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2 changes: 2 additions & 0 deletions .jenkins/build.sh
Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,8 @@ DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null && pwd )"
sudo apt-get update
sudo apt-get install -y pandoc

# export CUBLAS_WORKSPACE_CONFIG=:4096:8

# NS: Path to python runtime should already be part of docker container
# export PATH=/opt/conda/bin:$PATH

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21 changes: 12 additions & 9 deletions .jenkins/get_files_to_run.py
Original file line number Diff line number Diff line change
Expand Up @@ -41,24 +41,27 @@ def add_to_shard(i, filename):

all_other_files = all_files.copy()
needs_multigpu = list(
filter(lambda x: get_needs_machine(x) == "linux.16xlarge.nvidia.gpu", all_files,)
)
needs_a10g = list(
filter(lambda x: get_needs_machine(x) == "linux.g5.4xlarge.nvidia.gpu", all_files,)
filter(lambda x: get_needs_machine(x) == "multigpu", all_files,)
)
# Magic code for torchvision: for some reason, it needs to run after
# beginner_source/basics/data_tutorial.py. Very specifically:
# https://github.com/pytorch/tutorials/blob/edff1330ca6c198e8e29a3d574bfb4afbe191bfd/beginner_source/basics/data_tutorial.py#L49-L60
# So manually add them to the last shard. I think some other files also
# work but I'm too lazy to figure out which ones.
# add_to_shard(num_shards - 1, "beginner_source/basics/data_tutorial.py")
# add_to_shard(num_shards - 1, "intermediate_source/torchvision_tutorial.py")
# all_other_files.remove("beginner_source/basics/data_tutorial.py")
# all_other_files.remove("intermediate_source/torchvision_tutorial.py")

for filename in needs_multigpu:
# currently, the only job that has multigpu is the 0th worker,
# so we'll add all the jobs that need this machine to the 0th worker
add_to_shard(0, filename)
all_other_files.remove(filename)
for filename in needs_a10g:
# currently, workers 1-5 use linux.g5.4xlarge.nvidia.gpu (sm86, A10G),
# so we'll add all the jobs that need this machine to the 1st worker
add_to_shard(1, filename)
all_other_files.remove(filename)
sorted_files = sorted(all_other_files, key=get_duration, reverse=True,)

for filename in sorted_files:
# If you don't specify a machine, you get the default
min_shard_index = sorted(range(1, num_shards), key=lambda i: sharded_files[i][0])[
0
]
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