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‎cloud-infrastructure/ai-infra-gpu/GPU/conda-envs/LICENSE

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Copyright (c) 2024 Oracle and/or its affiliates.
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The Universal Permissive License (UPL), Version 1.0
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Subject to the condition set forth below, permission is hereby granted to any
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person obtaining a copy of this software, associated documentation and/or data
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(collectively the "Software"), free of charge and under any and all copyright
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rights in the Software, and any and all patent rights owned or freely
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licensable by each licensor hereunder covering either (i) the unmodified
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Software as contributed to or provided by such licensor, or (ii) the Larger
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Works (as defined below), to deal in both
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(a) the Software, and
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(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
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one is included with the Software (each a "Larger Work" to which the Software
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is contributed by such licensors),
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without restriction, including without limitation the rights to copy, create
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derivative works of, display, perform, and distribute the Software and make,
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use, sell, offer for sale, import, export, have made, and have sold the
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Software and the Larger Work(s), and to sublicense the foregoing rights on
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either these or other terms.
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This license is subject to the following condition:
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The above copyright notice and either this complete permission notice or at
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a minimum a reference to the UPL must be included in all copies or
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substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎cloud-infrastructure/ai-infra-gpu/GPU/conda-envs/OL8/pytorch.yml

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name: pytorch
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channels:
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- defaults
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dependencies:
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- _libgcc_mutex=0.1=main
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- _openmp_mutex=5.1=1_gnu
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- blas=1.0=mkl
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- ca-certificates=2024.3.11=h06a4308_0
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- cffi=1.16.0=py39h5eee18b_0
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- filelock=3.13.1=py39h06a4308_0
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- fsspec=2023.10.0=py39h06a4308_0
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- gmp=6.2.1=h295c915_3
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- gmpy2=2.1.2=py39heeb90bb_0
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- intel-openmp=2023.1.0=hdb19cb5_46306
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- jinja2=3.1.3=py39h06a4308_0
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- ld_impl_linux-64=2.38=h1181459_1
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- libffi=3.4.4=h6a678d5_0
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- libgcc-ng=11.2.0=h1234567_1
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- libgomp=11.2.0=h1234567_1
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- libprotobuf=3.20.3=he621ea3_0
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- libstdcxx-ng=11.2.0=h1234567_1
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- markupsafe=2.1.3=py39h5eee18b_0
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- mkl=2023.1.0=h213fc3f_46344
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- mkl-service=2.4.0=py39h5eee18b_1
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- mkl_fft=1.3.8=py39h5eee18b_0
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- mkl_random=1.2.4=py39hdb19cb5_0
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- mpc=1.1.0=h10f8cd9_1
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- mpfr=4.0.2=hb69a4c5_1
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- mpmath=1.3.0=py39h06a4308_0
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- ncurses=6.4=h6a678d5_0
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- networkx=3.1=py39h06a4308_0
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- ninja=1.10.2=h06a4308_5
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- ninja-base=1.10.2=hd09550d_5
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- numpy=1.26.4=py39h5f9d8c6_0
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- numpy-base=1.26.4=py39hb5e798b_0
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- openssl=3.0.13=h7f8727e_0
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- pip=23.3.1=py39h06a4308_0
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- pycparser=2.21=pyhd3eb1b0_0
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- python=3.9.19=h955ad1f_0
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- readline=8.2=h5eee18b_0
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- setuptools=68.2.2=py39h06a4308_0
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- sqlite=3.41.2=h5eee18b_0
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- sympy=1.12=py39h06a4308_0
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- tbb=2021.8.0=hdb19cb5_0
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- tk=8.6.12=h1ccaba5_0
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- typing_extensions=4.9.0=py39h06a4308_1
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- wheel=0.41.2=py39h06a4308_0
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- xz=5.4.6=h5eee18b_0
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- zlib=1.2.13=h5eee18b_0
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- pip:
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- accelerate==0.28.0
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- aiohttp==3.9.3
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- aiosignal==1.3.1
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- anyio==4.3.0
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- argon2-cffi==23.1.0
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- argon2-cffi-bindings==21.2.0
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- arrow==1.3.0
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- asttokens==2.4.1
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- async-lru==2.0.4
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- async-timeout==4.0.3
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- attrs==23.2.0
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- babel==2.14.0
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- beautifulsoup4==4.12.3
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- bleach==6.1.0
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- certifi==2024.2.2
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- charset-normalizer==3.3.2
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- comm==0.2.2
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- datasets==2.18.0
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- debugpy==1.8.1
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- decorator==5.1.1
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- defusedxml==0.7.1
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- dill==0.3.8
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- docstring-parser==0.16
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- exceptiongroup==1.2.0
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- executing==2.0.1
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- fastjsonschema==2.19.1
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- fqdn==1.5.1
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- frozenlist==1.4.1
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- h11==0.14.0
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- httpcore==1.0.4
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- httpx==0.27.0
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- huggingface-hub==0.22.0
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- idna==3.6
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- importlib-metadata==7.1.0
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- ipykernel==6.29.3
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- ipython==8.18.1
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- isoduration==20.11.0
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- jedi==0.19.1
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- json5==0.9.24
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- jsonpointer==2.4
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- jsonschema==4.21.1
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- jsonschema-specifications==2023.12.1
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- jupyter-client==8.6.1
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- jupyter-core==5.7.2
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- jupyter-events==0.10.0
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- jupyter-lsp==2.2.4
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- jupyter-server==2.13.0
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- jupyter-server-terminals==0.5.3
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- jupyterlab==4.1.5
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- jupyterlab-pygments==0.3.0
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- jupyterlab-server==2.25.4
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- markdown-it-py==3.0.0
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- matplotlib-inline==0.1.6
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- mdurl==0.1.2
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- mistune==3.0.2
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- multidict==6.0.5
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- multiprocess==0.70.16
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- nbclient==0.10.0
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- nbconvert==7.16.3
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- nbformat==5.10.3
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- nest-asyncio==1.6.0
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- notebook-shim==0.2.4
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- nvidia-cublas-cu12==12.1.3.1
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- nvidia-cuda-cupti-cu12==12.1.105
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- nvidia-cuda-nvrtc-cu12==12.1.105
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- nvidia-cuda-runtime-cu12==12.1.105
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- nvidia-cudnn-cu12==8.9.2.26
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- nvidia-cufft-cu12==11.0.2.54
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- nvidia-curand-cu12==10.3.2.106
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- nvidia-cusolver-cu12==11.4.5.107
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- nvidia-cusparse-cu12==12.1.0.106
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- nvidia-nccl-cu12==2.19.3
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- nvidia-nvjitlink-cu12==12.4.99
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- nvidia-nvtx-cu12==12.1.105
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- overrides==7.7.0
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- packaging==24.0
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- pandas==2.2.1
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- pandocfilters==1.5.1
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- parso==0.8.3
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- peft==0.10.0
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- pexpect==4.9.0
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- pillow==10.2.0
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- platformdirs==4.2.0
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- prometheus-client==0.20.0
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- prompt-toolkit==3.0.43
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- psutil==5.9.8
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- ptyprocess==0.7.0
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- pure-eval==0.2.2
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- pyarrow==15.0.2
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- pyarrow-hotfix==0.6
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- pygments==2.17.2
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- python-dateutil==2.9.0.post0
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- python-json-logger==2.0.7
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- pytz==2024.1
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- pyyaml==6.0.1
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- pyzmq==25.1.2
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- referencing==0.34.0
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- regex==2023.12.25
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- requests==2.31.0
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- rfc3339-validator==0.1.4
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- rfc3986-validator==0.1.1
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- rich==13.7.1
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- rpds-py==0.18.0
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- safetensors==0.4.2
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- send2trash==1.8.2
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- shtab==1.7.1
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- six==1.16.0
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- sniffio==1.3.1
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- soupsieve==2.5
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- stack-data==0.6.3
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- terminado==0.18.1
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- test-pytorch-gpu==0.0.4
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- tinycss2==1.2.1
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- tokenizers==0.15.2
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- tomli==2.0.1
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- torch==2.2.1
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- torchvision==0.17.1
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- tornado==6.4
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- tqdm==4.66.2
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- traitlets==5.14.2
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- transformers==4.39.1
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- triton==2.2.0
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- trl==0.8.1
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- types-python-dateutil==2.9.0.20240316
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- tyro==0.7.3
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- tzdata==2024.1
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- uri-template==1.3.0
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- urllib3==2.2.1
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- wcwidth==0.2.13
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- webcolors==1.13
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- webencodings==0.5.1
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- websocket-client==1.7.0
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- xxhash==3.4.1
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- yarl==1.9.4
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- zipp==3.18.1
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prefix: /home/opc/anaconda3/envs/pytorch

‎cloud-infrastructure/ai-infra-gpu/GPU/conda-envs/OL8/utilities/locustfile.py

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# Import the Locust dependencies
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from locust import HttpUser, task
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# Import general library from python
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import os
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import random
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# Import lorem ipsum library to generate some random texts
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from lorem_text import lorem
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# Import dotenv to load the environments variables
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from dotenv import load_dotenv
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load_dotenv()
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# Create a table with some lorem ipsum texts
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messages = []
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# Add 1000 random texts of 10 paragraphs each (simulate 1000 emails)
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for i in range(1000):
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messages.append(lorem.paragraphs(10))
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# Define the headers of the request. Token is stored as an environment variable here
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headers = {
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"Authorization": "Bearer {os.getenv('TOKEN')}", "Content-Type": "application/json"}
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class HelloWorldUser(HttpUser):
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# Definition of the first path where we do our post request
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@task
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def hello_world(self):
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# Define the body with the email choose randomly from the tab of all the emails
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body = {"model": "meta-llama/Llama-2-7b-chat-hf","prompt": "the following is a conversation with an AI research assistant. The assistant tone is technical and scientific. Human: Hello, who are you? AI: Greeting! I am an AI research assistant. How can I help you today? Human: Can you tell me about the creation of blackholes? AI:","max_tokens": 20,"temperature": 0}
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# Do the post request on the spam detection path
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self.client.post("/v1/completions",
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headers=headers, json=body)

‎cloud-infrastructure/ai-infra-gpu/GPU/conda-envs/OL8/utilities/test-vllm.py

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from vllm import LLM
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# Replace the model name with the one you want to use:
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# Mixtral simple: mistralai/Mixtral-8x7B-v0.1
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# Mixtral instruct: mistralai/Mixtral-8x7B-Instruct-v0.1
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# Mistral 7B simple: mistralai/Mistral-7B-v0.1
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# Mistral 7B instruct: mistralai/Mistral-7B-Instruct-v0.1
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# LLaMA 2 70B: meta-llama/Llama-2-70b-hf
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llm = LLM("meta-llama/Llama-2-7b-chat-hf", tensor_parallel_size=1, max_model_len=4096)
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print(llm.generate("What is batch inference?"))

‎cloud-infrastructure/ai-infra-gpu/GPU/conda-envs/OL8/utilities/vllm-langchain.py

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from langchain_community.llms import VLLM
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llm = VLLM(model="mosaicml/mpt-7b",
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trust_remote_code=True, # mandatory for hf models
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max_new_tokens=128,
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top_k=10,
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top_p=0.95,
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temperature=0.8,
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# tensor_parallel_size=... # for distributed inference
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)
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print(llm("What is the capital of France ?"))

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