A utility for tracking and reproducing Tensorflow runs.

tensorflow utilities development
pip install tf-run-manager==2.1.6



Lab Notebook

Researchers in computer science often need to compare results between different versions of a process. Lab Notebook helps document, track, and organize process these kinds of runs. This is essential for reproducibility and helps researchers figure out what changes in code led to different outcomes. The goals of Lab Notebook are reproducibility, modularity, and organization. Specifically, Lab Notebook provides the following functionality:

  • Maintain metadata about each run, including a description, a timestamp, and a git commit.
  • Automatically set up runs, building flags and directories with unique name corresponding to each run and launching runs in tmux.
  • Organize runs into hierarchical categories.
  • Synchronize runs with directories, so that directories are moved and deleted when runs are moved and deleted.


The only external dependencies of this tool are tmux and git. After that, pip install lab-notebook.


The program will default to any arguments specified in .runsrc. The user can always override the .runsrc file with command-line arguments. For descriptions of arguments, use runs -h or runs [command] -h The program searches for the .runsrc file in ancestors (inclusive) of the current working directory. If the program does not find a .runsrc file, it will create one with default values in the current working directory. The user can use two keyword in the .runsrc:

  • <path> will be replaced by the path to the run. Paths look just like ordinary file paths (/-delimited).
  • <name> will be replaced by the head of path.

Also users can interpolate strings from other sections of .runsrc using the syntax ${section:value}. For more details see configparser ExtendedInterpolation.

Here is an example .runsrc file:

root = /Users/ethan/demo-lab-notebook/.runs
db_path = /Users/ethan/demo-lab-notebook/runs.pkl
dir_names = tensorboard
prefix = Source ~/virtualenvs/demo-lab-notebook/bin/activate;


description = demo lab-notebook

This will pass the flag --logdir=/Users/ethan/baselines/.runs/tensorboard/<path> to any program launched with run, where <path> will be replaced by the path argument given by the user.


This is a simple wrapper around git that substitutes +your-path with runs lookup commit your-path. For example, to see changes since when you launched your-run:

runs-git diff +your-run

If you want to live on the wild side, use direnv to alias git to runs-git when you are in your project directory.

Example Usage

Setup environment:

mkdir ~/lab-notebook-demo/ && cd ~/lab-notebook-demo
wget https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/tutorials/mnist/mnist_with_summaries.py
pip install tensorflow lab-notebook
git init
echo 'runs.pkl .runs .runsrc' > .gitignore
git add -A
git commit -am init

Create a new run. The run will be launched in tmux:

runs new train 'python mnist_with_summaries.py' --description='demo new command'

Check out your run:

tmux attach -t train

Reproduce your run:

runs reproduce train
runs reproduce --no-overwrite train

Try modifying the .runsrc file to look like the example in the Configuration section with appropriate changes for your system. Then create a new run:

runs new subdir/train 'python mnist_with_summaries.py' --description='demo categorization'

Get an overview of what runs are in the database:

runs ls
runs ls 'tra*'
runs ls --show-attrs

Query information about current runs:

runs lookup description train
runs lookup commit train

runs-git: avoid typing runs lookup commit <path> all the time:

echo '# Hello' >> mnist_with_summaries.py
runs-git diff +train

Organize runs

runs mv train subdir/train2
runs ls
tree .runs  # note that directories are synchronized with database entries
runs mv subdir archive
runs ls

Delete runs

runs rm archive/train
runs killall


For an overview of subcommands, run

runs -h

For detailed descriptions of each subcommand and its arguments, run

runs <subcommand> -h

Tab autocompletion

If you are using Zsh, simpy copy the _runs to some place on your fpath. Then pressing tab will prompt you with the names of runs currently in your database

Why not just use git?

  • If processes are long-running, it is hard to know which commit a given run corresponds to.
  • Commit statements are really meant to describe changes to software, not runs. A description of a change may not actually tell you very much about the motivation for a software run.
  • Not all commits will correspond to runs, so you will need to fish through a large number of commits to find those that correspond to runs.
  • Often processes depend on specific file-structures (e.g. a logging directory). Setting up and removing these directories by hand is time-consuming and error-prone.
  • Commits cannot be organized hierarchically or categorized after their creation.