Awesome
light-the-torch
light-the-torch
is a small utility that wraps pip
to ease the installation process
for PyTorch distributions like torch
, torchvision
, torchaudio
, and so on as well
as third-party packages that depend on them. It auto-detects compatible CUDA versions
from the local setup and installs the correct PyTorch binaries without user
interference.
- Why do I need it?
- How do I install it?
- How do I use it?
- How does it work?
- Is it safe?
- How do I contribute?
Why do I need it?
PyTorch distributions like torch
, torchvision
, torchaudio
, and so on are fully
pip install
'able, but PyPI, the default pip
search index, has some limitations:
- PyPI regularly only allows binaries up to a size of approximately 60 MB. One can request a file size limit increase (and the PyTorch team probably does that for every release), but it is still not enough: although PyTorch has pre-built binaries for Windows with CUDA, they cannot be installed through PyPI due to their size.
- PyTorch uses local version specifiers to indicate for which computation backend the
binary was compiled, for example
torch==1.11.0+cpu
. Unfortunately, local specifiers are not allowed on PyPI. Thus, only the binaries compiled with one CUDA version are uploaded without an indication of the CUDA version. If you do not have a CUDA capable GPU, downloading this is only a waste of bandwidth and disk capacity. If on the other hand your NVIDIA driver version simply doesn't support the CUDA version the binary was compiled with, you can't use any of the GPU features.
To overcome this, PyTorch also hosts most1 binaries
on their own package indices. To access PyTorch's
package indices, you can still use pip install
, but some
additional options are needed:
pip install torch --index-url https://download.pytorch.org/whl/cu118
While this is certainly an improvement, it still has a few downsides:
- You need to know what computation backend, e.g. CUDA 11.8 (
cu118
), is supported on your local machine. This can be quite challenging for new users and at least tedious for more experienced ones. - Besides the stable binaries, PyTorch also offers nightly and test ones. To install
them, you need a different
--index-url
for each.
If any of these points don't sound appealing to you, and you just want to have the same
user experience as pip install
for PyTorch distributions, light-the-torch
was made
for you.
How do I install it?
Installing light-the-torch
is as easy as
pip install light-the-torch
Since it depends on pip
and it might be upgraded during installation,
Windows users should
install it with
py -m pip install light-the-torch
How do I use it?
After light-the-torch
is installed you can use its CLI interface ltt
as drop-in
replacement for pip
:
ltt install torch
In fact, ltt
is pip
with a few added options:
-
By default,
ltt
uses the local NVIDIA driver version to select the correct binary for you. You can pass the--pytorch-computation-backend
option to manually specify the computation backend you want to use:ltt install --pytorch-computation-backend=cu121 torch torchvision torchaudio
Borrowing from the mutex packages that PyTorch provides for
conda
installations,--cpuonly
is available as shorthand for--pytorch-computation-backend=cpu
.In addition, the computation backend to be installed can also be set through the
LTT_PYTORCH_COMPUTATION_BACKEND
environment variable. It will only be honored in case no CLI option for the computation backend is specified. -
By default,
ltt
installs stable PyTorch binaries. To install binaries from the nightly or test channels pass the--pytorch-channel
option:ltt install --pytorch-channel=nightly torch torchvision torchaudio
If
--pytorch-channel
is not passed, usingpip
's builtin--pre
option implies--pytorch-channel=test
.
Of course, you are not limited to install only PyTorch distributions. Everything shown above also works if you install packages that depend on PyTorch:
ltt install --pytorch-computation-backend=cpu --pytorch-channel=nightly pystiche
How does it work?
The authors of pip
do not condone the use of pip
internals as they might break
without warning. As a results of this, pip
has no capability for plugins to hook into
specific tasks.
light-the-torch
works by monkey-patching pip
internals at runtime:
- While searching for a download link for a PyTorch distribution,
light-the-torch
replaces the default search index with an official PyTorch download link. This is equivalent to callingpip install
with the--index-url
option only for PyTorch distributions. - While evaluating possible PyTorch installation candidates,
light-the-torch
culls binaries incompatible with the hardware.
Is it safe?
A project as large as PyTorch is attractive for malicious actors given the large user
base. For example in December 2022, PyTorch was hit by a
supply chain attack that
potentially extracted user information. The PyTorch team mitigated the attack as soon as
it was detected by temporarily hosting all third party dependencies on their own
indices. With that,
pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
wouldn't pull
anything from PyPI and thus avoiding malicious packages placed there. Ultimately, this
became the permanent solution and the official installation instructions now use
--index-url
and thus preventing installing anything not hosted on their indices.
However, due to light-the-torch
's index patching, this mitigation was initially
completely circumvented since only PyTorch distributions would have been installed from
the PyTorch indices. Since version 0.7.0
, light-the-torch
will only pull third-party
dependencies from PyPI in case they are specifically requested and pinned. For example
ltt install --pytorch-channel=nightly torch
and
ltt install --pytorch-channel=nightly torch sympy
will install everything from the
PyTorch indices. However, if you pin a third party dependency, e.g.
ltt install --pytorch-channel=nightly torch sympy==1.11.1
, it will be pulled from PyPI
regardless of whether the version matches the one on the PyTorch index.
In summary, light-the-torch
is usually as safe as the regular PyTorch installation
instructions. However, attacks on the supply chain can lead to situations where
light-the-torch
circumvents mitigations done by the PyTorch team. Unfortunately,
light-the-torch
is not officially supported by PyTorch and thus also not tested by
them.
How do I contribute?
Thanks a lot for your interest to contribute to light-the-torch
! All contributions are
appreciated, be it code or not. Especially in a project like this, we rely on user
reports for edge cases we didn't anticipate. Please feel free to
open an issue if you encounter
anything that you think should be working but doesn't.
If you want to contribute code, check out our contributing guidelines to learn more about the workflow.
Footnotes
-
Some distributions are not compiled against a specific computation backend and thus hosting them on PyPI is sufficient since they work in every environment. ↩