cml | ♾️ CML - Continuous Machine Learning | CI/CD for ML | Continous Integration library
kandi X-RAY | cml Summary
kandi X-RAY | cml Summary
What is CML? Continuous Machine Learning (CML) is an open-source CLI tool for implementing continuous integration & delivery (CI/CD) with a focus on MLOps. Use it to automate development workflows — including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. CML can help train and evaluate models — and then generate a visual report with results and metrics — automatically on every pull request.
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QUESTION
I am working on a project that calls an API (using C# ASP.NET), and the API returns an XML document. I know how to deserialize an response in JSON, but am running into issues with deserializing an XML response. Currently I am getting the below error when attempting to deserialize the XML:
InvalidOperationException: response xmlns="" was not expected.
The error code displayed just the empty quotes in the error message. I have the code samples below, and I would greatly appreciate any constructive feedback or advice on making this better, and any advice on where I went wrong on attempting to deserialize this!
Here is the XML response from the API call (This API only returns either CML or a .csv):
...ANSWER
Answered 2022-Jan-11 at 04:00try this, it was tested in Visual Studio
QUESTION
I am looking for a good way to split the following xml
...ANSWER
Answered 2021-Dec-19 at 12:50I would approach it in the following way. Note that you have to take into account namespaces, so the code reflects that:
QUESTION
Df1:
...ANSWER
Answered 2021-Nov-17 at 18:08Using data.table
:
Although this works for this example, you can't use it as-is for "any" other data set. It requires some knowledge of the data, which can be adjusted easily when following the preparation steps (see explanation).
QUESTION
Lisbon city has open data, regarding several parameters, of sensors around the city. The JSON query is the following
http://opendata-cml.qart.pt/measurements/RULAEQ0001?startDate=202104010000&endDate=202108310000
This will return the registered noise levels RULAEQ
for station 0001
between the those dates expressed in YYYYMMDDHHMM
There are 80 monitoring stations across the city and I would like to get the data for each one of them. The stations are numbered from 0001 to 0080.
What is the best way to automate this process in python?
Thank you in advance.
Note 1: I have the code working for one single query
...ANSWER
Answered 2021-Nov-04 at 13:58# -*- coding: utf-8 -*-
# Import libraries
import pandas as pd
import urllib.request, json
from flatten_json import flatten
for station in list(range(1, 80)):
# Query URL
url = f'http://opendata-cml.qart.pt/measurements/RULAEQ{str(station).zfill(4)}?startDate=202104010000&endDate=202108310000'
print(station)
# Read JSON from URL and decode
with urllib.request.urlopen(url) as url:
data = json.loads(url.read().decode())
# Flatten JSON data
data_flattened = [flatten(d) for d in data]
# Create dataframe
df = pd.DataFrame(data_flattened)
# Print Dataframe
print(df)
QUESTION
I'm trying to parallelize the training step of my model with tensorflow ParameterServerStrategy
. I work with GCP AI Platform
to create the cluster and launch the task.
As my dataset is huge, I use the bigquery tensorflow connector included in tensorflow-io
.
My script is inspired by the documentation of tensorflow bigquery reader and the documentation of tensorflow ParameterServerStrategy
Locally my script works well but when I launch it with AI Platform I get the following error :
{"created":"@1633444428.903993309","description":"Error received from peer ipv4:10.46.92.135:2222","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Op type not registered \'IO>BigQueryClient\' in binary running on gke-cml-1005-141531--n1-standard-16-2-644bc3f8-7h8p. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.","grpc_status":5}
The scripts works with fake data on AI platform and works locally with bigquery connector. I imagine that the compilation of the model including the bigquery connector and its calls on other devices creates the bug but I don't know how to fix it.
I read this error happens when devices don't have same tensorflow versions so I checked tensorflow and tensorflow-io version on each device.
tensorflow : 2.5.0
tensorflow-io : 0.19.1
I created a similar example which reproduce the bug on AI platform
...ANSWER
Answered 2021-Oct-06 at 22:04As far as I understand this happens because when you submit your training job to Cloud AI training, it is using a stock TensorFlow 2.5 environment that doesn't have tensorflow-io package installed. Therefore it is complaining that it doesn't know about 'IO>BigQueryClient' op defined in tensorflow-io package.
Instead you can submit your training job to be using a custom container: https://cloud.google.com/ai-platform/training/docs/custom-containers-training
You don't need to write a new Docker file, you can use gcr.io/deeplearning-platform-release/tf-cpu.2-5 or gcr.io/deeplearning-platform-release/tf-gpu.2-5 (if your training job needs GPU) that has the right version of tensorflow-io installed.
You can read more about these containers here: https://cloud.google.com/tensorflow-enterprise/docs/use-with-deep-learning-containers
Here is my old example showing how to run a distributed training on Cloud AI using BigQueryReader: https://github.com/vlasenkoalexey/criteo/blob/master/scripts/train-cloud.sh
It is no longer maintained, but should give you a general idea how it should look like.
QUESTION
When I use setup-miniconda, it uses shell: bash -l {0}
in GitHub Actions:
ANSWER
Answered 2021-Sep-06 at 16:33That could be linked to issue 128 which states:
I got stuck for a while because my run commands were not using a login bash shell.
So the conda environment was not active.Would be helpful to warn about that and recommend something like the following in the yaml:
QUESTION
Using the below works when I use a PS Task in the ADO build pipeline but the same fails in Command line task.
...ANSWER
Answered 2021-Aug-12 at 04:31Change the occurrence of %20 with %%20. The command shell is interpreting %2 as an argument that is empty. That leaves 0.
QUESTION
Im trying to build a project through Jenkins integration tool. I tried to run locally through batch Command line option. I had earlier installed newman package as well as jenkins.war package to run through local server. But, when i try to build the project im getting an error below. Im a beginner but has no idea how to get rid of this error.
newman run C:\Users\MKBMX500.jenkins\workspace\Exported Collection\CountriesAPcollection.postman_collection.json
This is the exact path entered in the Jenkins CML batch option and i have rechecked the json file has no error at all. Any solution will be appreciated.
...ANSWER
Answered 2021-Aug-07 at 06:56Figured out the solution, not sure it exactly the same procedure or not, but for me the problem was somehow the path of the .json file. I'd copied the json file & paste into to .jenkins workspace folder created in users dir of c:\ drive. Project's name should have to be similar to that of the .json file folder. That worked least for me. Here is the screenshot
As the name of folder is API_run, so created jenkins project with this same name. Build was completed & finished successfully.
QUESTION
I'm trying to launch a training job on Google AI Platform with a custom container. As I want to use GPUs for the training, the base image I've used for my container is:
...ANSWER
Answered 2021-Mar-11 at 01:05The suggested way to build the most reliable container is to use the officially maintained 'Deep Learning Containers'. I would suggest pulling 'gcr.io/deeplearning-platform-release/tf2-gpu.2-4'. This should already have CUDA, CUDNN, GPU Drivers, and TF 2.4 installed & tested. You'll just need to add your code into it.
- https://cloud.google.com/ai-platform/deep-learning-containers/docs/choosing-container
- https://console.cloud.google.com/gcr/images/deeplearning-platform-release?project=deeplearning-platform-release
- https://cloud.google.com/ai-platform/deep-learning-containers/docs/getting-started-local#create_your_container
QUESTION
I'm trying to use and convert following code in a Pine screener
...ANSWER
Answered 2021-Feb-04 at 09:07All calculations that are necessary for the function screenerFunc
must be included in this function. Also, if you use the history access operator [1]
, you must first declare the variable. The errors in the script below are fixed, but you should check for yourself whether the script creates the labels correctly.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
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Install cml
:warning: Note that if you are using GitLab, you will need to create a Personal Access Token for this example to work.
Fork our example project repository.
To create a CML workflow, copy the following into a new file, .github/workflows/cml.yaml:
In your text editor of choice, edit line 16 of train.py to depth = 5.
Commit and push the changes:
In GitHub, open up a pull request to compare the experiment branch to master.
you push changes to your GitHub repository,
the workflow in your .github/workflows/cml.yaml file gets run, and
a report is generated and posted to GitHub.
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