TensorDict

TensorDict is a batched, nested dict[str, Tensor] that behaves like a tensor.

Move it, slice it, reshape it, stack it, save it, compile it, or do arithmetic on it: every tensor leaf follows the same operation, and one shared batch_size keeps the structure honest.

TensorDict(batch_size=[32])
|-- obs:      Tensor[32, 128]
|-- action:   Tensor[32]
|-- reward:   Tensor[32]
`-- next:
    `-- obs:  Tensor[32, 128]

30-second demo

import torch
from tensordict import TensorDict

batch = TensorDict(
    {
        "obs": torch.randn(32, 128),
        "action": torch.randint(0, 4, (32,)),
        "reward": torch.randn(32),
        "next": {"obs": torch.randn(32, 128)},
    },
    batch_size=[32],
)

mini = batch[:8]                 # slices every leaf
device = "cuda" if torch.cuda.is_available() else "cpu"
on_device = batch.to(device)       # moves every leaf; non-blocking internally
scaled = batch * 0.5             # arithmetic on the whole structure
merged = batch + batch           # leaf-wise TensorDict arithmetic
stacked = torch.stack([batch, batch], 0)

print(mini.shape)                # torch.Size([8])
print(stacked.shape)             # torch.Size([2, 32])

The object remains a mapping, but the batch acts like a tensor. That is the point: write the operation once, apply it to every tensor that belongs to the same example, rollout, batch, parameter set, or dataset shard.

Why TensorDict

Plain dictionaries are flexible. TensorDict keeps that flexibility and adds the parts tensor programs need once the code gets serious.

With a plain `dict`	With `TensorDict`
Manually keep leading dimensions aligned	One `batch_size` validates the structure
Repeat `.to(device)` for every tensor	`td.to(device)` moves the full batch
Hand-roll slicing, stacking, reshaping	`td[:32]`, `torch.stack`, `td.reshape`
Manually recurse through nested state	Nested keys are first-class
Duplicate arithmetic over leaves	`td + td`, `td * scalar`, `td.abs()`
Invent checkpoint formats	`td.save`, `td.memmap`, `load_memmap`
Hope generic code keeps working	PyTorch-native APIs, `torch.compile` coverage

Use TensorDict when the unit of data is not one tensor anymore, but it should still move through your program like one tensor.

Performance is part of the API

TensorDict is not just syntax for recursive Python loops. Core paths are built for high-throughput PyTorch workloads:

Arithmetic dispatch: operations such as td + td, td * 0.5, td.abs() and in-place variants apply directly to leaves and use PyTorch foreach kernels where available.
Device and host transfers: D2H and H2D copies are dispatched across the full structure. TensorDict uses non-blocking leaf transfers internally when possible, so the common path is just td.to(device); pass non_blocking=False only when you need an explicitly synchronous transfer.
Shape operations without boilerplate: indexing, view, reshape, permute, unsqueeze, squeeze, flatten, unflatten, stack and cat operate on the batch structure rather than on hand-maintained lists of leaves.
Low-allocation workflows: lazy stacks, preallocation, memory mapping and inplace=True shape-changing operations help reduce peak memory in data-heavy pipelines.
Compile-aware internals: TensorDict is used in compiled training and RL loops, and the codebase carries dedicated torch.compile coverage for hot paths.

For deeper numbers, see the benchmark notes.

What is new in 0.14

TensorDict 0.14 adds new storage formats and autograd on a whole structure, and changes how module state and pickled memmap data are handled:

Zarr-backed PersistentTensorDict through td.to_zarr(), PersistentTensorDict.from_zarr() and TensorDict.from_schema(..., storage="zarr").
Portable single-file .tdz archives: td.save("x.tdz") and TensorDict.load_memmap("x.tdz"), plus pack_memmap / unpack_memmap to convert between memmap directories and archives.
TensorDict.backward() backpropagates through all differentiable leaves in a single autograd call.
Group-relative peers with group_dst / group_src for point-to-point send / recv.
Behavior changes: to_module() preserves existing parameter and buffer registrations by default (preserve_module_state=True; pass False for the previous replacement behavior), and copy_at_() uses its fast path by default (pass fast=False for the previous one).
Deprecation: calling load_memmap() without allow_pickle still loads pickled non-tensor data, with a FutureWarning; the default becomes allow_pickle=False in 0.15.

Patterns

One batch through the whole training step

TensorDict lets datasets, models and losses agree on one container instead of a long argument list.

for batch in dataloader:
    batch = batch.to(device)
    batch = model(batch)
    loss = loss_module(batch)

    loss.backward()
    optimizer.step()
    optimizer.zero_grad()

That loop can stay stable while the schema changes from classification to segmentation, RL rollouts, model-based prediction or LLM post-training batches.

Nested data without custom plumbing

td = TensorDict(
    {
        "agents": {
            "policy": torch.randn(64, 8),
            "value": torch.randn(64, 1),
        },
        "env": {
            "reward": torch.randn(64),
            "done": torch.zeros(64, dtype=torch.bool),
        },
    },
    batch_size=[64],
)

policy = td["agents", "policy"]
td["env", "reward"] = td["env", "reward"].clip(-1, 1)

Nested keys are part of the API, not an afterthought.

Functional modules and parameter sets

TensorDict can hold module parameters, swap them into modules, vectorize over ensembles and make model state explicit.

from tensordict import TensorDict

params = TensorDict.from_module(module)

with params.to_module(module, preserve_module_state=True):
    out = module(inputs)

This is the same foundation used by TorchRL modules and functional training utilities.

td = TensorDict({"tokens": tokens, "scores": scores}, batch_size=[n])
td.memmap("/path/to/private/batch")  # memory-map every leaf
reloaded = TensorDict.load_memmap("/path/to/private/batch")

Memory-mapped TensorDicts are useful for large offline datasets, replay buffers, inter-process handoff and checkpointed intermediate state.

Key features

Tensor-like collection ops: indexing, slicing, device casting, dtype casting, reshaping, stacking and concatenation. [tutorial]
Nested structures with tuple keys and predictable batch semantics. [tutorial]
Fast memory workflows: asynchronous transfers, memmap, consolidated tensors, lazy stacks and preallocation. [tutorial]
Functional programming with parameter TensorDicts, to_module and compatibility with torch.vmap. [tutorial]
@tensorclass: a tensor-aware dataclass for structured tensor objects. [tutorial]
Distributed and multiprocessed pipelines across workers, devices and machines. [doc]
Serialization and memory mapping for efficient checkpointing and dataset storage. [doc]

For a longer tour, start with GETTING_STARTED.md or the online documentation.

Installation

With pip:

pip install tensordict

With conda:

conda install -c conda-forge tensordict

Nightly builds:

pip install tensordict-nightly

From source with an existing PyTorch install:

Building from source compiles a C++ extension and needs CMake >= 3.22 and a C++20 compiler. The build requirements (pybind11, setuptools_scm) are installed by pip unless you pass --no-build-isolation. --no-deps also skips the runtime dependencies, so install them first:

pip install numpy cloudpickle packaging importlib_metadata orjson "pyvers>=0.2,<0.3"
pip install -e . --no-deps

(orjson is only listed as a dependency for Python < 3.13.)

If you use uv with PyTorch nightlies, keep torch pinned to the PyTorch wheel index or install TensorDict with --no-deps so the resolver does not replace your existing PyTorch build. Use one of:

# Option 1: skip dependency resolution (install the runtime dependencies first)
uv pip install -e . --no-deps
# Option 2: resolve against the PyTorch nightly index
uv pip install -e . --prerelease=allow -f "https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html"

Ecosystem

TensorDict started in reinforcement learning, where batches quickly become nested trajectories. It is now used anywhere tensor batches are structured data: RL rollouts, LLM post-training samples, robotics trajectories, simulation state, model parameters, checkpointed datasets and scientific pipelines.

Domain	Projects
Reinforcement Learning	TorchRL (PyTorch), DreamerV3-torch, Dreamer4, SkyRL
LLM Post-Training	verl, ROLL (Alibaba), LMFlow, LoongFlow (Baidu)
Robotics and Simulation	MuJoCo Playground (Google DeepMind), ProtoMotions (NVIDIA), holosoma (Amazon)
Physics and Scientific ML	PhysicsNeMo (NVIDIA)
Genomics	Medaka (Oxford Nanopore)

Citation

If you use TensorDict, please cite the TorchRL paper:

@misc{bou2023torchrl,
      title={TorchRL: A data-driven decision-making library for PyTorch},
      author={Albert Bou and Matteo Bettini and Sebastian Dittert and Vikash Kumar and Shagun Sodhani and Xiaomeng Yang and Gianni De Fabritiis and Vincent Moens},
      year={2023},
      eprint={2306.00577},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

License

TensorDict is licensed under the MIT License. See LICENSE for details.

Metadata

Release files for tensordict-nightly 2026.10.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distributions (wheels)

Table of built distributions (wheels) for tensordict-nightly 2026.10.8
File	Interpreter	ABI	Platform	Reset
tensordict_nightly-2026.10.8-cp314-cp314t-manylinux_2_28_aarch64.whl 609.3 kB Oct 8, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ ARM64	Details
tensordict_nightly-2026.10.8-cp314-cp314-win_amd64.whl 670.1 kB Oct 8, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
tensordict_nightly-2026.10.8-cp314-cp314-manylinux_2_28_aarch64.whl 608.1 kB Oct 8, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.28+ ARM64	Details
tensordict_nightly-2026.10.8-cp314-cp314-manylinux1_x86_64.whl 613.2 kB Oct 8, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.5+ x86-64	Details
tensordict_nightly-2026.10.8-cp314-cp314-macosx_11_0_universal2.whl 597.2 kB Oct 8, 2026	CPython 3.14	CPython 3.14	macOS 11.0+ universal2 (ARM64, x86-64)	Details
tensordict_nightly-2026.10.8-cp313-cp313-win_amd64.whl 667.9 kB Oct 8, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
tensordict_nightly-2026.10.8-cp313-cp313-manylinux_2_28_aarch64.whl 607.3 kB Oct 8, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.28+ ARM64	Details
tensordict_nightly-2026.10.8-cp313-cp313-manylinux1_x86_64.whl 613.2 kB Oct 8, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.5+ x86-64	Details
tensordict_nightly-2026.10.8-cp313-cp313-macosx_11_0_universal2.whl 597.1 kB Oct 8, 2026	CPython 3.13	CPython 3.13	macOS 11.0+ universal2 (ARM64, x86-64)	Details
tensordict_nightly-2026.10.8-cp312-cp312-win_amd64.whl 667.9 kB Oct 8, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
tensordict_nightly-2026.10.8-cp312-cp312-manylinux_2_28_aarch64.whl 607.1 kB Oct 8, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.28+ ARM64	Details
tensordict_nightly-2026.10.8-cp312-cp312-manylinux1_x86_64.whl 613.0 kB Oct 8, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.5+ x86-64	Details
tensordict_nightly-2026.10.8-cp312-cp312-macosx_11_0_universal2.whl 597.0 kB Oct 8, 2026	CPython 3.12	CPython 3.12	macOS 11.0+ universal2 (ARM64, x86-64)	Details
tensordict_nightly-2026.10.8-cp311-cp311-win_amd64.whl 666.8 kB Oct 8, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
tensordict_nightly-2026.10.8-cp311-cp311-manylinux_2_28_aarch64.whl 607.5 kB Oct 8, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.28+ ARM64	Details
tensordict_nightly-2026.10.8-cp311-cp311-manylinux1_x86_64.whl 613.1 kB Oct 8, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.5+ x86-64	Details
tensordict_nightly-2026.10.8-cp311-cp311-macosx_11_0_universal2.whl 596.3 kB Oct 8, 2026	CPython 3.11	CPython 3.11	macOS 11.0+ universal2 (ARM64, x86-64)	Details
tensordict_nightly-2026.10.8-cp310-cp310-win_amd64.whl 664.5 kB Oct 8, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
tensordict_nightly-2026.10.8-cp310-cp310-manylinux1_x86_64.whl 611.4 kB Oct 8, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.5+ x86-64	Details
tensordict_nightly-2026.10.8-cp310-cp310-macosx_11_0_universal2.whl 594.4 kB Oct 8, 2026	CPython 3.10	CPython 3.10	macOS 11.0+ universal2 (ARM64, x86-64)	Details

Total release size: 12.4 MB

Release files / tensordict_nightly-2026.10.8-cp314-cp314t-manylinux_2_28_aarch64.whl

Download URL	tensordict_nightly-2026.10.8-cp314-cp314t-manylinux_2_28_aarch64.whl
Size	609.3 kB
Tags	CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`40b98800b860e2e46dd480f2839ab0a6cced28f6fb9ff8e72dc0aafe5d284eb4`
BLAKE2b-256 checksum How to use checksums	`14221478b8372965cea3de9376bb277e3e1a17740e657eaacde7abac7ecc29a2`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.12`

Release files / tensordict_nightly-2026.10.8-cp314-cp314-win_amd64.whl

Download URL	tensordict_nightly-2026.10.8-cp314-cp314-win_amd64.whl
Size	670.1 kB
Tags	CPython 3.14 Windows x86-64
SHA-256 checksum How to use checksums	`713a8014de55692be6370b08ca3ba82ab7fb861619a22c3c9860f3417c9f2d35`
BLAKE2b-256 checksum How to use checksums	`7eb1f8054d1ee37e230528dc968dac827066dead27b0a70e89ec4c4f09e198ff`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp314-cp314-manylinux_2_28_aarch64.whl

Download URL	tensordict_nightly-2026.10.8-cp314-cp314-manylinux_2_28_aarch64.whl
Size	608.1 kB
Tags	CPython 3.14 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`7aaf1a38616321d6ec2f3e860744b1173e6a2445fd2b923283dced2d4c9ea3f1`
BLAKE2b-256 checksum How to use checksums	`96c31fb2ed59a5cd135125369129f9325a4a6d847808ebdedecbb9bac84bd037`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.12`

Release files / tensordict_nightly-2026.10.8-cp314-cp314-manylinux1_x86_64.whl

Download URL	tensordict_nightly-2026.10.8-cp314-cp314-manylinux1_x86_64.whl
Size	613.2 kB
Tags	CPython 3.14 Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`c6f69dd281955bf05b2512b76d67ee5b42f6a7508b19d8229355b3d58b62e586`
BLAKE2b-256 checksum How to use checksums	`8518562892e04a793e555f9b45e730bfd60069983e8b8e4760966c04d928c309`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.8`

Release files / tensordict_nightly-2026.10.8-cp314-cp314-macosx_11_0_universal2.whl

Download URL	tensordict_nightly-2026.10.8-cp314-cp314-macosx_11_0_universal2.whl
Size	597.2 kB
Tags	CPython 3.14 macOS 11.0+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`5cf126496264479197bae7bae2b6aa3d1d3d110c829e758e3131835349cc78dc`
BLAKE2b-256 checksum How to use checksums	`92ed33f18bb91745206250a6ea81b2d781ce3b3e55c68214e377afb5f5c2769a`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.14.7`

Release files / tensordict_nightly-2026.10.8-cp313-cp313-win_amd64.whl

Download URL	tensordict_nightly-2026.10.8-cp313-cp313-win_amd64.whl
Size	667.9 kB
Tags	CPython 3.13 Windows x86-64
SHA-256 checksum How to use checksums	`954cdb4cc90564f07fa29ff8461665af65626f546b6ac84e916ea00144de604b`
BLAKE2b-256 checksum How to use checksums	`97616b598d54af428604ed20fb827684c80668d483f3ce18db49b60a90723193`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp313-cp313-manylinux_2_28_aarch64.whl

Download URL	tensordict_nightly-2026.10.8-cp313-cp313-manylinux_2_28_aarch64.whl
Size	607.3 kB
Tags	CPython 3.13 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`8d7abdfc90fecb9890119e80cbb81ada4a640c44aff567340a1ecd6aa82db72c`
BLAKE2b-256 checksum How to use checksums	`d7c523f09658c73bdccdf24a1c08d1c04ce72c990f07d61bbac3ef36c61747fb`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.12`

Release files / tensordict_nightly-2026.10.8-cp313-cp313-manylinux1_x86_64.whl

Download URL	tensordict_nightly-2026.10.8-cp313-cp313-manylinux1_x86_64.whl
Size	613.2 kB
Tags	CPython 3.13 Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`68120c78625794ce5919966ade4b8db4a157cbe0062b7ff5f9066316b486a692`
BLAKE2b-256 checksum How to use checksums	`474127a10dc156b662e2cb6b47c14c84494f792a5e93ef016419ec7004ddec87`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.16`

Release files / tensordict_nightly-2026.10.8-cp313-cp313-macosx_11_0_universal2.whl

Download URL	tensordict_nightly-2026.10.8-cp313-cp313-macosx_11_0_universal2.whl
Size	597.1 kB
Tags	CPython 3.13 macOS 11.0+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`7e0d3496a2de92c527a67be41219be2bdb6f36b41e6605af547c6c3eaa97ceee`
BLAKE2b-256 checksum How to use checksums	`a762d192ac35b530f9b3f920c8b55e4ad624c009a5a8438a535da0eead730676`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.15`

Release files / tensordict_nightly-2026.10.8-cp312-cp312-win_amd64.whl

Download URL	tensordict_nightly-2026.10.8-cp312-cp312-win_amd64.whl
Size	667.9 kB
Tags	CPython 3.12 Windows x86-64
SHA-256 checksum How to use checksums	`d04e8da842a5e52d552b2ed534363311a8f00b118b11d6c33c2b6c50d75c1138`
BLAKE2b-256 checksum How to use checksums	`f9b962c6a1d09b0fb175bf04a32c7ab9d9eb7a80be04653b16a125b485951054`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp312-cp312-manylinux_2_28_aarch64.whl

Download URL	tensordict_nightly-2026.10.8-cp312-cp312-manylinux_2_28_aarch64.whl
Size	607.1 kB
Tags	CPython 3.12 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`abfb52d190c55691de423c2150952bbf85519bcaefa03a9fe4d3e6a98c593f81`
BLAKE2b-256 checksum How to use checksums	`a36fa93f83d21f09975638854f7e67533ce43a0b13c836a470086888a32f571f`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.12`

Release files / tensordict_nightly-2026.10.8-cp312-cp312-manylinux1_x86_64.whl

Download URL	tensordict_nightly-2026.10.8-cp312-cp312-manylinux1_x86_64.whl
Size	613.0 kB
Tags	CPython 3.12 Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`1b7434ff01e3f2a01104b423f50be7d63ce6a3346fa3e00832de8fbf749c744e`
BLAKE2b-256 checksum How to use checksums	`e6ab44c8882406b35cc30b143d9e1eebaec4177cd9839d60c9c808598f86582f`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.14`

Release files / tensordict_nightly-2026.10.8-cp312-cp312-macosx_11_0_universal2.whl

Download URL	tensordict_nightly-2026.10.8-cp312-cp312-macosx_11_0_universal2.whl
Size	597.0 kB
Tags	CPython 3.12 macOS 11.0+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`095e0f79b43443746eede27b11845c9a9225fa01d3c67b5271b07e2801b98f59`
BLAKE2b-256 checksum How to use checksums	`ae00fa61423d0788f8daaed17158e3794656b4f3406e9549161e697b4db539bd`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp311-cp311-win_amd64.whl

Download URL	tensordict_nightly-2026.10.8-cp311-cp311-win_amd64.whl
Size	666.8 kB
Tags	CPython 3.11 Windows x86-64
SHA-256 checksum How to use checksums	`74641ec9b7657b36ba9b97c31248ee53dc799779b130eef294db8edc17d0e09c`
BLAKE2b-256 checksum How to use checksums	`acc26fa0ca5ba7ed79760ef22ddb408b5cd0003238a10a2740f68787f4551f1b`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp311-cp311-manylinux_2_28_aarch64.whl

Download URL	tensordict_nightly-2026.10.8-cp311-cp311-manylinux_2_28_aarch64.whl
Size	607.5 kB
Tags	CPython 3.11 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`eafc6ffc68bdaf5aa9f32e0e0e438cd50884036200a8122ce925e808d9409cb3`
BLAKE2b-256 checksum How to use checksums	`bee9912b408b970a99af10a249c6935b296450195bea90e7bed189f2759336bd`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.12`

Release files / tensordict_nightly-2026.10.8-cp311-cp311-manylinux1_x86_64.whl

Download URL	tensordict_nightly-2026.10.8-cp311-cp311-manylinux1_x86_64.whl
Size	613.1 kB
Tags	CPython 3.11 Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`49d24ab8bb93a0ae755b8336b17ba6853829d731d8cced74d393a9ecf3f4adc8`
BLAKE2b-256 checksum How to use checksums	`b7e940dfbb6a587d0fe01ecc6cddab8352c2b39bd1412c1453a0c91ce9b6bc15`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.17`

Release files / tensordict_nightly-2026.10.8-cp311-cp311-macosx_11_0_universal2.whl

Download URL	tensordict_nightly-2026.10.8-cp311-cp311-macosx_11_0_universal2.whl
Size	596.3 kB
Tags	CPython 3.11 macOS 11.0+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`28920b7bf1a2f4625268fcaac48ecb0bcafe4a648612605741a6bda58ebfbd6b`
BLAKE2b-256 checksum How to use checksums	`94d32aac803d43205309c020517207e391d6ba1db8775128f01377c127e2ec23`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.11.9`

Release files / tensordict_nightly-2026.10.8-cp310-cp310-win_amd64.whl

Download URL	tensordict_nightly-2026.10.8-cp310-cp310-win_amd64.whl
Size	664.5 kB
Tags	CPython 3.10 Windows x86-64
SHA-256 checksum How to use checksums	`bc3446cec4ef3592a9c89830add2e87bab95699f49cad29889650d8f6143c9a4`
BLAKE2b-256 checksum How to use checksums	`e8ab15a9bd73c4829ff6a3410a62b730c43a213bdb56e35721e25d7e5e7ce2d8`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.12.10`

Release files / tensordict_nightly-2026.10.8-cp310-cp310-manylinux1_x86_64.whl

Download URL	tensordict_nightly-2026.10.8-cp310-cp310-manylinux1_x86_64.whl
Size	611.4 kB
Tags	CPython 3.10 Linux glibc 2.5+ x86-64
SHA-256 checksum How to use checksums	`4c21c11a9bc2d71b7a9c33ca40f32be70963106cdc1b83b40f40c41048ad2fd3`
BLAKE2b-256 checksum How to use checksums	`0a7d284b3d3236abe1bdfb6618c9e7ce9fc6eb1dc6ac588399146a6343abc09f`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.22`

Release files / tensordict_nightly-2026.10.8-cp310-cp310-macosx_11_0_universal2.whl

Download URL	tensordict_nightly-2026.10.8-cp310-cp310-macosx_11_0_universal2.whl
Size	594.4 kB
Tags	CPython 3.10 macOS 11.0+ universal2 (ARM64, x86-64)
SHA-256 checksum How to use checksums	`d431e5bab5c6c31cbae4a8be3df549cc951f037a117220511309056063ee172d`
BLAKE2b-256 checksum How to use checksums	`95b1fd72a19df1269da5f420f7c66c79546fd61f68e8238c05469fff83920a87`
Upload date	Oct 8, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.10.11`