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 any Python found in the $PATH. This is usually the system Python (i.e., on a cluster where it has already been installed for you).

>>> 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/dask-worker 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.

Dask Clusters

The previous methods can be combined to launch a full distributed dask cluster on YARN with code like the following

from dask_yarn import DaskYARNCluster
cluster = DaskYARNCluster(env='my/conda/')
cluster.start(8, cpu=2, memory=2048)

The object cluster starts a dask scheduler, and can also be used to start or stop more containers than the original 8 referenced above. The same set of config options apply as for a Knit object, in addition to conda creation options, which will define the environment in which the workers run.

To start a dask client in the same session, you can simply do

from dask.distributed import Client
c = Client(cluster)

and use as usual, or look at cluster.scheduler_address for clients connecting from other sessions.

Note that DaskYARNCluster can also be used as a context manager, which will ensure that it gets closed (and the corresponding YARN application killed) when the with context finishes.

Instance Connections

The main instances that you will handle in this library have attributes which are instances of other classes, and expose functionality. Generally, parameters are passed down, so that the constructor parameters for DaskYarnCluster will also be used for Knit (e.g., replication_factor), CondaCreator (e.g., channels) and YARNAPI (e.g., rm).


  • .knit is an instance of Knit, and exposes methods to check the yarn application

state, logs and to increase/decrease the container count - an instance of CondaCreator is created on-the-fly if making/zipping a conda environment - .local_cluster is an instance of dask.distributed.LocalCluster, with no local workers. The only parameter passed in is ip.


  • .yarn_api is an instance of YARNAPI, which provides commands to be directly

executed by the ResourceManager, including several informational calls, mostly via REST - an instance of CondaCreator is created on-the-fly if making/zipping a conda environment