We Keep Coding

How to use Ansible Style dynamic inventory with Nornir?

I want to move from Ansible to Nornir. In Ansbile I use dynamic inventory, where I use this python script to reference the host_var folder:
import json
import yaml
import glob
groups = {}
for hostfilename in glob.glob('./host_vars/*.yml'):
with open(hostfilename, 'r') as hostfile:
host = yaml.load(hostfile, Loader=yaml.FullLoader)
for hostgroup in host['host_groups']:
if hostgroup not in groups.keys():
groups[ hostgroup ] = { 'hosts': [] }
groups[ hostgroup ]['hosts'].append( host['hostname'] )
print(json.dumps(groups))
Question:
How can I use my existing Ansible Inventory in Nornir.
nornir.plugins.inventory.ansible.AnsibleInventory can only be used with 1x host.yaml file not with many, at least this is my understanding
Edit: Goal is to create always new Inventory files on every run. The workflow would be to generate the inventory yaml files in host_vars and then use it during the play.
Can somebody please help me?
Thanks
F.

If I understood you correctly, you want each yaml file in the host_vars folder to be interpreted as one host and its data. This feature is not part of base Nornir, but can be implemented via a custom inventory plugin.
The custom inventory plugin should implement a load() method that returns an Inventory-type object that Nornir can then use normally (see here for an example of the SimpleInventory implementation). I came up with this snippet adapted from the code that was given:
import os
import yaml
import glob
import pathlib
from nornir.core.inventory import (
Inventory,
Hosts,
Host,
Groups,
Group)
def map_host_data(host_dict):
return({
'hostname' : host_dict['hostname'],
'port': host_dict.get('port',22),
'username' : host_dict['username'],
'password' : host_dict['password'],
'platform' : host_dict['platform'],
'data' : host_dict.get('data', None)
})
class DynamicInventory:
def __init__(self, inventory_dir: str = "host_vars/") -> None:
self.inventory_dir = pathlib.Path(inventory_dir).expanduser()
def load(self):
hosts = Hosts()
groups = Groups()
for hostfilename in glob.glob(f"{self.inventory_dir}/*.yaml"):
with open(hostfilename,'r') as hostfile:
host_name = os.path.basename(hostfilename).replace('.yaml','')
host = yaml.load(hostfile, Loader=yaml.FullLoader)
for hostgroup in host['host_groups']:
if hostgroup not in groups.keys():
group = Group(name=hostgroup)
groups[hostgroup] = group
hosts[host_name] = Host(name=host_name, **map_host_data(host))
return Inventory(hosts=hosts,groups=groups,defaults={})
I'm assuming you're using Nornir >= 3 (which you really should), so don't forget to register your plugin if using it on your configuration. Assuming you put the above code under plugins/inventory.py:
from nornir import InitNornir
from plugins.inventory import DynamicInventory
from nornir.core.plugins.inventory import InventoryPluginRegister
InventoryPluginRegister.register("DynamicInventoryPlugin",DynamicInventory)
nr = InitNornir(inventory={'plugin': 'DynamicInventoryPlugin'},
runner={'plugin': 'threaded','options': {'num_workers': 20}})
This of course ignores some features (such as setting defaults), but can be modified to add more features that better match your current setup.

Python 3 and Azure table storage tablestorageaccount not working

I'm trying to use the sample provided by Microsoft to connect to an Azure storage table using Python. The code below fail because of tablestorageaccount not found. What I'm missing I installed the azure package but still complaining that it's not found.
import azure.common
from azure.storage import CloudStorageAccount
from tablestorageaccount import TableStorageAccount
print('Azure Table Storage samples for Python')
# Create the storage account object and specify its credentials
# to either point to the local Emulator or your Azure subscription
if IS_EMULATED:
account = TableStorageAccount(is_emulated=True)
else:
account_connection_string = STORAGE_CONNECTION_STRING
# Split into key=value pairs removing empties, then split the pairs into a dict
config = dict(s.split('=', 1) for s in account_connection_string.split(';') if s)
# Authentication
account_name = config.get('AccountName')
account_key = config.get('AccountKey')
# Basic URL Configuration
endpoint_suffix = config.get('EndpointSuffix')
if endpoint_suffix == None:
table_endpoint = config.get('TableEndpoint')
table_prefix = '.table.'
start_index = table_endpoint.find(table_prefix)
end_index = table_endpoint.endswith(':') and len(table_endpoint) or table_endpoint.rfind(':')
endpoint_suffix = table_endpoint[start_index+len(table_prefix):end_index]
account = TableStorageAccount(account_name = account_name, connection_string = account_connection_string, endpoint_suffix=endpoint_suffix)

I find the source sample code, and in the sample code there is still a custom module tablestorageaccount.py, it's just used to return TableService. If you already have the storage connection string and want to have a test, you could connect to table directly.
Sample:
from azure.storage.table import TableService, Entity
account_connection_string = 'DefaultEndpointsProtocol=https;AccountName=account name;AccountKey=account key;EndpointSuffix=core.windows.net'
tableservice=TableService(connection_string=account_connection_string)
Also you could refer to the new sdk to connect table. Here is the official tutorial about Get started with Azure Table storage.

TensorFlow Serving: Update model_config (add additional models) at runtime

I'm busy configuring a TensorFlow Serving client that asks a TensorFlow Serving server to produce predictions on a given input image, for a given model.
If the model being requested has not yet been served, it is downloaded from a remote URL to a folder where the server's models are located. (The client does this). At this point I need to update the model_config and trigger the server to reload it.
This functionality appears to exist (based on https://github.com/tensorflow/serving/pull/885 and https://github.com/tensorflow/serving/blob/master/tensorflow_serving/apis/model_service.proto#L22), but I can't find any documentation on how to actually use it.
I am essentially looking for a python script with which I can trigger the reload from client side (or otherwise to configure the server to listen for changes and trigger the reload itself).

So it took me ages of trawling through pull requests to finally find a code example for this. For the next person who has the same question as me, here is an example of how to do this. (You'll need the tensorflow_serving package for this; pip install tensorflow-serving-api).
Based on this pull request (which at the time of writing hadn't been accepted and was closed since it needed review): https://github.com/tensorflow/serving/pull/1065
from tensorflow_serving.apis import model_service_pb2_grpc
from tensorflow_serving.apis import model_management_pb2
from tensorflow_serving.config import model_server_config_pb2
import grpc
def add_model_config(host, name, base_path, model_platform):
channel = grpc.insecure_channel(host)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
#Create a config to add to the list of served models
config_list = model_server_config_pb2.ModelConfigList()
one_config = config_list.config.add()
one_config.name= name
one_config.base_path=base_path
one_config.model_platform=model_platform
model_server_config.model_config_list.CopyFrom(config_list)
request.config.CopyFrom(model_server_config)
print(request.IsInitialized())
print(request.ListFields())
response = stub.HandleReloadConfigRequest(request,10)
if response.status.error_code == 0:
print("Reload sucessfully")
else:
print("Reload failed!")
print(response.status.error_code)
print(response.status.error_message)
add_model_config(host="localhost:8500",
name="my_model",
base_path="/models/my_model",
model_platform="tensorflow")

Add a model to TF Serving server and to the existing config file conf_filepath: Use arguments name, base_path, model_platform for the new model. Keeps the original models intact.
Notice a small difference from #Karl 's answer - using MergeFrom instead of CopyFrom
pip install tensorflow-serving-api
import grpc
from google.protobuf import text_format
from tensorflow_serving.apis import model_service_pb2_grpc, model_management_pb2
from tensorflow_serving.config import model_server_config_pb2
def add_model_config(conf_filepath, host, name, base_path, model_platform):
with open(conf_filepath, 'r+') as f:
config_ini = f.read()
channel = grpc.insecure_channel(host)
stub = model_service_pb2_grpc.ModelServiceStub(channel)
request = model_management_pb2.ReloadConfigRequest()
model_server_config = model_server_config_pb2.ModelServerConfig()
config_list = model_server_config_pb2.ModelConfigList()
model_server_config = text_format.Parse(text=config_ini, message=model_server_config)
# Create a config to add to the list of served models
one_config = config_list.config.add()
one_config.name = name
one_config.base_path = base_path
one_config.model_platform = model_platform
model_server_config.model_config_list.MergeFrom(config_list)
request.config.CopyFrom(model_server_config)
response = stub.HandleReloadConfigRequest(request, 10)
if response.status.error_code == 0:
with open(conf_filepath, 'w+') as f:
f.write(request.config.__str__())
print("Updated TF Serving conf file")
else:
print("Failed to update model_config_list!")
print(response.status.error_code)
print(response.status.error_message)

While the solutions mentioned here works fine, there is one more method that you can use to hot-reload your models. You can use --model_config_file_poll_wait_seconds
As mentioned here in the documentation -
By setting the --model_config_file_poll_wait_seconds flag to instruct the server to periodically check for a new config file at --model_config_file filepath.
So, you just have to update the config file at model_config_path and tf-serving will load any new models and unload any models removed from the config file.
Edit 1: I looked at the source code and it seems that the flag is present from the very early version of tf-serving but there have been instances where some users were not able to use this flag (see this). So, try to use the latest version if possible.

If you're using the method described in this answer, please note that you're actually launching multiple tensorflow model server instances instead of a single model server, effectively making the servers compete for resources instead of working together to optimize tail latency.

FileUploadMiscError while persisting output file from Azure Batch

I'm facing the following error while trying to persist log files to Azure Blob storage from Azure Batch execution - "FileUploadMiscError - A miscellaneous error was encountered while uploading one of the output files". This error doesn't give a lot of information as to what might be going wrong. I tried checking the Microsoft Documentation for this error code, but it doesn't mention this particular error code.
Below is the relevant code for adding the task to Azure Batch that I have ported from C# to Python for persisting the log files.
Note: The container that I have configured gets created when the task is added, but there's no blob inside.
import datetime
import logging
import os
import azure.storage.blob.models as blob_model
import yaml
from azure.batch import models
from azure.storage.blob.baseblobservice import BaseBlobService
from azure.storage.common.cloudstorageaccount import CloudStorageAccount
from dotenv import load_dotenv
LOG = logging.getLogger(__name__)
def add_tasks(batch_client, job_id, task_id, io_details, blob_details):
task_commands = "This is a placeholder. Actual code has an actual task. This gets completed successfully."
LOG.info("Configuring the blob storage details")
base_blob_service = BaseBlobService(
account_name=blob_details['account_name'],
account_key=blob_details['account_key'])
LOG.info("Base blob service created")
base_blob_service.create_container(
container_name=blob_details['container_name'], fail_on_exist=False)
LOG.info("Container present")
container_sas = base_blob_service.generate_container_shared_access_signature(
container_name=blob_details['container_name'],
permission=blob_model.ContainerPermissions(write=True),
expiry=datetime.datetime.now() + datetime.timedelta(days=1))
LOG.info(f"Container SAS created: {container_sas}")
container_url = base_blob_service.make_container_url(
container_name=blob_details['container_name'], sas_token=container_sas)
LOG.info(f"Container URL created: {container_url}")
# fpath = task_id + '/output.txt'
fpath = task_id
LOG.info(f"Creating output file object:")
out_files_list = list()
out_files = models.OutputFile(
file_pattern=r"../stderr.txt",
destination=models.OutputFileDestination(
container=models.OutputFileBlobContainerDestination(
container_url=container_url, path=fpath)),
upload_options=models.OutputFileUploadOptions(
upload_condition=models.OutputFileUploadCondition.task_completion))
out_files_list.append(out_files)
LOG.info(f"Output files: {out_files_list}")
LOG.info(f"Creating the task now: {task_id}")
task = models.TaskAddParameter(
id=task_id, command_line=task_commands, output_files=out_files_list)
batch_client.task.add(job_id=job_id, task=task)
LOG.info(f"Added task: {task_id}")

There is a bug in Batch's OutputFile handling which causes it to fail to upload to containers if the full container URL includes any query-string parameters other than the ones included in the SAS token. Unfortunately, the azure-storage-blob Python module includes an extra query string parameter when generating the URL via make_container_url.
This issue was just raised to us, and a fix will be released in the coming weeks, but an easy workaround is instead of using make_container_url to craft the URL, craft it yourself like so: container_url = 'https://{}/{}?{}'.format(blob_service.primary_endpoint, blob_details['container_name'], container_sas).
The resulting URL should look something like this: https://<account>.blob.core.windows.net/<container>?se=2019-01-12T01%3A34%3A05Z&sp=w&sv=2018-03-28&sr=c&sig=<sig> - specifically it shouldn't have restype=container in it (which is what the azure-storage-blob package is including)

Azure Speaker Recognition - How to add a new key/value to a users profile?

I'm using the python sdk for speaker recognition using Microsoft cognitive service [I'm working in the Identification Folder]; When I run the CreateProfile.py file I'm trying give a profile a user-name. So for example, I would run my main.py file and then I would try to pass the locale and name.
import sys
sys.path.append('./Identification')
from CreateProfile import create_profile
name="Jane Doe"
subscriptionKey = "<my subscription key>"
locale = "en-us"
create_profile(name, subscriptionKey, locale)
Once I run the get profile GetProfile.py I want it to output all the user's info including their user-name. But I get this returned in the terminal.
Profile Name = None
Profile ID = 93affed1-ceb2-4538-9e6b-f0bd22d123b0
Locale = en-us
Enrollments Speech Time = 0.0
Remaining Enrollment Time = 30.0
Created = 2017-10-07T02:03:51.956Z
Last Action = 2017-10-07T02:03:51.956Z
Enrollment Status = Enrolling
I've tried editing the IdentificationProfile.py by adding things like _PROFILE_NAME = 'identificationProfileName' in the class, I've added a change related to this in a lot of places but I still don't see the name returning when I run GetProfile.py
This is my IdentificationProfile.py
import IdentificationServiceHttpClientHelper
import sys
def get_profile(subscription_key, profile_id):
"""Get a speaker's profile with given profile ID
Arguments:
subscription_key -- the subscription key string
profile_id -- the profile ID of the profile to resets
"""
helper = IdentificationServiceHttpClientHelper.IdentificationServiceHttpClientHelper(
subscription_key)
profile = helper.get_profile(profile_id)
print('Profile Name = {0}\n Profile ID = {1}\nLocale = {2}\nEnrollments Speech Time = {3}\nRemaining Enrollment Time = {4}\nCreated = {5}\nLast Action = {6}\nEnrollment Status = {7}\nName\n'.format(
profile._profile_name,
profile._profile_id,
profile._locale,
profile._name,
profile._enrollment_speech_time,
profile._remaining_enrollment_time,
profile._created_date_time,
profile._last_action_date_time,
profile._enrollment_status))
if __name__ == "__main__":
if len(sys.argv) < 3:
print('Usage: python DeleteProfile.py <subscription_key> <profile_id> ')
print('\t<subscription_key> is the subscription key for the service')
print('\t<profile_id> the ID for a profile to delete from the sevice')
sys.exit('Error: Incorrect usage.')
get_profile(sys.argv[1], sys.argv[2])

You cannot give a name to a identification profile. The HTTP endpoint that the python SDK uses does not accept any parameters other than the locale for the voice.
You would have to map user names to identification profile IDs yourself. You can use a database for that, maybe an Azure MySQL db. Or if you're just testing things out, use an in-memory map (aka dictionary).