Running the ToolΒΆ
To run the tool, ensure that the required Docker volumes are set up. You can do this by using the following command on compute:
docker pull veda504/finding_eml:v1.1
docker run -it -v "/home/files_dir_path:/mnt" veda504/finding_eml:v1.1 /bin/bash
This command starts a container using the image veda504/finding_eml:v1.1 and mounts the local directory of files into the container at /mnt. It is suggested to use mentioned Docker for running the tool for better results.
Click to view library versions used in Docker
Package Version Editable project location
----------------------------- ------------ -------------------------
absl-py 2.0.0
aiohttp 3.9.1
aiosignal 1.3.1
alabaster 0.7.13
anndata 0.9.2
asttokens 2.4.1
async-timeout 4.0.3
attrs 23.1.0
Babel 2.14.0
backcall 0.2.0
beautifulsoup4 4.12.2
bleach 6.1.0
cached-property 1.5.2
certifi 2023.11.17
charset-normalizer 3.3.2
chex 0.1.7
cmake 3.28.1
comm 0.2.0
contextlib2 21.6.0
contourpy 1.1.1
cycler 0.12.1
Cython 3.0.7
debugpy 1.8.0
decorator 5.1.1
defusedxml 0.7.1
dm-tree 0.1.8
docrep 0.3.2
docutils 0.20.1
et-xmlfile 1.1.0
etils 1.3.0
executing 2.0.1
fastjsonschema 2.19.0
filelock 3.13.1
finding-eML 0.1 /app/src
flax 0.7.2
fonttools 4.47.0
frozenlist 1.4.1
fsspec 2023.12.2
furo 2023.9.10
gdown 4.7.1
get-annotations 0.1.2
googleDriveFileDownloader 1.2
h5py 3.10.0
idna 3.6
igraph 0.10.8
imagesize 1.4.1
imbalanced-learn 0.11.0
importlib-metadata 7.0.0
importlib-resources 6.1.1
ipykernel 6.27.1
ipython 8.12.3
jax 0.4.13
jaxlib 0.4.13
jedi 0.19.1
Jinja2 3.1.2
joblib 1.3.2
jsonschema 4.20.0
jsonschema-specifications 2023.11.2
jupyter_client 8.6.0
jupyter_core 5.5.1
jupyterlab_pygments 0.3.0
kiwisolver 1.4.5
leidenalg 0.10.1
lightning-utilities 0.10.0
lit 17.0.6
llvmlite 0.41.1
markdown-it-py 3.0.0
MarkupSafe 2.1.3
matplotlib 3.7.4
matplotlib-inline 0.1.6
mdurl 0.1.2
mistune 3.0.2
ml-collections 0.1.1
ml-dtypes 0.2.0
mpmath 1.3.0
msgpack 1.0.7
mudata 0.2.3
multidict 6.0.4
multipledispatch 1.0.0
muon 0.1.5
my-package 0.1
natsort 8.4.0
nbclient 0.9.0
nbconvert 7.13.0
nbformat 5.9.2
nbsphinx 0.9.3
nest-asyncio 1.5.8
networkx 3.1
newick 1.0.0
numba 0.58.1
numpy 1.24.4
numpydoc 1.6.0
numpyro 0.12.1
nvidia-cublas-cu11 11.10.3.66
nvidia-cublas-cu12 12.1.3.1
nvidia-cuda-cupti-cu11 11.7.101
nvidia-cuda-cupti-cu12 12.1.105
nvidia-cuda-nvrtc-cu11 11.7.99
nvidia-cuda-nvrtc-cu12 12.1.105
nvidia-cuda-runtime-cu11 11.7.99
nvidia-cuda-runtime-cu12 12.1.105
nvidia-cudnn-cu11 8.5.0.96
nvidia-cudnn-cu12 8.9.2.26
nvidia-cufft-cu11 10.9.0.58
nvidia-cufft-cu12 11.0.2.54
nvidia-curand-cu11 10.2.10.91
nvidia-curand-cu12 10.3.2.106
nvidia-cusolver-cu11 11.4.0.1
nvidia-cusolver-cu12 11.4.5.107
nvidia-cusparse-cu11 11.7.4.91
nvidia-cusparse-cu12 12.1.0.106
nvidia-nccl-cu11 2.14.3
nvidia-nccl-cu12 2.18.1
nvidia-nvjitlink-cu12 12.3.101
nvidia-nvtx-cu11 11.7.91
nvidia-nvtx-cu12 12.1.105
openpyxl 3.1.2
opt-einsum 3.3.0
optax 0.1.7
orbax-checkpoint 0.2.3
packaging 23.2
pandas 1.5.3
pandocfilters 1.5.0
parso 0.8.3
patsy 0.5.4
pexpect 4.9.0
pickleshare 0.7.5
Pillow 10.1.0
pip 23.3.2
pkgconfig 1.5.5
pkgutil_resolve_name 1.3.10
platformdirs 4.1.0
prompt-toolkit 3.0.43
protobuf 4.25.1
psutil 5.9.7
ptyprocess 0.7.0
pure-eval 0.2.2
Pygments 2.17.2
pynndescent 0.5.11
pyparsing 3.1.1
pyro-api 0.1.2
pyro-ppl 1.8.6
PySocks 1.7.1
python-dateutil 2.8.2
pytorch-lightning 1.9.5
pytz 2023.3.post1
PyYAML 6.0.1
pyzmq 25.1.2
referencing 0.32.0
requests 2.31.0
rich 13.7.0
rpds-py 0.15.2
scanpy 1.9.6
scArches 0.5.10
scHPL 1.0.4
scikit-learn 1.3.2
scikit-misc 0.2.0
scipy 1.10.1
scvi-tools 0.20.3
seaborn 0.12.2
session-info 1.0.0
setuptools 57.5.0
six 1.16.0
snowballstemmer 2.2.0
soupsieve 2.5
Sphinx 7.1.2
sphinx-basic-ng 1.0.0b2
sphinxcontrib-applehelp 1.0.4
sphinxcontrib-devhelp 1.0.2
sphinxcontrib-htmlhelp 2.0.1
sphinxcontrib-jsmath 1.0.1
sphinxcontrib-qthelp 1.0.3
sphinxcontrib-serializinghtml 1.1.5
stack-data 0.6.3
statsmodels 0.14.1
stdlib-list 0.10.0
sympy 1.12
tabulate 0.9.0
tensorstore 0.1.45
texttable 1.7.0
threadpoolctl 3.2.0
tinycss2 1.2.1
tomli 2.0.1
toolz 0.12.0
torch 2.0.1
torchmetrics 1.2.1
tornado 6.4
tqdm 4.66.1
traitlets 5.14.0
triton 2.0.0
typing_extensions 4.9.0
umap-learn 0.5.5
urllib3 2.1.0
wcwidth 0.2.12
webencodings 0.5.1
wheel 0.40.0
yarl 1.9.4
zipp 3.17.0
Once you have set up this environment, you can run the tool either interactively or directly in batch mode.
To run Finding eML locally, you can choose one of two methods:
1. Direct Execution: Execute the script directly for automated processing.
2. Interactive Execution: Run the Python shell for real-time interaction.