{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"accelerate","slug":"accelerate","name":"Accelerate","type":"framework","url":"https://github.com/huggingface/accelerate","page_url":"https://unfragile.ai/accelerate","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"accelerate__cap_0","uri":"capability://automation.workflow.hardware.agnostic.distributed.training.abstraction","name":"hardware-agnostic distributed training abstraction","description":"Abstracts PyTorch's distributed training backends (DDP, FSDP, DeepSpeed, Megatron-LM) behind a unified Accelerator class that auto-detects hardware and selects the appropriate backend without code changes. The Accelerator wraps models, optimizers, and dataloaders with backend-specific logic while preserving the user's training loop structure, enabling the same script to run on single GPU, multi-GPU, TPU, or multi-node clusters by only changing launch configuration.","intents":["Write a training script once and run it on any hardware without modifying training logic","Switch from single-GPU to distributed training without rewriting the training loop","Automatically select the best distributed backend for available hardware"],"best_for":["ML researchers and engineers building custom training loops","Teams managing models across heterogeneous hardware (on-prem GPUs, cloud TPUs, Apple Silicon)","Organizations wanting to avoid vendor lock-in to specific distributed frameworks"],"limitations":["Requires PyTorch training loop structure — incompatible with high-level frameworks like Keras/TensorFlow","Backend selection is automatic but may not be optimal for all use cases (e.g., FSDP vs DeepSpeed trade-offs)","Adds ~5-10% overhead per distributed backend due to abstraction layer"],"requires":["PyTorch 1.10+","Python 3.7+","For multi-GPU: CUDA 11.0+ or compatible GPU drivers","For TPU: Google Cloud TPU access and appropriate credentials"],"input_types":["PyTorch model (torch.nn.Module)","PyTorch optimizer (torch.optim.Optimizer)","PyTorch DataLoader","Training loop code"],"output_types":["Wrapped model with distributed-aware forward pass","Wrapped optimizer with gradient synchronization","Wrapped DataLoader with automatic sharding"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_1","uri":"capability://automation.workflow.automatic.mixed.precision.training.with.multi.backend.support","name":"automatic mixed-precision training with multi-backend support","description":"Implements FP16, BF16, and FP8 mixed-precision training by wrapping the backward pass and optimizer step with automatic casting logic that varies by backend and hardware. Uses native PyTorch autocast for DDP, DeepSpeed's native FP16 handler for DeepSpeed training, and FSDP's built-in mixed-precision APIs for FSDP, automatically selecting the optimal implementation based on detected hardware capabilities (e.g., BF16 support on newer GPUs).","intents":["Train models with reduced memory footprint using FP16/BF16 without manual casting","Automatically select the best precision format for available hardware","Maintain numerical stability while reducing training time and memory usage"],"best_for":["Teams training large models on memory-constrained GPUs","Researchers requiring reproducible mixed-precision training across different hardware","Production training pipelines where memory efficiency directly impacts cost"],"limitations":["FP8 training requires NVIDIA H100 or newer GPUs with native FP8 support","BF16 requires hardware support (NVIDIA A100+, newer AMD GPUs); falls back to FP16 on unsupported hardware","Numerical instability possible with very deep models or certain loss functions — requires manual loss scaling tuning"],"requires":["PyTorch 1.10+ with autocast support","For FP16: NVIDIA GPU with compute capability 7.0+ (V100, A100, RTX series)","For BF16: NVIDIA A100 or newer, or AMD MI100+","For FP8: NVIDIA H100 or newer"],"input_types":["PyTorch model","Loss function","Optimizer"],"output_types":["Mixed-precision wrapped backward pass","Scaled gradients (if using loss scaling)","FP32 model weights (stored in full precision)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_10","uri":"capability://automation.workflow.fsdp.integration.with.automatic.sharding.strategies","name":"fsdp integration with automatic sharding strategies","description":"Wraps PyTorch's Fully Sharded Data Parallel (FSDP) with automatic sharding strategy selection based on model size and available hardware. Handles FSDP-specific configuration (sharding strategy, backward prefetch, CPU offloading) transparently, and provides utilities for saving/loading sharded checkpoints and managing FSDP-specific state (e.g., full_state_dict for inference).","intents":["Train very large models using FSDP without manual sharding strategy tuning","Automatically select between FULL_SHARD, SHARD_GRAD_OP, and NO_SHARD strategies","Save and load FSDP checkpoints without manual state consolidation"],"best_for":["Teams training models larger than single-GPU memory (100B+ parameters)","Researchers requiring fine-grained control over gradient sharding","Production systems where FSDP's memory efficiency is critical"],"limitations":["FSDP requires all-reduce communication at every backward pass; communication overhead scales with model size and number of GPUs","Sharding strategy selection is heuristic-based; optimal strategy depends on model architecture and hardware topology","Saving full_state_dict (for inference) requires consolidating sharded state on a single process, which can be memory-intensive"],"requires":["PyTorch 1.12+ (for stable FSDP support)","Multi-GPU setup (FSDP is not beneficial for single GPU)","Models with sufficient parameters to benefit from sharding (typically 10B+)"],"input_types":["PyTorch model (torch.nn.Module)","Model size estimate (bytes)"],"output_types":["FSDP-wrapped model with automatic sharding","Sharded checkpoint (or full_state_dict for inference)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_11","uri":"capability://automation.workflow.deepspeed.integration.with.zero.optimization.stages","name":"deepspeed integration with zero optimization stages","description":"Wraps DeepSpeed's ZeRO optimizer with automatic stage selection (Stage 1: gradient partitioning, Stage 2: optimizer state partitioning, Stage 3: parameter partitioning) based on model size and available memory. Handles DeepSpeed-specific configuration (activation checkpointing, gradient accumulation, communication hooks) transparently, and provides utilities for DeepSpeed checkpoint management and inference optimization.","intents":["Train very large models using DeepSpeed ZeRO without manual configuration","Automatically select ZeRO stage based on model size and memory constraints","Optimize inference with DeepSpeed's inference engine"],"best_for":["Teams training 10B+ parameter models with memory constraints","Production systems requiring maximum memory efficiency (ZeRO Stage 3)","Researchers experimenting with different ZeRO stages"],"limitations":["ZeRO Stage 3 adds significant communication overhead; communication time can exceed computation time on slow networks","DeepSpeed configuration is complex; automatic stage selection may not be optimal for all models","Activation checkpointing (enabled by default in Stage 3) adds ~20-30% compute overhead to reduce memory"],"requires":["DeepSpeed 0.5.0+","NVIDIA GPU with compute capability 7.0+ (V100 or newer)","For multi-node: fast interconnect (InfiniBand or high-bandwidth Ethernet)"],"input_types":["PyTorch model (torch.nn.Module)","DeepSpeed config dict (optional; auto-generated if not provided)"],"output_types":["DeepSpeed-wrapped model with ZeRO optimization","DeepSpeed checkpoint (with ZeRO state)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_12","uri":"capability://automation.workflow.notebook.launcher.with.interactive.environment.detection","name":"notebook launcher with interactive environment detection","description":"Provides a notebook_launcher function that detects the notebook environment (Jupyter, Colab, Kaggle) and launches distributed training within the notebook process, handling process spawning and environment setup automatically. Enables distributed training experimentation in notebooks without manual process management, with support for multiple GPUs and TPUs.","intents":["Run distributed training experiments in Jupyter notebooks without manual process setup","Automatically detect notebook environment and configure distributed training","Experiment with multi-GPU training in interactive notebooks"],"best_for":["Researchers prototyping distributed training in notebooks","Teams using Colab or Kaggle for training experimentation","Educational settings where interactive training is preferred"],"limitations":["Notebook launcher spawns processes within the notebook kernel; can cause kernel crashes if not handled carefully","Multi-GPU support is limited in notebooks; some notebook environments don't support CUDA","Debugging distributed code in notebooks is difficult; errors in worker processes may not be visible"],"requires":["Jupyter notebook or compatible environment (Colab, Kaggle)","For multi-GPU: notebook environment with GPU access","Training code must be defined in the notebook (not imported from external modules)"],"input_types":["Training function (callable)","Number of processes (integer, optional)"],"output_types":["Launched distributed training within notebook"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_13","uri":"capability://automation.workflow.optimizer.integration.with.gradient.accumulation.and.synchronization","name":"optimizer integration with gradient accumulation and synchronization","description":"Wraps PyTorch optimizers with AcceleratedOptimizer that handles distributed gradient synchronization, gradient accumulation step counting, and backend-specific optimizer state management. Automatically defers optimizer steps until gradient accumulation threshold is reached, and handles gradient scaling for mixed-precision training without requiring manual loss scaling logic.","intents":["Use standard PyTorch optimizers in distributed training without manual gradient synchronization","Automatically handle gradient accumulation step counting","Integrate gradient scaling for mixed-precision training"],"best_for":["Teams using standard PyTorch optimizers (SGD, Adam, AdamW) in distributed training","Training pipelines requiring gradient accumulation without manual step counting","Mixed-precision training requiring automatic loss scaling"],"limitations":["AcceleratedOptimizer adds ~1-2% overhead per optimizer step due to wrapper logic","Gradient accumulation requires manual step counting in training loop; easy to misconfigure","Custom optimizer implementations may not work with AcceleratedOptimizer wrapper"],"requires":["PyTorch optimizer (torch.optim.Optimizer subclass)","Distributed training setup (DDP, FSDP, or DeepSpeed)"],"input_types":["PyTorch optimizer","Gradient accumulation steps (integer)"],"output_types":["Wrapped optimizer with distributed synchronization","Optimizer step (deferred until accumulation threshold)"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_2","uri":"capability://data.processing.analysis.automatic.dataloader.sharding.with.stateful.resumption","name":"automatic dataloader sharding with stateful resumption","description":"Wraps PyTorch DataLoaders to automatically partition data across distributed processes using DistributedSampler under the hood, with support for multiple sharding strategies (by-index, by-node, custom). Maintains DataLoader state (current batch index, epoch) across checkpoints, enabling exact resumption from a checkpoint without data duplication or skipping, even in distributed settings where process counts may change between runs.","intents":["Automatically shard training data across GPUs without manual DistributedSampler setup","Resume training from a checkpoint without re-processing already-seen data","Handle dynamic process counts (e.g., adding/removing GPUs) without data leakage"],"best_for":["Teams training on large datasets where data duplication wastes compute","Long-running training jobs requiring frequent checkpointing and resumption","Distributed training with variable cluster sizes"],"limitations":["Requires DataLoader to be wrapped before training loop — incompatible with lazy-loaded or streaming datasets without custom adapters","Stateful resumption only works if checkpoint includes DataLoader state; standard PyTorch checkpoints won't restore position","Sharding strategies are limited to index-based and node-based; custom sampling logic requires subclassing"],"requires":["PyTorch DataLoader","Checkpoint saving/loading integration (manual or via Accelerate's checkpoint API)","For stateful resumption: explicit DataLoader state serialization"],"input_types":["PyTorch DataLoader","Dataset (any torch.utils.data.Dataset subclass)"],"output_types":["Sharded DataLoader with DistributedSampler","DataLoader state dict (for resumption)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_3","uri":"capability://automation.workflow.gradient.accumulation.with.distributed.synchronization","name":"gradient accumulation with distributed synchronization","description":"Implements gradient accumulation by deferring gradient synchronization across processes until the accumulation step count is reached, reducing communication overhead. Uses backend-specific synchronization hooks (DDP's no_sync context manager, DeepSpeed's gradient accumulation steps, FSDP's reduce-scatter timing) to avoid redundant all-reduce operations, enabling effective batch size scaling without proportional communication cost.","intents":["Simulate larger batch sizes on memory-constrained hardware by accumulating gradients","Reduce communication overhead in distributed training by batching gradient synchronization","Train with effective batch sizes larger than what fits in GPU memory"],"best_for":["Teams training large models (LLMs, vision transformers) with memory constraints","Distributed training on high-latency networks where communication is a bottleneck","Researchers requiring specific effective batch sizes that don't align with GPU memory"],"limitations":["Requires manual step counting logic in training loop — easy to misconfigure and cause gradient staleness","Synchronization timing varies by backend; DeepSpeed and FSDP handle it automatically, but DDP requires explicit no_sync context","Accumulated gradients consume more GPU memory than single-step gradients (roughly proportional to accumulation steps)"],"requires":["PyTorch 1.5+ (for DDP no_sync support)","Distributed training setup (DDP, FSDP, or DeepSpeed)","Manual step counter in training loop"],"input_types":["Loss tensor","Accumulation step count (integer)"],"output_types":["Synchronized gradients (after accumulation threshold)","Optimizer step"],"categories":["automation-workflow","model-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_4","uri":"capability://automation.workflow.device.mapping.and.memory.offloading.for.large.model.inference","name":"device mapping and memory offloading for large model inference","description":"Implements automatic device mapping for models larger than GPU memory by partitioning model layers across GPUs and CPU using a cost model that estimates layer memory and compute time. Supports CPU offloading (layer swapping between GPU and CPU) and NVMe offloading (via DeepSpeed) for models that exceed total system memory, with hooks to manage data movement and minimize latency impact during inference.","intents":["Run inference on models larger than single GPU memory (e.g., 70B LLMs on single A100)","Automatically partition model layers across available GPUs to maximize throughput","Trade inference latency for memory efficiency by offloading inactive layers to CPU/NVMe"],"best_for":["Teams deploying large language models on resource-constrained hardware","Inference services requiring flexible memory-latency trade-offs","Researchers experimenting with models larger than available GPU memory"],"limitations":["Device mapping is heuristic-based; optimal partitioning requires profiling and manual tuning for specific models","CPU offloading adds 50-200ms latency per layer swap due to PCIe bandwidth limits (~16 GB/s on PCIe 4.0)","NVMe offloading requires fast NVMe (PCIe 4.0+) and is significantly slower than CPU offloading; only viable for very large models with low throughput requirements","Incompatible with models using dynamic control flow or custom CUDA kernels"],"requires":["PyTorch 1.10+","For multi-GPU mapping: multiple GPUs with peer-to-peer access","For CPU offloading: sufficient CPU RAM (typically 2-3x model size)","For NVMe offloading: fast NVMe storage and DeepSpeed integration"],"input_types":["PyTorch model (torch.nn.Module)","Model size estimate (bytes)","Available device memory (bytes)"],"output_types":["Device-mapped model with layer-to-device assignments","Offloading hooks for data movement"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_5","uri":"capability://automation.workflow.tied.parameter.and.shared.weight.memory.optimization","name":"tied parameter and shared weight memory optimization","description":"Detects and optimizes models with tied parameters (e.g., shared embeddings in language models) by tracking parameter aliases and ensuring gradients are correctly accumulated across all uses without duplication. Implements a hook system that intercepts backward passes to merge gradients from tied parameters before optimizer steps, reducing memory overhead and preventing gradient inconsistencies in distributed settings.","intents":["Train models with tied weights (e.g., BERT-style embeddings) without memory duplication","Ensure gradient correctness for shared parameters in distributed training","Automatically detect and optimize tied parameters without manual configuration"],"best_for":["Teams training transformer models with tied embeddings or output layers","Researchers experimenting with parameter sharing for memory efficiency","Production training where tied parameters are common (BERT, GPT variants)"],"limitations":["Tied parameter detection is based on Python object identity; dynamically created tied parameters may not be detected","Hook-based gradient merging adds ~1-2% overhead per backward pass","Incompatible with custom autograd functions that don't properly propagate gradients through aliases"],"requires":["PyTorch 1.9+ (for robust parameter aliasing support)","Models explicitly using parameter sharing (e.g., model.embedding = model.output_layer)"],"input_types":["PyTorch model with tied parameters"],"output_types":["Optimized model with gradient hooks","Correctly accumulated gradients for tied parameters"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_6","uri":"capability://automation.workflow.checkpoint.saving.and.loading.with.state.management","name":"checkpoint saving and loading with state management","description":"Provides a unified checkpoint API that serializes model, optimizer, and DataLoader state across distributed processes, with support for custom checkpoint hooks and project-level configuration. Handles backend-specific state serialization (e.g., DeepSpeed's checkpoint format, FSDP's sharded checkpoints) transparently, and supports resuming from checkpoints with different process counts or hardware configurations.","intents":["Save training state (model, optimizer, DataLoader position) in a single call","Resume training from checkpoint without manual state reconstruction","Handle backend-specific checkpoint formats (DeepSpeed, FSDP) transparently"],"best_for":["Teams running long-training jobs requiring frequent checkpointing","Production training pipelines with automated checkpoint management","Researchers experimenting with different hardware configurations"],"limitations":["Checkpoint size scales with model size and optimizer state; full-precision checkpoints can be 3-4x model size","Custom checkpoint hooks require manual implementation; no built-in compression or deduplication","Resuming with different process counts requires careful state redistribution; not all backends support this seamlessly"],"requires":["Filesystem with sufficient space (typically 3-4x model size per checkpoint)","For distributed checkpointing: shared filesystem (NFS, cloud storage) or manual synchronization","Optional: custom checkpoint hooks (subclass CheckpointHandler)"],"input_types":["Model state dict","Optimizer state dict","DataLoader state","Custom state (optional)"],"output_types":["Checkpoint directory with model, optimizer, and metadata files","Restored state dicts and DataLoader position"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_7","uri":"capability://automation.workflow.experiment.tracking.and.multi.process.logging","name":"experiment tracking and multi-process logging","description":"Integrates with experiment tracking platforms (Weights & Biases, TensorBoard, Comet, MLflow) via a unified Tracker API that handles multi-process logging coordination, ensuring only the main process logs to avoid duplicate entries. Provides context managers and decorators for tracking custom metrics, and automatically logs training hyperparameters and system information across all supported backends.","intents":["Log training metrics to experiment tracking platform without manual process coordination","Avoid duplicate logging in distributed training (only main process logs)","Automatically capture hyperparameters and system info for reproducibility"],"best_for":["Teams using experiment tracking for model development and comparison","Production training pipelines requiring audit trails and reproducibility","Researchers comparing models across different hardware configurations"],"limitations":["Only main process logs; metrics from worker processes are lost unless explicitly gathered","Tracker initialization requires API keys or credentials; no built-in credential management","Custom metrics require manual logging calls; no automatic gradient or activation tracking"],"requires":["Experiment tracking platform account (W&B, TensorBoard, Comet, MLflow, etc.)","API key or credentials for the tracking platform","Optional: tracker-specific Python SDK (e.g., wandb, comet_ml)"],"input_types":["Metric name (string)","Metric value (float, dict, or tensor)","Step number (integer)"],"output_types":["Logged metrics in experiment tracking platform","Hyperparameter and system info metadata"],"categories":["automation-workflow","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_8","uri":"capability://automation.workflow.distributed.collective.operations.and.tensor.utilities","name":"distributed collective operations and tensor utilities","description":"Provides high-level APIs for distributed collective operations (all-reduce, all-gather, broadcast, scatter) that abstract backend differences (DDP, FSDP, DeepSpeed) and handle tensor type conversions automatically. Includes utilities for gathering metrics across processes, broadcasting model state, and synchronizing random number generators to ensure reproducibility in distributed settings.","intents":["Perform distributed collective operations without backend-specific code","Gather metrics from all processes for logging and evaluation","Synchronize random state across processes for reproducible distributed training"],"best_for":["Teams implementing custom distributed algorithms requiring collective operations","Researchers needing fine-grained control over distributed communication","Production systems requiring deterministic distributed behavior"],"limitations":["Collective operations add communication latency; all-reduce on large tensors can add 10-100ms per operation","Synchronizing RNG state requires explicit calls; easy to miss and cause non-determinism","Custom collective patterns (e.g., ring all-reduce) not supported; limited to standard operations"],"requires":["Distributed training setup (DDP, FSDP, or DeepSpeed)","For RNG synchronization: explicit seed management"],"input_types":["Tensor (for collective operations)","Metric dict (for gathering)","Seed value (for RNG sync)"],"output_types":["Reduced/gathered/broadcast tensor","Aggregated metrics dict","Synchronized RNG state"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"accelerate__cap_9","uri":"capability://automation.workflow.command.line.launcher.with.environment.configuration","name":"command-line launcher with environment configuration","description":"Provides the accelerate launch CLI that configures and launches distributed training scripts with automatic environment variable setup. Detects available hardware (GPUs, TPUs, CPU count) and prompts for configuration (backend, precision, etc.), then sets up process groups and environment variables before launching the training script, eliminating manual torchrun or torch.distributed.launch setup.","intents":["Launch distributed training without manually setting RANK, WORLD_SIZE, MASTER_ADDR environment variables","Auto-detect hardware and suggest optimal distributed configuration","Switch between single-GPU, multi-GPU, and TPU training with a single command"],"best_for":["Teams running training scripts on diverse hardware without manual environment setup","Researchers prototyping distributed training without deep distributed systems knowledge","Production training pipelines requiring reproducible launch configuration"],"limitations":["Launcher assumes standard PyTorch distributed setup; incompatible with custom process management","Configuration prompts are interactive; requires manual input or pre-saved config file for automation","TPU support requires Google Cloud SDK and appropriate credentials; 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