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‎README.md‎

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# PyTorch-Ignite playground
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# PyTorch-Ignite Playground
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Click on the badge to launch the playground in Binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pytorch-ignite/playground/HEAD?filepath=index.ipynb)
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- Click on the badge to launch the playground in Binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/pytorch-ignite/playground/HEAD?labpath=index.ipynb)
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- Click on the badge to launch the playground in Google Colab [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytorch-ignite/playground/blob/main/index.ipynb)
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## About this repository
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Access this Binder by clicking the blue badge above or at the following URL:
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https://mybinder.org/v2/gh/pytorch-ignite/playground/HEAD?filepath=index.ipynb
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https://mybinder.org/v2/gh/pytorch-ignite/playground/HEAD?labpath=index.ipynb
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### Notes
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‎environment.yml‎

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- pytorch
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- cpuonly
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- ignite
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- numpy

‎index.ipynb‎

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"print(torch.__version__, ignite.__version__)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"source": [
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"### Basic setup with various events filtering use-cases"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"from ignite.engine import Engine, Events\n",
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"from ignite.utils import setup_logger, logging\n",
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"\n",
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"\n",
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"train_data = range(10)\n",
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"eval_data = range(4)\n",
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"max_epochs = 5\n",
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"\n",
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"\n",
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"def train_step(engine, batch):\n",
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" print(f\"{engine.state.epoch} / {engine.state.max_epochs} | {engine.state.iteration} - batch: {batch}\", flush=True)\n",
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"\n",
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"trainer = Engine(train_step)\n",
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"\n",
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"# Enable trainer logger for a debug mode\n",
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"# trainer.logger = setup_logger(\"trainer\", level=logging.DEBUG)\n",
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"\n",
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"evaluator = Engine(lambda e, b: None)\n",
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"\n",
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"\n",
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"@trainer.on(Events.EPOCH_COMPLETED(every=2))\n",
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"def run_validation():\n",
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" print(f\"{trainer.state.epoch} / {trainer.state.max_epochs} | {trainer.state.iteration} - run validation\", flush=True)\n",
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" evaluator.run(eval_data)\n",
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"\n",
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"\n",
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"@trainer.on(Events.ITERATION_COMPLETED(every=7))\n",
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"def log_events_filtering__every():\n",
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" print(f\"{trainer.state.epoch} / {trainer.state.max_epochs} | {trainer.state.iteration} - calling log_events_filtering__every\", flush=True)\n",
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"\n",
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" \n",
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"@trainer.on(Events.EPOCH_COMPLETED(once=3))\n",
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"def log_events_filtering__once():\n",
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" print(f\"{trainer.state.epoch} / {trainer.state.max_epochs} | {trainer.state.iteration} - calling log_events_filtering__once\", flush=True)\n",
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"\n",
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"\n",
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"def custom_event_filter(engine, event):\n",
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" if trainer.state.epoch == 2 and event in (1, 3):\n",
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" return True\n",
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" return False\n",
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" \n",
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"\n",
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"@evaluator.on(Events.ITERATION_COMPLETED(event_filter=custom_event_filter))\n",
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"def log_events_filtering__event_filter():\n",
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" print(f\"{trainer.state.epoch} / {trainer.state.max_epochs} | {evaluator.state.iteration} - calling log_events_filtering__event_filter\", flush=True)\n",
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" \n",
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"\n",
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"trainer.run(train_data, max_epochs=max_epochs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": []
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Distributed run with 2 processes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"vscode": {
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"languageId": "plaintext"
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}
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},
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"outputs": [],
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"source": [
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"import time\n",
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"import ignite.distributed as idist\n",
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"\n",
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"from ignite.engine import Engine, Events\n",
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"from ignite.utils import setup_logger, logging\n",
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"\n",
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"\n",
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"def pprint(*args, **kwargs):\n",
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" rank = idist.get_rank()\n",
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" time.sleep(rank * 0.1)\n",
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" print(f\"Rank {rank}:\", end=\" \")\n",
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" print(*args, **kwargs)\n",
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"\n",
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"\n",
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"def run(local_rank): \n",
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" rank = idist.get_rank() \n",
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" torch.manual_seed(12 + rank)\n",
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" \n",
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" train_data = range(10)\n",
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" eval_data = range(4)\n",
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" max_epochs = 5\n",
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"\n",
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" def train_step(engine, batch):\n",
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" pprint(f\"{engine.state.epoch} / {engine.state.max_epochs} | {engine.state.iteration} - batch: {batch}\", flush=True)\n",
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"\n",
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" trainer = Engine(train_step)\n",
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" \n",
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" @trainer.on(Events.EPOCH_COMPLETED)\n",
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" def sync():\n",
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" idist.barrier()\n",
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"\n",
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" trainer.run(train_data, max_epochs=max_epochs) \n",
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" \n",
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" \n",
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"idist.spawn(\"gloo\", run, (), nproc_per_node=2) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,

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