tensordict-nightly
TensorDict is a pytorch dedicated tensor container.
Decision gist · record as of 2026-08-14
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.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires PyTorch (torch) and modern Python (3.10+); compiled wheels available for common platforms.
- Nightly release with active maintenance (last commit 2026-08-14) and broad platform coverage across Python 3.10–3.14 on macOS, Linux, and Windows.
- Medium install friction due to compiled wheels; no known vulnerabilities.
License · maintenance · safety
BSD (permissive) — BSD permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 1,034 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 188,521 downloads/mo, #9,942 on PyPI
Alternatives
Verify before relying
pip install tensordict-nightly
import torch
from tensordict import TensorDict
batch = TensorDict(
{"obs": torch.randn(32, 128), "action": torch.randint(0, 4, (32,))},
batch_size=[32],
)
mini = batch[:8] # slices all leaves
on_device = batch.to("cuda") # moves all leaves- Whether nightly releases receive the same stability guarantees as stable releases
- Performance overhead of TensorDict operations compared to manual tensor handling in specific workloads
What it is and 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.
It is designed for data-heavy PyTorch workflows—training loops, reinforcement learning rollouts, parameter ensembles, and offline datasets—where 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.
Use it for
- 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.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
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.
Install
tensordict-nightly on PyPI
Before you install
Nightly release with active maintenance (last commit 2026-08-14) and broad platform coverage across Python 3.10–3.14 on macOS, Linux, and Windows. Medium install friction due to compiled wheels; no known vulnerabilities.
Requires PyTorch (torch) and modern Python (3.10+); compiled wheels available for common platforms.
License in practice
BSD permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install tensordict-nightly
import torch
from tensordict import TensorDict
batch = TensorDict(
{"obs": torch.randn(32, 128), "action": torch.randint(0, 4, (32,))},
batch_size=[32],
)
mini = batch[:8] # slices all leaves
on_device = batch.to("cuda") # moves all leavesVerify before relying
- Whether nightly releases receive the same stability guarantees as stable releases
- Performance overhead of TensorDict operations compared to manual tensor handling in specific workloads
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 7 packagestorchnumpycloudpicklepackagingimportlib_metadataorjsonpyvers |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 188,521 / month, #9,942 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: tensordict_nightly-2026.8.14-cp310-cp310-macosx_11_0_universal2.whl; tensordict_nightly-2026.8.14-cp310-cp310-manylinux1_x86_64.whl; tensordict_nightly-2026.8.14-cp310-cp310-manylinux_2_28_aarch64.whl; tensordict_nightly-2026.8.14-cp310-cp310-win_amd64.whl; tensordict_nightly-2026.8.14-cp311-cp311-macosx_11_0_universal2.whl; tensordict_nightly-2026.8.14-cp311-cp311-manylinux1_x86_64.whl; tensordict_nightly-2026.8.14-cp311-cp311-manylinux_2_28_aarch64.whl; tensordict_nightly-2026.8.14-cp311-cp311-win_amd64.whl; tensordict_nightly-2026.8.14-cp312-cp312-macosx_11_0_universal2.whl; tensordict_nightly-2026.8.14-cp312-cp312-manylinux1_x86_64.whl; tensordict_nightly-2026.8.14-cp312-cp312-manylinux_2_28_aarch64.whl; tensordict_nightly-2026.8.14-cp312-cp312-win_amd64.whl; tensordict_nightly-2026.8.14-cp313-cp313-macosx_11_0_universal2.whl; tensordict_nightly-2026.8.14-cp313-cp313-manylinux1_x86_64.whl; tensordict_nightly-2026.8.14-cp313-cp313-manylinux_2_28_aarch64.whl; tensordict_nightly-2026.8.14-cp313-cp313-win_amd64.whl; tensordict_nightly-2026.8.14-cp314-cp314-macosx_11_0_universal2.whl; tensordict_nightly-2026.8.14-cp314-cp314-manylinux1_x86_64.whl; tensordict_nightly-2026.8.14-cp314-cp314-manylinux_2_28_aarch64.whl; tensordict_nightly-2026.8.14-cp314-cp314t-manylinux_2_28_aarch64.whl
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See also tensordict · docarray · torchrl · torch · tensorly · rotary-embedding-torch · einops · linear-operator · torchao · pytorch-forecasting