Knit can be used in several novel ways. Our primary concern is supporting easy deployment of distributed Python runtimes; though, we can also consider other languages (R, Julia, etc) should interest develop. Below are a few novels ways we can currently use Knit


The example below use Python found in the $PATH. This is usually the system Python.

>>> import knit
>>> k = knit.Knit()
>>> cmd = "python -c 'import sys; print(sys.path); import socket; print(socket.gethostname())'"
>>> appId = k.start(cmd)

Zipped Conda Envs

Often nodes managed under YARN may not have desired Python libraries or the Python binary at all! In these cases, we want to package up an environment to be shipped along with the command. knit allows us to declare a zipped directory with the following structure typical of Python environments:

$ ll dev/
drwxr-xr-x+ 23 ubuntu  ubuntu   782B Jan 30 17:55 bin
drwxr-xr-x+ 20 ubuntu  ubuntu   680B Jan 30 17:55 include
drwxr-xr-x+ 39 ubuntu  staff    1.3K Jan 30 17:55 lib
drwxr-xr-x+  4 ubuntu  staff    136B Jan 30 17:55 share
drwxr-xr-x+  6 ubuntu  ubuntu   204B Jan 30 17:55 ssl
>>> appId = k.start(cmd, env='<full-path>/')

When we ship <full-path>/, knit uploads to a temporary directory within the user’s home HDFS space e.g. /Users/ubuntu/.knitDeps and the following bash ENVIRONMENT variables will be available:

  • $CONDA_PREFIX: full path to prefix location of zipped directory
  • $PYTHON_BIN: full path to Python binary

With the ENVIRONMENT variables available users can build more nuanced commands like the following:

>>> cmd = '$PYTHON_BIN $CONDA_PREFIX/bin/dworker 8786'

knit also provides a convenience method with conda to help build zipped environments. The following builds an environment with Python 3.5 and a variety of popular data Python libraries:

>>> env_zip = k.create_env(env_name='dev', packages=['python=3', 'distributed',
...                                                  'dask', 'pandas', 'scikit-learn'])

Adding Files

Knit can also pass local files to each container.

>>> files = ['creds.txt', 'data.csv']
>>> k.start(cmd, files=files)

With the above, we are send files creds.txt and data.csv to each container and can reference them as local file paths in the cmd command.


We can also call out to the HADOOP jar directly:

$ hadoop jar ./knit-1.0-SNAPSHOT.jar io.continuum.knit.Client --help
   knit x.1
   Usage: scopt [options]

     -n <value> | --numContainers <value>
           Number of YARN containers
     -m <value> | --memory <value>
           Amount of memory per container
     -c <value> | --virtualCores <value>
           Virtual cores per container
     -C <value> | --command <value>
           Command to run in containers
     -p <value> | --pythonEnv <value>
           Number of YARN containers
           command line for launching distributed python

$ hadoop jar ./knit-1.0-SNAPSHOT.jar io.continuum.knit.Client --numContainers 1 \
  --command "python -c 'import sys; print(sys.path); import random; print(str(random.random()))'"

Helpful aliases

$ alias yarn-status='yarn application -status'
$ alias yarn-log='yarn logs -applicationId'
$ alias yarn-kill='yarn application -kill'