From 8878a3f80670ef49813854d01b0cfbe1740b41b2 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Mon, 6 Jul 2020 02:10:41 -0700 Subject: [PATCH 1/5] CML blog post --- content/blog/2020-07-07-cml-release.md | 197 ++++++++++++++++++ .../images/2020-07-07/cml-report-metrics.png | Bin 0 -> 58583 bytes .../images/2020-07-07/cml-report-params.png | Bin 0 -> 1045286 bytes 3 files changed, 197 insertions(+) create mode 100644 content/blog/2020-07-07-cml-release.md create mode 100644 static/uploads/images/2020-07-07/cml-report-metrics.png create mode 100644 static/uploads/images/2020-07-07/cml-report-params.png diff --git a/content/blog/2020-07-07-cml-release.md b/content/blog/2020-07-07-cml-release.md new file mode 100644 index 0000000000..b16daf5ade --- /dev/null +++ b/content/blog/2020-07-07-cml-release.md @@ -0,0 +1,197 @@ +--- +title: 'New Release: Continuous Machine Learning (CML) is CI/CD for ML' +date: 2020-07-07 +description: | + The DVC team is releasing a new open-source project called Continuous Machine + Learning. CML helps to organize MLOps infrastructure on top of the traditional + software engineering stack. + +descriptionLong: | + Today, the DVC team is releasing a new open-source project called Continuous + Machine Learning, or CML (https://cml.dev) to mainstream the best engineering + practices of CI/CD to AI and ML teams. CML helps to organize MLOps + infrastructure on top of the traditional software engineering stack. + +picture: 2020-06-22/release.png +pictureComment: CML release +author: dmitry_petrov +commentsUrl: https://discuss.dvc.org/t/dvc-1-0-release/412 +tags: + - Release + - CI/CD for ML + - MLOps + - DataOps +--- + +## 1. CI/CD for machine learning problems + +Continuous integration and continuous delivery (CI/CD) is a widely-used software +engineering practice. It's a validated approach to increasing the agility of +software development without sacrificing stability. **But why haven't CI/CD +practices taken root in machine learning and data science so far?** + +We see three substantial technical barriers to using standard CI systems with +machine learning projects: + +1. **Data dependencies.** In ML, data plays a similar role as code: ML results + critically depend on datasets, and changes in data need to trigger feedback + just like changes in source code. Furthermore, multi-GB datasets are + challenging to manage with Git-centric CI systems. +2. **Metrics-driven.** The traditional software engineering idea of pass/fail + tests does not apply in ML. As an example, `+0.72% accuracy` and + `-0.35% precision` does not answer the question if the ML model is good or + not. Detailed reports with metrics and plots are needed to make a good/bad + model discussion +3. **CPU/GPU resources**. ML training often requires more resources to train + then is typical to have in CI/CD runners. CI/CD must be connected with cloud + computing instances or Kubernetes clusters for ML training. + +## 2. CI/CD for ML is the next step for DVC team + +Since the beginning, our motivation has been helping ML teams benefit from +DevOps. We started DVC because we knew that data management would be a crucial +bottleneck, and sure enough, DVC was a big step towards making pipelines and +experiments manageable and reproducible. But conversations with our community +have brought us to one conclusion again and again: CI/CD for ML is the holy +grail. + +Over the last 3 years, we've reached some big milestones: + +1. We built DVC to address the ML data management problem. Recently, we + [released DVC 1.0](https://dvc.org/blog/dvc-1-0-release), marking a new and + more stable era for our API. +2. DVC has become a core part of many ML team's daily operations. The latest + [ThoughtWorks Technology Radar](https://www.thoughtworks.com/radar/tools) + says: + + _"... it [DVC] has become a favorite tool for managing experiments in machine + learning (ML) projects. Since it's based on Git, DVC is a familiar + environment for software developers to bring their engineering practices to + ML practice."_ + +3. An extraordinary team and community have emerged around DVC: + - 15 employees in our organization https://iterative.ai + - 100+ open-source contributors to DVC https://github.com/iterative/dvc and + another 100+ open-source contributors to docs + https://github.com/iterative/dvc.org + - 2000+ community members in our Discord https://dvc.org/chat and GitHub + issue tracker https://github.com/iterative/dvc + - 4000+ regular users of DVC + +Now that DVC is maturing, we're ready to take the next step: we want to +revolutionize ML development processes. We want ML experiments to have greater +visibility to teammates, shorter feedback loops, and more reproducibility. We +want teams to spend less time managing their computing resources and +experiments, and more time building value. The goal is to extend the amazing +results of DevOps from software development to ML and MLOps. + +## 3. Continuous Machine Learning release + +Today, we're releasing an open-source project https://CML.DEV to close the gap +between machine learning and software development practices. + +CML is a library of functions used inside CI/CD runners to make ML compatible +with **GitHub Actions** and **GitLab CI**. We've created functions to: + +1. Generate informative reports on every Pull/Merge Request with metrics, plots, + and hyperparameters changes. +2. Provision GPU\CPU resources from cloud service providers (**AWS, GCP, Azure, + Ali**) and deploy CI runners using **docker-machine**. +3. Bring datasets from cloud storage to runners for model training (using + **DVC**), as well as save the resulting model in cloud storage. + +![Auto-generated metrics-driven report in GitLab Merge Request](/uploads/images/2020-07-07/cml-report-metrics.png) + +The workflow and visual reports are customizable by modifying the CI +configuration file in your GitHub `./github/workflows/*.yaml` or GitLab +`.gitlab-ci.yml` project. Use CML functions in conjunction with your own ML +model training and testing scripts to create your own automated workflow and +reporting system. + +```yaml +# GitLab workflow in '.gitlab-ci.yml' file + +stages: + - cml_run + +cml: + stage: cml_run + image: dvcorg/cml-py3:latest + script: + - dvc pull data --run-cache + + - pip install -r requirements.txt + - dvc repro + + # Compare metrics to master + - git fetch --prune + - dvc metrics diff --show-md master >> report.md + + # Visualize loss function diff + - dvc plots diff --target loss.csv --show-vega master > vega.json + - vl2png vega.json | cml-publish --md >> report.md + - dvc push data --run-cache + - cml-send-comment report.md +``` + +![Hyperparameter change with a result image in GitHub Pull request report](/uploads/images/2020-07-07/cml-report-params.png) + +In this example all the CML functions are defined in the **docker images** that +is used in the workflow - `dvcorg/cml-py3`. Users can specify any docker image. +The only restriction is that the CML library need to be installed to enable all +the CML commands for the reporting and graphs: + +```bash +npm i @dvcorg/cml +``` + +Graphs and image commands might require additional packages like `vega-cli` or +image file convertors. This is how the original CML docker image installs all +the important the dependencies: + +```yaml +# Install update pip and nodejs, install dvc and cml +ADD "./" "/cml" RUN wget https://dvc.org/deb/dvc.list -O +/etc/apt/sources.list.d/dvc.list && \ apt update && \ apt -y install dvc && \ +apt -y install build-essential libcairo2-dev libpango1.0-dev libjpeg-dev +libgif-dev librsvg2-dev && \ npm config set user 0 && \ npm config set +unsafe-perm true && \ npm install -g /cml && \ npm install -g vega-cli && \ npm +install -g vega-lite && \ apt-get install -y libfontconfig-dev && \ apt-get +clean && \ rm -rf /var/lib/apt/lists/* +``` + +Examples of docker images can be found in the +[CML repository](https://github.com/iterative/cml/). + +CML is based on the assumption that MLOps can work with traditional engineering +tools. It shouldn't require an entirely separate platform. We're excited about a +world where DevOps practitioners can work fluently on both software and ML +aspects of a project. + +## 4. The relationship between CML and DVC + +CML and DVC are related projects under the umbrella of the same team, but will +have separate websites and independent development. The CML project is hosted on +a new web site: https://cml.dev. The source code and issue tracker is on GitHub: +https://github.com/iterative/cml + +For support and communications, the DVC Discord server is still the place to go: +https://dvc.org/chat. We've made a new `#cml` channel there to discuss CML, +CI/CD for ML and other MLOps related questions. + +## 5. Conclusion + +With the rise of AI/ML teams and ML platforms in addition to the software +engineering stack, we believe that the industry needs a single technology stack +to work with software as well as AI projects. A simple layer of a tool is +required to close the gap between AI projects and software projects to fit them +into the existing stack and CML is the way to make it. + +Our philosophy is that ML projects, and MLOps practices, should be built on top +of traditional engineering tools and not as a separate stack. A simple layer of +tools will be required to close the gap, and CML is part of this ecosystem. We +think this is the future of MLOps. + +As always, thanks for reading and for being part of the DVC community. We'd love +to hear what you think about CML. Please be in touch on +[Twitter](https://twitter.com/dvcorg) and [Discord](https://dvc.org/chat)! diff --git a/static/uploads/images/2020-07-07/cml-report-metrics.png b/static/uploads/images/2020-07-07/cml-report-metrics.png new file mode 100644 index 0000000000000000000000000000000000000000..9b8e0a6758ff77dadad95127fe0ec62fdf90231b GIT binary patch literal 58583 zcmb4}1B@t5*XPH!ZQHhO+qUhwGjqqbZTpUG+qUh!&-?ECCHw6ryW2^nEB~tMI;T2a zr*nQ4p&%y?3xx#*0000hDIuZ+007kZx0peI{Z-^qAi@9ua2C8&G+dMn-3c6=?9D7~ zObJ{(9ZU&KJuJ-t06f-*GA*4*S`$Kk4p5vyE+-u8GNxdUUmrLdB+xo3%T4Vk#ZWSb zs%S_MBE!4^SU&u}Khl2odyur*7?U%!r&E56BB$K-R6ageu6w43KW@J4K`!*wdvw`h z?*8nxqr76~uuJ*-bxc1!J_W{_x7~epl-IiZ_`*JV9T@lKkzXiY_$KJQ%($ZK12=#D z8LdvSm-aZeJh>R=PF``Rn4#hWo4Yyw5*oB}B)x{(zS(6Mf#dsQhLCeJ?S8C}j`Y$# z>wzoG{0WiYWfmmZO?z`t+I$1{Zgv=}eyo0RlWOb7+I>^Y-A~^#NsoQEKful;p)EG&0m@+!}xv$&EJ*Fw`t9avhxXZue_Mb-Mw3nO4Xg^ zhwXzF{F4>gIb-|cxuBZ!2dC8wZ8G>v-#@*_`(ei?-T9ccKB|478TH3Ylx`vJSFEAZxPg+qdp^5$PTD_TL=AkWb-}|)-rqvK)q;%C= 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CML helps to organize MLOps infrastructure on top of the traditional software engineering stack. -picture: 2020-06-22/release.png +picture: 2020-07-07/cover.png pictureComment: CML release author: dmitry_petrov commentsUrl: https://discuss.dvc.org/t/dvc-1-0-release/412 @@ -161,7 +161,7 @@ clean && \ rm -rf /var/lib/apt/lists/* ``` Examples of docker images can be found in the -[CML repository](https://github.com/iterative/cml/). +[CML repository](https://github.com/iterative/cml). CML is based on the assumption that MLOps can work with traditional engineering tools. It shouldn't require an entirely separate platform. We're excited about a @@ -176,8 +176,8 @@ a new web site: https://cml.dev. The source code and issue tracker is on GitHub: https://github.com/iterative/cml For support and communications, the DVC Discord server is still the place to go: -https://dvc.org/chat. We've made a new `#cml` channel there to discuss CML, -CI/CD for ML and other MLOps related questions. +https://dvc.org/chat We've made a new `#cml` channel there to discuss CML, CI/CD +for ML and other MLOps related questions. ## 5. 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zcVGn)UcsgRM{r!dMDNO9o_bczhhO+Pl7=w5e-e>c2U_jA&qJv>2Vx!4wgSQjX Date: Mon, 6 Jul 2020 16:07:29 -0700 Subject: [PATCH 3/5] cut the docker part & change dvc report image --- content/blog/2020-07-07-cml-release.md | 27 ++++-------------- .../images/2020-07-07/cml-report-metrics.png | Bin 58583 -> 213999 bytes 2 files changed, 6 insertions(+), 21 deletions(-) diff --git a/content/blog/2020-07-07-cml-release.md b/content/blog/2020-07-07-cml-release.md index d23488cffe..f4c0bda0b5 100644 --- a/content/blog/2020-07-07-cml-release.md +++ b/content/blog/2020-07-07-cml-release.md @@ -145,28 +145,13 @@ the CML commands for the reporting and graphs: npm i @dvcorg/cml ``` -Graphs and image commands might require additional packages like `vega-cli` or -image file convertors. This is how the original CML docker image installs all -the important the dependencies: +Examples of docker images can be found in `docker` directory of the CML the +repository: [CML repository](https://github.com/iterative/cml). -```yaml -# Install update pip and nodejs, install dvc and cml -ADD "./" "/cml" RUN wget https://dvc.org/deb/dvc.list -O -/etc/apt/sources.list.d/dvc.list && \ apt update && \ apt -y install dvc && \ -apt -y install build-essential libcairo2-dev libpango1.0-dev libjpeg-dev -libgif-dev librsvg2-dev && \ npm config set user 0 && \ npm config set -unsafe-perm true && \ npm install -g /cml && \ npm install -g vega-cli && \ npm -install -g vega-lite && \ apt-get install -y libfontconfig-dev && \ apt-get -clean && \ rm -rf /var/lib/apt/lists/* -``` - -Examples of docker images can be found in the -[CML repository](https://github.com/iterative/cml). - -CML is based on the assumption that MLOps can work with traditional engineering -tools. It shouldn't require an entirely separate platform. We're excited about a -world where DevOps practitioners can work fluently on both software and ML -aspects of a project. +As you can see, CML is based on the assumption that MLOps can work with +traditional engineering tools. It shouldn't require an entirely separate +platform. We're excited about a world where DevOps practitioners can work +fluently on both software and ML aspects of a project. ## 4. 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zsc+Za-o8=PTNY@r%T2#S^;6c?)+CpXc1-k7cXxNPYYFPo>V^I%6C0ZX2&I@%0Nj^B zIU!p|hz`p_V64)!p+m-q98j5mfYmTgca04l7FEmN$|zy-%}AjwML0*5@AgJmk{&_a z{#~rYr%RjuWs>%nnbP-|`M)>#{yj@mDQqymIK{E&KCxxZqfgS!y9@P%xkeYO>UT`? z=_|{P3$C&_qv~RGZGTf47v@jPDPourlGiYDKnkn>`<3E| Date: Tue, 7 Jul 2020 00:55:03 -0700 Subject: [PATCH 4/5] more reviews --- content/blog/2020-07-07-cml-release.md | 49 +++++++++++++++----------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/content/blog/2020-07-07-cml-release.md b/content/blog/2020-07-07-cml-release.md index f4c0bda0b5..6e8e5ae7f5 100644 --- a/content/blog/2020-07-07-cml-release.md +++ b/content/blog/2020-07-07-cml-release.md @@ -2,15 +2,15 @@ title: 'New Release: Continuous Machine Learning (CML) is CI/CD for ML' date: 2020-07-07 description: | - The DVC team is releasing a new open-source project called Continuous Machine - Learning. CML helps to organize MLOps infrastructure on top of the traditional - software engineering stack. + Today we're launching Continuous Machine Learning (CML), a new open-source + project for CI/CD with ML. Let's bring the power of DevOps to ML or MLOps. descriptionLong: | - Today, the DVC team is releasing a new open-source project called Continuous - Machine Learning, or CML (https://cml.dev) to mainstream the best engineering - practices of CI/CD to AI and ML teams. CML helps to organize MLOps - infrastructure on top of the traditional software engineering stack. + Today we're launching Continuous Machine Learning (CML), a new open-source + project for CI/CD with ML. Use it to automate parts of your ML workflow, + including model training and evaluation, comparing ML experiments across your + project history, and monitoring changing datasets. Let's bring the power of + DevOps to ML or MLOps. picture: 2020-07-07/cover.png pictureComment: CML release @@ -23,7 +23,13 @@ tags: - DataOps --- -## 1. CI/CD for machine learning problems +## CI/CD for machine learning + +Today, the DVC team is releasing a new open-source project called Continuous +Machine Learning, or CML (https://cml.dev) to mainstream the best engineering +practices of CI/CD to AI and ML teams. CML helps to organize MLOps +infrastructure on top of the traditional software engineering stack instead of +creating separate AI platforms. Continuous integration and continuous delivery (CI/CD) is a widely-used software engineering practice. It's a validated approach to increasing the agility of @@ -46,7 +52,7 @@ machine learning projects: then is typical to have in CI/CD runners. CI/CD must be connected with cloud computing instances or Kubernetes clusters for ML training. -## 2. CI/CD for ML is the next step for DVC team +## CI/CD for ML is the next step for the DVC team Since the beginning, our motivation has been helping ML teams benefit from DevOps. We started DVC because we knew that data management would be a crucial @@ -79,15 +85,15 @@ Over the last 3 years, we've reached some big milestones: - 4000+ regular users of DVC Now that DVC is maturing, we're ready to take the next step: we want to -revolutionize ML development processes. We want ML experiments to have greater -visibility to teammates, shorter feedback loops, and more reproducibility. We -want teams to spend less time managing their computing resources and -experiments, and more time building value. The goal is to extend the amazing -results of DevOps from software development to ML and MLOps. +revolutionize the ML development processes. We want ML experiments to have +greater visibility to teammates, shorter feedback loops, and more +reproducibility. We want teams to spend less time managing their computing +resources and experiments, and more time building value. The goal is to extend +the amazing results of DevOps from software development to ML and MLOps. -## 3. Continuous Machine Learning release +## _Continuous Machine Learning_ release -Today, we're releasing an open-source project https://CML.DEV to close the gap +Today, we're releasing an open-source project https://cml.dev to close the gap between machine learning and software development practices. CML is a library of functions used inside CI/CD runners to make ML compatible @@ -96,9 +102,10 @@ with **GitHub Actions** and **GitLab CI**. We've created functions to: 1. Generate informative reports on every Pull/Merge Request with metrics, plots, and hyperparameters changes. 2. Provision GPU\CPU resources from cloud service providers (**AWS, GCP, Azure, - Ali**) and deploy CI runners using **docker-machine**. -3. Bring datasets from cloud storage to runners for model training (using - **DVC**), as well as save the resulting model in cloud storage. + Ali**) and deploy CI runners using + [Docker Machine](https://github.com/docker/machine). +3. Bring datasets from cloud storage to runners (using **DVC**) for model + trainin, as well as save the resulting model in cloud storage. ![Auto-generated metrics-driven report in GitLab Merge Request](/uploads/images/2020-07-07/cml-report-metrics.png) @@ -153,7 +160,7 @@ traditional engineering tools. It shouldn't require an entirely separate platform. We're excited about a world where DevOps practitioners can work fluently on both software and ML aspects of a project. -## 4. The relationship between CML and DVC +## The relationship between CML and DVC CML and DVC are related projects under the umbrella of the same team, but will have separate websites and independent development. The CML project is hosted on @@ -164,7 +171,7 @@ For support and communications, the DVC Discord server is still the place to go: https://dvc.org/chat We've made a new `#cml` channel there to discuss CML, CI/CD for ML and other MLOps related questions. -## 5. Conclusion +## Conclusion With the rise of AI/ML teams and ML platforms in addition to the software engineering stack, we believe that the industry needs a single technology stack From 22f8d465a87232d05fe7c4c0837b9e9e4962f975 Mon Sep 17 00:00:00 2001 From: Dmitry Petrov Date: Tue, 7 Jul 2020 00:58:35 -0700 Subject: [PATCH 5/5] Link to discuss --- content/blog/2020-07-07-cml-release.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2020-07-07-cml-release.md b/content/blog/2020-07-07-cml-release.md index 6e8e5ae7f5..a678666291 100644 --- a/content/blog/2020-07-07-cml-release.md +++ b/content/blog/2020-07-07-cml-release.md @@ -15,7 +15,7 @@ descriptionLong: | picture: 2020-07-07/cover.png pictureComment: CML release author: dmitry_petrov -commentsUrl: https://discuss.dvc.org/t/dvc-1-0-release/412 +commentsUrl: https://discuss.dvc.org/t/continuous-machine-learning-release/429 tags: - Release - CI/CD for ML