Micromamba setup¶

These instructions should work on Linux, macOS, and Windows.

Install and configure Micromamba¶

Install Micromamba. Then add this to ~/.condarc:

ssl_verify: true
auto_activate_base: true
auto_update_conda: true
pip_interop_enabled: false #(1)!
channel_priority: strict   #(2)!

channels:
  - conda-forge            #(3)!

1. This feature is extremely slow and often doesn’t work anyway.
2. Do not use the default channel.
3. Only list conda-forge.

Create a build environment¶

Create an environment for just building and packaging Python projects.

conda create \
  --name build \
  --force \
  --yes \
  python=3.11

conda activate build
pip install poetry hatch tox pytest pytest-cov

Create a playground environment¶

You can add packages in here and wreck it. If it’s ruined, just rebuild by running the same command.

Add and remove packages at the bottom as needed.

conda create \
  --name ds \
  --force \
  --yes \
  python=3.11 \
  numpy \
  pandas[performance,computation,parquet,feather,excel,compression] \
  polars[numpy,pandas,pyarrow] \
  pytorch scikit-learn tensorflow-gpu keras opencv3 \
  jupyterlab ipympl nb_conda_kernels plotly

conda activate ds

Configure Jupyter¶

Copy this to ~/.jupyter/jupyter_lab_config.py:

from pathlib import Path

c.ServerApp.root_dir = str(Path.home())
c.ServerApp.token = ""
c.ServerApp.password = ""
c.ServerApp.port = 8888
c.ServerApp.port_retries = 0
c.ServerApp.autoreload = False

Run a Jupyter server:

jupyter lab --no-browser &!

(The &! will keep it running even when the session quits.) There’s no reason to set a password: It’s unlikely to add any security, and you shouldn’t rely on it. Instead, if you want to access Jupyter remotely, use an SSH tunnel. First, make sure to set up SSH keys to your server. Now open the tunnel by running

ssh -L 8899:localhost:8888 myservername

Then leave that open and go to: https://localhost:8899. Now you can lose the connection and your notebooks will still be there.

Build real packages¶

The playground environment is your unreliable I don’t care workspace.

You should use an isolated build procedure whenever you care about reproducibility. Your project should list dependencies with version ranges and be rebuilt from scratch on each build.

Unfortunately, Conda’s dependency solver is buggy bugs, and Conda provides far fewer packages than PyPi. Unfortunately, mixing Conda and pip is problematic and can result in undetected dependency conflicts and package clobbering.

Consider instead using Poetry or Hatch. These have better dependency resolution, better performance, and access to all packages on PyPi or are distributed in wheels. You can use Tyrannosaurus as a template.