## Summary
- Added --no-install-recommends flag to all apt-get install commands to reduce unnecessary dependencies
- Added apt-get clean after package installations to remove package cache and reduce image size
- Combined multiple apt commands into single instructions to reduce Docker image layers
## Test plan
Test by building the devcontainer and verifying functionality while ensuring reduced image size
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152882
Approved by: https://github.com/cyyever, https://github.com/atalman, https://github.com/Skylion007
## Summary
- Replaced miniconda base image with base Ubuntu 22.04 image
- Installed Python and required dependencies using apt
- Replaced conda-based CUDA installation with apt-based version
- Updated paths in install-dev-tools.sh to reflect the new non-conda environment
- Removed conda-specific files and added requirements.txt for Python dependencies
## Test plan
Test by building and running the devcontainer in VS Code with both CPU and CUDA configurations
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152881
Approved by: https://github.com/atalman
## Summary
- Changed the devcontainer context path from '../..' to './' for both CPU and CUDA configurations
- Added workspace mount configuration to properly mount the repository in the container
- Added containerEnv to disable implicit --user pip flag
## Test plan
Test by building and running the devcontainer in VS Code
Pull Request resolved: https://github.com/pytorch/pytorch/pull/152880
Approved by: https://github.com/atalman
Add a new option `--cuda` to `tools/nightly.py` to pull the nightly packages with CUDA support.
```bash
# installs pytorch-nightly with cpuonly
tools/nightly.py pull
# The following only available on Linux and Windows
# installs pytorch-nightly with latest CUDA we support
tools/nightly.py pull --cuda
# installs pytorch-nightly with CUDA 12.1
tools/nightly.py pull --cuda 12.1
```
Also add targets in `Makefile` and instructions in constribution guidelines.
```bash
# setup conda environment with pytorch-nightly
make setup-env
# setup conda environment with pytorch-nightly with CUDA support
make setup-env-cuda
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/131133
Approved by: https://github.com/ezyang
Without this the build will freeze with prompt:
Proceed ([y]/n)?
I'm using rootless podman in vscode instead of docker but I think it should not affect this.
..or does conda somehow detect Docker but not Podman? Anyway, this should not break anything.
Btw, I also had to uncomment the line: "remoteUser": "root" in devcontainer.json to finish the post installation properly but I guess there might be other workarounds - and perhaps you don't want to run as root if your container has root privileges.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/121128
Approved by: https://github.com/drisspg
Building with CUDA in dev container leads to error: `cannot find -lcudart_static`. This is because the libraries are under a custom CUDA_HOME, and `ld` cannot find it.
Updating the `LDFLAGS` environment variable works.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/108766
Approved by: https://github.com/drisspg
# Summary
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### <samp>🤖 Generated by Copilot at 293ded1</samp>
This pull request adds support for using Visual Studio Code Remote - Containers extension with the pytorch project. It adds a `.devcontainer` folder with a `devcontainer.json` file, a `Dockerfile`, and a `noop.txt` file that configure and create a dev container with Anaconda and Python 3.
<!--
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### <samp>🤖 Generated by Copilot at d6b9cd7</samp>
> _`devcontainer.json`_
> _Configures PyTorch containers_
> _For CPU or GPU_
## Related to:
https://github.com/pytorch/pytorch/issues/92838
Pull Request resolved: https://github.com/pytorch/pytorch/pull/98252
Approved by: https://github.com/ZainRizvi
