Use cephfs-ci storageClass instead of ceph-filesystem

[furiosamg]

Use cephfs-ci storageClass instead of ceph-filesystem

[furiosa-infra]

Pip freeze result for all

pip freeze result

aiobotocore==2.7.0
aiofiles==23.2.1
aiohttp==3.8.6
aiohttp-retry==2.8.3
aioitertools==0.11.0
aiosignal==1.3.1
amqp==5.2.0
annotated-types==0.6.0
antlr4-python3-runtime==4.9.3
appdirs==1.4.4
asttokens==2.4.1
async-timeout==4.0.3
asyncssh==2.14.1
atpublic==4.0
attrs==23.1.0
billiard==4.2.0
boto3==1.28.64
botocore==1.31.64
celery==5.3.5
certifi==2023.7.22
cffi==1.16.0
charset-normalizer==3.3.2
click==8.1.7
click-didyoumean==0.3.0
click-plugins==1.1.1
click-repl==0.3.0
cmake==3.27.7
colorama==0.4.6
comm==0.2.0
configobj==5.0.8
contourpy==1.2.0
cryptography==41.0.5
cycler==0.12.1
Cython==3.0.5
debugpy==1.8.0
decorator==5.1.1
dictdiffer==0.9.0
diskcache==5.6.3
distro==1.8.0
dpath==2.1.6
dulwich==0.21.6
dvc==3.29.0
dvc-data==2.20.0
dvc-http==2.30.2
dvc-objects==1.2.0
dvc-render==0.6.0
dvc-s3==2.23.0
dvc-studio-client==0.15.0
dvc-task==0.3.0
entrypoints==0.4
exceptiongroup==1.1.3
executing==2.0.1
fastjsonschema==2.18.1
filelock==3.13.1
flatten-dict==0.4.2
flufl.lock==7.1.1
fonttools==4.44.0
frozenlist==1.4.0
fsspec==2023.10.0
funcy==2.0
furiosa-common==0.10.0
furiosa-models @ file:///app
furiosa-native-postprocess==0.9.0
furiosa-native-runtime==0.10.2
furiosa-quantizer==0.10.0
furiosa-quantizer-impl==0.10.1
furiosa-runtime==0.10.0
gitdb==4.0.11
GitPython==3.1.40
grandalf==0.8
gto==1.5.0
hydra-core==1.3.2
idna==3.4
importlib-metadata==6.8.0
importlib-resources==6.1.1
iniconfig==2.0.0
ipykernel==6.26.0
ipython==8.17.2
iterative-telemetry==0.0.8
jedi==0.19.1
Jinja2==3.1.2
jmespath==1.0.1
jsonschema==4.19.2
jsonschema-specifications==2023.7.1
jupyter_client==8.6.0
jupyter_core==5.5.0
kiwisolver==1.4.5
kombu==5.3.3
lit==17.0.5
markdown-it-py==3.0.0
MarkupSafe==2.1.3
matplotlib==3.8.1
matplotlib-inline==0.1.6
mdurl==0.1.2
mpmath==1.3.0
multidict==6.0.4
multipledispatch==1.0.0
nbclient==0.6.8
nbformat==5.9.2
nbmake==1.4.6
nest-asyncio==1.5.8
networkx==3.2.1
numpy==1.25.2
nvidia-cublas-cu11==11.10.3.66
nvidia-cuda-cupti-cu11==11.7.101
nvidia-cuda-nvrtc-cu11==11.7.99
nvidia-cuda-runtime-cu11==11.7.99
nvidia-cudnn-cu11==8.5.0.96
nvidia-cufft-cu11==10.9.0.58
nvidia-curand-cu11==10.2.10.91
nvidia-cusolver-cu11==11.4.0.1
nvidia-cusparse-cu11==11.7.4.91
nvidia-nccl-cu11==2.14.3
nvidia-nvtx-cu11==11.7.91
omegaconf==2.3.0
onnx==1.14.1
opencv-python-headless==4.8.0.76
orjson==3.9.10
packaging==23.2
pandas==2.0.3
parso==0.8.3
pathspec==0.11.2
pexpect==4.8.0
Pillow==10.1.0
platformdirs==3.11.0
pluggy==1.3.0
prompt-toolkit==3.0.41
protobuf==4.25.0
psutil==5.9.6
ptyprocess==0.7.0
pure-eval==0.2.2
py-cpuinfo==9.0.0
pyarrow==12.0.1
pycocotools==2.0.7
pycparser==2.21
pydantic==2.5.0
pydantic_core==2.14.1
pydot==1.4.2
pygit2==1.13.2
Pygments==2.16.1
pygtrie==2.5.0
pyparsing==3.1.1
pytest==7.4.3
pytest-asyncio==0.17.2
pytest-benchmark==4.0.0
python-dateutil==2.8.2
pytz==2023.3.post1
PyYAML==6.0.1
pyzmq==25.1.1
referencing==0.30.2
requests==2.31.0
rich==13.6.0
rpds-py==0.12.0
ruamel.yaml==0.18.5
ruamel.yaml.clib==0.2.8
s3fs==2023.10.0
s3transfer==0.7.0
scmrepo==1.4.1
semver==3.0.2
shortuuid==1.0.11
shtab==1.6.4
six==1.16.0
smmap==5.0.1
sqltrie==0.8.0
stack-data==0.6.3
sympy==1.12
tabulate==0.9.0
tomli==2.0.1
tomlkit==0.12.2
torch==2.0.1
torchvision==0.15.2
tornado==6.3.3
tqdm==4.66.1
traitlets==5.13.0
triton==2.0.0
typer==0.9.0
typing_extensions==4.8.0
tzdata==2023.3
urllib3==1.26.18
vine==5.1.0
voluptuous==0.14.0
wcwidth==0.2.10
wrapt==1.16.0
yarl==1.9.2
zc.lockfile==3.0.post1
zipp==3.17.0

[furiosa-infra]

fc31a38 WIP: use new PVC and run regression tests

--------------------------------------------------------------------------------------------------------- benchmark: 9 tests --------------------------------------------------------------------------------------------------------
Name (time in ms)                                                 Min                   Max                Mean             StdDev              Median                IQR            Outliers       OPS            Rounds  Iterations
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
test_mlcommons_resnet50_accuracy                               1.2727 (1.0)         72.3006 (5.80)       5.2407 (1.0)       1.8776 (2.22)       4.8868 (1.0)       1.4968 (2.30)    5909;3718  190.8139 (1.0)       50000           1
test_mlcommons_ssd_mobilenet_with_native_rust_pp_accuracy      4.0567 (3.19)        12.4615 (1.0)        8.0037 (1.53)      0.8469 (1.0)        8.0417 (1.65)      0.6500 (1.0)       986;633  124.9424 (0.65)       5000           1
test_efficientnetb0_accuracy                                   4.5938 (3.61)     3,193.7534 (256.29)    16.2317 (3.10)     26.0472 (30.76)     13.5194 (2.77)      3.8784 (5.97)     781;2667   61.6079 (0.32)      50000           1
test_mlcommons_ssd_mobilenet_accuracy                          5.3209 (4.18)       226.5520 (18.18)     10.8848 (2.08)      3.2083 (3.79)      10.8549 (2.22)      0.8258 (1.27)       46;542   91.8716 (0.48)       5000           1
test_efficientnetv2s_accuracy                                  6.6764 (5.25)       714.4169 (57.33)     21.3216 (4.07)     23.4077 (27.64)     16.6419 (3.41)      5.1365 (7.90)    2419;4555   46.9009 (0.25)      50000           1
test_yolov5m_accuracy                                          7.9854 (6.27)        25.3976 (2.04)      11.0138 (2.10)      1.4750 (1.74)      10.7632 (2.20)      1.7777 (2.73)     2733;280   90.7952 (0.48)      10000           1
test_yolov5l_accuracy                                         10.8855 (8.55)        52.3222 (4.20)      14.7350 (2.81)      1.6811 (1.99)      14.4566 (2.96)      1.9766 (3.04)     2539;260   67.8657 (0.36)      10000           1
test_mlcommons_ssd_resnet34_with_native_rust_pp_accuracy      36.2243 (28.46)      762.0383 (61.15)     52.7862 (10.07)    17.0206 (20.10)     52.1312 (10.67)     3.0451 (4.68)        6;221   18.9444 (0.10)       5000           1
test_mlcommons_ssd_resnet34_accuracy                          93.7459 (73.66)    1,151.5121 (92.41)    123.1531 (23.50)    26.4997 (31.29)    120.1101 (24.58)    16.9555 (26.09)     244;148    8.1200 (0.04)       5000           1
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Legend:
  Outliers: 1 Standard Deviation from Mean; 1.5 IQR (InterQuartile Range) from 1st Quartile and 3rd Quartile.
  OPS: Operations Per Second, computed as 1 / Mean

[furiosamg]

I've confirmed that it's a simple modification and it works, so will merge it.