pytorch3d/.circleci/regenerate.py
Jeremy Reizenstein d049cd2e01 PyTorch 1.10 + CUDA 11.1 builds
Summary: Although the PyTorch website, which describes the current version 1.10, suggests CUDA 10.2 and 11.3 are supported, it would appear that we need to include builds for CUDA 11.1 to avoid surprises. This is because these builds are on anaconda, and this combination is used on Google Colab.

Reviewed By: nikhilaravi

Differential Revision: D33063932

fbshipit-source-id: 1b22d1f06e22bd18fb53ceecb58e78ac6a5d1693
2021-12-13 10:11:00 -08:00

168 lines
4.5 KiB
Python
Executable File

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.
"""
This script is adapted from the torchvision one.
"""
import os.path
import jinja2
import yaml
# The CUDA versions which have pytorch conda packages available for linux for each
# version of pytorch.
# Pytorch 1.4 also supports cuda 10.0 but we no longer build for cuda 10.0 at all.
CONDA_CUDA_VERSIONS = {
"1.6.0": ["cu92", "cu101", "cu102"],
"1.7.0": ["cu101", "cu102", "cu110"],
"1.7.1": ["cu101", "cu102", "cu110"],
"1.8.0": ["cu101", "cu102", "cu111"],
"1.8.1": ["cu101", "cu102", "cu111"],
"1.9.0": ["cu102", "cu111"],
"1.9.1": ["cu102", "cu111"],
"1.10.0": ["cu102", "cu111", "cu113"],
}
def conda_docker_image_for_cuda(cuda_version):
if cuda_version == "cu113":
return "pytorch/conda-builder:cuda113"
return None
def pytorch_versions_for_python(python_version):
if python_version in ["3.6", "3.7", "3.8"]:
return list(CONDA_CUDA_VERSIONS)
pytorch_without_py39 = ["1.4", "1.5.0", "1.5.1", "1.6.0", "1.7.0"]
return [i for i in CONDA_CUDA_VERSIONS if i not in pytorch_without_py39]
def workflows(prefix="", filter_branch=None, upload=False, indentation=6):
w = []
for btype in ["conda"]:
for python_version in ["3.6", "3.7", "3.8", "3.9"]:
for pytorch_version in pytorch_versions_for_python(python_version):
for cu_version in CONDA_CUDA_VERSIONS[pytorch_version]:
w += workflow_pair(
btype=btype,
python_version=python_version,
pytorch_version=pytorch_version,
cu_version=cu_version,
prefix=prefix,
upload=upload,
filter_branch=filter_branch,
)
return indent(indentation, w)
def workflow_pair(
*,
btype,
python_version,
pytorch_version,
cu_version,
prefix="",
upload=False,
filter_branch,
):
w = []
py = python_version.replace(".", "")
pyt = pytorch_version.replace(".", "")
base_workflow_name = f"{prefix}linux_{btype}_py{py}_{cu_version}_pyt{pyt}"
w.append(
generate_base_workflow(
base_workflow_name=base_workflow_name,
python_version=python_version,
pytorch_version=pytorch_version,
cu_version=cu_version,
btype=btype,
filter_branch=filter_branch,
)
)
if upload:
w.append(
generate_upload_workflow(
base_workflow_name=base_workflow_name,
btype=btype,
cu_version=cu_version,
filter_branch=filter_branch,
)
)
return w
def generate_base_workflow(
*,
base_workflow_name,
python_version,
cu_version,
pytorch_version,
btype,
filter_branch=None,
):
d = {
"name": base_workflow_name,
"python_version": python_version,
"cu_version": cu_version,
"pytorch_version": pytorch_version,
"context": "DOCKERHUB_TOKEN",
}
conda_docker_image = conda_docker_image_for_cuda(cu_version)
if conda_docker_image is not None:
d["conda_docker_image"] = conda_docker_image
if filter_branch is not None:
d["filters"] = {"branches": {"only": filter_branch}}
return {f"binary_linux_{btype}": d}
def generate_upload_workflow(*, base_workflow_name, btype, cu_version, filter_branch):
d = {
"name": f"{base_workflow_name}_upload",
"context": "org-member",
"requires": [base_workflow_name],
}
if btype == "wheel":
d["subfolder"] = cu_version + "/"
if filter_branch is not None:
d["filters"] = {"branches": {"only": filter_branch}}
return {f"binary_{btype}_upload": d}
def indent(indentation, data_list):
if len(data_list) == 0:
return ""
return ("\n" + " " * indentation).join(
yaml.dump(data_list, default_flow_style=False).splitlines()
)
if __name__ == "__main__":
d = os.path.dirname(__file__)
env = jinja2.Environment(
loader=jinja2.FileSystemLoader(d),
lstrip_blocks=True,
autoescape=False,
keep_trailing_newline=True,
)
with open(os.path.join(d, "config.yml"), "w") as f:
f.write(env.get_template("config.in.yml").render(workflows=workflows))