_end():\n           ...\n\n       def test_step():\n           ...\n\n       def test_step_end():\n           ...\n\n       def on_test_epoch_end():\n           ...\n\n       def configure_optimizers():\n           ...\n\n       def any_extra_hook():\n           ...\n   ```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eVersion control your data and models with DVC\u003c/b\u003e\u003c/summary\u003e\n\nUse [DVC](https://dvc.org) to version control big files, like your data or trained ML models.\u003cbr\u003e\nTo initialize the dvc repository:\n\n```bash\ndvc init\n```\n\nTo start tracking a file or directory, use `dvc add`:\n\n```bash\ndvc add data/MNIST\n```\n\nDVC stores information about the added file (or a directory) in a special .dvc file named data/MNIST.dvc, a small text file with a human-readable format. This file can be easily versioned like source code with Git, as a placeholder for the original data:\n\n```bash\ngit add data/MNIST.dvc data/.gitignore\ngit commit -m \"Add raw data\"\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eSupport installing project as a package\u003c/b\u003e\u003c/summary\u003e\n\nIt allows other people to easily use your modules in their own projects.\nChange name of the `src` folder to your project name and complete the `setup.py` file.\n\nNow your project can be installed from local files:\n\n```bash\npip install -e .\n```\n\nOr directly from git repository:\n\n```bash\npip install git+git://github.com/YourGithubName/your-repo-name.git --upgrade\n```\n\nSo any file can be easily imported into any other file like so:\n\n```python\nfrom project_name.models.mnist_module import MNISTLitModule\nfrom project_name.data.mnist_datamodule import MNISTDataModule\n```\n\n\u003c/details\u003e\n\n\u003cdetails\u003e\n\u003csummary\u003e\u003cb\u003eKeep local configs out of code versioning\u003c/b\u003e\u003c/summary\u003e\n\nSome configurations are user/machine/installation specific (e.g. configuration of local cluster, or harddrive paths on a specific machine). For such scenarios, a file [configs/local/default.yaml](configs/local/) can be created which is automatically loaded but not tracked by Git.\n\nFor example, you can use it for a SLURM cluster config:\n\n```yaml\n# @package _global_\n\ndefaults:\n  - override /hydra/launcher@_here_: submitit_slurm\n\ndata_dir: /mnt/scratch/data/\n\nhydra:\n  launcher:\n    timeout_min: 1440\n    gpus_per_task: 1\n    gres: gpu:1\n  job:\n    env_set:\n      MY_VAR: /home/user/my/system/path\n      MY_KEY: asdgjhawi8y23ihsghsueity23ihwd\n```\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n## Resources\n\nThis template was inspired by:\n\n- [PyTorchLightning/deep-learning-project-template](https://github.com/PyTorchLightning/deep-learning-project-template)\n- [drivendata/cookiecutter-data-science](https://github.com/drivendata/cookiecutter-data-science)\n- [lucmos/nn-template](https://github.com/lucmos/nn-template)\n\nOther useful repositories:\n\n- [jxpress/lightning-hydra-template-vertex-ai](https://github.com/jxpress/lightning-hydra-template-vertex-ai) - lightning-hydra-template integration with Vertex AI hyperparameter tuning and custom training job\n\n\u003c/details\u003e\n\n\u003cbr\u003e\n\n## License\n\nLightning-Hydra-Template is licensed under the MIT License.\n\n```\nMIT License\n\nCopyright (c) 2021 ashleve\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n```\n\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\u003cbr\u003e\n\n**DELETE EVERYTHING ABOVE FOR YOUR PROJECT**\n\n______________________________________________________________________\n\n\u003cdiv align=\"center\"\u003e\n\n# Your Project Name\n\n\u003ca href=\"https://pytorch.org/get-started/locally/\"\u003e\u003cimg alt=\"PyTorch\" src=\"https://img.shields.io/badge/PyTorch-ee4c2c?logo=pytorch\u0026logoColor=white\"\u003e\u003c/a\u003e\n\u003ca href=\"https://pytorchlightning.ai/\"\u003e\u003cimg alt=\"Lightning\" src=\"https://img.shields.io/badge/-Lightning-792ee5?logo=pytorchlightning\u0026logoColor=white\"\u003e\u003c/a\u003e\n\u003ca href=\"https://hydra.cc/\"\u003e\u003cimg alt=\"Config: Hydra\" src=\"https://img.shields.io/badge/Config-Hydra-89b8cd\"\u003e\u003c/a\u003e\n\u003ca href=\"https://github.com/ashleve/lightning-hydra-template\"\u003e\u003cimg alt=\"Template\" src=\"https://img.shields.io/badge/-Lightning--Hydra--Template-017F2F?style=flat\u0026logo=github\u0026labelColor=gray\"\u003e\u003c/a\u003e\u003cbr\u003e\n[![Paper](http://img.shields.io/badge/paper-arxiv.1001.2234-B31B1B.svg)](https://www.nature.com/articles/nature14539)\n[![Conference](http://img.shields.io/badge/AnyConference-year-4b44ce.svg)](https://papers.nips.cc/paper/2020)\n\n\u003c/div\u003e\n\n## Description\n\nWhat it does\n\n## Installation\n\n#### Pip\n\n```bash\n# clone project\ngit clone https://github.com/YourGithubName/your-repo-name\ncd your-repo-name\n\n# [OPTIONAL] create conda environment\nconda create -n myenv python=3.9\nconda activate myenv\n\n# install pytorch according to instructions\n# https://pytorch.org/get-started/\n\n# install requirements\npip install -r requirements.txt\n```\n\n#### Conda\n\n```bash\n# clone project\ngit clone https://github.com/YourGithubName/your-repo-name\ncd your-repo-name\n\n# create conda environment and install dependencies\nconda env create -f environment.yaml -n myenv\n\n# activate conda environment\nconda activate myenv\n```\n\n## How to run\n\nTrain model with default configuration\n\n```bash\n# train on CPU\npython src/train.py trainer=cpu\n\n# train on GPU\npython src/train.py trainer=gpu\n```\n\nTrain model with chosen experiment configuration from [configs/experiment/](configs/experiment/)\n\n```bash\npython src/train.py experiment=experiment_name.yaml\n```\n\nYou can override any parameter from command line like this\n\n```bash\npython src/train.py trainer.max_epochs=20 data.batch_size=64\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashleve%2Flightning-hydra-template","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashleve%2Flightning-hydra-template","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashleve%2Flightning-hydra-template/lists"}