I am working in TravisCI, MlFlow and Databricks environment where .tavis.yml sits at git master branch and detects any change in .py file and whenever it gets updated, It will run mlflow command to run .py file in databricks environment.
my MLProject file looks as following:
name: mercury_cltv_lib
conda_env: conda-env.yml
entry_points:
main:
command: "python3 run-multiple-notebooks.py"
Workflow is as following:
TravisCI detects change in master branch-->triggers build which will run MLFlow command and it'll spin up a job cluster in databricks to run .py file from repo.
It worked fine with one .py file but when I tried to run multiple notebook using dbutils, it is throwing
File "run-multiple-notebooks.py", line 3, in <module>
from pyspark.dbutils import DBUtils
ModuleNotFoundError: No module named 'pyspark.dbutils'
Please find below the relevant code section from run-multiple-notebooks.py
def get_spark_session():
from pyspark.sql import SparkSession
return SparkSession.builder.getOrCreate()
def get_dbutils(self, spark = None):
try:
if spark == None:
spark = spark
from pyspark.dbutils import DBUtils #error line
dbutils = DBUtils(spark) #error line
except ImportError:
import IPython
dbutils = IPython.get_ipython().user_ns["dbutils"]
return dbutils
def submitNotebook(notebook):
print("Running notebook %s" % notebook.path)
spark = get_spark_session()
dbutils = get_dbutils(spark)
I tried all the options and tried
https://stackoverflow.com/questions/61546680/modulenotfounderror-no-module-named-pyspark-dbutils
as well. It is not working :(
Can someone please suggest if there is fix for the above-mentioned error while running .py in job cluster. My code works fine inside databricks local notebook but running from outside using TravisCI and MLFlow isn't working which is must requirement for pipeline automation.
Related
The following code is intended to run unit tests in Databricks notebooks, using pytest.
import pytest
import os
import sys
repo_name = "Databricks-Code-Repo"
# Get the path to this notebook, for example "/Workspace/Repos/{username}/{repo-name}".
notebook_path = dbutils.notebook.entry_point.getDbutils().notebook().getContext().notebookPath().get()
# Get the repo's root directory name.
repo_root = os.path.dirname(os.path.dirname(notebook_path))
# Prepare to run pytest from the repo.
os.chdir(f"/Workspace/{repo_root}/{repo_name}")
print(os.getcwd())
# Skip writing pyc files on a readonly filesystem.
sys.dont_write_bytecode = True
# Run pytest.
retcode = pytest.main([".", "-v", "-p", "no:cacheprovider"])
# Fail the cell execution if there are any test failures.
assert retcode == 0, "The pytest invocation failed. See the log for details."
This code snippet is in the guide provided by Databricks.
However, it produces the following error:
PermissionError: [Errno 1] Operation not permitted: '/Workspace//Repos/<email_address>/Databricks-Code-Repo/Databricks-Code-Repo'
This notebook is inside Databricks Repos. I have two other notebooks:
functions (where I have defined three data transformation functions);
test_functions (where I have defined test function for each of the data transformation functions from the previous notebook).
I get that the error has something to do with permissions, but I can't figure out what is causing it. I will appreciate any suggestions.
In the official Databricks example a notebook is created which runs tests with unittest:
The notebook imports another notebook (containing the test_* methods) with %run
The test is launched in the notebook with unittest.main(...
I would like to do the same thing with pytest:
I imported the notebook containing th test_* methods using %run
I tried to launch the tests with retcode = pytest.main([]) but I always get no tests ran
Notebook containing the tests (notebook_my_test)
def test_trivial2():
myvar=True
assert myvar == True
Main notebook:
%run ./notebook_my_test
import pytest
retcode = pytest.main([])
I am trying to deploy Google Dataflow streaming for use in my machine learning streaming pipeline, but cannot seem to deploy the worker with a file already loaded into memory. Currently, I have setup the job to pull a pickle file from a GCS bucket, load it into memory, and use it for model prediction. But this is executed on every cycle of the job, i.e. pull from GCS every time a new object enters the dataflow pipeline - meaning that the current execution of the pipeline is much slower than it needs to be.
What I really need, is a way to allocate a variable within the worker nodes on setup of each worker. Then use that variable within the pipeline, without having to re-load on every execution of the pipeline.
Is there a way to do this step before the job is deployed, something like
with open('model.pkl', 'rb') as file:
pickle_model = pickle.load(file)
But within my setup.py file?
##### based on - https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/complete/juliaset/setup.py
"""Setup.py module for the workflow's worker utilities.
All the workflow related code is gathered in a package that will be built as a
source distribution, staged in the staging area for the workflow being run and
then installed in the workers when they start running.
This behavior is triggered by specifying the --setup_file command line option
when running the workflow for remote execution.
"""
# pytype: skip-file
from __future__ import absolute_import
from __future__ import print_function
import subprocess
from distutils.command.build import build as _build # type: ignore
import setuptools
# This class handles the pip install mechanism.
class build(_build): # pylint: disable=invalid-name
"""A build command class that will be invoked during package install.
The package built using the current setup.py will be staged and later
installed in the worker using `pip install package'. This class will be
instantiated during install for this specific scenario and will trigger
running the custom commands specified.
"""
sub_commands = _build.sub_commands + [('CustomCommands', None)]
CUSTOM_COMMANDS = [['pip', 'install', 'scikit-learn==0.23.1']]
CUSTOM_COMMANDS = [['pip', 'install', 'google-cloud-storage']]
CUSTOM_COMMANDS = [['pip', 'install', 'mlxtend']]
class CustomCommands(setuptools.Command):
"""A setuptools Command class able to run arbitrary commands."""
def initialize_options(self):
pass
def finalize_options(self):
pass
def RunCustomCommand(self, command_list):
print('Running command: %s' % command_list)
p = subprocess.Popen(
command_list,
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
# Can use communicate(input='y\n'.encode()) if the command run requires
# some confirmation.
stdout_data, _ = p.communicate()
print('Command output: %s' % stdout_data)
if p.returncode != 0:
raise RuntimeError(
'Command %s failed: exit code: %s' % (command_list, p.returncode))
def run(self):
for command in CUSTOM_COMMANDS:
self.RunCustomCommand(command)
REQUIRED_PACKAGES = [
'google-cloud-storage',
'mlxtend',
'scikit-learn==0.23.1',
]
setuptools.setup(
name='ML pipeline',
version='0.0.1',
description='ML set workflow package.',
install_requires=REQUIRED_PACKAGES,
packages=setuptools.find_packages(),
cmdclass={
'build': build,
'CustomCommands': CustomCommands,
})
Snippet of current ML load mechanism:
class MlModel(beam.DoFn):
def __init__(self):
self._model = None
from google.cloud import storage
import pandas as pd
import pickle as pkl
self._storage = storage
self._pkl = pkl
self._pd = pd
def process(self,element):
if self._model is None:
bucket = self._storage.Client().get_bucket(myBucket)
blob = bucket.get_blob(myBlob)
self._model = self._pkl.loads(blob.download_as_string())
new_df = self._pd.read_json(element, orient='records').iloc[:, 3:-1]
predict = self._model.predict(new_df)
df = self._pd.DataFrame(data=predict, columns=["A", "B"])
A = df.iloc[0]['A']
B = df.iloc[0]['B']
d = {'A':A, 'B':B}
return [d]
You can use the #Setup method in your MlModel DoFn method where you can load your model and then use it in your #Process method. The #Setup method is called once per worker initialization.
I had written a similar answer here
HTH
When I run the example code in cmd, everything is ok.
>>> import pyspark
>>> l = [('Alice', 1)]
>>> spark.createDataFrame(l).collect()
[Row(_1='Alice', _2=1)]
But when I execute the code in pycharm, I get an error.
spark.createDataFrame(l).collect()
NameError: name 'spark' is not defined
Maybe something wrong when I link Pycharm to pyspark.
Environment Variable
Project Structure
Project Interpreter
When you start pyspark from the command line, you have a sparkSession object and a sparkContext available to you as spark and sc respectively.
For using it in pycharm, you should create these variables first so you can use them.
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
sc = spark.sparkContext
EDIT:
Please have a look at : Failed to locate the winutils binary in the hadoop binary path
I am a beginner in Spark and trying to follow instructions from here on how to initialize Spark shell from Python using cmd: http://spark.apache.org/docs/latest/quick-start.html
But when I run in cmd the following:
C:\Users\Alex\Desktop\spark-1.4.1-bin-hadoop2.4\>c:\Python27\python bin\pyspark
then I receive the following error message:
File "bin\pyspark", line 21
export SPARK_HOME="$(cd ="$(cd "`dirname "$0"`"/..; pwd)"
SyntaxError: invalid syntax
What am I doing wrong here?
P.S. When in cmd I try just C:\Users\Alex\Desktop\spark-1.4.1-bin-hadoop2.4>bin\pyspark
then I receive ""python" is not recognized as internal or external command, operable program or batch file".
You need to have Python available in the system path, you can add it with setx:
setx path "%path%;C:\Python27"
I'm a fairly new Spark user (as of today, really). I am using spark 1.6.0 on Windows 10 and 7 machines. The following worked for me:
import os
import sys
spark_home = os.environ.get('SPARK_HOME', None)
if not spark_home:
raise ValueError('SPARK_HOME environment variable is not set')
sys.path.insert(0, os.path.join(spark_home, 'python'))
sys.path.insert(0, os.path.join(spark_home, 'C:/spark-1.6.0-bin-hadoop2.6/python/lib/py4j-0.9-src.zip'))
execfile(os.path.join(spark_home, 'python/pyspark/shell.py'))
Using the code above, I was able to launch Spark in an IPython notebook and my Enthought Canopy Python IDE. Before, this, I was only able to launch pyspark through a cmd prompt. The code above will only work if you have your Environment Variables set correctly for Python and Spark (pyspark).
I run these set of path settings whenever I start pyspark in ipython:
import os
import sys
# Sys.setenv('SPARKR_SUBMIT_ARGS'='"--packages" "com.databricks:spark-csv_2.10:1.0.3" "sparkr-shell"') for R
### MANNN restart spart using ipython notebook --profile=pyspark --packages com.databricks:spark-csv_2.10:1.0.3
os.environ['SPARK_HOME']="G:/Spark/spark-1.5.1-bin-hadoop2.6"
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/bin")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/sql")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/pyspark/mllib")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/py4j-0.8.2.1-src.zip")
sys.path.append("G:/Spark/spark-1.5.1-bin-hadoop2.6/python/lib/pyspark.zip")
from pyspark import SparkContext
from pyspark import SparkConf
from pyspark import SQLContext
##sc.stop() # IF you wish to stop the context
sc = SparkContext("local", "Simple App")
With the reference and help of the user "maxymoo" I was able to find a way to set a PERMANENT path is Windows 7 as well. The instructions are here:
http://geekswithblogs.net/renso/archive/2009/10/21/how-to-set-the-windows-path-in-windows-7.aspx
Simply set path in System -> Environment Variables -> Path
R Path in my system C:\Program Files\R\R-3.2.3\bin
Python Path in my system c:\python27
Spark Path in my system c:\spark-2
The path must be separated by ";" and there must be no space between paths