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Metaflow, Netflix's Python framework for data science, is now open source
- ZenPsycho 7y agothis seems like it's very similar to http://metaflow.fr http://metaflow.fr is there any relation, or is this a name collision?
- vtuulos 7y agono relation
- vtuulos 7y agohey, I'm one of the authors of Metaflow. Happy to answer any questions! Netflix has been using Metaflow internally for about two years, so we have many war stories :)
- deleted 7y ago[deleted]
- bhtucker 7y agoThe centralized DAG scheduler seems like a pretty important part. How much will not having Meson hamper the usability?
- pela 7y agoHi bhtucker, We do have plans to integrate with production-grade schedulers in the very near future. https://github.com/Netflix/metaflow/issues/2 https://github.com/Netflix/metaflow/issues/2
- vtuulos 7y agonote that Metaflow comes with a built-in DAG scheduler, so it is perfectly usable without a centralized scheduler like Meson. Integrations to Airflow or StepFunctions are on the roadmap, depending on what seems to resonate with people outside Netflix.
- ageofwant 7y agoG'day, seems like an cool tool, thanks - the links to the github tuts are currently broken... I'm of the opinion that adopting some standard DAG meta format for data science may make a positive impact on the reproduceability issues we have in science generally. So its good to see the idea has real world merit as well.
- pela 7y agoHi ageofwant, Thanks for taking a look at Metaflow. We will fix these links. Can you point us to the offending links?
- pela 7y agoI have updated the links.
- LegitShady 7y agoPray you do not update them further?
- omarhaneef 7y agoThanks for open sourcing this. Can you say a little about which niche this would occupy, and what the motivation is? Is it intended to compete with Tensorflow and Pytorch or to be an industrial strength version of SKlearn. I looked through the tutorial on my mobile and the answer was not immediately clear. Is the benefit that it auto scales on AWS without having to think through the infrastructure?
- savin-goyal 7y agoHi omarhaneef, We don't intend to compete with Tensorflow, PyTorch, SKLearn. What we offer is a way to iterate and productionize your models written using any of the aforementioned libraries (and more). https://docs.metaflow.org/introduction/what-is-metaflow https://docs.metaflow.org/introduction/what-is-metaflow contains further elaboration of our philosophy. Auto-scaling infrastructure is one piece of the puzzle, and Metaflow goes beyond that offering a comprehensive solution for model management.
- kprybol 7y agoDoes this do experiment tracking similar to MLflow? I’m trying to figure where these two overlap and where they diverge.
- vtuulos 7y agoyep, Metaflow tracks everything: Your code, dependencies, and the internal state of the workflow automatically. A big difference between Metaflow and other workflow frameworks for ML is that Metaflow doesn't only execute your DAG, it helps you to design and implement the code that runs inside the DAG. Many frameworks leave these details to the data scientist to decide.
- jmpeax 7y agoWhy are my Netflix suggestions and % so bad? Because I like "There Will Be Blood" doesn't mean I'm going to like "Ridiculous 6" just because both a westerns. Or, because I like "Fight Club" doesn't mean I'm going to like "By The Sea" just because both have Brad Pitt in them.
- thundergolfer 7y agoThis is pretty off-topic, and you're asking an infrastructure engineer who is not going to be able to answer this.
- Supermancho 7y agoNo, it is not offtopic in the slightest...especially in response to an "author". The underlying questions remain obvious. Is metaflow not used in netflix suggestions? What utility does it offer that other tooling doesnt or at least how has netflix extracted value? These are interesting questions.
- thundergolfer 7y agoI replied to someone complaining about their Netflix movie recommendations being bad. Did you think I was replying to you?
- ordx 7y agoThank you for sharing this. Would this be useful for me if I only need the deployment management part? Don't really need to track experiments, just looking for an easy way to deploy my models to Fargate.
- pela 7y agoYes indeed. You can access your model via the Metaflow client inside your service - https://docs.metaflow.org/metaflow/client https://docs.metaflow.org/metaflow/client. We are looking into releasing the hosting component on Metaflow in the future - https://github.com/Netflix/metaflow/issues/3 https://github.com/Netflix/metaflow/issues/3
- zmjjmz 7y agoHey I meant to track one of y'all down at the MLOps conference, but didn't get the chance. I've built a very shitty version of a cached-execution DAG thing internally, and one of the design decisions I made was to have it so that parent nodes in the DAG don't need to know anything about child nodes. This allows for larger DAG builders to be more easily subclassed. MetaFlow doesn't do that -- instead each 'step' has to know what to call next, which means that if I wanted to subclass e.g. the MovieStatsFlow in [here](https://github.com/Netflix/metaflow/blob/master/metaflow/tutorials/02-statistics/stats.py#L57 https://github.com/Netflix/metaflow/blob/master/metaflow/tut...) and say, add some sort of input pre-processing before the compute_statistics call, I'd essentially end up having to either override what compute_statistics does to not match its name _or_ copy-past e that first step just to replace that last line. I'm sure this design decision was considered and/or that use-case doesn't come up a lot at Netflix (although I've encountered that a lot), or maybe I'm missing something very obvious, but I'd love to hear your thoughts on that.
- savin-goyal 7y agoWe erred on the side of simplicity to keep things manageable for our users.
- beshrkayali 7y agoThat seems more complicated.
- faizshah 7y agoI have been looking for something exactly like this, but I use GCP not AWS. Is there a way to deploy outside of AWS? What would be involved in getting it to work with a different cloud?
- savin-goyal 7y agoAll of our integrations are driven by plugins. We are exploring our roadmap with regards to integrations with other clouds.
- rgudic 7y agoHi Ville, I cannot express how happy I am that you open-sourced Metaflow. I have three questions: 1) Do you know ETA of R API release? 2) Would you recommend using both Metaflow nad MLFlow in projects? Could you please explain why yes or why no :) 3) Do you plan to release integration with Spark/Yarn? Thanks in advance, Roko
- tristanz 7y agoThis looks like a fantastically clean API for Python data and ML pipelines. Congratulations! It would be great to have a scheduler and monitoring UI that are equally lightweight.
- vtuulos 7y agoMetaflow comes with a built in scheduler. If your company has an existing scheduler, e.g. for ETL, you can translate Metaflow DAGs to the production scheduler automatically. This is what we do at Netflix. We could provide similar support e.g. for Airflow, if there's interest. For monitoring, we have relied on notebooks this far. Their flexibility and customizability is unbeatable. We might build some lightweight discovery / debugging UI later, if there's demand. We are a bit on the fence about it internally.
- thundergolfer 7y ago> Metaflow comes with a built in scheduler. This isn't a scheduler that will trigger 'flows' though is it? If I search "Scheduler" in your docs the top result is a roadmap item, and searching "Cron" turns up nothing. Is it true that right now that to run a DAG "every day at 3am UTC" requires an external service?
- pela 7y agoRight now, yes.
- thundergolfer 7y agoThanks for confirmation.
- thundergolfer 7y ago> For monitoring, we have relied on notebooks this far. This is pretty interesting approach. Notebooks are a natural environment of Data Scientist and for monitoring they'd provide really good discoverability, flexibility, and interactivity. Do you use more traditional monitoring that will alert you somehow if a workflow fails, or a workflow hasn't run at all for X hrs/days?
- vtuulos 7y agobtw, if you happen to be at AWS Reinvent right now, you can get a stylish, collector's edition Metaflow t-shirt if you drop by at the Netflix booth at the expo hall and/or ping us otherwise!
- purple-again 7y agoWe are on Azure using Spark via Databricks. We had to abandon sci kit learn because of this choice. Does your service require AWS and can it be used in conjunction with Spark? Thank you for your time and consideration.
- pela 7y agoWe currently provide integrations with AWS (S3 and Batch) and it is easy to extend Metaflow to work with other cloud providers. https://docs.metaflow.org/internals-of-metaflow/technical-overview#decorators https://docs.metaflow.org/internals-of-metaflow/technical-ov...
- missosoup 7y agoWhat about databricks made you abandon sklearn?
- manojlds 7y agoMLLib I would think - https://spark.apache.org/docs/latest/ml-guide.html https://spark.apache.org/docs/latest/ml-guide.html
- rxin 7y agoWhat's the reason you need to abandon scikit-learn? You can run scikit-learn on Databricks, and many of our customers do. Disclaimer: Databricks cofounder.
- Thorentis 7y agoHow is this different / better to existing tools or workflows? I don't like to criticise new frameworks / tools without first understanding them, but I like to know what some key differences are without the marketing/PR fluff before giving one a go. For instance, this tutorial example here (https://github.com/Netflix/metaflow/blob/master/metaflow/tutorials/01-playlist/playlist.py https://github.com/Netflix/metaflow/blob/master/metaflow/tut...) does not look substantially different to what I could achieve just as easily in R, or other Python data wrangling frameworks. Is the main feature the fact I can quickly put my workflows into the cloud?
- vtuulos 7y agoHere are some key features: - Metaflow snapshots your code, data, and dependencies automatically in a content-addressed datastore, which is typically backed by S3, although local filesystem is supported too. This allows you to resume workflows, reproduce past results, and inspect anything about the workflow e.g. in a notebook. This is a core feature of Metaflow. - Metaflow is designed to work well with a cloud backend. We support AWS today but technically other clouds could be supported too. There's quite a bit of engineering that has gone into building this integration. For instance, using the Metaflow's built-in S3 client, you can pull over 10Gbps, which is more than you can get with e.g. aws CLI today easily. - We have spent time and effort in keeping the API surface area clean and highly usable. YMMV but it has been an appealing feature to many users this far. Hope this makes sense!
- elamje 7y agoYour first bullet point should be highlighted on your project front page! I read over the page a couple of times, and couldn’t deduce that, and I think it’s a really enticing feature!
- savin-goyal 7y agoThanks. Feedback noted.
- missosoup 7y ago
- aniketpanjwani 7y agoThis looks exciting! I'll play around with the tutorial and try to set up the AWS environment this weekend. I have several questions. 1. At what sort of scale does Metaflow become useful? Would you expect Metaflow to augment the productivity of a lone data scientist working by himself? Or is it more likely that you would need 3, 10, 25, or more data scientists before Metaflow is likely to become useful? 2. When you move to a new text editor, there are some initial frictions while you're trying to wrap your head around how things work. So, it can take some time before you become productive. Analogously, I imagine there are initial frictions when moving to Metaflow. In your experience, after Metaflow's environment has already been established, how long does it take for data scientists to get back to their initial productivity? It would be useful to have a sense of this for the data scientist who would want to sell their organization on adopting Metaflow. 3. Many data scientists work in organizations which have far less mature data infrastructure than Netflix, and/or data science needs of a much smaller scale than Netflix. In particular, I may not even have batch processing needs (e.g. a social scientist working on datasets which can be held entirely in memory). In that case, is Metaflow useful? 4. What's the closest open-source alternative to Metaflow on the market? Off the top of my head, I can't think of anything which quite matches.
- deleted 7y ago[deleted]
- seeravikiran 7y ago1. Metaflow should best help when there is an element of collaboration - so small to medium team of data scientists. Collaborating with your self is also another scenario when Metaflow can be useful since it takes care of versioning and archiving various artifacts. 2. Keeping the language pythonic, without any additional need to learn a DSL has definitely been key to Metaflow's adoption internally. That said, this is something we are open to hearing back, esp. with this OSS launch. 3. Yes - definitely think so. Personally my favorite is the local prototyping experience part; when everything can fit in memory and is blazing fast. There is an also an open issue for fast-data access, which you can upvote if interested in seeing it open-sourced. 4. We don't think there is an exact equivalent as well. :)
- cpintomammee 7y agoHow does this compare to snakemake[1] and nextflow[2]? [1] https://snakemake.readthedocs.io/en/stable/ https://snakemake.readthedocs.io/en/stable/ [2] https://www.nextflow.io/ https://www.nextflow.io/
- savin-goyal 7y agoI am not familiar with them.
- misterdoubt 7y agoThe fact that metaflow works directly in Python piques my interest. I can lint it, I can test it, I can format it, I can easily extend it. I've been hesitant to commit myself and my collaborators to yet another DSL -- and that's part of why I haven't seen much to offer in snakemake and nextflow.
- seeravikiran 7y agoYes - that’s our thinking too. Compilers finding your typos for variable names seems helpful for user productivity.
- russfink 7y agoI am disappointed that when I click on documentation, "why metaflow," I get a bunch of cartoony BS instead of a simple text explanation. Glad these folks don't write RFC'S. Edit: just went to the Amazon CodeGuru homepage. Fantastic! Wish they were all like that.
- amirathi 7y agoAfter going through a lot of marketing fluff, I landed on this useful page which explains Metaflow basics: https://docs.metaflow.org/metaflow/basics https://docs.metaflow.org/metaflow/basics Here's my understanding: - It's a python library for creating & executing DAGs - Each node is a processing step & the results are stored after each step so you can restart failed workflows from where it failed - Tight integration with AWS ECS to run the whole DAG on cloud I don't know why their .org site oddly feels like a paid SaaS tool. Anyway, thank you Netflix for open sourcing Metaflow.
- savin-goyal 7y agoAddendum - You can mix and match what steps of the DAG run on the cloud.
- seeravikiran 7y agoI would also add - dependency management (certain degree of reproducibility) as a first class feature leveraging conda.
- est 7y agolooks like airflow with ML tools?
- thundergolfer 7y agoIt provides a Python DAG building library like Airflow, but doesn't do Airflow's 'Operator ecosystem' thing. It also is very opinionated about dependency management (Conda-only) and is Python-only, where Airflow I think has operators to run arbitrary containers. So Metaflow is a non-starter I think if you don't want to exclusively use Python. Airflow also ships with built-in scheduler support (Celery?) or can run on K8s. Metaflow doesn't have this. Seems to rely on AWS Batch for production DAG execution. Airflow ships with a pretty rich UI. Metaflow seems to be anti-UI, and provides a novel Notebook-oriented workflow interaction model. Metaflow has pretty nice code artifact + params snapshotting functionality which is a core selling point. Airflow is not as supportive of this so it's harder to do reproducibility (I think). This is encapsulated by their "Datastore" model which can locally or in S3 persist flow code, config and data.
- deleted 7y ago[deleted]
- MostlyAmiable 7y agoThe link in the docs to the CloudFormation template source is broken: https://docs.metaflow.org/metaflow-on-aws/deploy-to-aws#cloudformation-template https://docs.metaflow.org/metaflow-on-aws/deploy-to-aws#clou... Instead of /Netflix/metaflow-tools/aws it should probably be /Netflix/metaflow-tools/tree/master/aws
- seeravikiran 7y agoThanks for reporting it. We ll fix it. Sorry for the inconvenience.
- MostlyAmiable 7y agoNo worries, on the whole the documentation is top notch.
- savin-goyal 7y agoFixed
- MostlyAmiable 7y agoWow, wasn't expecting that quick of a turnaround.
- bitfhacker 7y agoIt's so simple and intuitive to run two steps in parallel. Thank you, Netflix!
- savin-goyal 7y agoYou’re welcome! :)
- posedge 7y agoVery interesting project. I love that this allows you to transparently switch "runtime" from local to cloud, like spark does, but integrated with common python tools like sklearn/tf etc. Looking forward to test metaflow out myself.
- seeravikiran 7y agoThanks. Let us know how you like the prototyping -> scaling out & up journey.
- manojlds 7y agoHow does it compare to dragster.io? https://github.com/dagster-io/dagster https://github.com/dagster-io/dagster
- vtuulos 7y agoOrchestrating a workflow, which is what Dagster does, is just one part of Metaflow. Other important parts are dependency management, cloud integration, state transfer, inspecting and organizing results - features that are central to data science workflows. Metaflow helps data scientists build and manage data science workflows, not just execute a DAG.
- softwarelimits 7y agoCan anybody provide a good comparison e.g. with Meltano? I am not affiliated with the Meltano people, but I like the idea of keeping the system modular, what seems to make it easier to replace components. I have no doubt that we will see better replacements for every component of a data pipeline in the coming years. If there is only one thing to do right, then it´s to not bet on one tool but keep the whole stack flexible. I am still missing well established standards for data formats, workflow definitions and project descriptions - hopefully open source ninjas will deliver on this front before proprietary pirats will destroy the field with progress-inhibiting closed things. It seems to be too late to create an "Autocad" or "Word" file format for datascience, but I see no clear winner atm, but hopefully my sight is bad - please enlighten me!
- savin-goyal 7y agoI am not familiar with Meltano, sorry.
- Datenstrom 7y agoIs there a reason to use this over DVC[1] which is language and framework agnostic and supports a large number of storage backends? It works with any git repo and even polyglot implementations and can run the DAG on any system. Currently using DVC, MLflow just for metadata visualization and notes on experiments, and Anaconda for (python) dependency management. We are an embedded shop so we don't deploy to the "cloud." [1]: https://dvc.org/ https://dvc.org/
- vtuulos 7y agoWe are good friends with the DVC folks! If the DVC + MLFlow + Anaconda stack works for you, that's great. Metaflow provides similar features. The cloud integration is really important at Netflix's scale.
- elwell 7y agoAt first glance, I see BASIC's GOTO statement.
- dj18 7y agoSeems like a cool addition to the DAG ML tooling family. Thanks for sharing! Do you support, or plan to support, features commonly found in data science platform tools like Domino (https://www.dominodatalab.com/ https://www.dominodatalab.com/)? I'm thinking of container management, automatic publishing of web apps and API endpoints, providing a search for artifacts like code or projects, etc.
- vtuulos 7y agoGood question. We have many common features covered: - Container management: See https://docs.metaflow.org/metaflow/dependencies https://docs.metaflow.org/metaflow/dependencies - Search for artifacts: see https://docs.metaflow.org/metaflow/client https://docs.metaflow.org/metaflow/client - Automatic publishing of web apps: we have this internally but it is not open-source yet. If it interests you, react to this issue (https://github.com/Netflix/metaflow/issues/3 https://github.com/Netflix/metaflow/issues/3) Let us know if you notice any other interesting features missing! Feel free to open a GitHub issue or reach out to us on http://chat.metaflow.org http://chat.metaflow.org
- somurzakov 7y agoi looked over the tutorials and curious to know, whether the tutorials are representative of how Netflix does ML ? is data really being read in .csv format and processed in memory with pandas ? because I see "petabytes of data" being thrown everywhere, and i am just trying to understand how one can read gigabytes in .csv process do simple stats like grouping by in pandas - shouldn't simple SQL DWH do the same thing more efficiently with partitioned tables, clustered indexes and the power of SQL language ? i would love to take a look at one representative ML pipeline (even with masked names of datasets, features) just to see how "terabytes" of data get processed into a model
- vtuulos 7y agoGood question! A typical Metaflow workflow at Netflix starts by reading data from our data warehouse, either by executing a (Spark)SQL query or by fetching Parquet files directly from S3 using the built-in S3 client. We have some additional Python tooling to make this easy (see https://github.com/Netflix/metaflow/issues/4 https://github.com/Netflix/metaflow/issues/4) After the data is loaded, there are bunch of steps related to data transformations. Training happens with an off-the-shelf ML library like Scikit Learn or Tensorflow for training. Many workflows train a suite of models using the foreach construct. The results can be pushed to various other systems. Typically they are either pushed to another table or as a microservice (see https://github.com/Netflix/metaflow/issues/3 https://github.com/Netflix/metaflow/issues/3)
- edparcell 7y agoMy team has a similar library called Loman, which we open-sourced. Instead of nodes representing tasks, they represent data, and the library keeps track of which nodes are up-to-date or stale as you provide new inputs or change how nodes are computed. Each node is either an input node with a provided value, or a computed node with a function to calculate its value. Think of it as a grown-up Excel calculation tree. We've found it quite useful for quant research, and in production it works nicely because you can serialize entire computation graph which gives an easy way to diagnose what failed and why in hundreds of interdependent computations. It's also useful for real-time displays, where you can bind market and UI inputs to nodes and calculated nodes back to the UI - some things you want to recalculate frequently, whereas some are slow and need to happen infrequently in the background. [1] Github: https://github.com/janushendersonassetallocation/loman https://github.com/janushendersonassetallocation/loman [2] Docs: https://loman.readthedocs.io/en/latest/ https://loman.readthedocs.io/en/latest/ [3] Examples: https://github.com/janushendersonassetallocation/loman/tree/master/examples https://github.com/janushendersonassetallocation/loman/tree/...
- sriharshams 7y agoAwesome!!!
- firedup 7y agoHow does this compare to Kedro?