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Thanks for the PR @Priyansi !
I've added some suggestions related to coding style.
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| " trainset = CIFAR10(\n", | ||
| " root=data_dir, train=True, download=True, transform=transform)\n", | ||
| "\n", | ||
| " testset = CIFAR10(\n", | ||
| " root=data_dir, train=False, download=True, transform=transform)\n", | ||
| "\n", | ||
| " return trainset, testset" |
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Suggested change
| " trainset = CIFAR10(\n", | |
| " root=data_dir, train=True, download=True, transform=transform)\n", | |
| "\n", | |
| " testset = CIFAR10(\n", | |
| " root=data_dir, train=False, download=True, transform=transform)\n", | |
| "\n", | |
| " return trainset, testset" | |
| " trainset = CIFAR10(\n", | |
| " root=data_dir, train=True, download=True, transform=transform\n", | |
| " )\n", | |
| " testset = CIFAR10(\n", | |
| " root=data_dir, train=False, download=True, transform=transform\n", | |
| " )\n", | |
| " return trainset, testset" |
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| " train_subset, val_subset = random_split(\n", | ||
| " trainset, [test_abs, len(trainset) - test_abs])\n", | ||
| "\n", | ||
| " trainloader = idist.auto_dataloader(\n", | ||
| " train_subset,\n", | ||
| " batch_size=int(config[\"batch_size\"]),\n", | ||
| " shuffle=True,\n", | ||
| " num_workers=8)\n", | ||
| " valloader = idist.auto_dataloader(\n", | ||
| " val_subset,\n", | ||
| " batch_size=int(config[\"batch_size\"]),\n", | ||
| " shuffle=True,\n", | ||
| " num_workers=8)\n", | ||
| " \n", | ||
| " return trainloader, valloader" |
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Suggested change
| " train_subset, val_subset = random_split(\n", | |
| " trainset, [test_abs, len(trainset) - test_abs])\n", | |
| "\n", | |
| " trainloader = idist.auto_dataloader(\n", | |
| " train_subset,\n", | |
| " batch_size=int(config[\"batch_size\"]),\n", | |
| " shuffle=True,\n", | |
| " num_workers=8)\n", | |
| " valloader = idist.auto_dataloader(\n", | |
| " val_subset,\n", | |
| " batch_size=int(config[\"batch_size\"]),\n", | |
| " shuffle=True,\n", | |
| " num_workers=8)\n", | |
| " \n", | |
| " return trainloader, valloader" | |
| " train_subset, val_subset = random_split(\n", | |
| " trainset, [test_abs, len(trainset) - test_abs]\n", | |
| " )\n", | |
| " trainloader = idist.auto_dataloader(\n", | |
| " train_subset,\n", | |
| " batch_size=int(config[\"batch_size\"]),\n", | |
| " shuffle=True,\n", | |
| " num_workers=8\n", | |
| " )\n", | |
| " valloader = idist.auto_dataloader(\n", | |
| " val_subset,\n", | |
| " batch_size=int(config[\"batch_size\"]),\n", | |
| " shuffle=True,\n", | |
| " num_workers=8\n", | |
| " )\n", | |
| " return trainloader, valloader" |
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| "def initialize(config, checkpoint_dir):\n", | ||
| " model = idist.auto_model(Net(config[\"l1\"], config[\"l2\"]))\n", | ||
| "\n", | ||
| " device = idist.device()\n", | ||
| "\n", | ||
| " criterion = nn.CrossEntropyLoss()\n", | ||
| " optimizer = idist.auto_optim(optim.SGD(model.parameters(), lr=config[\"lr\"], momentum=0.9))\n", | ||
| "\n", | ||
| " if checkpoint_dir:\n", | ||
| " model_state, optimizer_state = torch.load(\n", | ||
| " os.path.join(checkpoint_dir, \"checkpoint\"))\n", | ||
| " model.load_state_dict(model_state)\n", | ||
| " optimizer.load_state_dict(optimizer_state)\n", | ||
| " \n", | ||
| " return model, device, criterion, optimizer" |
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Suggested change
| "def initialize(config, checkpoint_dir):\n", | |
| " model = idist.auto_model(Net(config[\"l1\"], config[\"l2\"]))\n", | |
| "\n", | |
| " device = idist.device()\n", | |
| "\n", | |
| " criterion = nn.CrossEntropyLoss()\n", | |
| " optimizer = idist.auto_optim(optim.SGD(model.parameters(), lr=config[\"lr\"], momentum=0.9))\n", | |
| "\n", | |
| " if checkpoint_dir:\n", | |
| " model_state, optimizer_state = torch.load(\n", | |
| " os.path.join(checkpoint_dir, \"checkpoint\"))\n", | |
| " model.load_state_dict(model_state)\n", | |
| " optimizer.load_state_dict(optimizer_state)\n", | |
| " \n", | |
| " return model, device, criterion, optimizer" | |
| "def initialize(config, checkpoint_dir):\n", | |
| " model = idist.auto_model(Net(config[\"l1\"], config[\"l2\"]))\n", | |
| "\n", | |
| " device = idist.device()\n", | |
| "\n", | |
| " criterion = nn.CrossEntropyLoss()\n", | |
| " optimizer = idist.auto_optim(\n", | |
| " optim.SGD(model.parameters(), lr=config[\"lr\"], momentum=0.9)\n", | |
| " )\n", | |
| "\n", | |
| " if checkpoint_dir:\n", | |
| " model_state, optimizer_state = torch.load(\n", | |
| " os.path.join(checkpoint_dir, \"checkpoint\")\n", | |
| " )\n", | |
| " model.load_state_dict(model_state)\n", | |
| " optimizer.load_state_dict(optimizer_state)\n", | |
| "\n", | |
| " return model, device, criterion, optimizer" |
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| "def train_cifar(config, data_dir=None, checkpoint_dir=None):\n", | ||
| " trainloader, valloader = get_train_val_loaders(config, data_dir)\n", | ||
| " model, device, criterion, optimizer = initialize(config, checkpoint_dir)\n", | ||
| " \n", | ||
| " trainer = create_supervised_trainer(model, optimizer, criterion, device=device, non_blocking=True)\n", | ||
| " \n", | ||
| " avg_output = RunningAverage(output_transform=lambda x: x)\n", | ||
| " avg_output.attach(trainer, 'running_avg_loss')\n", | ||
| " \n", | ||
| " val_evaluator = create_supervised_evaluator(model, metrics={ \"accuracy\": Accuracy(), \"loss\": Loss(criterion)}, device=device, non_blocking=True)\n", | ||
| " \n", | ||
| " @trainer.on(Events.ITERATION_COMPLETED(every=2000))\n", | ||
| " def log_training_loss(engine):\n", | ||
| " print(f\"Epoch[{engine.state.epoch}], Iter[{engine.state.iteration}] Loss: {engine.state.output:.2f} Running Avg Loss: {engine.state.metrics['running_avg_loss']:.2f}\")\n", | ||
| "\n", | ||
| "\n", | ||
| " @trainer.on(Events.EPOCH_COMPLETED)\n", | ||
| " def log_validation_results(trainer):\n", | ||
| " val_evaluator.run(valloader)\n", | ||
| " metrics = val_evaluator.state.metrics\n", | ||
| " print(f\"Validation Results - Epoch[{trainer.state.epoch}] Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['loss']:.2f}\")\n", | ||
| "\n", | ||
| " with tune.checkpoint_dir(trainer.state.epoch) as checkpoint_dir:\n", | ||
| " path = os.path.join(checkpoint_dir, \"checkpoint\")\n", | ||
| " torch.save((model.state_dict(), optimizer.state_dict()), path)\n", | ||
| " \n", | ||
| " tune.report(loss=metrics['loss'], accuracy=metrics['accuracy']) \n", | ||
| "\n", | ||
| " trainer.run(trainloader, max_epochs=10) " |
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Suggested change
| "def train_cifar(config, data_dir=None, checkpoint_dir=None):\n", | |
| " trainloader, valloader = get_train_val_loaders(config, data_dir)\n", | |
| " model, device, criterion, optimizer = initialize(config, checkpoint_dir)\n", | |
| " \n", | |
| " trainer = create_supervised_trainer(model, optimizer, criterion, device=device, non_blocking=True)\n", | |
| " \n", | |
| " avg_output = RunningAverage(output_transform=lambda x: x)\n", | |
| " avg_output.attach(trainer, 'running_avg_loss')\n", | |
| " \n", | |
| " val_evaluator = create_supervised_evaluator(model, metrics={ \"accuracy\": Accuracy(), \"loss\": Loss(criterion)}, device=device, non_blocking=True)\n", | |
| " \n", | |
| " @trainer.on(Events.ITERATION_COMPLETED(every=2000))\n", | |
| " def log_training_loss(engine):\n", | |
| " print(f\"Epoch[{engine.state.epoch}], Iter[{engine.state.iteration}] Loss: {engine.state.output:.2f} Running Avg Loss: {engine.state.metrics['running_avg_loss']:.2f}\")\n", | |
| "\n", | |
| "\n", | |
| " @trainer.on(Events.EPOCH_COMPLETED)\n", | |
| " def log_validation_results(trainer):\n", | |
| " val_evaluator.run(valloader)\n", | |
| " metrics = val_evaluator.state.metrics\n", | |
| " print(f\"Validation Results - Epoch[{trainer.state.epoch}] Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['loss']:.2f}\")\n", | |
| "\n", | |
| " with tune.checkpoint_dir(trainer.state.epoch) as checkpoint_dir:\n", | |
| " path = os.path.join(checkpoint_dir, \"checkpoint\")\n", | |
| " torch.save((model.state_dict(), optimizer.state_dict()), path)\n", | |
| " \n", | |
| " tune.report(loss=metrics['loss'], accuracy=metrics['accuracy']) \n", | |
| "\n", | |
| " trainer.run(trainloader, max_epochs=10) " | |
| "def train_cifar(config, data_dir=None, checkpoint_dir=None):\n", | |
| " trainloader, valloader = get_train_val_loaders(config, data_dir)\n", | |
| " model, device, criterion, optimizer = initialize(config, checkpoint_dir)\n", | |
| "\n", | |
| " trainer = create_supervised_trainer(\n", | |
| " model, optimizer, criterion, device=device, non_blocking=True\n", | |
| " )\n", | |
| "\n", | |
| " avg_output = RunningAverage(output_transform=lambda x: x)\n", | |
| " avg_output.attach(trainer, \"running_avg_loss\")\n", | |
| "\n", | |
| " val_evaluator = create_supervised_evaluator(\n", | |
| " model,\n", | |
| " metrics={\"accuracy\": Accuracy(), \"loss\": Loss(criterion)},\n", | |
| " device=device,\n", | |
| " non_blocking=True,\n", | |
| " )\n", | |
| "\n", | |
| " @trainer.on(Events.ITERATION_COMPLETED(every=2000))\n", | |
| " def log_training_loss(engine):\n", | |
| " print(\n", | |
| " f\"Epoch[{engine.state.epoch}], Iter[{engine.state.iteration}] Loss: {engine.state.output:.2f} Running Avg Loss: {engine.state.metrics['running_avg_loss']:.2f}\"\n", | |
| " )\n", | |
| "\n", | |
| " @trainer.on(Events.EPOCH_COMPLETED)\n", | |
| " def log_validation_results(trainer):\n", | |
| " val_evaluator.run(valloader)\n", | |
| " metrics = val_evaluator.state.metrics\n", | |
| " print(\n", | |
| " f\"Validation Results - Epoch[{trainer.state.epoch}] Avg accuracy: {metrics['accuracy']:.2f} Avg loss: {metrics['loss']:.2f}\"\n", | |
| " )\n", | |
| "\n", | |
| " with tune.checkpoint_dir(trainer.state.epoch) as checkpoint_dir:\n", | |
| " path = os.path.join(checkpoint_dir, \"checkpoint\")\n", | |
| " torch.save((model.state_dict(), optimizer.state_dict()), path)\n", | |
| "\n", | |
| " tune.report(loss=metrics[\"loss\"], accuracy=metrics[\"accuracy\"])\n", | |
| "\n", | |
| " trainer.run(trainloader, max_epochs=10)" |
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| "def test_best_model(best_trial, data_dir=None):\n", | ||
| " _, testset = load_data(data_dir)\n", | ||
| " \n", | ||
| " best_trained_model = idist.auto_model(Net(best_trial.config[\"l1\"], best_trial.config[\"l2\"]))\n", | ||
| " device = idist.device()\n", | ||
| "\n", | ||
| " best_checkpoint_dir = best_trial.checkpoint.value\n", | ||
| " model_state, optimizer_state = torch.load(os.path.join(\n", | ||
| " best_checkpoint_dir, \"checkpoint\"))\n", | ||
| " best_trained_model.load_state_dict(model_state)\n", | ||
| "\n", | ||
| " test_evaluator = create_supervised_evaluator(best_trained_model, metrics={\"Accuracy\": Accuracy()}, device=device, non_blocking=True)\n", | ||
| "\n", | ||
| " testloader = idist.auto_dataloader(testset, batch_size=4, shuffle=False, num_workers=2)\n", | ||
| "\n", | ||
| " test_evaluator.run(testloader)\n", | ||
| " print(f\"Best trial test set accuracy: {test_evaluator.state.metrics}\")" |
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Suggested change
| "def test_best_model(best_trial, data_dir=None):\n", | |
| " _, testset = load_data(data_dir)\n", | |
| " \n", | |
| " best_trained_model = idist.auto_model(Net(best_trial.config[\"l1\"], best_trial.config[\"l2\"]))\n", | |
| " device = idist.device()\n", | |
| "\n", | |
| " best_checkpoint_dir = best_trial.checkpoint.value\n", | |
| " model_state, optimizer_state = torch.load(os.path.join(\n", | |
| " best_checkpoint_dir, \"checkpoint\"))\n", | |
| " best_trained_model.load_state_dict(model_state)\n", | |
| "\n", | |
| " test_evaluator = create_supervised_evaluator(best_trained_model, metrics={\"Accuracy\": Accuracy()}, device=device, non_blocking=True)\n", | |
| "\n", | |
| " testloader = idist.auto_dataloader(testset, batch_size=4, shuffle=False, num_workers=2)\n", | |
| "\n", | |
| " test_evaluator.run(testloader)\n", | |
| " print(f\"Best trial test set accuracy: {test_evaluator.state.metrics}\")" | |
| "def test_best_model(best_trial, data_dir=None):\n", | |
| " _, testset = load_data(data_dir)\n", | |
| "\n", | |
| " best_trained_model = idist.auto_model(\n", | |
| " Net(best_trial.config[\"l1\"], best_trial.config[\"l2\"])\n", | |
| " )\n", | |
| " device = idist.device()\n", | |
| "\n", | |
| " best_checkpoint_dir = best_trial.checkpoint.value\n", | |
| " model_state, optimizer_state = torch.load(\n", | |
| " os.path.join(best_checkpoint_dir, \"checkpoint\")\n", | |
| " )\n", | |
| " best_trained_model.load_state_dict(model_state)\n", | |
| "\n", | |
| " test_evaluator = create_supervised_evaluator(\n", | |
| " best_trained_model,\n", | |
| " metrics={\"Accuracy\": Accuracy()},\n", | |
| " device=device,\n", | |
| " non_blocking=True,\n", | |
| " )\n", | |
| "\n", | |
| " testloader = idist.auto_dataloader(\n", | |
| " testset, batch_size=4, shuffle=False, num_workers=2\n", | |
| " )\n", | |
| "\n", | |
| " test_evaluator.run(testloader)\n", | |
| " print(f\"Best trial test set accuracy: {test_evaluator.state.metrics}\")" |
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| "def main(num_samples=10, max_num_epochs=10, gpus_per_trial=1):\n", | ||
| " data_dir = os.path.abspath(\"./data\")\n", | ||
| " load_data(data_dir)\n", | ||
| " \n", | ||
| " config = {\n", | ||
| " \"l1\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n", | ||
| " \"l2\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n", | ||
| " \"lr\": tune.loguniform(1e-4, 1e-1),\n", | ||
| " \"batch_size\": tune.choice([2, 4, 8, 16])\n", | ||
| " }\n", | ||
| " scheduler = ASHAScheduler(\n", | ||
| " metric=\"loss\",\n", | ||
| " mode=\"min\",\n", | ||
| " max_t=max_num_epochs,\n", | ||
| " grace_period=1,\n", | ||
| " reduction_factor=2)\n", | ||
| " reporter = CLIReporter(\n", | ||
| " metric_columns=[\"loss\", \"accuracy\", \"training_iteration\"])\n", | ||
| " result = tune.run(\n", | ||
| " partial(train_cifar, data_dir=data_dir),\n", | ||
| " resources_per_trial={\"cpu\": 2, \"gpu\": gpus_per_trial},\n", | ||
| " config=config,\n", | ||
| " num_samples=num_samples,\n", | ||
| " scheduler=scheduler,\n", | ||
| " progress_reporter=reporter)\n", | ||
| "\n", | ||
| " best_trial = result.get_best_trial(\"loss\", \"min\", \"last\")\n", | ||
| " print(f\"Best trial config: {best_trial.config}\")\n", | ||
| " print(f\"Best trial final validation loss: {best_trial.last_result['loss']}\")\n", | ||
| " print(f\"Best trial final validation accuracy: {best_trial.last_result['accuracy']}\")\n", | ||
| " \n", | ||
| " test_best_model(best_trial, data_dir)" |
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Suggested change
| "def main(num_samples=10, max_num_epochs=10, gpus_per_trial=1):\n", | |
| " data_dir = os.path.abspath(\"./data\")\n", | |
| " load_data(data_dir)\n", | |
| " \n", | |
| " config = {\n", | |
| " \"l1\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n", | |
| " \"l2\": tune.sample_from(lambda _: 2**np.random.randint(2, 9)),\n", | |
| " \"lr\": tune.loguniform(1e-4, 1e-1),\n", | |
| " \"batch_size\": tune.choice([2, 4, 8, 16])\n", | |
| " }\n", | |
| " scheduler = ASHAScheduler(\n", | |
| " metric=\"loss\",\n", | |
| " mode=\"min\",\n", | |
| " max_t=max_num_epochs,\n", | |
| " grace_period=1,\n", | |
| " reduction_factor=2)\n", | |
| " reporter = CLIReporter(\n", | |
| " metric_columns=[\"loss\", \"accuracy\", \"training_iteration\"])\n", | |
| " result = tune.run(\n", | |
| " partial(train_cifar, data_dir=data_dir),\n", | |
| " resources_per_trial={\"cpu\": 2, \"gpu\": gpus_per_trial},\n", | |
| " config=config,\n", | |
| " num_samples=num_samples,\n", | |
| " scheduler=scheduler,\n", | |
| " progress_reporter=reporter)\n", | |
| "\n", | |
| " best_trial = result.get_best_trial(\"loss\", \"min\", \"last\")\n", | |
| " print(f\"Best trial config: {best_trial.config}\")\n", | |
| " print(f\"Best trial final validation loss: {best_trial.last_result['loss']}\")\n", | |
| " print(f\"Best trial final validation accuracy: {best_trial.last_result['accuracy']}\")\n", | |
| " \n", | |
| " test_best_model(best_trial, data_dir)" | |
| "def main(num_samples=10, max_num_epochs=10, gpus_per_trial=1):\n", | |
| " data_dir = os.path.abspath(\"./data\")\n", | |
| " load_data(data_dir)\n", | |
| "\n", | |
| " config = {\n", | |
| " \"l1\": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),\n", | |
| " \"l2\": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),\n", | |
| " \"lr\": tune.loguniform(1e-4, 1e-1),\n", | |
| " \"batch_size\": tune.choice([2, 4, 8, 16]),\n", | |
| " }\n", | |
| " scheduler = ASHAScheduler(\n", | |
| " metric=\"loss\",\n", | |
| " mode=\"min\",\n", | |
| " max_t=max_num_epochs,\n", | |
| " grace_period=1,\n", | |
| " reduction_factor=2,\n", | |
| " )\n", | |
| " reporter = CLIReporter(metric_columns=[\"loss\", \"accuracy\", \"training_iteration\"])\n", | |
| " result = tune.run(\n", | |
| " partial(train_cifar, data_dir=data_dir),\n", | |
| " resources_per_trial={\"cpu\": 2, \"gpu\": gpus_per_trial},\n", | |
| " config=config,\n", | |
| " num_samples=num_samples,\n", | |
| " scheduler=scheduler,\n", | |
| " progress_reporter=reporter,\n", | |
| " )\n", | |
| "\n", | |
| " best_trial = result.get_best_trial(\"loss\", \"min\", \"last\")\n", | |
| " print(f\"Best trial config: {best_trial.config}\")\n", | |
| " print(f\"Best trial final validation loss: {best_trial.last_result['loss']}\")\n", | |
| " print(f\"Best trial final validation accuracy: {best_trial.last_result['accuracy']}\")\n", | |
| "\n", | |
| " test_best_model(best_trial, data_dir)" |
Member
|
What about adding some summarizing sentences about the best trial, how to interpret the results? |
| "For every trial, Ray Tune will randomly sample a combination of parameters from these search spaces. It will then train a number of models in parallel and find the best performing one among these. \n", | ||
| "We also use the `ASHAScheduler()` which is one of the trial schedulers that aggressively terminate low-performing trials.\n", | ||
| "Apart from that, we leverage the `CLIReporter()` to prettify our outputs.\n", | ||
| "And then, we wrap `train_cifar` in functools.partial and pass it to `tune.run` along with other resources like the CPUs and GPUs available to use, the configurable parameters, the number of trials, scheduler and reporter.\n", |
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nit
Suggested change
| "And then, we wrap `train_cifar` in functools.partial and pass it to `tune.run` along with other resources like the CPUs and GPUs available to use, the configurable parameters, the number of trials, scheduler and reporter.\n", | |
| "And then, we wrap `train_cifar` in `functools.partial` and pass it to `tune.run` along with other resources like the CPUs and GPUs available to use, the configurable parameters, the number of trials, scheduler and reporter.\n", |
| "id": "vJgTaKWU8Doq" | ||
| }, | ||
| "source": [ | ||
| "In this tutorial, we will see how [Ray Tune](https://docs.ray.io/en/stable/tune.html) can be used with Ignite for hyperparameter tuning. We will also compare it with other frameworks like [Optuna](https://optuna.org/) and [Ax](https://ax.dev/) for hyperparameter optimization.\n", |
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are we going to add comparisons with ax and optuna?
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Fixes #29