Use pip or conda to install:

$ pip install knit --upgrade
$ conda install knit -c conda-forge



Instantiate knit with valid ResourceManager/Namenode IP/Ports and create a command string to run in all YARN containers

>>> from knit import Knit
>>> k = Knit(autodetect=True) # autodetect IP/Ports for YARN/HADOOP
>>> cmd = 'date'
>>> k.start(cmd)

start also takes parameters: num_containers, memory, virtual_cores, env, and files


After starting/submitting a command you can monitor its progress. The status method communicates with YARN’s ResourceManager and returns a python dictionary with current monitoring data.

 >>> k.status()
{'allocatedMB': 512,
'allocatedVCores': 1,
'amContainerLogs': '',
'amHostHttpAddress': '',
'applicationTags': '',
'applicationType': 'YARN',
'clusterId': 1454100653858,
'diagnostics': '',
'elapsedTime': 123800,
'finalStatus': 'UNDEFINED',
'finishedTime': 0,
'id': 'application_1454100653858_0011',
'memorySeconds': 63247,
'name': 'knit',
'numAMContainerPreempted': 0,
'numNonAMContainerPreempted': 0,
'preemptedResourceMB': 0,
'preemptedResourceVCores': 0,
'progress': 0.0,
'queue': 'default',
'runningContainers': 1,
'startedTime': 1454276990907,
'state': 'ACCEPTED',
'trackingUI': 'UNASSIGNED',
'user': 'ubuntu',
'vcoreSeconds': 123}

Often we track the state of an application. Possible states include: NEW, NEW_SAVING, SUBMITTED, ACCEPTED, RUNNING, FINISHED, FAILED, KILLED

Further details on the current functioning of the cluster are available via the connected yarn_api class which can help with trouble shooting: cluster_metrics(), nodes(), systems_logs.


We retrieve log data directly from a RUNNING Application Master:

>>> k.logs()

Or, if log aggregation is enabled, we retrieve the resulting aggregated log data stored in HDFS. Note: aggregated log data is only available after the application has finished or been terminated, usually with a small lag of a few seconds while log aggregation takes place.


To stop an application from executing immediately, use the kill method:

>>> k.kill()

Python Applications

Python applications can be created by first making a conda environment for them to run within. This can be done directly with CondaCreator (and such environments are cached and reused) or with the knit instance itself.

A simple Python based application:

from knit import Knit
k = Knit()

env = k.create_env('test', packages=['python=3.5']])
cmd = 'python -c "import sys; print(sys.version_info); import random; print(str(random.random()))"'
app_id = k.start(cmd, num_containers=2, env=env)

A long running Python application. Here we reuse the same environment create above:

from knit import Knit
k = Knit()

cmd = 'python -m SimpleHTTPServer'
app_id = k.start(cmd, num_containers=2, env=env)

Dask Cluster

Run a distributed dask cluster on YARN with a few lines like:

To start a dask cluster on YARN

import dask_yarn

# Specify conda packages and channels for execution environment
cluster = dask_yarn.DaskYARNCluster(packages=['python=3.6', 'scikit-learn', 'pandas', 'dask'],

# each worker gets 4GB and two cores
cluster.start(nworkers=10, memory=4096, cpus=2)

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