Command linesΒΆ
Creates an falcon application and starts it through a wsgi application
Creates an falcon application to store machine learned models
Creates an falcon application and starts it through a wsgi application
Creates an falcon application and starts it through a wsgi server.
<<<
python -m lightmlrestapi start_mlrestapi --help
>>>
usage: start_mlrestapi [-h] [-n NAME] [-ho HOST] [-p PORT] [-no NOSTART]
[-w WSGI] [-o OPTIONS] [-s SECRET] [-c CCALL]
[-u USERS]
Creates an `falcon` application and runs it through a `wsgi` server.
optional arguments:
-h, --help show this help message and exit
-n NAME, --name NAME class name or filename which defines the application
(default: dummy)
-ho HOST, --host HOST
host (default: 127.0.0.1)
-p PORT, --port PORT port (default: 8081)
-no NOSTART, --nostart NOSTART
do not start the wsgi server (default: False)
-w WSGI, --wsgi WSGI `wsgi` framework which runs the falcon application
(default: waitress)
-o OPTIONS, --options OPTIONS
additional options as a string (depends on the
application) (default: )
-s SECRET, --secret SECRET
secret used to encrypt the logging, default is empty
which disables the encryption (default: )
-c CCALL, --ccall CCALL
calling convention, 'single', 'multi' or 'both'
depending on the fact that the prediction function can
predict for only one observation, multiple ones or
both (default: single)
-u USERS, --users USERS
registred users, file with two columns login,
encrypted password, and no header (default: )
(original entry : make_ml_app.py:docstring of lightmlrestapi.cli.make_ml_app.start_mlrestapi, line 23)
Creates an falcon application to store machine learned models
Creates an falcon application and starts it through a wsgi server. The appplication stores machine learned models and runs predictions assuming all the necessary packages were installed.
<<<
python -m lightmlrestapi start_mlreststor --help
>>>
usage: start_mlreststor [-h] [-l LOCATION] [-ho HOST] [-p PORT] [-n NAME]
[-no NOSTART] [-w WSGI] [-s SECRET] [-u USERS]
[-a ALGO]
Creates an `falcon` application and runs it through a `wsgi` server. The
appplication stores machine learned models and runs predictions assuming all
the necessary packages were installed.
optional arguments:
-h, --help show this help message and exit
-l LOCATION, --location LOCATION
location of the storage (default: .)
-ho HOST, --host HOST
host (default: 127.0.0.1)
-p PORT, --port PORT port (default: 8081)
-n NAME, --name NAME only one option is implemented 'ml' (default: ml)
-no NOSTART, --nostart NOSTART
do not start the wsgi server (for debug purpose)
(default: False)
-w WSGI, --wsgi WSGI `wsgi` framework which runs the falcon application
(default: waitress)
-s SECRET, --secret SECRET
secret used to encrypt the logging, default is empty
which disables the encryption (default: )
-u USERS, --users USERS
list of authorized users stored in a text file with
two columns: login and encrypted password (default: )
-a ALGO, --algo ALGO algorithm used to encrypt the passwords (default:
sha224)
(original entry : make_ml_store.py:docstring of lightmlrestapi.cli.make_ml_store.start_mlreststor, line 25)
Encrypts password
Encrypts passwords for a REST API created by lightmlrestapi.
<<<
python -m lightmlrestapi encrypt_pwd --help
>>>
usage: encrypt_pwd [-h] [-i INPUT] [-o OUTPUT] [-a ALGO]
Encrypts passwords to setup a REST API with *lightmlrestapi*.
optional arguments:
-h, --help show this help message and exit
-i INPUT, --input INPUT
file containing two columns <login>,<clear password>
(comma separated values), no header, encoding is
*utf-8* (default: )
-o OUTPUT, --output OUTPUT
file containing two columns <login>,<encrypted
password>, csv, encoding is *utf-8* (default: )
-a ALGO, --algo ALGO algorithm used to hash the passwords (default: sha224)
(original entry : make_encrypt_pwd.py:docstring of lightmlrestapi.cli.make_encrypt_pwd.encrypt_pwd, line 11)
Uploads a machine model
Uploads a machine learned model to a REST API created with lightmlrestapi. The code of this command line is equivalent to:
from lightmlrestapi.netrest import submit_rest_request, json_upload_model
req = json_upload_model(name=name, pyfile=pyfile, data=data)
submit_rest_request(req, login=login, pwd=pwd, url=url)
<<<
python -m lightmlrestapi upload_model --help
>>>
usage: upload_model [-h] [-l LOGIN] [--pwd PWD] [-n NAME] [-p PYFILE]
[-d DATA] [--url URL] [-t TIMEOUT]
Uplaods a machine learned models to a REST API defined by
:class:`MLStoragePost <lightmlrestapi.mlapp.mlstorage_rest.MLStoragePost>`.
optional arguments:
-h, --help show this help message and exit
-l LOGIN, --login LOGIN
user login (default: )
--pwd PWD user pasword (default: )
-n NAME, --name NAME name of the model, should be unique and not already
used (default: )
-p PYFILE, --pyfile PYFILE
python file which computes the prediction, the file
must follows the specification defined in
:ref:`l-template-ml` (default: )
-d DATA, --data DATA files to upload (default: )
--url URL url of the REST API (default: 127.0.0.1:8081)
-t TIMEOUT, --timeout TIMEOUT
timeout (default: 50)
(original entry : make_ml_upload.py:docstring of lightmlrestapi.cli.make_ml_upload.upload_model, line 15)
