Databricks

• all-purpose clusters (notebook/job clusters) :: block by block code execution
• standard job clusters :: job execution (~3-4 times cheaper)

miscellanious

• path to root: file:/databricks/driver/
• use your own IDE (e.g. Jupyterlab): https://databricks.com/blog/2019/12/03/jupyterlab-databricks-integration-bridge-local-and-remote-workflows.html
• Koalas: pandas API on Apache Spark
• secrets: credentials manager
• the comment/uncomment shortcut Cmd + / doesn't work with German / Swiss mac keyboards.

-> German fix [credits: Franziska] Option + select beginning of lines with mouse + #

-> Swiss fix Ctrl + -

Jobs

• chose a new job cluster (the all purpose ones are more expensive and can also be notebook clusters).
• Saving logs: advanced options -> logging -> destination -> DBFS

job clusters, unlike all-purpose notebooks, don't have a "stop when idle" option, which means your job may run indefinitely

Databricks + AWS

permanently mount a bucket (notebook and job clusters are handled differently)

bucket = "path/to/bucket"
mount = "folder_name"

# mount if not already mounted
m = dbutils.fs.mounts()
if(not any('/mnt/' + mount in s for s in m)):
  dbutils.fs.mount("s3a://" + bucket, '/mnt/' + mount)
  display(dbutils.fs.ls('/mnt/' + mount))

temporarily copy from a bucket (while cluster is active)

dbutils.fs.cp("s3a://path/to/bucket", "file:/databricks/driver/", True)

download files from folder

1. go to user settings under the little man top right. setup a new token

2. in terminal:

pip install databricks-cli
databricks configure --token
westeurope.azuredatabricks.net/?o=5728379491119130

3. copy paste the token

---

• for help:

databricks fs -h

• to download a file:

databricks fs cp dbfs:/FileStore/... local/file/directory

For Python

import a .py file as a module

1. right click in the workspace and click import (creating a .py file from scratch is not possible)
2. write %run /Users/firstName.lastName@company.com/path/to/external.py or %run ./relative/path/to/external.pyin a notebook block.

install a package on a live notebook cluster from the pypi repository

go to main page -> import library -> pypi -> "package_name"

whenever you restart the cluster that is attached to the notebook, you have to "detach reattach" to the cluster on the upper left corner.

Once one is done testing, it is easier to move to a standard cluster, so as to start scheduling and always have the packages installed.

install a package on a standard cluster from the pypi repository

create a job -> associate a clsuter to it -> add a dependent library that was imported with the steps above.

hyperparameter tuning

https://docs.databricks.com/notebooks/widgets.html

silence the message "py4j.java_gateway - Received command c on object id p0" in the logs

import logging
logger = spark._jvm.org.apache.log4j
logging.getLogger("py4j").setLevel(logging.ERROR)

For R

Install and Load Packages dynamically

packages <- c("data.table", "profvis", ...)

package.check <- lapply(packages, FUN = function(x) {
  if (!require(x, character.only = T)) install.packages(x)
  if (! (x %in% (.packages() )))  library(x, character.only = T)
})

install Keras

# making sure that the cluster's default python is used to install keras and run models
# without this command, keras_model.compile(), install_keras() and install_tensorflow() conflict by using different python installations.
Sys.setenv(RETICULATE_PYTHON = system("which python", intern = T))

install.packages("tensorflow")
library(tensorflow)
install_tensorflow()

# version = "gpu"

install.packages("keras")
library(keras)
install_keras(tensorflow = "gpu")

### check

k = backend()
sess = k$get_session()
sess$list_devices()

workflow example

Databricks does not offer an integration of an entire github repo. Instead, it works on a file by file basis. To manage that, to use your favourite IDE and to separate the script into several text files for readability (e.g. one text file per function / class definition), one solution is the following: Have a central R notebook on databricks that calls scripts from other R files in a central scriptdirectory. Workflow is then the following:

1. modify script files locally on favourite IDE
2. in your shell (to install the CLI, see "download files from folder"):

databricks fs mkdirs dbfs:/FileStore/video2vec/script
databricks fs cp -r script dbfs:/FileStore/video2vec/script

3. in databricks

setwd("/dbfs/FileStore/video2vec/script")
lapply(list.files(pattern = "[.][rR]$", recursive = FALSE), source)

Another option is an R-Package, but that would have required an extra step of de- and re-installing the package at every modification. This workflow sounds a bit more tedious than usual, but requires just a few seconds of time and is not that much slower than full repo git integration (until it comes to Databricks). Please do message me in case you found a nicer workflow for R or Python.