pip or conda ?

[Karim-53]

Hi,

I am confused:

• the CONTRIBUTING.md ask me to install stuff using conda and environment.yml (file updated in 2019) containing numpy=1.15.4

• on the other hand, if I follow the tutorial in the README.md file, then I will use pip to install the requirements.txt (file updated in 2020) and thus numpy==1.18.2

I would like to know, please, what I should do.
From my personal experience, it is better to use pip as it is more stable and more up-to-date.

And maybe my first contribution would be to fix that :)

[murtraja]

@Karim-53
I personally used pip to download the dependencies. Also, I used a virtual environment whose requirements I am pasting below for your reference

absl-py==0.11.0
appdirs==1.4.4
argon2-cffi==20.1.0
astunparse==1.6.3
async-generator==1.10
attrs==20.3.0
audioread==2.1.9
backcall==0.2.0
bleach==3.2.1
cachetools==4.1.1
certifi==2020.11.8
cffi==1.14.3
chardet==3.0.4
decorator==4.4.2
deepspeech==0.7.1
defusedxml==0.6.0
entrypoints==0.3
gast==0.3.3
google-api-core==1.23.0
google-auth==1.23.0
google-auth-oauthlib==0.4.2
google-cloud-speech==1.3.2
google-pasta==0.2.0
googleapis-common-protos==1.52.0
grpcio==1.33.2
h5py==2.10.0
idna==2.10
importlib-metadata==2.0.0
ipykernel==5.3.4
ipython==7.16.1
ipython-genutils==0.2.0
jedi==0.17.2
Jinja2==2.11.2
joblib==0.17.0
jsonschema==3.2.0
jupyter-client==6.1.7
jupyter-core==4.6.3
jupyterlab-pygments==0.1.2
Keras-Preprocessing==1.1.2
librosa==0.8.0
llvmlite==0.34.0
Markdown==3.3.3
MarkupSafe==1.1.1
mistune==0.8.4
nbclient==0.5.1
nbconvert==6.0.7
nbformat==5.0.8
nest-asyncio==1.4.3
notebook==6.1.5
numba==0.51.2
numpy==1.18.2
oauthlib==3.1.0
opt-einsum==3.3.0
packaging==20.4
pandas==0.25.3
pandocfilters==1.4.3
parso==0.7.1
pexpect==4.8.0
pickleshare==0.7.5
pooch==1.2.0
prometheus-client==0.8.0
prompt-toolkit==3.0.8
protobuf==3.13.0
ptyprocess==0.6.0
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycparser==2.20
pydub==0.23.1
Pygments==2.7.2
pyparsing==2.4.7
pyrsistent==0.17.3
python-dateutil==2.8.1
python-Levenshtein==0.12.0
pytz==2020.4
PyYAML==5.3
pyzmq==19.0.2
requests==2.24.0
requests-oauthlib==1.3.0
resampy==0.2.2
rsa==4.6
scikit-learn==0.23.2
scipy==1.5.4
Send2Trash==1.5.0
six==1.15.0
SoundFile==0.10.3.post1
tensorboard==2.3.0
tensorboard-plugin-wit==1.7.0
tensorflow-cpu==2.3.1
tensorflow-estimator==2.3.0
termcolor==1.1.0
terminado==0.9.1
testpath==0.4.4
threadpoolctl==2.1.0
tornado==6.1
tqdm==4.43.0
traitlets==4.3.3
urllib3==1.25.11
wcwidth==0.2.5
webencodings==0.5.1
Werkzeug==1.0.1
wrapt==1.12.1
zipp==3.4.0

[Karim-53]

Thanks for the clarification
I will install one environment
and maybe in few days, we could agree on how to solve the conflict between the 2 tutorials

[piraka9011]

Salam @Karim-53!
We haven't been able to keep the repo up to date unfortunately.
I'm still happy to review PRs including documentation improvements :)
I personally prefer pip and is what we use internally at Tarteel so we can stick to that and remove references to conda.

[Karim-53]

Ok I will create a PR to update that,
in 2 or 4 days

[Karim-53]

#58