{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/8"}],"enrichment":{"capability":"TensorDict is a batched, nested dictionary container for PyTorch tensors that behaves like a single tensor\u2014enabling slicing, reshaping, device transfer, and arithmetic operations across all leaves simultaneously while maintaining a shared batch size.","skillfed_tags":["pytorch-ecosystem","tensor-containers","batch-operations"],"use_cases":["Training loops where a single batch object flows through dataset, model, and loss without unpacking and repacking tensors.","Reinforcement learning environments that need to track nested state (agent policy, value, environment reward, done flags) with synchronized batch operations.","Offline datasets and replay buffers that benefit from memory-mapped storage and efficient slicing across many tensor leaves.","Functional training with parameter ensembles, where module weights are held in a TensorDict and swapped in and out of modules.","Multi-agent or hierarchical systems with nested data structures (e.g., agents.policy, env.reward) that need uniform batch semantics."],"what_it_does":"TensorDict is a container that wraps multiple PyTorch tensors into a single batched, nested structure while preserving tensor semantics. Instead of manually managing a collection of tensors with aligned batch dimensions, you create a TensorDict once and then slice, reshape, move to device, or perform arithmetic on the entire structure as if it were a single tensor. Every operation applies uniformly to all leaf tensors, keeping the batch dimension honest.\n\nIt is designed for data-heavy PyTorch workflows\u2014training loops, reinforcement learning rollouts, parameter ensembles, and offline datasets\u2014where the natural unit of data is not one tensor but a structured collection. The package includes memory-mapped I/O, lazy stacking, functional parameter handling, and torch.compile support to reduce boilerplate and improve performance in large systems.","worth_installing":"Yes. TensorDict is actively maintained, has no known vulnerabilities, and is widely used in PyTorch RL and training systems. Install the nightly release if you are comfortable with frequent updates; otherwise, wait for a stable release. It is worth installing if you work with structured tensor batches and want to eliminate manual dimension tracking and repetitive tensor operations."},"id":"tensordict-nightly","links":{"html":"https://skillfed.io/packages/tensordict-nightly","md":"https://skillfed.io/packages/tensordict-nightly.md","pypi":"https://pypi.org/project/tensordict-nightly/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-14","license_spdx":null,"license_treatment":"permissive","name":"tensordict-nightly","python_support":"supports_current","summary":"TensorDict is a pytorch dedicated tensor container."},"popularity":{"monthly_downloads":188521,"position":9942,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.8.14"}