Python package for automatically generating and deploying nuclio
serverless functions from code, archives or Jupyter notebooks.
Providing a powerful mechanism for automating code and function generation,
simple debugging, lifecycle management, and native integration into data-science tools.
- Automatically convert code/files + dependencies (environment, packages configuration, data/files)
into nuclio function spec or archive - Automatically build and deploy nuclio functions (code, spec, or archive) onto a cluster
- Provide native integration into Jupyter IDE (Menu and %magic commands)
- Handle function+spec versioning and archiving against an external object storage (s3, http/s, git or iguazio)
nuclio is a high performance serverless platform which runs over docker or kubernetes
and automate the development, operation, and scaling of code (written in multiple supported languages).
nuclio functions can be triggered via HTTP, popular messaging/streaming protocols, scheduled events, and in batch.
nuclio can run in the cloud as a managed offering, or on any Kubernetes cluster (cloud, on-prem, or edge)
read more about nuclio ...
nuclio and this package are an integral part of iguazio managed data science platform, you can see many end to end usage examples and notebooks in iguazio tutorial repo.
nuclio take code + function spec + optional file artifacts and automatically convert them to auto-scaling services over Kubernetes.
the artifacts can be provided as a YAML file (with embedded code), as Dockerfiles, or as archives (Git or Zip).
function spec allow you to define everything from CPU/Mem/GPU requirements, package dependencies, environment variables, secrets, shared volumes, API gateway config, and more.
this package is trying to simplify the configuration and deployment through more abstract APIs and %nuclio magic commands which eventually build the code + spec artifacts in YAML or Archive formats
(archives are best used when additional files need to be packaged or for version control)
the %nuclio magic commands are simple to use, but may be limited, if you want more
programmability use the python API calls
(build_file, deploy_file and deploy_code) from your code or a notebook cell.
- Installing
- Creating and debugging functions inside a notebook using
%nucliomagic - Exporting functions using Jupyter UI
- Creating and deploying functions using the python API
- Exporting, versioning, and archiving functions
- Deploy functions or versions directly from archive or git
- Controlling function code and configuration:
# nuclio:markers - mark relevant code cells (ignore, start-code, end-code)%nuclio cmd- defining package dependencies%nuclio envandenv_file- configuring local and remote env variables%nuclio handler- function handler wrapper%nuclio mount- mounting shared volumes into a function%nuclio config- resources, spec, and triggers configuration%nuclio build- generate and/or upload function spec or archive%nuclio deploy- deploy functions onto the cluster%nuclio show- show generated function code and spec (YAML)
- Advanced topics
- nuclio
init_context()hook for initializing resources (across invocations) - changing
context.loggerverbosity level to DEBUG - using Docker
- nuclio
- Links
- Developing
- Licence
pip install --upgrade nuclio-jupyter
Install in a Jupyter Notebook by running the following in a cell
# nuclio: ignore
!pip install --upgrade nuclio-jupyter
to access the library use import nuclio
%nuclio magic commands and some comment notations (e.g. # nuclio: ignore)
help us provide non-intrusive hints as to how we want to convert the notebook into a full function + spec.
cells which we do not plan to include in the final function (e.g. prints, plots, debug code, etc.) are prefixed with # nuclio: ignore
if we want settings such as environment variables and package installations to automatically appear in the fucntion spec
we use the env or cmd commands and those will copy them self into the function spec.
Note: if we want to ignore many cells at the beginning of the notebook (e.g. data exploration and model training) we can use
# nuclio: startat the first relevant code cell instead of marking all the cells above with# nuclio: ignore.
after we finish writing the code we can simulate the code with the built-in nuclio context object
(see: debugging functions) and when we are done we can use the export command to generate the function YAML/archive
or use deploy to automatically deploy the function on a nuclio/kubernetes cluster.
we can use other commands like show to print out the generated function + spec,
config to set various spec params (like cpu/mem/gpu requirements, triggers, etc.),
and mount to auto-mount shared volumes into the function.
for more details use the %nuclio help or %nuclio help <command>.
Can see the following example for configuring resources, writing and testing code,
deploying the function, and testing the final function.
note serverless functions have an entry point (handler) which is called by the run time engine and triggers.
the handler carry two objects, a context (run-time objects like logger) and event
(the body and other attributes delivered by the client or trigger).
We start with, import nucilo package, this initialize the %nuclio magic commands and context object
this section should not be copied to the function so we mark this cell with # nuclio: ignore (or we can use
# nuclio: start-code or # nuclio: end-code to mark the code section in the notebook).
# nuclio: ignore
import nucliothe following sections set an environment variable, install desired package,
and set some special configuration (e.g. set the base docker image used for the function).
note the environment variables and packages will be deployed in the notebook AND in the function,
we can specify that we are interested in having them only locally (-l) or in nuclio spec (-c).
we can use local environment variables in those commands with ${VAR_NAME}, see help for details.
note:
%is used for single line commands and%%means the command apply to the entire cell, see details
%nuclio cmd pip install textblob
%nuclio env TO_LANG=fr
%nuclio config spec.build.baseImage = "python:3.6-jessie"
magic commands only accept constant values or local environment variables as parameters
if you are interested in more flexibility use nuclio.build_file() or nuclio.deploy_file()
API calls, see python API calls
In the cell you'd like to become the handler, you can use one of two ways:
- create a
def handler(context, event)function (the traditional nuclio way) - or mark a cell with
%%nuclio handlerwhich means this cell is the handler function (the Jupyter way)
when using the 2nd approach we mark the return line using # nuclio:return at the end of it.
we can use the built-in nuclio context and nuclio.Event objects to simulate our functions locally,
we create an event object (message body, headers, etc.) and call our function with
the context object and our new event object, this will simulate a generated event trigger.
we can also simulate the function with multiple events simply by using a for loop
# nuclio: ignore
event = nuclio.Event(body=b'good morninng')
handler(context, event)once we are done we use the %nuclio deploy command to build the function and run it on a real cluster,
note the deploy command return a valid HTTP end-point which can be used to test/use our real function.
deploy the code as nuclio function nlp under project ai:
%nuclio deploy -n nlp -p ai
we can use %nuclio build if we only want to generate the function code + spec or
archive and/or upload/commit them to an external repository without running them on the cluster
this can also be used for automated CI/CD, functions can be built and pushed to GIT
and trigger a CI process which will only deploy the function after it passed tests.
if you would like to see the generated code and YAML configuration file before you deploy use %nuclio show command
for more flexibility use the nuclio.build_file() or nuclio.deploy_file() API calls, see the example below:
# nuclio: ignore
# deploy the notebook code with extra configuration (env vars, config, etc.)
spec = nuclio.ConfigSpec(config={'spec.maxReplicas': 2}, env={'EXTRA_VAR': 'something'})
addr = nuclio.deploy_file(name='nlp',project='ai',verbose=True, spec=spec, tag='v1.1')
# invoke the generated function
resp = requests.get('http://' + addr)
print(resp.text)Note: Cells containing
# nuclio: ignorecomment will be omitted in the build process.
visit this link to see the complete notebook , or check out this other example
The generated function spec for the above notebook will look like:
apiVersion: nuclio.io/v1
kind: Function
metadata:
name: nuclio-example
spec:
build:
baseImage: python:3.6-jessie
commands:
- pip install textblob
noBaseImagesPull: true
env:
- name: TO_LANG
value: fr
handler: handler:handler
runtime: python:3.6in many cases we just want to export the function into a YAML/Zip file and loaded manually to nuclio (e.g. via nuclio UI).
this package automatically register it self as a Jupyter converter, which allow exporting a notebook into nuclio format,
see example below, choose File/Download as/Nuclio in Jupyter notebook
Note: you might need to mark the notebook as
Trustedin order for the Nuclio option to show
Or you can run
jupyter nbconvert --to nuclio example.ipynb
This will create example.yaml or example.zip (if the function include extra files) with your code, spec, and extra files.
We currently don't support Google Colaboratory, Kaggle Notebooks and other custom Jupyter versions.
in some cases working from a notebook is an overkill, or we may want to generate code and configurations programmatically,
the nuclio package provide two main function calls deploy_code and deploy_file which allow us direct access as shown below:
import requests
import nuclio
# define my function code template
code='''
import glob
def handler(context, event):
context.logger.info('{}')
return str(glob.glob('/data/*'))
'''
# substitute a string in the template
code = code.format('Hello World!')
# define a file share (mount my shared fs home dir into the function /data dir)
vol = nuclio.Volume('data','~/')
# deploy my code with extra configuration (env vars, mount)
spec = nuclio.ConfigSpec(env={'MYENV_VAR': 'something'}, mount=vol)
addr = nuclio.deploy_code(code,name='myfunc',project='proj',verbose=True, spec=spec)
# invoke the generated function
resp = requests.get('http://' + addr)
print(resp.text)the deploy_file API allow deploying functions from various file formats (.ipynb, .py, .go, .js, .java, .yaml, or .zip archives)
the build_file API is the equivalent of %nuclio build magic command (create deployable function or archive and can upload it)
The first attribute in those functions is the filename, if left blank it will use the current notebook as source
the ConfigSpec provide a structures API for configuring functions, the class have the following methods:
set_env(name, value)- add environment variableset_config(key, value)- add spec attributeadd_volume(local, remote, kind, name, key, readonly)- add a Kubernetes Volume (v3io or PVC)add_trigger(name, spec)- add a trigger (see below)with_v3io()- addv3ioenvironment variables
methods can be nested, example:
# nuclio: ignore
spec = nuclio.ConfigSpec(cmd=build_commands)\
.set_config('build.baseImage', 'python:3.6-jessie')\
.add_volume(local='User', remote='~/')
spec.with_v3io()
spec.add_trigger('web', nuclio.triggers.HttpTrigger(workers=4, port=32005).ingress(name, host, paths))
spec.add_trigger('cron', nuclio.triggers.CronTrigger(interval='20s', body='scheduled_retraining'))
spec.add_trigger('kafka', nuclio.triggers.KafkaTrigger(url, topic, partitions).sasl(user, password))
addr = nuclio.deploy_file(name='training', project='netops', verbose=False, spec=spec)Note: such a section must be outside the code cells, use code section markers.
the library is not limited to python code, it support other languages such as go,
node.js, java, and bash. see the following example with bash code. note that
you can install any package/binary inside the function using the build commands (cmd=).
import requests
import nuclio
code = '''
echo "good morning"
echo $SOME_ENV
'''
spec = nuclio.ConfigSpec(env={'SOME_ENV':'env text'},
cmd=['apk --update --no-cache add imagemagick'])
addr = nuclio.deploy_code(code, lang='.sh', name='mysh', project='demo', spec=spec)
# invoke the generated function
resp = requests.get('http://' + addr)
print(resp.text)output:
good morning
env text
nuclio functions are a great way to provide well defined code + dependencies + environment definitions, functions can be versioned, archived, and restored by simply storing and re-applying their artifacts.
after we defined a functions using the %nuclio magic commands or directly from the API, we can build them,
we can also use the add magic command or files API attribute to pack multiple files in the same zip archive with the code and spec,
store it locally or upload the archive to cloud storage using a single command.
when we want to deploy a function from a URL or archive we use the deploy command or API,
specify the URL (vs the code or notebook) as source and the function will be deployed automatically.
we currently support the following archive options:
local/shared file system, http(s) unauthenticated or with Basic auth, Github, AWS S3, and iguazio PaaS.
note: that at this point nuclio doesnt support pulling archives directly from secret protected S3 buckets
see build and deploy commands help below for details
specify additional files to pack with the function (will force the use of zip)
%nuclio add -f model.json -f mylib.py
convert the current notebook into a function archive and upload into remote object store
%nuclio build -p myproj -t v1.1 -o s3://my-bucket/sub-dir -a
deploy and older version from an archive and name it newfunc
%nuclio deploy https://my-bucket.s3.amazonaws.com/sub-dir/myproj/funcname_v1.1.zip -n newfunc
note:
buildanddeploycommands dont have to run from the same notebook, seehelpfor detailed command options.
# nuclio: ignore
spec = nuclio.ConfigSpec(env={'TO_LANG':'de'})
nuclio.build_file(name='nlp', spec=spec, project='proj',tag='v7', verbose=True,
output_dir='v3io:///bigdata', archive=True)
# deploy the archived function
spec = nuclio.ConfigSpec(env={'TO_LANG':'fr'})
addr = nuclio.deploy_file('v3io:///bigdata/proj/nlp_v7.zip', name='nlp2', spec=spec, project='p1',tag='v8')
# invoke the generated function
resp = requests.get('http://' + addr)
print(resp.text)users can deploy functions from an archive (.zip file) or Git repository,
and override various function spec parameters (e.g. change environment variables,
add a custom trigger, etc.).
functions are versioned (using the tag attribute), we can load from a version
and deploy under a version (which can have a different tag)
in order to deploy from an archive you must specify an archive source URL (must start with
http(s)://, v3io://, or git://)
example:
%nuclio deploy https://myurl.com/projects/myfunc-v1.zip -n myfunc -p myproj
example: deploying functions from nuclio examples repository
addr = nuclio.deploy_file('git://github.com/nuclio/nuclio#master:/hack/examples/python/helloworld',name='hw', project='myproj')
resp = requests.get('http://' + addr)
print(resp.text)the Git URL convention is: git://[token@]github.com/org/repo#master[:<workdir>],
tokens are only needed for private repositories and can also be provided through the
GIT_ACCESS_TOKEN environment variable.
workdir is optional, specify the sub directory for the function within the repository
To upload function artifact (function.yaml + code + extra files) to git you can generate the artifacts using the build
command (specify desired output-dir), followed by committing and pushing the updates into your Git repo.
note that you can specify the desired branch in the url.
A user may want to include code sections in the notebook which should not translate to function code, for that he can use the following code markers (comment lines):
# nuclio : ignore- ignore the current cell when building the function# nuclio : code-start- ignore any lines prior to this line/cell# nuclio : code-end- ignore any cell from this cell (included) to the end
Run a command, add it to "build.Commands" in exported configuration.
Examples:
In [1]: %nuclio cmd pip install chardet==1.0.1
In [2]: %%nuclio cmd
...: apt-get install -y libyaml-dev
...: pip install pyyaml==3.13
If you'd like to only to add the instructions to function.yaml without running it locally, use the '--config-only' or '-c' flag
In [3]: %nuclio cmd --config-only apt-get install -y libyaml-dev
Set environment variable. Will update "spec.env" in configuration.
Examples:
In [1]: %nuclio env USER=iguzaio
%nuclio: setting 'iguazio' environment variable
In [2]: %%nuclio env
...: USER=iguazio
...: PASSWORD=t0ps3cr3t
...:
...:
%nuclio: setting 'USER' environment variable
%nuclio: setting 'PASSWORD' environment variable
Note: there is a magic environment variable
%v3iowhich adds the v3io connection env vars to the function, e.g.%nuclio env %v3io
If you'd like to only to add the instructions to function.yaml without running it locally, use the '--config-only' or '-c' flag
In [3]: %nuclio env --config-only MODEL_DIR=/home
If you'd like to only run locally and not to add the instructions to function.yaml, use the '--local-only' or '-l' flag
Set environment from file(s). Will update "spec.env" in configuration.
Examples:
In [1]: %nuclio env_file env.yml
In [2]: %%nuclio env_file
...: env.yml
...: dev-env.yml
Mark this cell as handler function. You can give optional name
%%nuclio handler
context.logger.info('handler called')
# nuclio:return
'Hello ' + event.body
Will become
def handler(context, event):
context.logger.info('handler called')
# nuclio:return
return 'Hello ' + event.body
Mount a shared file Volume into the function.
Example:
In [1]: %nuclio mount /data /projects/netops/data
mounting volume path /projects/netops/data as /data
Set function configuration value (resources, triggers, build, etc.). Values need to numeric, strings, or json strings (1, "debug", 3.3, {..}) You can use += to append values to a list.
see the nuclio configuration reference
Example:
In [1] %nuclio config spec.maxReplicas = 5
In [2]: %%nuclio config
...: spec.maxReplicas = 5
...: spec.runtime = "python2.7"
...: build.commands += "apk --update --no-cache add ca-certificates"
Build notebook/code + config, and generate/upload yaml or archive.
%nuclio build [filename] [flags]
when running inside a notebook the the default filename will be the
notebook it self
-n, --name path
function name, optional (default is filename)
-t, --tag tag
version tag (label) for the function
-p, --project
project name (required for archives)
-a, --archive
indicate that the output is an archive (zip)
-o, --output-dir path
Output directory/file or upload URL (see below)
--handler name
Name of handler function (if other than 'handler')
-e, --env key=value
add/override environment variable, can be repeated
-v, --verbose
emit more logs
supported output options:
format: [scheme://[username:secret@]path/to/dir/[name[.zip|yaml]]
name will be derived from function name if not specified
.zip extensions are used for archives (multiple files)
supported schemes and examples:
local file: my-dir/func
AWS S3: s3://<bucket>/<key-path>
http(s): http://<api-url>/path
iguazio: v3io://<api-url>/<data-container>/path
Example:
In [1] %nuclio build -v
In [2] %nuclio build --output-dir .
In [3] %nuclio build /path/to/code.py --handler faces
In [4] %nuclio build --tag v1.1 -e ENV_VAR1="some text" -e ENV_VAR2=xx
In [5] %nuclio build -p myproj -t v1.1 --output-dir v3io:///bigdata -a
Deploy notebook/file with configuration as nuclio function.
%nuclio deploy [file-path|url] [options]
parameters:
-n, --name path
function name, optional (default is filename)
-p, --project
project name (required)
-t, --tag tag
version tag (label) for the function
-d, --dashboard-url
nuclio dashboard url
-o, --output-dir path
Output directory/file or upload URL (see below)
-a, --archive
indicate that the output is an archive (zip)
--handler name
Name of handler function (if other than 'handler')
-e, --env key=value
add/override environment variable, can be repeated
-v, --verbose
emit more logs
when deploying a function which contains extra files or if we want to
archive/version functions we specify output-dir with archiving option (-a)
(or pre-set the output using the NUCLIO_ARCHIVE_PATH env var
supported output options include local path, S3, and iguazio v3io
following urls can be used to deploy functions from a remote archive:
http(s): http://<api-url>/path.zip[#workdir]
iguazio: v3io://<api-url>/<data-container>/project/name_v1.zip[#workdir]
git: git://[token@]github.com/org/repo#master[:<workdir>]
Examples:
In [1]: %nuclio deploy
In [2] %nuclio deploy -d http://localhost:8080 -p tango
In [3] %nuclio deploy myfunc.py -n new-name -p faces
In [4] %nuclio deploy git://github.com/myorg/repo#master -n myfunc -p proj
Print out the function code and spec (YAML). You should save the notebook before calling this function.
TBD
by default the built-in context object is set to print logs at INFO level and above, if we want to print out the debug level logs we can type the following
nuclio.context.set_logger_level(True)
this logging level only apply to the notebook/emulation, to change the function runtime
log level you should use the config or nuclio UI.
You can build a docker image and try it out
$ docker build -t jupyter-nuclio .
$ docker run -p 8888:8888 jupyter-nuclio
Then open your browser at http://localhost:8888
- iguazio tutorial repo - hosts many usage examples
We're using pipenv as package manager. To install dependencies run
$ pipenv sync -d
To run the tests run
$ pipenv run python -m pytest -v tests
To upload to pypi either run make upload after changing version in
nuclio/__init__.py or python cut_release <version>. The latter will update
the version in nuclio/__init__.py. You can use + for the next version. Ask
around for pypi credentials.
Apache 2.0 (see LICENSE.txt)