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Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-ray-train\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"distributed","training","Distributed Training","distributed training ray train","Ray","Ray Train","train","tuning","Clusters","running","when","tolerance","fault tolerance","scaling","across","node","tensorflow","elastic","Transformers","pytorch","orchestration","research","2026-11-10 10:12:30",false,"---\nname: distributed-training-ray-train\ndescription: Distributed training orchestration across clusters. Scales PyTorch\u002FTensorFlow\u002FHuggingFace from laptop to 1000s of nodes. Built-in hyperparameter tuning with Ray Tune, fault tolerance, elastic scaling. Use when training massive models across multiple machines or running distributed hyperparameter sweeps.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Orchestra Research\n  tags: [Ray Train, Distributed Training, Orchestration, Ray, Hyperparameter Tuning, Fault Tolerance, Elastic Scaling, Multi-Node, PyTorch, TensorFlow]\n  dependencies: [\"ray[train]\", torch, transformers]\n---\n\n# Ray Train - Distributed Training Orchestration\n\n## Quick start\n\nRay Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.\n\n**Installation**:\n```bash\npip install -U \"ray[train]\"\n```\n\n**Basic PyTorch training** (single node):\n\n```python\nimport ray\nfrom ray import train\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\nimport torch\nimport torch.nn as nn\n\n# Define training function\ndef train_func(config):\n    # Your normal PyTorch code\n    model = nn.Linear(10, 1)\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n    # Prepare for distributed (Ray handles device placement)\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(10):\n        # Your training loop\n        output = model(torch.randn(32, 10))\n        loss = output.sum()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n\n        # Report metrics (logged automatically)\n        train.report({\"loss\": loss.item(), \"epoch\": epoch})\n\n# Run distributed training\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=4,  # 4 GPUs\u002Fworkers\n        use_gpu=True\n    )\n)\n\nresult = trainer.fit()\nprint(f\"Final loss: {result.metrics['loss']}\")\n```\n\n**That's it!** Ray handles:\n- Distributed coordination\n- GPU allocation\n- Fault tolerance\n- Checkpointing\n- Metric aggregation\n\n## Common workflows\n\n### Workflow 1: Scale existing PyTorch code\n\n**Original single-GPU code**:\n```python\nmodel = MyModel().cuda()\noptimizer = torch.optim.Adam(model.parameters())\n\nfor epoch in range(epochs):\n    for batch in dataloader:\n        loss = model(batch)\n        loss.backward()\n        optimizer.step()\n```\n\n**Ray Train version** (scales to multi-GPU\u002Fmulti-node):\n```python\nfrom ray.train.torch import TorchTrainer\nfrom ray import train\n\ndef train_func(config):\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters())\n\n    # Prepare for distributed (automatic device placement)\n    model = train.torch.prepare_model(model)\n    dataloader = train.torch.prepare_data_loader(dataloader)\n\n    for epoch in range(epochs):\n        for batch in dataloader:\n            loss = model(batch)\n            loss.backward()\n            optimizer.step()\n\n            # Report metrics\n            train.report({\"loss\": loss.item()})\n\n# Scale to 8 GPUs\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(num_workers=8, use_gpu=True)\n)\ntrainer.fit()\n```\n\n**Benefits**: Same code runs on 1 GPU or 1000 GPUs\n\n### Workflow 2: HuggingFace Transformers integration\n\n```python\nfrom ray.train.huggingface import TransformersTrainer\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments\n\ndef train_func(config):\n    # Load model and tokenizer\n    model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n    tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n\n    # Training arguments (HuggingFace API)\n    training_args = TrainingArguments(\n        output_dir=\".\u002Foutput\",\n        num_train_epochs=3,\n        per_device_train_batch_size=8,\n        learning_rate=2e-5,\n    )\n\n    # Ray automatically handles distributed training\n    from transformers import Trainer\n    trainer = Trainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n    )\n\n    trainer.train()\n\n# Scale to multi-node (2 nodes × 8 GPUs = 16 workers)\ntrainer = TransformersTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=16,\n        use_gpu=True,\n        resources_per_worker={\"GPU\": 1}\n    )\n)\n\nresult = trainer.fit()\n```\n\n### Workflow 3: Hyperparameter tuning with Ray Tune\n\n```python\nfrom ray import tune\nfrom ray.train.torch import TorchTrainer\nfrom ray.tune.schedulers import ASHAScheduler\n\ndef train_func(config):\n    # Use hyperparameters from config\n    lr = config[\"lr\"]\n    batch_size = config[\"batch_size\"]\n\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(10):\n        # Training loop\n        loss = train_epoch(model, optimizer, batch_size)\n        train.report({\"loss\": loss, \"epoch\": epoch})\n\n# Define search space\nparam_space = {\n    \"lr\": tune.loguniform(1e-5, 1e-2),\n    \"batch_size\": tune.choice([16, 32, 64, 128])\n}\n\n# Run 20 trials with early stopping\ntuner = tune.Tuner(\n    TorchTrainer(\n        train_func,\n        scaling_config=ScalingConfig(num_workers=4, use_gpu=True)\n    ),\n    param_space=param_space,\n    tune_config=tune.TuneConfig(\n        num_samples=20,\n        scheduler=ASHAScheduler(metric=\"loss\", mode=\"min\")\n    )\n)\n\nresults = tuner.fit()\nbest = results.get_best_result(metric=\"loss\", mode=\"min\")\nprint(f\"Best hyperparameters: {best.config}\")\n```\n\n**Result**: Distributed hyperparameter search across cluster\n\n### Workflow 4: Checkpointing and fault tolerance\n\n```python\nfrom ray import train\nfrom ray.train import Checkpoint\n\ndef train_func(config):\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters())\n\n    # Try to resume from checkpoint\n    checkpoint = train.get_checkpoint()\n    if checkpoint:\n        with checkpoint.as_directory() as checkpoint_dir:\n            state = torch.load(f\"{checkpoint_dir}\u002Fmodel.pt\")\n            model.load_state_dict(state[\"model\"])\n            optimizer.load_state_dict(state[\"optimizer\"])\n            start_epoch = state[\"epoch\"]\n    else:\n        start_epoch = 0\n\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(start_epoch, 100):\n        loss = train_epoch(model, optimizer)\n\n        # Save checkpoint every 10 epochs\n        if epoch % 10 == 0:\n            checkpoint = Checkpoint.from_directory(\n                train.get_context().get_trial_dir()\n            )\n            torch.save({\n                \"model\": model.state_dict(),\n                \"optimizer\": optimizer.state_dict(),\n                \"epoch\": epoch\n            }, checkpoint.path \u002F \"model.pt\")\n\n            train.report({\"loss\": loss}, checkpoint=checkpoint)\n\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(num_workers=8, use_gpu=True)\n)\n\n# Automatically resumes from checkpoint if training fails\nresult = trainer.fit()\n```\n\n### Workflow 5: Multi-node training\n\n```python\nfrom ray.train import ScalingConfig\n\n# Connect to Ray cluster\nray.init(address=\"auto\")  # Or ray.init(\"ray:\u002F\u002Fhead-node:10001\")\n\n# Train across 4 nodes × 8 GPUs = 32 workers\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=32,\n        use_gpu=True,\n        resources_per_worker={\"GPU\": 1, \"CPU\": 4},\n        placement_strategy=\"SPREAD\"  # Spread across nodes\n    )\n)\n\nresult = trainer.fit()\n```\n\n**Launch Ray cluster**:\n```bash\n# On head node\nray start --head --port=6379\n\n# On worker nodes\nray start --address=\u003Chead-node-ip>:6379\n```\n\n## When to use vs alternatives\n\n**Use Ray Train when**:\n- Training across multiple machines (multi-node)\n- Need hyperparameter tuning at scale\n- Want fault tolerance (auto-restart failed workers)\n- Elastic scaling (add\u002Fremove nodes during training)\n- Unified framework (same code for PyTorch\u002FTF\u002FHF)\n\n**Key advantages**:\n- **Multi-node orchestration**: Easiest multi-node setup\n- **Ray Tune integration**: Best-in-class hyperparameter tuning\n- **Fault tolerance**: Automatic recovery from failures\n- **Elastic**: Add\u002Fremove nodes without restarting\n- **Framework agnostic**: PyTorch, TensorFlow, HuggingFace, XGBoost\n\n**Use alternatives instead**:\n- **Accelerate**: Single-node multi-GPU, simpler\n- **PyTorch Lightning**: High-level abstractions, callbacks\n- **DeepSpeed**: Maximum performance, complex setup\n- **Raw DDP**: Maximum control, minimal overhead\n\n## Common issues\n\n**Issue: Ray cluster not connecting**\n\nCheck ray status:\n```bash\nray status\n\n# Should show:\n# - Nodes: 4\n# - GPUs: 32\n# - Workers: Ready\n```\n\nIf not connected:\n```bash\n# Restart head node\nray stop\nray start --head --port=6379 --dashboard-host=0.0.0.0\n\n# Restart worker nodes\nray stop\nray start --address=\u003Chead-ip>:6379\n```\n\n**Issue: Out of memory**\n\nReduce workers or use gradient accumulation:\n```python\nscaling_config=ScalingConfig(\n    num_workers=4,  # Reduce from 8\n    use_gpu=True\n)\n\n# In train_func, accumulate gradients\nfor i, batch in enumerate(dataloader):\n    loss = model(batch) \u002F accumulation_steps\n    loss.backward()\n\n    if (i + 1) % accumulation_steps == 0:\n        optimizer.step()\n        optimizer.zero_grad()\n```\n\n**Issue: Slow training**\n\nCheck if data loading is bottleneck:\n```python\nimport time\n\ndef train_func(config):\n    for epoch in range(epochs):\n        start = time.time()\n        for batch in dataloader:\n            data_time = time.time() - start\n            # Train...\n            start = time.time()\n            print(f\"Data loading: {data_time:.3f}s\")\n```\n\nIf data loading is slow, increase workers:\n```python\ndataloader = DataLoader(dataset, num_workers=8)\n```\n\n## Advanced topics\n\n**Multi-node setup**: See [references\u002Fmulti-node.md](references\u002Fmulti-node.md) for Ray cluster deployment on AWS, GCP, Kubernetes, and SLURM.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameter-tuning.md](references\u002Fhyperparameter-tuning.md) for Ray Tune integration, search algorithms (Optuna, HyperOpt), and population-based training.\n\n**Custom training loops**: See [references\u002Fcustom-loops.md](references\u002Fcustom-loops.md) for advanced Ray Train usage, custom backends, and integration with other frameworks.\n\n## Hardware requirements\n\n- **Single node**: 1+ GPUs (or CPUs)\n- **Multi-node**: 2+ machines with network connectivity\n- **Cloud**: AWS, GCP, Azure (Ray autoscaling)\n- **On-prem**: Kubernetes, SLURM clusters\n\n**Supported accelerators**:\n- NVIDIA GPUs (CUDA)\n- AMD GPUs (ROCm)\n- TPUs (Google Cloud)\n- CPUs\n\n## Resources\n\n- Docs: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftrain\u002Ftrain.html\n- GitHub: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray ⭐ 36,000+\n- Version: 2.40.0+\n- Examples: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftrain\u002Fexamples.html\n- Slack: https:\u002F\u002Fforms.gle\u002F9TSdDYUgxYs8SA9e8\n- Used by: OpenAI, Uber, Spotify, Shopify, Instacart\n\n\n","# Ray Train - Distributed Training Orchestration\n\n## Quick start\n\nRay Train scales machine learning training from single GPU to multi-node clusters with minimal code changes.\n\n**Installation**:\n```bash\npip install -U \"ray[train]\"\n```\n\n**Basic PyTorch training** (single node):\n\n```python\nimport ray\nfrom ray import train\nfrom ray.train import ScalingConfig\nfrom ray.train.torch import TorchTrainer\nimport torch\nimport torch.nn as nn\n\n# Define training function\ndef train_func(config):\n    # Your normal PyTorch code\n    model = nn.Linear(10, 1)\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n\n    # Prepare for distributed (Ray handles device placement)\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(10):\n        # Your training loop\n        output = model(torch.randn(32, 10))\n        loss = output.sum()\n        loss.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n\n        # Report metrics (logged automatically)\n        train.report({\"loss\": loss.item(), \"epoch\": epoch})\n\n# Run distributed training\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=4,  # 4 GPUs\u002Fworkers\n        use_gpu=True\n    )\n)\n\nresult = trainer.fit()\nprint(f\"Final loss: {result.metrics['loss']}\")\n```\n\n**That's it!** Ray handles:\n- Distributed coordination\n- GPU allocation\n- Fault tolerance\n- Checkpointing\n- Metric aggregation\n\n## Common workflows\n\n### Workflow 1: Scale existing PyTorch code\n\n**Original single-GPU code**:\n```python\nmodel = MyModel().cuda()\noptimizer = torch.optim.Adam(model.parameters())\n\nfor epoch in range(epochs):\n    for batch in dataloader:\n        loss = model(batch)\n        loss.backward()\n        optimizer.step()\n```\n\n**Ray Train version** (scales to multi-GPU\u002Fmulti-node):\n```python\nfrom ray.train.torch import TorchTrainer\nfrom ray import train\n\ndef train_func(config):\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters())\n\n    # Prepare for distributed (automatic device placement)\n    model = train.torch.prepare_model(model)\n    dataloader = train.torch.prepare_data_loader(dataloader)\n\n    for epoch in range(epochs):\n        for batch in dataloader:\n            loss = model(batch)\n            loss.backward()\n            optimizer.step()\n\n            # Report metrics\n            train.report({\"loss\": loss.item()})\n\n# Scale to 8 GPUs\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(num_workers=8, use_gpu=True)\n)\ntrainer.fit()\n```\n\n**Benefits**: Same code runs on 1 GPU or 1000 GPUs\n\n### Workflow 2: HuggingFace Transformers integration\n\n```python\nfrom ray.train.huggingface import TransformersTrainer\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments\n\ndef train_func(config):\n    # Load model and tokenizer\n    model = AutoModelForCausalLM.from_pretrained(\"gpt2\")\n    tokenizer = AutoTokenizer.from_pretrained(\"gpt2\")\n\n    # Training arguments (HuggingFace API)\n    training_args = TrainingArguments(\n        output_dir=\".\u002Foutput\",\n        num_train_epochs=3,\n        per_device_train_batch_size=8,\n        learning_rate=2e-5,\n    )\n\n    # Ray automatically handles distributed training\n    from transformers import Trainer\n    trainer = Trainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n    )\n\n    trainer.train()\n\n# Scale to multi-node (2 nodes × 8 GPUs = 16 workers)\ntrainer = TransformersTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=16,\n        use_gpu=True,\n        resources_per_worker={\"GPU\": 1}\n    )\n)\n\nresult = trainer.fit()\n```\n\n### Workflow 3: Hyperparameter tuning with Ray Tune\n\n```python\nfrom ray import tune\nfrom ray.train.torch import TorchTrainer\nfrom ray.tune.schedulers import ASHAScheduler\n\ndef train_func(config):\n    # Use hyperparameters from config\n    lr = config[\"lr\"]\n    batch_size = config[\"batch_size\"]\n\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(10):\n        # Training loop\n        loss = train_epoch(model, optimizer, batch_size)\n        train.report({\"loss\": loss, \"epoch\": epoch})\n\n# Define search space\nparam_space = {\n    \"lr\": tune.loguniform(1e-5, 1e-2),\n    \"batch_size\": tune.choice([16, 32, 64, 128])\n}\n\n# Run 20 trials with early stopping\ntuner = tune.Tuner(\n    TorchTrainer(\n        train_func,\n        scaling_config=ScalingConfig(num_workers=4, use_gpu=True)\n    ),\n    param_space=param_space,\n    tune_config=tune.TuneConfig(\n        num_samples=20,\n        scheduler=ASHAScheduler(metric=\"loss\", mode=\"min\")\n    )\n)\n\nresults = tuner.fit()\nbest = results.get_best_result(metric=\"loss\", mode=\"min\")\nprint(f\"Best hyperparameters: {best.config}\")\n```\n\n**Result**: Distributed hyperparameter search across cluster\n\n### Workflow 4: Checkpointing and fault tolerance\n\n```python\nfrom ray import train\nfrom ray.train import Checkpoint\n\ndef train_func(config):\n    model = MyModel()\n    optimizer = torch.optim.Adam(model.parameters())\n\n    # Try to resume from checkpoint\n    checkpoint = train.get_checkpoint()\n    if checkpoint:\n        with checkpoint.as_directory() as checkpoint_dir:\n            state = torch.load(f\"{checkpoint_dir}\u002Fmodel.pt\")\n            model.load_state_dict(state[\"model\"])\n            optimizer.load_state_dict(state[\"optimizer\"])\n            start_epoch = state[\"epoch\"]\n    else:\n        start_epoch = 0\n\n    model = train.torch.prepare_model(model)\n\n    for epoch in range(start_epoch, 100):\n        loss = train_epoch(model, optimizer)\n\n        # Save checkpoint every 10 epochs\n        if epoch % 10 == 0:\n            checkpoint = Checkpoint.from_directory(\n                train.get_context().get_trial_dir()\n            )\n            torch.save({\n                \"model\": model.state_dict(),\n                \"optimizer\": optimizer.state_dict(),\n                \"epoch\": epoch\n            }, checkpoint.path \u002F \"model.pt\")\n\n            train.report({\"loss\": loss}, checkpoint=checkpoint)\n\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(num_workers=8, use_gpu=True)\n)\n\n# Automatically resumes from checkpoint if training fails\nresult = trainer.fit()\n```\n\n### Workflow 5: Multi-node training\n\n```python\nfrom ray.train import ScalingConfig\n\n# Connect to Ray cluster\nray.init(address=\"auto\")  # Or ray.init(\"ray:\u002F\u002Fhead-node:10001\")\n\n# Train across 4 nodes × 8 GPUs = 32 workers\ntrainer = TorchTrainer(\n    train_func,\n    scaling_config=ScalingConfig(\n        num_workers=32,\n        use_gpu=True,\n        resources_per_worker={\"GPU\": 1, \"CPU\": 4},\n        placement_strategy=\"SPREAD\"  # Spread across nodes\n    )\n)\n\nresult = trainer.fit()\n```\n\n**Launch Ray cluster**:\n```bash\n# On head node\nray start --head --port=6379\n\n# On worker nodes\nray start --address=\u003Chead-node-ip>:6379\n```\n\n## When to use vs alternatives\n\n**Use Ray Train when**:\n- Training across multiple machines (multi-node)\n- Need hyperparameter tuning at scale\n- Want fault tolerance (auto-restart failed workers)\n- Elastic scaling (add\u002Fremove nodes during training)\n- Unified framework (same code for PyTorch\u002FTF\u002FHF)\n\n**Key advantages**:\n- **Multi-node orchestration**: Easiest multi-node setup\n- **Ray Tune integration**: Best-in-class hyperparameter tuning\n- **Fault tolerance**: Automatic recovery from failures\n- **Elastic**: Add\u002Fremove nodes without restarting\n- **Framework agnostic**: PyTorch, TensorFlow, HuggingFace, XGBoost\n\n**Use alternatives instead**:\n- **Accelerate**: Single-node multi-GPU, simpler\n- **PyTorch Lightning**: High-level abstractions, callbacks\n- **DeepSpeed**: Maximum performance, complex setup\n- **Raw DDP**: Maximum control, minimal overhead\n\n## Common issues\n\n**Issue: Ray cluster not connecting**\n\nCheck ray status:\n```bash\nray status\n\n# Should show:\n# - Nodes: 4\n# - GPUs: 32\n# - Workers: Ready\n```\n\nIf not connected:\n```bash\n# Restart head node\nray stop\nray start --head --port=6379 --dashboard-host=0.0.0.0\n\n# Restart worker nodes\nray stop\nray start --address=\u003Chead-ip>:6379\n```\n\n**Issue: Out of memory**\n\nReduce workers or use gradient accumulation:\n```python\nscaling_config=ScalingConfig(\n    num_workers=4,  # Reduce from 8\n    use_gpu=True\n)\n\n# In train_func, accumulate gradients\nfor i, batch in enumerate(dataloader):\n    loss = model(batch) \u002F accumulation_steps\n    loss.backward()\n\n    if (i + 1) % accumulation_steps == 0:\n        optimizer.step()\n        optimizer.zero_grad()\n```\n\n**Issue: Slow training**\n\nCheck if data loading is bottleneck:\n```python\nimport time\n\ndef train_func(config):\n    for epoch in range(epochs):\n        start = time.time()\n        for batch in dataloader:\n            data_time = time.time() - start\n            # Train...\n            start = time.time()\n            print(f\"Data loading: {data_time:.3f}s\")\n```\n\nIf data loading is slow, increase workers:\n```python\ndataloader = DataLoader(dataset, num_workers=8)\n```\n\n## Advanced topics\n\n**Multi-node setup**: See [references\u002Fmulti-node.md](references\u002Fmulti-node.md) for Ray cluster deployment on AWS, GCP, Kubernetes, and SLURM.\n\n**Hyperparameter tuning**: See [references\u002Fhyperparameter-tuning.md](references\u002Fhyperparameter-tuning.md) for Ray Tune integration, search algorithms (Optuna, HyperOpt), and population-based training.\n\n**Custom training loops**: See [references\u002Fcustom-loops.md](references\u002Fcustom-loops.md) for advanced Ray Train usage, custom backends, and integration with other frameworks.\n\n## Hardware requirements\n\n- **Single node**: 1+ GPUs (or CPUs)\n- **Multi-node**: 2+ machines with network connectivity\n- **Cloud**: AWS, GCP, Azure (Ray autoscaling)\n- **On-prem**: Kubernetes, SLURM clusters\n\n**Supported accelerators**:\n- NVIDIA GPUs (CUDA)\n- AMD GPUs (ROCm)\n- TPUs (Google Cloud)\n- CPUs\n\n## Resources\n\n- Docs: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftrain\u002Ftrain.html\n- GitHub: https:\u002F\u002Fgithub.com\u002Fray-project\u002Fray ⭐ 36,000+\n- Version: 2.40.0+\n- Examples: https:\u002F\u002Fdocs.ray.io\u002Fen\u002Flatest\u002Ftrain\u002Fexamples.html\n- Slack: https:\u002F\u002Fforms.gle\u002F9TSdDYUgxYs8SA9e8\n- Used by: OpenAI, Uber, Spotify, Shopify, Instacart\n\n\n",{"name":5,"license":15,"metadata":46,"description":7},{"tags":47,"author":55,"version":56,"dependencies":57},[24,21,48,23,49,50,51,52,53,54],"Orchestration","Hyperparameter Tuning","Fault Tolerance","Elastic Scaling","Multi-Node","PyTorch","TensorFlow","Orchestra Research","1.0.0",[58,59,60],"ray[train]","torch","transformers","main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-ray-train",3911,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":72,"prompt":82,"zipAvailable":83},[73,76,79],{"tool":74,"command":75},"npx","npx skills add davila7\u002Fclaude-code-templates --skill distributed-training-ray-train",{"tool":77,"command":78},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill distributed-training-ray-train",{"tool":80,"command":81},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill distributed-training-ray-train","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-ray-train\u002FSKILL.md",true,[85,91,95],{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"LOGIC.md","REFERENCE","参考文档",6093,"https:\u002F\u002Fgithub.com\u002Fnodetool-ai\u002Fnodetool\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fprototype\u002FLOGIC.md",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"references\u002Fmulti-node.md",13526,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fai-research\u002Fdistributed-training-ray-train\u002Freferences\u002Fmulti-node.md",{"path":96,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":97,"sourceHtmlUrl":98},"UI.md",6563,"https:\u002F\u002Fgithub.com\u002Fnodetool-ai\u002Fnodetool\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fprototype\u002FUI.md",[100,131,158,191,226,250],{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":111,"publishedAt":41},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[106],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[112,113,114,115,116,117,118,119,120,29,121,122,123,124,125,126,127,128,129,130],"scoring","pool","wealthville pool scoring","liquidity","which","for","exit","now","use","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":132,"name":133,"nameZh":6,"description":134,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":135,"categories":136,"keywords":138,"publishedAt":41},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[137],{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},[139,140,141,142,143,144,145,146,147,148,149,29,150,151,152,153,154,155,156,157],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":173,"publishedAt":41},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[164,167,168],{"categoryNo":165,"categoryName":166,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},{"categoryNo":169,"categoryName":170,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[174,175,176,177,178,179,180,117,181,182,183,184,185,186,187,120,188,189,29,190],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":192,"name":193,"nameZh":6,"description":194,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":195,"licenseFileUrl":196,"sourceHtmlUrl":197,"categories":198,"keywords":202,"publishedAt":41},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[199],{"categoryNo":200,"categoryName":201,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000009","前端开发",[203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,117,225],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":227,"name":228,"nameZh":6,"description":229,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":195,"licenseFileUrl":230,"sourceHtmlUrl":231,"categories":232,"keywords":237,"publishedAt":41},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[233,236],{"categoryNo":234,"categoryName":235,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":200,"categoryName":201,"taxonomyType":109,"taxonomyTypeDesc":110},[238,239,240,241,242,204,243,213,214,244,245,246,247,117,248,122,249],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":251,"name":252,"nameZh":6,"description":253,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":195,"licenseFileUrl":254,"sourceHtmlUrl":255,"categories":256,"keywords":264,"publishedAt":41},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[257,260,261],{"categoryNo":258,"categoryName":259,"taxonomyType":109,"taxonomyTypeDesc":110},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":107,"categoryName":108,"taxonomyType":109,"taxonomyTypeDesc":110},{"categoryNo":262,"categoryName":263,"taxonomyType":171,"taxonomyTypeDesc":172},"01JBSEEDCAT000000000000036","内容创作者",[265,266,267,268,269,270,271,272,206,273,216,150,274,275,276,277,278,186,279,29,280,281,282,283,284,128,285],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]