metaflow | manage real-life data science projects | Machine Learning library

by Netflix Python Version: 2.12.5 License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(7)Vulnerabilities Install Support

kandi X-RAY | metaflow Summary

metaflow is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. metaflow has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install metaflow' or download it from GitHub, PyPI.

Metaflow is a human-friendly Python/R library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. For more information, see Metaflow's website and documentation.

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metaflow has a highly active ecosystem.

It has 6722 star(s) with 644 fork(s). There are 264 watchers for this library.

There were 10 major release(s) in the last 12 months.

There are 227 open issues and 299 have been closed. On average issues are closed in 226 days. There are 49 open pull requests and 0 closed requests.

It has a negative sentiment in the developer community.

The latest version of metaflow is 2.12.5

Quality

metaflow has 0 bugs and 0 code smells.

Security

metaflow has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

metaflow code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

metaflow is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

metaflow releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

It has 33251 lines of code, 2668 functions and 268 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed metaflow and discovered the below as its top functions. This is intended to give you an instant insight into metaflow implemented functionality, and help decide if they suit your requirements.

Return a list of MFExtPackage objects .
Return a list of container templates .
Process a single node .
Run a single step .
Registers a new job definition .
Returns the next result from dst .
Queue a task join .
Tokenize a template .
A worker that reads the results from S3 .
Create a progress bar .

Get all kandi verified functions for this library.

metaflow Key Features

No Key Features are available at this moment for metaflow.

metaflow Examples and Code Snippets

No Code Snippets are available at this moment for metaflow.

Community Discussions

Trending Discussions on metaflow

Metaflow from Netflix vs Apache Airflow

How to disable automatic retries of failed Metaflow tasks?

How to store and retrieve figures as artifacts in MetaFlow?

How to set MetaFlow's --max-workers flag from within Python definition?

How to create nested branches in metaflow?

Metaflow - AWS batch task fails after deleting conda folder from S3

Stop Metaflow from parallelising foreach steps

QUESTION

Metaflow from Netflix vs Apache Airflow

Asked 2022-Jan-03 at 22:10

I have a questions regarding differences between Apache Airflow and Metaflow(https://docs.metaflow.org/). As far as I understood Apache airflow is just a job scheduler, that runs tasks. Metaflow from Netflix is as a dataflow library, which creates machine learning pipeline(dataflow is available) in forms of DAGs. Basically it means, that Metaflow can be executed on the Apache Airflow?

is my understanding correct? If yes, is it possible to convert Metaflow DAG into Apache Airflow DAG?

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ANSWER

Answered 2022-Jan-03 at 22:10

Honestly, I haven't worked with Metaflow and thank you for introducing it to me! There is a nice introduction video you can find on Youtube.

Airflow is a framework for creating scheduled pipelines. A pipeline is a set of tasks, linked between each other that represent an Directed Acyclic Graph. Pipeline can be scheduled, you can tell how often or when it should run, you can tell when it should've ran in the past and what time period it should backfill. You can run the whole Airflow as one single docker container or you can have multi-node cluster, it has bunch of already existing operators to integrate with 3rd party services. I recommend to look into Airflow Architecture and concepts.

Metaflow looks like something similar, but created specifically for data-scientists. I can be wrong here, but looking at the Metaflow Basics it looks like I can the same way create a scheduled pipeline similar to Airflow.

I would look in specific tools you want to integrate with and which one of both integrates better. As mentioned, Airflow has lots of already made connectors and operators, as well as, powerful scheduler with backfill and Jinja template language to design your DB queries for enter link description here.

Hope that is somewhat helpful. Here is also some nice article with feature comparison.

Source https://stackoverflow.com/questions/70569957

QUESTION

How to disable automatic retries of failed Metaflow tasks?

Asked 2021-Aug-13 at 18:29

By default, MetaFlow retries failed steps multiple times before the pipeline errors out. However, this is undesired when I am CI testing my flows using pytest-- I just want the flows to fail fast. How do I temporarily disable retries (without hard-coding @retry(times=0) on all steps)?

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ANSWER

Answered 2021-Aug-13 at 18:29

You can disable it with by setting the METAFLOW_DECOSPECS environment variable: METAFLOW_DECOSPECS=retry:times=0.

This temporarily decorates all steps with @retry(times=0)-- unless they are already decorated, in which case this won't override the hard-coded retry settings.

Source: @Ville in the MetaFlow Slack.

Source https://stackoverflow.com/questions/68776921

QUESTION

How to store and retrieve figures as artifacts in MetaFlow?

Asked 2021-Aug-09 at 22:46

How do I save a plot as a data artifact in MetaFlow? Plotting libraries usually have you write out to a file on disk. How do I view the figure afterwards?

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ANSWER

Answered 2021-Aug-09 at 22:46

The trick is to serialize the figure's bytes as an artifact:

Source https://stackoverflow.com/questions/68719323

QUESTION

How to set MetaFlow's --max-workers flag from within Python definition?

Asked 2021-Jul-15 at 16:12

MetaFlow permits you to set the maximum number of concurrent tasks using the --max-workers CLI flag (ref: https://docs.metaflow.org/metaflow/scaling#safeguard-flags). However, I would like to avoid setting this every time.

Is it possible to set the --max-workers flag from the Python definition of the FlowSpec (without the CLI)?

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ANSWER

Answered 2021-Jul-15 at 16:12

The CLI flag sets the METAFLOW_MAX_WORKERS environment variable. You can set this within Python by setting this environment variable before you define your FlowSpec:

Source https://stackoverflow.com/questions/68397295

QUESTION

How to create nested branches in metaflow?

Asked 2021-Apr-30 at 15:28

I am using metaflow to create a text processing pipeline as follows:-

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ANSWER

Answered 2021-Apr-30 at 15:27

After going through docs again carefully, I realised that I wasn't handling joins properly. As per docs for metaflow-2.2.10:-

Note that you can nest branches arbitrarily, that is, you can branch inside a branch. Just remember to join all the branches that you create.

which means every branch should be joined. In order to join values from branches, metaflow provides merge_artifacts utility function to aid in propagating unambiguous values.

Since, there are three branches in the workflow, therefore added three join steps to merge results.

Following changes worked for me:-

Source https://stackoverflow.com/questions/67333103

QUESTION

Metaflow - AWS batch task fails after deleting conda folder from S3

Asked 2020-Feb-11 at 20:12

I am using Metaflow on AWS in batch mode. I deleted the conda folder from s3. Now when I try to run a batch task, it fails in the bootstrapping environment step.

Apparently metaflow.plugins.conda.batch_bootstrap tries to download conda packages using the cache_urls associated with the environment id from the conda.dependencies file. The issue is described in some more detail here.

How can I fix this problem so that I can run a metaflow batch task again?

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ANSWER

Answered 2020-Feb-11 at 20:12

@kanimbla metaflow maintains a manifest folder .metaflow either in the same directory as your flow or in one of the parent directories. Deleting it resets your flow's dependencies.

Source https://stackoverflow.com/questions/60100900

QUESTION

Stop Metaflow from parallelising foreach steps

Asked 2020-Feb-11 at 20:08

I recently started using Metaflow for my hyperparameter searches. I'm using a foreach for all my parameters as follows:

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ANSWER

Answered 2020-Feb-11 at 20:08

@BBQuercus You can limit parallelization by using the --max-workers flag.

Currently, we run no more than 16 tasks in parallel and you can override it as python myflow.py run --max-workers 32 for example.

Source https://stackoverflow.com/questions/60053025

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