{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"pypi_pypi-accelerate","slug":"pypi-accelerate","name":"accelerate","type":"framework","url":"https://github.com/huggingface/accelerate","page_url":"https://unfragile.ai/pypi-accelerate","categories":["model-training"],"tags":["deep","learning"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"pypi_pypi-accelerate__cap_0","uri":"capability://automation.workflow.unified.distributed.training.abstraction.with.minimal.code.changes","name":"unified distributed training abstraction with minimal code changes","description":"Provides a thin wrapper API (Accelerator class) that abstracts distributed training boilerplate across CPU, single GPU, multi-GPU (DDP), TPU, and multi-node clusters. Users integrate by wrapping models, optimizers, and dataloaders with accelerator.prepare() and replacing backward() with accelerator.backward(), enabling the same training script to run on any hardware without modification. Internally detects the distributed backend (DDP, FSDP, DeepSpeed, Megatron) and configures process groups, device placement, and communication patterns automatically.","intents":["Write a training script once and run it on CPU, single GPU, or multi-GPU without code changes","Avoid managing PyTorch distributed training boilerplate (DistributedDataParallel, process groups, device setup)","Retain full control over training loop logic while delegating hardware-specific concerns","Switch between distributed backends (DDP to FSDP to DeepSpeed) via configuration only"],"best_for":["PyTorch researchers and engineers writing custom training loops","Teams needing hardware-agnostic training code for multi-environment deployment","Developers migrating from single-GPU to distributed training without rewriting scripts"],"limitations":["Requires PyTorch training loop structure — incompatible with high-level frameworks (Trainer, Lightning) that manage loops internally","Abstraction adds ~5-10ms overhead per training step for distributed synchronization checks","No automatic hyperparameter tuning or learning rate scheduling — users must implement or integrate separately","Limited support for custom distributed algorithms requiring fine-grained communication control"],"requires":["Python 3.8+","PyTorch 1.10+","For multi-GPU: CUDA 11.0+ or compatible GPU drivers","For TPU: torch-xla library and TPU access","For DeepSpeed backend: DeepSpeed 0.5.0+ installed separately"],"input_types":["PyTorch nn.Module (models)","torch.optim.Optimizer instances","torch.utils.data.DataLoader objects","torch.Tensor batches"],"output_types":["Wrapped nn.Module with distributed communication hooks","Wrapped Optimizer with gradient synchronization","Wrapped DataLoader with automatic sharding","Training loss tensors synchronized across processes"],"categories":["automation-workflow","distributed-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_1","uri":"capability://automation.workflow.automatic.distributed.backend.detection.and.configuration","name":"automatic distributed backend detection and configuration","description":"Detects the distributed training environment (single-process, multi-GPU DDP, FSDP, DeepSpeed, Megatron-LM, TPU) by inspecting environment variables (RANK, WORLD_SIZE, MASTER_ADDR, etc.) and hardware availability. Automatically selects and initializes the appropriate backend's process group, communication primitives, and device placement without user intervention. Supports mixed-precision training (FP16, BF16, FP8) and gradient accumulation patterns specific to each backend.","intents":["Run the same training script on different hardware (CPU, single GPU, 8-GPU node, multi-node cluster) without configuration changes","Automatically initialize distributed process groups and communication backends","Switch from DDP to FSDP or DeepSpeed via environment variables or config file, not code changes","Detect TPU availability and configure torch-xla communication automatically"],"best_for":["DevOps and ML engineers managing training infrastructure across heterogeneous hardware","Researchers running experiments on multiple clusters with different topologies","Teams using container orchestration (Kubernetes, SLURM) that set environment variables"],"limitations":["Relies on correct environment variable setup — misconfigured RANK or WORLD_SIZE causes silent failures or hangs","Backend selection is deterministic but not always optimal — may choose DDP over FSDP for memory-constrained scenarios","Custom distributed algorithms requiring non-standard communication patterns must bypass auto-detection","TPU detection requires torch-xla to be installed; falls back to CPU if unavailable"],"requires":["Environment variables: RANK, WORLD_SIZE, MASTER_ADDR, MASTER_PORT (for multi-node)","For FSDP: PyTorch 1.12+","For DeepSpeed: DeepSpeed package installed and configured","For Megatron: Megatron-LM fork installed","For TPU: torch-xla and TPU access"],"input_types":["Environment variables (RANK, WORLD_SIZE, MASTER_ADDR, MASTER_PORT, LOCAL_RANK)","Hardware introspection (GPU count, TPU availability, CPU cores)","Optional config file (accelerate config output)"],"output_types":["Initialized torch.distributed process group","Backend-specific communication primitives (NCCL, Gloo, MPI)","Device assignment (cuda:0, cuda:1, etc. or tpu:0)","Distributed state object with process metadata"],"categories":["automation-workflow","environment-configuration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_10","uri":"capability://automation.workflow.deepspeed.integration.with.automatic.configuration.generation","name":"deepspeed integration with automatic configuration generation","description":"Integrates DeepSpeed distributed training framework with automatic configuration generation based on model size, hardware, and training requirements. Handles DeepSpeed initialization, ZeRO optimizer state sharding (stages 1-3), gradient checkpointing, and activation checkpointing. Automatically selects optimal DeepSpeed configuration for memory efficiency and training speed.","intents":["Train very large models using DeepSpeed's ZeRO optimizer state sharding","Automatically generate DeepSpeed configuration without manual JSON editing","Combine DeepSpeed with Accelerate for simplified distributed training setup","Switch between DeepSpeed ZeRO stages (1, 2, 3) via configuration"],"best_for":["Teams training very large language models (100B+ parameters) with DeepSpeed","Production systems requiring maximum memory efficiency and training speed","Researchers experimenting with different ZeRO stages and configurations"],"limitations":["DeepSpeed configuration is complex — automatic generation may not be optimal for all models","DeepSpeed requires separate installation and configuration","Some model architectures may not be compatible with DeepSpeed","Debugging DeepSpeed issues requires deep understanding of ZeRO stages and communication patterns","DeepSpeed updates may break compatibility with Accelerate"],"requires":["DeepSpeed 0.5.0+ installed separately","Multi-GPU setup (DeepSpeed requires at least 2 GPUs)","Model architecture compatible with DeepSpeed (standard transformer architectures work well)"],"input_types":["Model size (number of parameters)","Hardware configuration (GPU count, GPU memory)","Training configuration (batch size, learning rate, etc.)","DeepSpeed configuration (optional, auto-generated if not provided)"],"output_types":["DeepSpeed configuration dict","DeepSpeed-wrapped model and optimizer","ZeRO optimizer state sharding across processes"],"categories":["automation-workflow","distributed-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_11","uri":"capability://automation.workflow.megatron.lm.integration.for.tensor.and.pipeline.parallelism","name":"megatron-lm integration for tensor and pipeline parallelism","description":"Integrates Megatron-LM framework for tensor parallelism (sharding model weights across GPUs) and pipeline parallelism (splitting model layers across GPUs). Handles Megatron initialization, tensor parallel group setup, and pipeline parallel scheduling. Automatically determines optimal tensor and pipeline parallel configurations based on model size and hardware topology.","intents":["Train extremely large models using Megatron's tensor and pipeline parallelism","Automatically configure tensor and pipeline parallel groups based on hardware","Combine Megatron with Accelerate for simplified setup","Optimize training speed and memory usage with Megatron parallelism strategies"],"best_for":["Teams training models at extreme scale (500B+ parameters) with Megatron","Production systems requiring maximum training speed with tensor/pipeline parallelism","Researchers experimenting with different parallelism strategies"],"limitations":["Megatron integration is experimental and may have compatibility issues","Megatron requires significant engineering effort to set up and debug","Tensor and pipeline parallelism add communication overhead","Model architecture must be compatible with Megatron (custom models may require modifications)","Requires Megatron-LM fork (not standard PyTorch)"],"requires":["Megatron-LM fork installed separately","Multi-GPU setup with high-bandwidth interconnect (NVLink recommended)","Model architecture compatible with Megatron"],"input_types":["Model size and architecture","Hardware topology (GPU count, interconnect bandwidth)","Tensor and pipeline parallel configuration"],"output_types":["Megatron-wrapped model with tensor/pipeline parallelism","Tensor parallel groups and pipeline parallel schedules","Communication operations for parallelism"],"categories":["automation-workflow","distributed-training"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_12","uri":"capability://automation.workflow.random.number.generator.synchronization.across.processes","name":"random number generator synchronization across processes","description":"Synchronizes random number generator (RNG) states across distributed processes to ensure deterministic behavior and reproducibility. Handles seeding of PyTorch RNG, NumPy RNG, and Python random module across all processes. Supports both deterministic seeding (same seed on all processes) and process-specific seeding (different seed per process for data augmentation).","intents":["Ensure reproducible training across distributed processes with synchronized random seeds","Implement data augmentation with different random values per process (e.g., different crops per GPU)","Debug training issues by reproducing exact same random behavior","Ensure model initialization is identical across all processes"],"best_for":["Researchers requiring exact reproducibility across training runs","Production systems requiring deterministic behavior for debugging","Teams implementing custom data augmentation with per-process randomness"],"limitations":["Synchronizing RNG state adds minimal overhead but requires explicit calls","Some operations (e.g., dropout) may have different behavior with synchronized vs unsynchronized RNG","RNG synchronization must be done before training loop for reproducibility","Different random libraries (PyTorch, NumPy, Python random) must be synchronized separately"],"requires":["Distributed training setup with initialized process groups","Explicit RNG synchronization calls in training script"],"input_types":["Random seed (integer)","Process rank (for process-specific seeding)"],"output_types":["Synchronized RNG state across all processes","Deterministic random values for model initialization and data augmentation"],"categories":["automation-workflow","reproducibility"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_13","uri":"capability://automation.workflow.notebook.based.distributed.training.launcher","name":"notebook-based distributed training launcher","description":"Provides notebook_launcher function that enables distributed training within Jupyter notebooks by spawning child processes and coordinating training across them. Handles process spawning, output redirection, and error handling within notebook environment. Allows users to write distributed training code in notebooks without external launcher scripts.","intents":["Run distributed training experiments directly in Jupyter notebooks without external scripts","Debug distributed training issues interactively in notebook environment","Prototype distributed training code before moving to production scripts","Combine notebook exploration with distributed training for rapid iteration"],"best_for":["Researchers prototyping distributed training in notebooks","Teams debugging distributed training issues interactively","Educational settings teaching distributed training concepts"],"limitations":["Notebook launcher spawns child processes that don't have direct notebook access — debugging is limited","Output from child processes may be difficult to capture and display in notebook","Notebook environment may not be suitable for long-running training jobs","Some distributed backends may not work correctly in notebook environment","Performance may be degraded compared to script-based training"],"requires":["Jupyter notebook environment","Python 3.8+","PyTorch with distributed training support"],"input_types":["Training function (callable)","Function arguments (passed to training function)","Number of processes (for distributed training)"],"output_types":["Training output (printed to notebook)","Training results (returned from training function)","Error messages and stack traces"],"categories":["automation-workflow","notebook-support"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_14","uri":"capability://automation.workflow.memory.profiling.and.system.resource.monitoring","name":"memory profiling and system resource monitoring","description":"Provides utilities to profile GPU and CPU memory usage during training, detect memory leaks, and monitor system resources (temperature, power consumption). Tracks peak memory usage, memory allocation patterns, and identifies memory bottlenecks. Integrates with experiment tracking for memory usage visualization and analysis.","intents":["Identify memory bottlenecks in distributed training to optimize batch size and model size","Detect memory leaks in training code that cause out-of-memory errors","Monitor GPU temperature and power consumption to prevent hardware damage","Compare memory efficiency of different training configurations"],"best_for":["Teams optimizing training for memory-constrained hardware","Production systems requiring memory monitoring and alerting","Researchers analyzing memory efficiency of different training approaches"],"limitations":["Memory profiling adds overhead and may slow down training","GPU memory profiling requires CUDA profiling tools (nvidia-smi, torch.cuda.memory_stats)","Memory tracking across distributed processes requires manual aggregation","Some memory allocations (e.g., from C++ extensions) may not be tracked accurately"],"requires":["GPU with memory profiling support (NVIDIA GPUs with CUDA)","nvidia-smi or equivalent GPU monitoring tool","PyTorch with CUDA memory tracking enabled"],"input_types":["Training loop (for memory profiling)","Memory thresholds (for alerting)"],"output_types":["Memory usage statistics (peak, average, allocated)","Memory allocation timeline","System resource metrics (temperature, power consumption)"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_2","uri":"capability://data.processing.analysis.stateful.dataloader.sharding.and.resumption","name":"stateful dataloader sharding and resumption","description":"Automatically shards datasets across distributed processes using DistributedSampler, ensuring each process receives a unique subset of data without overlap. Supports stateful resumption by saving and restoring dataloader state (current batch index, epoch, sampler state) to enable training continuation from checkpoints without data duplication or skipping. Implements multiple sharding strategies (sequential, random, custom) and dispatching strategies (synchronous, asynchronous) to optimize data loading for different hardware topologies.","intents":["Automatically split training data across GPU processes without manual sampler configuration","Resume training from a checkpoint and continue from the exact batch where training stopped, without re-processing earlier batches","Ensure deterministic data ordering across distributed processes for reproducibility","Optimize data loading performance by choosing appropriate sharding and dispatching strategies for the hardware"],"best_for":["Teams training large models on multi-GPU setups requiring checkpoint-and-resume workflows","Researchers needing reproducible distributed training with deterministic data ordering","Production ML systems where training interruptions (hardware failures, job preemption) are common"],"limitations":["Stateful resumption requires explicit checkpoint saving of dataloader state — automatic checkpointing not built-in","Sharding strategies assume uniform batch sizes across processes — dynamic batching not supported","Custom samplers must implement __getstate__/__setstate__ for resumption to work correctly","Asynchronous dispatching adds latency and memory overhead for prefetching"],"requires":["torch.utils.data.DataLoader instance","torch.utils.data.distributed.DistributedSampler or compatible sampler","For resumption: manual checkpoint save/load of dataloader state","Batch size must be divisible by world size for even sharding"],"input_types":["torch.utils.data.Dataset","torch.utils.data.DataLoader","Sampler state (batch index, epoch, random state)"],"output_types":["Sharded DataLoader with DistributedSampler","Dataloader state dict (for checkpointing)","Batches distributed to correct process rank"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_3","uri":"capability://automation.workflow.mixed.precision.training.with.automatic.loss.scaling","name":"mixed-precision training with automatic loss scaling","description":"Enables FP16, BF16, and FP8 mixed-precision training by automatically casting forward passes to lower precision while keeping optimizer state in FP32. Implements automatic loss scaling (dynamic or static) to prevent gradient underflow in FP16 training, automatically adjusting scale factors based on gradient overflow detection. Integrates with distributed backends to synchronize loss scaling across processes and handle gradient clipping in mixed precision.","intents":["Reduce memory usage and increase training throughput by training in FP16 or BF16 instead of FP32","Automatically handle loss scaling to prevent gradient underflow without manual tuning","Train large models that don't fit in GPU memory at FP32 precision","Maintain numerical stability across distributed training with synchronized loss scaling"],"best_for":["Teams training large language models or vision transformers on memory-constrained GPUs","Production systems requiring maximum training throughput and minimal memory footprint","Researchers experimenting with different precision levels (FP16 vs BF16 vs FP8)"],"limitations":["FP16 training requires GPU support for half-precision operations (most modern GPUs support this)","BF16 requires newer hardware (A100, H100, or newer); not available on older V100/P100","FP8 support is limited to specific hardware (H100) and requires careful tuning of loss scale","Automatic loss scaling can cause training instability if gradients are very large or very small","Some operations (batch normalization, layer normalization) may have numerical issues in FP16"],"requires":["GPU with FP16 support (nearly all modern GPUs)","For BF16: GPU with native BF16 support (A100, H100, or newer)","For FP8: H100 GPU with FP8 support","PyTorch 1.10+ with mixed precision support"],"input_types":["Model parameters (nn.Module)","Loss values (scalar tensors)","Gradients (computed during backward pass)"],"output_types":["Scaled loss values (for backward pass)","Unscaled gradients (for optimizer step)","Loss scale factor (dynamic or static)","Gradient overflow flags"],"categories":["automation-workflow","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_4","uri":"capability://automation.workflow.gradient.accumulation.with.distributed.synchronization","name":"gradient accumulation with distributed synchronization","description":"Implements gradient accumulation by deferring optimizer steps across multiple backward passes, reducing memory usage and enabling larger effective batch sizes. Automatically synchronizes gradients across distributed processes only when accumulation steps are complete, reducing communication overhead. Handles gradient clipping, optimizer state updates, and learning rate scheduling in the context of accumulated gradients.","intents":["Train with larger effective batch sizes than GPU memory allows by accumulating gradients over multiple steps","Reduce communication overhead in distributed training by synchronizing gradients less frequently","Implement gradient clipping and learning rate scheduling that accounts for accumulated gradients","Maintain training stability when using very large batch sizes"],"best_for":["Teams training large models (LLMs, vision transformers) with memory constraints","Distributed training setups where communication is a bottleneck","Researchers experimenting with different batch sizes and accumulation strategies"],"limitations":["Requires manual loop structure to track accumulation steps — no automatic accumulation","Gradient synchronization must be explicitly triggered after accumulation steps complete","Learning rate scheduling becomes complex because effective batch size differs from per-step batch size","Gradient clipping must account for accumulated gradients to be numerically correct"],"requires":["Manual training loop with accumulation step counter","Explicit calls to accelerator.backward() for each accumulated step","Explicit calls to optimizer.step() after accumulation is complete"],"input_types":["Loss values (scalar tensors) from multiple forward passes","Gradients (accumulated across multiple backward passes)","Accumulation step counter"],"output_types":["Accumulated gradients (synchronized across processes)","Updated model parameters (after optimizer step)","Gradient norms (for clipping and monitoring)"],"categories":["automation-workflow","optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_5","uri":"capability://automation.workflow.big.model.support.with.device.mapping.and.memory.offloading","name":"big model support with device mapping and memory offloading","description":"Enables training and inference of models larger than GPU memory by automatically mapping model layers to different devices (GPU, CPU, disk) based on memory constraints. Implements memory offloading strategies (CPU offloading, disk offloading) that move activations and parameters between devices during forward/backward passes. Supports tied parameters (weight sharing) and hook-based memory optimization to minimize redundant copies.","intents":["Train or infer models larger than single GPU memory (e.g., 70B parameter models on single A100)","Automatically determine optimal device placement for model layers based on available memory","Offload intermediate activations to CPU or disk to reduce peak GPU memory usage","Handle weight-tied layers efficiently without duplicating shared parameters"],"best_for":["Researchers training very large language models (70B+) on limited GPU memory","Teams running inference on models that don't fit in GPU memory","Production systems requiring memory-efficient model serving"],"limitations":["Device mapping and offloading add significant latency (10-50% slower than single-device training)","Requires careful tuning of offloading strategies for optimal performance","CPU/disk offloading requires sufficient CPU RAM and fast storage (NVMe SSD)","Not compatible with some model architectures (e.g., models with dynamic control flow)","Gradient checkpointing may conflict with offloading strategies"],"requires":["Model architecture that supports layer-wise device mapping","Sufficient CPU RAM for CPU offloading (typically 2-3x model size)","For disk offloading: fast NVMe SSD with sufficient space","PyTorch 1.13+ with device mapping support"],"input_types":["nn.Module with layer-wise structure","Memory constraints (GPU memory, CPU memory, disk space)","Device mapping configuration (manual or automatic)"],"output_types":["Device-mapped model with layers on different devices","Offloading hooks for activation/parameter movement","Memory usage estimates and optimization reports"],"categories":["automation-workflow","memory-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_6","uri":"capability://automation.workflow.checkpoint.saving.and.loading.with.distributed.state.management","name":"checkpoint saving and loading with distributed state management","description":"Provides utilities to save and load training state (model weights, optimizer state, random number generator state, dataloader state) across distributed processes. Handles consolidation of distributed state (e.g., gathering optimizer state from all processes) and safe resumption from checkpoints. Supports custom checkpoint hooks for user-defined state and integrates with experiment tracking systems for metadata logging.","intents":["Save training checkpoints that can be resumed on different hardware configurations (e.g., checkpoint from 8-GPU training resumed on 4-GPU)","Safely save distributed optimizer state without race conditions or data corruption","Resume training with exact reproducibility (same random seeds, same dataloader position)","Implement custom checkpoint logic for model-specific state (e.g., custom buffers, auxiliary models)"],"best_for":["Production training systems requiring reliable checkpoint-and-resume workflows","Teams training models across multiple hardware configurations","Researchers requiring exact reproducibility across training runs"],"limitations":["Checkpoint files can be very large (model size + optimizer state, typically 2-3x model size)","Resuming on different world size (e.g., 8 GPUs → 4 GPUs) requires manual state reshaping","Custom checkpoint hooks must be implemented by users for model-specific state","No built-in compression or deduplication of checkpoint files"],"requires":["Distributed training setup with initialized process groups","Sufficient disk space for checkpoint files (2-3x model size)","For resumption: matching or compatible model architecture"],"input_types":["Model state dict (nn.Module.state_dict())","Optimizer state dict (torch.optim.Optimizer.state_dict())","Random number generator state (torch.get_rng_state())","Dataloader state (batch index, epoch, sampler state)"],"output_types":["Checkpoint file (typically .pt or .safetensors format)","Metadata file (training step, epoch, loss, etc.)","Distributed state dict (consolidated across processes)"],"categories":["automation-workflow","state-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_7","uri":"capability://automation.workflow.command.line.launcher.for.distributed.training","name":"command-line launcher for distributed training","description":"Provides accelerate launch CLI that automatically configures and launches distributed training scripts without manual environment variable setup. Detects hardware (GPUs, TPUs, CPUs) and prompts users for configuration (number of processes, mixed precision, backend selection). Generates launcher commands for different environments (single-node multi-GPU, multi-node SLURM, Kubernetes) and handles process spawning and monitoring.","intents":["Launch distributed training with a single command without manually setting environment variables","Automatically detect available hardware and suggest optimal configuration","Generate launcher commands for different cluster environments (SLURM, Kubernetes, etc.)","Debug distributed training by inspecting generated configuration and environment"],"best_for":["ML engineers and researchers unfamiliar with distributed training setup","Teams using multiple cluster environments (local, SLURM, Kubernetes)","Production systems requiring reproducible training launch procedures"],"limitations":["Launcher assumes standard distributed training setup — custom process spawning not supported","SLURM and Kubernetes support requires cluster-specific configuration","Debugging launcher issues requires understanding environment variables and process groups","No built-in support for dynamic scaling or elastic training"],"requires":["accelerate package installed and in PATH","Python training script with Accelerator integration","For multi-node: SLURM, Kubernetes, or other cluster manager with environment variable support"],"input_types":["Training script path (Python file)","Script arguments (passed through to training script)","Configuration file (from accelerate config)"],"output_types":["Launcher command (shell command to execute)","Environment variables (RANK, WORLD_SIZE, MASTER_ADDR, etc.)","Process spawning and monitoring output"],"categories":["automation-workflow","cli-tools"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_8","uri":"capability://automation.workflow.experiment.tracking.integration.with.multi.process.coordination","name":"experiment tracking integration with multi-process coordination","description":"Integrates with experiment tracking systems (Weights & Biases, TensorBoard, Comet, MLflow, Neptune) and automatically coordinates logging across distributed processes to avoid duplicate logs. Ensures only the main process logs to avoid race conditions and duplicate entries. Provides unified logging API that works across different tracking backends and handles metric aggregation across processes.","intents":["Log training metrics to experiment tracking systems without manual process rank checks","Automatically aggregate metrics across distributed processes (e.g., average loss across GPUs)","Avoid duplicate logs and race conditions in distributed training","Switch between tracking backends (W&B, TensorBoard, etc.) without code changes"],"best_for":["Teams using experiment tracking for hyperparameter tuning and model comparison","Production ML systems requiring centralized logging and monitoring","Researchers tracking multiple distributed training runs"],"limitations":["Only main process (rank 0) logs by default — custom logging on other ranks requires manual implementation","Metric aggregation is manual — users must explicitly compute and log aggregated metrics","Some tracking backends have rate limits that may be exceeded with frequent logging","Custom metrics require manual implementation for each tracking backend"],"requires":["Tracking backend library installed (e.g., wandb, tensorboard, comet-ml)","API credentials or configuration for tracking backend","Distributed training setup with initialized process groups"],"input_types":["Metric names and values (scalars, tensors)","Hyperparameters (dict)","Model configuration (dict)"],"output_types":["Logged metrics in tracking backend","Aggregated metrics across processes","Training run metadata and configuration"],"categories":["automation-workflow","monitoring"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"pypi_pypi-accelerate__cap_9","uri":"capability://automation.workflow.fsdp.fully.sharded.data.parallel.integration.with.automatic.sharding.configuration","name":"fsdp (fully sharded data parallel) integration with automatic sharding configuration","description":"Integrates PyTorch's Fully Sharded Data Parallel (FSDP) backend with automatic sharding strategy selection (FULL_SHARD, SHARD_GRAD_OP, NO_SHARD) based on model size and hardware. Handles parameter and gradient sharding across processes, automatic all-gather operations during forward passes, and reduce-scatter during backward passes. Supports mixed precision with FSDP and integrates with gradient checkpointing for memory optimization.","intents":["Train models larger than single GPU memory by sharding parameters and gradients across multiple GPUs","Automatically select optimal FSDP sharding strategy based on model size and hardware","Reduce memory usage compared to DDP by sharding optimizer state across processes","Combine FSDP with gradient checkpointing for maximum memory efficiency"],"best_for":["Teams training very large models (10B+ parameters) on multi-GPU setups","Production systems requiring memory-efficient distributed training","Researchers experimenting with different sharding strategies"],"limitations":["FSDP adds communication overhead compared to DDP (all-gather and reduce-scatter operations)","Sharding strategy selection is heuristic-based — may not be optimal for all models","Some model architectures (e.g., models with custom communication) may not work with FSDP","Debugging FSDP issues is complex due to distributed nature of sharding","Requires PyTorch 1.12+ with FSDP support"],"requires":["PyTorch 1.12+","Multi-GPU setup (FSDP requires at least 2 GPUs to be useful)","Model architecture compatible with FSDP (standard transformer architectures work well)"],"input_types":["nn.Module (model to shard)","Sharding strategy configuration (FULL_SHARD, SHARD_GRAD_OP, NO_SHARD)","Mixed precision configuration (FP16, BF16)"],"output_types":["FSDP-wrapped model with sharded parameters","Sharded optimizer state","Communication operations (all-gather, reduce-scatter)"],"categories":["automation-workflow","distributed-training"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":27,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.10+","For multi-GPU: CUDA 11.0+ or compatible GPU drivers","For TPU: torch-xla library and TPU access","For DeepSpeed backend: DeepSpeed 0.5.0+ installed separately","Environment variables: RANK, WORLD_SIZE, MASTER_ADDR, MASTER_PORT (for multi-node)","For FSDP: PyTorch 1.12+","For DeepSpeed: DeepSpeed package installed and configured","For Megatron: Megatron-LM fork installed","For TPU: torch-xla and TPU access"],"failure_modes":["Requires PyTorch training loop structure — incompatible with high-level frameworks (Trainer, Lightning) that manage loops internally","Abstraction adds ~5-10ms overhead per training step for distributed synchronization checks","No automatic hyperparameter tuning or learning rate scheduling — users must implement or integrate separately","Limited support for custom distributed algorithms requiring fine-grained communication control","Relies on correct environment variable setup — misconfigured RANK or WORLD_SIZE causes silent failures or hangs","Backend selection is deterministic but not always optimal — may choose DDP over FSDP for memory-constrained scenarios","Custom distributed algorithms requiring non-standard communication patterns must bypass auto-detection","TPU detection requires torch-xla to be installed; falls back to CPU if unavailable","DeepSpeed configuration is complex — automatic generation may not be optimal for all models","DeepSpeed requires separate installation and configuration","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.35,"ecosystem":0.46,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:05.295Z","last_scraped_at":"2026-05-03T15:20:15.343Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pypi-accelerate","compare_url":"https://unfragile.ai/compare?artifact=pypi-accelerate"}},"signature":"XSPliS8jfYv4/0gddUdC30Z0HLuKSHHOa1otgdzobVPP56ZJBYMpGZ4wB8DCCZnwtnXQF0QivWqAl+JfYsVZCA==","signedAt":"2026-06-20T20:01:43.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pypi-accelerate","artifact":"https://unfragile.ai/pypi-accelerate","verify":"https://unfragile.ai/api/v1/verify?slug=pypi-accelerate","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}