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Self-hosted Runner Edits #676

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Self-hosted Runner Edits #676

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e237964
temp sh runner location on falcon
akshay-krishnan Jul 26, 2023
f44b2af
fake assert
akshay-krishnan Jul 26, 2023
1672120
update cacher
akshay-krishnan Jul 26, 2023
81d9c15
update from master
akshay-krishnan Jul 27, 2023
99d4262
add installation
akshay-krishnan Jul 28, 2023
6be5d5d
Update benchmark-self-hosted.yml
akshay-krishnan Jul 28, 2023
e70fdba
netvlad link update
akshay-krishnan Jul 28, 2023
dc5f001
remove debug lines
akshay-krishnan Jul 28, 2023
e13a941
Merge branch 'ak-falcon' of github.com:borglab/gtsfm into ak-falcon
akshay-krishnan Jul 28, 2023
089590a
Merge branch 'master' into ak-falcon
travisdriver Jul 28, 2023
483ce62
Move cluster environment settings to new file
travisdriver Jul 28, 2023
ddc35d8
Move cluster environment settings to new file
travisdriver Jul 28, 2023
ff4a53b
Cleanup
travisdriver Jul 28, 2023
e7faf63
Cleanup
travisdriver Jul 28, 2023
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2 changes: 1 addition & 1 deletion .github/scripts/execute_single_benchmark_self_hosted.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@
# Datasets are stored in /usr/local/gtsfm-data in order to reduce runtimes by not
# redownloading each (very large) dataset every CI run. Any new datasets must be
# downloaded and stored in /usr/local/gtsfm-data before running this action.
DATASET_PREFIX=/usr/local/gtsfm-data
DATASET_PREFIX=/home/akrishnan86/gtsfm/data

DATASET_NAME=$1
CONFIG_NAME=$2
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3 changes: 3 additions & 0 deletions .github/workflows/benchmark-self-hosted.yml
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Expand Up @@ -53,6 +53,9 @@ jobs:
# bash
# cache-environment: true
# post-cleanup: "all"
- name: Enter gtsfm directory + install it
run:
cd $GITHUB_WORKSPACE && pip install -e .
- name: Download third-party weights
run: |
./download_model_weights.sh
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59 changes: 59 additions & 0 deletions environment_cluster.yml
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Original file line number Diff line number Diff line change
@@ -0,0 +1,59 @@
name: gtsfm-v1
channels:
# This environment is identical to the default Linux environment except that
# we install a different PyTorch version that works on the cluster.
#
# for priority order, we prefer pytorch as the highest priority as it supplies
# latest stable packages for numerous deep learning based methods. conda-forge
# supplies higher versions of packages like opencv compared to the defaults
# channel.
- pytorch
- conda-forge
- nvidia
dependencies:
# python essentials
- python=3.8
- pip
# formatting and dev environment
- black
- coverage
- mypy
- pylint
- pytest
- flake8
- isort
# dask and related
- dask # same as dask[complete] pip distribution
- asyncssh
- python-graphviz
# core functionality and APIs
- matplotlib==3.4.2
- networkx
- numpy
- nodejs
- pandas
- pillow>=8.0.1
- scikit-learn
- scipy
- hydra-core
# 3rd party algorithms for different modules
- pytorch-cuda=11.8
- pytorch
- torchvision>=0.13.0
- kornia
# io
- h5py
- plotly=4.14.3
- tabulate
- simplejson
# testing
- parameterized
- pip:
- open3d
- opencv-python>=4.5.4.58
- pydegensac
- colour
- pycolmap>=0.1.0
- trimesh[easy]
- gtsam==4.2a8
- pydot
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