Summary:
The aim is being able to inspect a container image and determine immediately
which version of pytorch it contains.
Closes https://github.com/pytorch/pytorch/issues/48324
Signed-off-by: Felix Abecassis <fabecassis@nvidia.com>
seemethere PTAL.
As you requested in https://github.com/pytorch/pytorch/issues/48324#issuecomment-754237156, I'm submitting the patch. But I could only do limited testing as I'm not sure these Makefile/Dockerfile are used for pushing the Docker Hub images (since the Makefile tags the image with a `v` prefix for the version, as in: `pytorch:v1.7.1`, but Docker Hub images don't have this prefix).
Also on the master branch we currently have the following:
```
$ git describe --tags
v1.4.0a0-11171-g68a6e46379
```
So it's a little off, but it behaves as expected on the `release/1.7` branch.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50154
Reviewed By: walterddr
Differential Revision: D25828491
Pulled By: seemethere
fbshipit-source-id: 500ec96cb5f5da1321610002d5e3678f4b0b94b5
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45438
Adds torchelastic (as well as its dependencies) to the official docker
images
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Test Plan: Imported from OSS
Reviewed By: tierex
Differential Revision: D23963787
Pulled By: seemethere
fbshipit-source-id: 54ebb4b9c50699e543f264975dadf99badf55753
Summary:
Although PyTorch already supports CUDA 11, the Dockerfile still relies on CUDA 10. This pull request upgrades all the necessary versions such that recent NVIDIA GPUs like A100 can be used.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45071
Reviewed By: ezyang
Differential Revision: D23873224
Pulled By: seemethere
fbshipit-source-id: 822c25f183dcc3b4c5b780c00cd37744d34c6e00
Summary:
Miniconda repo has moved from continuum.io to anaconda.com
Also we should be specific about cudatoolkit version so that it installs
the right CUDA version.
Resolves https://github.com/pytorch/pytorch/issues/37047
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/37186
Differential Revision: D21443147
Pulled By: seemethere
fbshipit-source-id: 856718822bdd3ce51bbc6e59b0609fe6af77bd79
Summary:
## Commit Message:
Refactors Dockerfile to be as parallel as possible with caching and adds a new Makefile to build said Dockerfile.
Also updated the README.md to reflect the changes as well as updated some of the verbage around running our latest Docker images.
Adds the new Dockerfile process to our CircleCI workflows
## How to build:
Building the new images is pretty simple, just requires `docker` > 18.06 since the new build process relies on `buildkit` caching and multi-stage build resolving.
### Development images
For `runtime` images:
```
make -f docker.Makefile runtime-image
```
For `devel` images:
```
make -f docker.Makefile devel-image
```
Builds are tagged as follows:
```bash
docker.io/${docker_user:-whoami}/pytorch:$(git describe --tags)-${image_type}
```
Example:
```
docker.io/seemethere/pytorch:v1.4.0a0-2225-g9eba97b61d-runtime
```
### Official images
Official images are the ones hosted on [`docker.io/pytorch/pytorch`](https://hub.docker.com/r/pytorch/pytorch)
To do official images builds you can simply add set the `BUILD_TYPE` variable to `official` and it will do the correct build without building the local binaries:
Example:
```
make -f docker.Makefile BUILD_TYPE=official runtime-image
```
## How to push:
Pushing is also super simple (And will automatically tag the right thing based off of the git tag):
```
make -f docker.Makefile runtime-push
make -f docker.Makefile devel-push
```
Signed-off-by: Eli Uriegas <eliuriegas@fb.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/32515
Differential Revision: D19558619
Pulled By: seemethere
fbshipit-source-id: a06b25cd39ae9890751a60f8f36739ad6ab9ac99
This is the first of three PRs that #5537 will be split into.
This PR adds mkl headers to included files, and provides helper functions for MKL fft and cuFFT.
In particular, on POSIX, headers are using mkl-include from conda, and on Windows, it is from a new file @yf225 and I made and uploaded to s3.
* add mkl-include to required packages
* include MKL headers; add AT_MKL_ENABLED flag; add a method to query MKL availability
* Add MKL and CUFFT helpers
