From 2c62a0ce0949bdd297e81779163240c681ade5ec Mon Sep 17 00:00:00 2001 From: MKhalusova Date: Tue, 1 Feb 2022 09:46:37 -0500 Subject: [PATCH 01/23] first draft TPI Docker tutorial --- ...terative-terraform-provider-with-docker.md | 204 ++++++++++++++++++ .../images/2022-02-28/unsplash-containers.jpg | Bin 0 -> 6397189 bytes 2 files changed, 204 insertions(+) create mode 100644 content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md create mode 100644 static/uploads/images/2022-02-28/unsplash-containers.jpg diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md new file mode 100644 index 0000000000..fd3819d282 --- /dev/null +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -0,0 +1,204 @@ +--- +title: Using Iterative Terraform Provider with Docker +date: 2022-02-28 +description: > + In this tutorial you'll learn how you can move use an existing Docker image with + Iterative Terraform Provider, as well as build your own Docker image. +descriptionLong: > + In this tutorial you'll learn how you can move use an existing Docker image with + Iterative Terraform Provider, as well as build your own Docker image. +picture: 2022-02-28/unsplash-containers.jpg +author: maria_khalusova +# todo: commentsUrl: +tags: + - MLOps + - Iterative Terraform Provider + - AWS + - Docker +--- + +Recently we have [published a tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) +that demonstrated how you can move a machine learning experiment from a local computer to a more powerful cloud machine with +some help from the [Iterative Terraform Provider](https://registry.terraform.io/providers/iterative/iterative/latest). We've +talked about [Terraform](https://www.terraform.io), Iterative Terraform Provider and how you can configure the `iterative_task` +resource to provision infrastructure, sync data and run your training script. + +To make it easy to get started, we used a pre-configured [Deep Learning AMI from AWS](https://aws.amazon.com/machine-learning/amis/) +to simplify the environment setup. In most cases, however, instead of using a pre-configured Deep Learning AMI, you will +ship your environment to a remote machine using [Docker](https://www.docker.com) which has become an essential tool not only +in traditional software development, but also in machine learning. If you're not sure why it would be a good idea to use +Docker for machine learning development, check out +[this article from AWS](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) +that should answer this question for you. As a side note, using Docker to encapsulate your environment doesn't mean you won't + be using AMIs at all. In fact, AMI (Amazon Machine Image) is actually a required component of setting up an EC2, and at the + minimum you will need an Ubuntu distribution AMI (which is a configurable default for the `iterative_task`). However, we will + use Docker for the machine learning specific setup: drivers, GPU-specific libraries for accelerated math, and Python libraries. + This separation will not only give you more control over the versions of the frameworks that you choose to use (or not use), + but also over the cloud provider you provision the resources with. Your Docker image is not cloud provider specific while a + Deep Learning AMI is a part of the AWS ecosystem. + +If you have a DevOps team, they can build a Docker image suited for your needs, so you'll only need to use it in your +`iterative_task` script. We'll show you how to do this in the first part of this tutorial. But if you need (or want) to build +your own Docker image, we've got you too! The second part of this tutorial will illustrate some basics required for building +and using your own Docker image. Without further ado, let's get into it! + +## Setting up environment for utilizing GPUs with Docker +If you haven't read the [previous tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) +yet and are not familiar with Terraform, I would encourage you to check it out and learn some basics, i.e. how to let Terraform +know that you'll be using the Iterative Provider, how to initialize the working directory with `terraform init`, how to use +other essential Terraform commands (`plan`, `apply`, `refresh`, `show`, `destroy`). All these will apply here, we'll only modify +the script part of the `main.tf`. + +Let's say we have a DevOps team that has carefully prepared a Docker image suitable for data science and machine learning. To +illustrate this, we can use a publicly available CML Docker image (`docker://iterativeai/cml`) provided by Iterative. This image +comes loaded with Python, CUDA, git, node and other essentials for full-stack data science. There are several versions of this +image, but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, which includes Ubuntu 20.04, Python 3.8 +(CUDA 11.0.3, CuDNN 8). + +Here's what the script is going to look like: + +```hcl + script = <<-END + #!/bin/bash + sudo apt-get update + sudo apt-get install -y software-properties-common build-essential ubuntu-drivers-common + sudo ubuntu-drivers autoinstall + sudo curl -fsSL https://get.docker.com -o get-docker.sh && sudo sh get-docker.sh && + sudo usermod -aG docker ubuntu + sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock + curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - + curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list + sudo apt update && sudo apt install -y nvidia-docker2 + sudo systemctl restart docker + rm get-docker.sh + + nvidia-smi + docker run --rm --gpus all -v "$PWD:/tpi" iterativeai/cml:0-dvc2-base1-gpu \ + /bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py" + END +``` + +As you can see, we still need to make sure to install the required drivers first. Then in order to use a Docker image, we'll need +to install Docker itself, and sort out users, groups and permissions. Next, we'll install the `nvidia-docker` that will enable us +to run GPU-accelerated Docker containers. Yes, GPUs will make you work for them before you can enjoy the accelerated training! + +Finally, we can use Iterative's image to run our own container with this line: +``` docker run --rm --gpus all -v "$PWD:/tpi" iterativeai/cml:0-dvc2-base1-gpu \ + /bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py" +``` +Let's unpack this line. +* `docker run`: this command will essentially create a container from the specified image, and then let us use it. +* `–rm flag` will make Docker automatically clean up the container and remove the file system when the container exits, +* `–gpus all` will allow us to use GPUs of the host machine from the container +* `-v "$PWD:/tpi"`: the Docker container we'll be running is only aware of what is included inside of it, and it doesn't have our +training script nor the data which we synced to the host machine. How do we pass the data and the training code into the Docker +container? We can do it by mounting a volume. The `-v` flag lets you configure a volume, and has the following structure: +`-v "[path on the host machine to files to be shared with Docker container]:[the path where the directory is mounted in the container]"` +* `iterativeai/cml:0-dvc2-base1-gpu`: full name of the Docker image including the tag +* `/bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py"`: bash commands to run inside of the container. +We'll need to navigate to the mounted volume, install the dependencies and launch the training script. + +Now if we've initialized the working directory, we can call `terraform apply` to provision infrastructure, sync data and code to +it, set up the environment, and run the training process. If you’d like to tinker with this example you can +[find it on GitHub](https://github.com/iterative/blog-tpi-bees/blob/docker-examples/main.tf). + +## Building your own Docker image +Suppose, we wanted our Docker container to include all the Python libraries required to run our training pipeline? Well, these would +be quite specific to our project, so we'd have to build our own Docker image. Luckily we don't have to build one completely from +scratch, instead we can use one of the existing publicly available images as a base to build on top of. + +For this example, let's use `nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04` from NVIDIA. Earlier, we didn't need to install Docker +on the local machine because it was only used on the cloud machine, but if you're going to build your own image, you'll need to start by +[installing Docker locally](https://docs.docker.com/get-docker/). + +To build an image, create a file called `Dockerfile` in your project's root directory. This is a text-based script of instructions +that will be used to create a container image. In our case it'll be quite simple as most of the complex setup is taken care of +by NVIDIA's image we're using as a base: + +``` +FROM --platform=linux/amd64 nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04 + +# Install wget and python +RUN apt-get update && apt-get install -y wget python3-pip + +# Add the required packages to the environment +COPY ./requirements.txt /tmp/requirements.txt +RUN pip3 install -r /tmp/requirements.txt + +WORKDIR /app +``` + +Unlike Terraform, where you had a declarative description of the desired infrastructure in the main.tf, `Dockerfile` is a script, +so the order of the steps matters. The `FROM` instruction sets the base image for subsequent instructions, and it is a required first +instruction. To execute commands in a new layer on top of the current image and commit the results, we use the `RUN` instruction. +The resulting committed image will be used for the next step in the `Dockerfile`. + +To install the project-specific Python packages, we'll need to copy the `requirements.txt` to the filesystem of the container, and +we can do this with the `COPY` instruction. Then we'll use `RUN` again to install them. + +Finally, with `WORKDIR` we can specify what we want to call the directory inside the container where we'll run things inside the container. + +To build your own image locally use the following command: `docker build -t bees:latest .`, where `bees` is the name of my Docker image +but you can name yours however you like. + +Hooray! We've got our own Docker image, but we're not done just yet. To use this image from the Terraform config file, we should +publish it on [Docker Hub](https://hub.docker.com/). Your company may already have a business account for Docker Hub, but if you're learning +Docker and want to try it as an individual, you can create your own free [personal account here](https://hub.docker.com/). + +Once you have ayour account, +1. Log in to the Docker public registry on your local machine from command line with `docker login`. +2. Tag your image (aka give it a name): `docker tag bees [YOUR_DOCKERHUB_ID]/bees:bees`. +3. Publish the image: `docker push [YOUR_DOCKERHUB_ID]/bees:bees` + +Once complete, this bees image becomes publicly available which means we can now use it in our Terraform config, and it is going to be +nearly identical to the previous example: + +```hcl +terraform { + required_providers { iterative = { source = "iterative/iterative"} } +} +provider "iterative" {} + +resource "iterative_task" "tpi-docker-examples" { + name = "tpi-docker-examples" + cloud = "aws" + region = "us-east-2" + machine = "m+k80" + workdir { input = "." } + + script = <<-END + #!/bin/bash + sudo apt update + sudo apt-get install -y software-properties-common build-essential ubuntu-drivers-common + sudo ubuntu-drivers autoinstall + sudo curl -fsSL https://get.docker.com -o get-docker.sh && sudo sh get-docker.sh && + sudo usermod -aG docker ubuntu + sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock + curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - + curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list + sudo apt update && sudo apt install -y nvidia-docker2 + sudo systemctl restart docker + rm get-docker.sh + + nvidia-smi + + docker run --rm --gpus all -v "$PWD":/app [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py + END +} +``` + +Note how we are mounting the volume to the working directory that we have specified when creating an image. This is not strictly required, +but makes things neat and tidy. Find this example together with the Dockerfile in this [GitHub repo](https://github.com/iterative/blog-tpi-bees/tree/own-docker-image). + +Now you know the basics of using Docker images together with Iterative Terraform Provider for provisioning your MLOps infrastructure, and you +can start building your own Docker images too! +Hope you found this tutorial useful, now it's time to build great things! + + + + + + + + + diff --git a/static/uploads/images/2022-02-28/unsplash-containers.jpg b/static/uploads/images/2022-02-28/unsplash-containers.jpg new file mode 100644 index 0000000000000000000000000000000000000000..18f40e10cb1ae7c66144f87c8c9e2e18806e2aa5 GIT binary patch literal 6397189 zcmb^YcT^K!)ISPGQBhG)5m2fMNE5KpOOz^Ix=4vMY0^8SSSTVTNR=9C(uwq*sEBkT zHS|ag5FkK+5JJM6@9%lndhWZ{d;hxkOeWdcXRn#F&YYROKl`)iWa8u>;EJx6juzn5 zsZ)Swj16$IaGF;~T^;n?#7Ij=Uz5=R0Gz(`#?jON^c4WW(<{KwL|g6F3#*s6m}dZ| z05<_l05O1qy`#U+)926i0spt{V4PviXd{yU9qa$r?EinqMJH!}M@H8%W4Ckk@e2R| zPT4SImf!%N|72H&eBHt0|H$C~WIx6L8T!QkWS9Su3;&PJ|Hw`M$HvS=onh0@ke|5x zUu1{>i~RpRFT(`DW zI`4b{Kz^U0PyAnde`5%;zthOaU%2G5W7CF%~$0+*lRX6N?wWD~H{JT{_N(y{8}3 zjx32d^jSKMx(OOYLCR?)^xHv2qZU*%b6mi?*Sf%Xu65RwAs>i zykj4u11GpJ*?XqsLB$Ig7Dy>ZnSPCk->UK@=N8g1#NiV_Fh&N1f>5-vTOr~E#LTIp zQ*x)0UvB1haa01-4TjqW@{@;dAq{*j^K#WGtPRF9^=!4djlhrL*509MKlhUSMO
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    I6-|Kc`Ybr~1G zVmf3zSo!Hp4!`_J{nvj0dhMqlhNPR<8&Zqw57|#~4Zoix#vWGh2ma3akh8IM^~_{L z#t+5013|O=wW8}ykGUtiBYzgxXnn(KyexUWKF-^WCH2I-Hoa^UW#zLi^K0{+mE6Ug z>iJ{IAHZ1Y_|5p_pOu#(uXyJ##XSBG5JblQK4J1GtlIJF i!8^VGs@EyDe@=EA#9i&as&(?liP;5XwLHv!%l`*O!R>Yc literal 0 HcmV?d00001 From 85a106931007e906f8069a91aff6fdb652ad34cb Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Thu, 3 Feb 2022 09:57:42 -0500 Subject: [PATCH 02/23] Apply suggestions from code review Co-authored-by: Casper da Costa-Luis --- ...using-iterative-terraform-provider-with-docker.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index fd3819d282..1016bd24a9 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -34,8 +34,7 @@ that should answer this question for you. As a side note, using Docker to encaps minimum you will need an Ubuntu distribution AMI (which is a configurable default for the `iterative_task`). However, we will use Docker for the machine learning specific setup: drivers, GPU-specific libraries for accelerated math, and Python libraries. This separation will not only give you more control over the versions of the frameworks that you choose to use (or not use), - but also over the cloud provider you provision the resources with. Your Docker image is not cloud provider specific while a - Deep Learning AMI is a part of the AWS ecosystem. + but also over the cloud provider you provision the resources with. Your Docker image is cloud provider agnostic. If you have a DevOps team, they can build a Docker image suited for your needs, so you'll only need to use it in your `iterative_task` script. We'll show you how to do this in the first part of this tutorial. But if you need (or want) to build @@ -51,8 +50,9 @@ the script part of the `main.tf`. Let's say we have a DevOps team that has carefully prepared a Docker image suitable for data science and machine learning. To illustrate this, we can use a publicly available CML Docker image (`docker://iterativeai/cml`) provided by Iterative. This image -comes loaded with Python, CUDA, git, node and other essentials for full-stack data science. There are several versions of this -image, but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, which includes Ubuntu 20.04, Python 3.8 +comes loaded with Python, CUDA, git, node and other essentials for full-stack data science. There are +[several versions of this image](https://cml.dev/doc/self-hosted-runners#docker-images), +but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, which includes Ubuntu 20.04, Python 3.8 (CUDA 11.0.3, CuDNN 8). Here's what the script is going to look like: @@ -88,8 +88,8 @@ Finally, we can use Iterative's image to run our own container with this line: ``` Let's unpack this line. * `docker run`: this command will essentially create a container from the specified image, and then let us use it. -* `–rm flag` will make Docker automatically clean up the container and remove the file system when the container exits, -* `–gpus all` will allow us to use GPUs of the host machine from the container +* `--rm` flag will make Docker automatically clean up the container and remove the file system when the container exits, +* `--gpus all` will allow us to use GPUs of the host machine from the container * `-v "$PWD:/tpi"`: the Docker container we'll be running is only aware of what is included inside of it, and it doesn't have our training script nor the data which we synced to the host machine. How do we pass the data and the training code into the Docker container? We can do it by mounting a volume. The `-v` flag lets you configure a volume, and has the following structure: From 71c147432325608b79ebeb3d43115faddb2961a3 Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 10:31:02 -0500 Subject: [PATCH 03/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Updating in the repo example as well Co-authored-by: Casper da Costa-Luis --- ...022-02-28-using-iterative-terraform-provider-with-docker.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 1016bd24a9..1de1b29692 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -60,8 +60,7 @@ Here's what the script is going to look like: ```hcl script = <<-END #!/bin/bash - sudo apt-get update - sudo apt-get install -y software-properties-common build-essential ubuntu-drivers-common + sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common sudo ubuntu-drivers autoinstall sudo curl -fsSL https://get.docker.com -o get-docker.sh && sudo sh get-docker.sh && sudo usermod -aG docker ubuntu From 86de4780d0efbc8d5d89258c4f1738143ca87d87 Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 11:41:23 -0500 Subject: [PATCH 04/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Tested and added the change to the example repo Co-authored-by: Casper da Costa-Luis --- ...2022-02-28-using-iterative-terraform-provider-with-docker.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 1de1b29692..37362507a8 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -62,7 +62,7 @@ Here's what the script is going to look like: #!/bin/bash sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common sudo ubuntu-drivers autoinstall - sudo curl -fsSL https://get.docker.com -o get-docker.sh && sudo sh get-docker.sh && + sudo curl -fsSL https://get.docker.com | sudo sh - sudo usermod -aG docker ubuntu sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - From 3d1f66baaff89a5a6fe323d3bcad301934556fbf Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 12:27:31 -0500 Subject: [PATCH 05/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Co-authored-by: Casper da Costa-Luis --- ...2022-02-28-using-iterative-terraform-provider-with-docker.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 37362507a8..d4f81e94ae 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -124,7 +124,7 @@ RUN apt-get update && apt-get install -y wget python3-pip COPY ./requirements.txt /tmp/requirements.txt RUN pip3 install -r /tmp/requirements.txt -WORKDIR /app +WORKDIR /tpi ``` Unlike Terraform, where you had a declarative description of the desired infrastructure in the main.tf, `Dockerfile` is a script, From 48de7a94b070e44f85150aa661c1cdb277605a4f Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 12:27:42 -0500 Subject: [PATCH 06/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Co-authored-by: Casper da Costa-Luis --- ...-02-28-using-iterative-terraform-provider-with-docker.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index d4f81e94ae..7f4a403c4d 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -121,9 +121,9 @@ FROM --platform=linux/amd64 nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04 RUN apt-get update && apt-get install -y wget python3-pip # Add the required packages to the environment -COPY ./requirements.txt /tmp/requirements.txt -RUN pip3 install -r /tmp/requirements.txt - +COPY requirements.txt . +RUN pip3 install -r requirements.txt && rm requirements.txt + WORKDIR /tpi ``` From 6677c98b8cd72b7e149c55a489b0f79e74118630 Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 12:27:57 -0500 Subject: [PATCH 07/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Co-authored-by: Casper da Costa-Luis --- ...2022-02-28-using-iterative-terraform-provider-with-docker.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 7f4a403c4d..21e561d4a7 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -67,7 +67,7 @@ Here's what the script is going to look like: sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list - sudo apt update && sudo apt install -y nvidia-docker2 + sudo apt update -qq && sudo apt install -yqq nvidia-docker2 sudo systemctl restart docker rm get-docker.sh From 4473e92ae87265e35efc62531934b18687777011 Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 12:28:05 -0500 Subject: [PATCH 08/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Co-authored-by: Casper da Costa-Luis --- .../2022-02-28-using-iterative-terraform-provider-with-docker.md | 1 - 1 file changed, 1 deletion(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 21e561d4a7..29586956a7 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -69,7 +69,6 @@ Here's what the script is going to look like: curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt update -qq && sudo apt install -yqq nvidia-docker2 sudo systemctl restart docker - rm get-docker.sh nvidia-smi docker run --rm --gpus all -v "$PWD:/tpi" iterativeai/cml:0-dvc2-base1-gpu \ From 88f4e08b93154b8629ed032d19838c0ea1f31c1f Mon Sep 17 00:00:00 2001 From: Maria Khalusova Date: Mon, 7 Feb 2022 12:28:19 -0500 Subject: [PATCH 09/23] Update content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md Co-authored-by: Casper da Costa-Luis --- ...22-02-28-using-iterative-terraform-provider-with-docker.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 29586956a7..86e5ad99c9 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -71,8 +71,8 @@ Here's what the script is going to look like: sudo systemctl restart docker nvidia-smi - docker run --rm --gpus all -v "$PWD:/tpi" iterativeai/cml:0-dvc2-base1-gpu \ - /bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py" + docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ + /bin/bash -c "pip install -r requirements.txt; python src/train.py" END ``` From 4cecc184bf98cdd3948211e5f6097c2c8e052c55 Mon Sep 17 00:00:00 2001 From: MKhalusova Date: Mon, 7 Feb 2022 12:30:26 -0500 Subject: [PATCH 10/23] polishing examples based on feedback --- ...8-using-iterative-terraform-provider-with-docker.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 86e5ad99c9..e686b66d79 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -166,21 +166,19 @@ resource "iterative_task" "tpi-docker-examples" { script = <<-END #!/bin/bash - sudo apt update - sudo apt-get install -y software-properties-common build-essential ubuntu-drivers-common + sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common sudo ubuntu-drivers autoinstall - sudo curl -fsSL https://get.docker.com -o get-docker.sh && sudo sh get-docker.sh && + sudo curl -fsSL https://get.docker.com | sudo sh - sudo usermod -aG docker ubuntu sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list - sudo apt update && sudo apt install -y nvidia-docker2 + sudo apt update -qq && sudo apt install -yqq nvidia-docker2 sudo systemctl restart docker - rm get-docker.sh nvidia-smi - docker run --rm --gpus all -v "$PWD":/app [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py + docker run --rm --gpus all -v "$PWD":/tpi [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py END } ``` From 6229eec156d4b8f7745e123fa053bf3206be76eb Mon Sep 17 00:00:00 2001 From: MKhalusova Date: Mon, 7 Feb 2022 12:31:59 -0500 Subject: [PATCH 11/23] fixed formatting issue --- ...2-02-28-using-iterative-terraform-provider-with-docker.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index e686b66d79..9fca71c8bd 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -81,8 +81,9 @@ to install Docker itself, and sort out users, groups and permissions. Next, we'l to run GPU-accelerated Docker containers. Yes, GPUs will make you work for them before you can enjoy the accelerated training! Finally, we can use Iterative's image to run our own container with this line: -``` docker run --rm --gpus all -v "$PWD:/tpi" iterativeai/cml:0-dvc2-base1-gpu \ - /bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py" +``` +docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ + /bin/bash -c "pip install -r requirements.txt; python src/train.py" ``` Let's unpack this line. * `docker run`: this command will essentially create a container from the specified image, and then let us use it. From 279c79d613a8e30a58335fb7ce8cd5060886a136 Mon Sep 17 00:00:00 2001 From: "restyled-io[bot]" <32688539+restyled-io[bot]@users.noreply.github.com> Date: Wed, 9 Feb 2022 20:19:14 -0800 Subject: [PATCH 12/23] Restyled by prettier (#3247) Co-authored-by: Restyled.io --- ...terative-terraform-provider-with-docker.md | 254 ++++++++++-------- 1 file changed, 149 insertions(+), 105 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 9fca71c8bd..c332e7ad95 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -2,11 +2,11 @@ title: Using Iterative Terraform Provider with Docker date: 2022-02-28 description: > - In this tutorial you'll learn how you can move use an existing Docker image with - Iterative Terraform Provider, as well as build your own Docker image. + In this tutorial you'll learn how you can move use an existing Docker image + with Iterative Terraform Provider, as well as build your own Docker image. descriptionLong: > - In this tutorial you'll learn how you can move use an existing Docker image with - Iterative Terraform Provider, as well as build your own Docker image. + In this tutorial you'll learn how you can move use an existing Docker image + with Iterative Terraform Provider, as well as build your own Docker image. picture: 2022-02-28/unsplash-containers.jpg author: maria_khalusova # todo: commentsUrl: @@ -17,45 +17,63 @@ tags: - Docker --- -Recently we have [published a tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) -that demonstrated how you can move a machine learning experiment from a local computer to a more powerful cloud machine with -some help from the [Iterative Terraform Provider](https://registry.terraform.io/providers/iterative/iterative/latest). We've -talked about [Terraform](https://www.terraform.io), Iterative Terraform Provider and how you can configure the `iterative_task` -resource to provision infrastructure, sync data and run your training script. - -To make it easy to get started, we used a pre-configured [Deep Learning AMI from AWS](https://aws.amazon.com/machine-learning/amis/) -to simplify the environment setup. In most cases, however, instead of using a pre-configured Deep Learning AMI, you will -ship your environment to a remote machine using [Docker](https://www.docker.com) which has become an essential tool not only -in traditional software development, but also in machine learning. If you're not sure why it would be a good idea to use -Docker for machine learning development, check out -[this article from AWS](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) -that should answer this question for you. As a side note, using Docker to encapsulate your environment doesn't mean you won't - be using AMIs at all. In fact, AMI (Amazon Machine Image) is actually a required component of setting up an EC2, and at the - minimum you will need an Ubuntu distribution AMI (which is a configurable default for the `iterative_task`). However, we will - use Docker for the machine learning specific setup: drivers, GPU-specific libraries for accelerated math, and Python libraries. - This separation will not only give you more control over the versions of the frameworks that you choose to use (or not use), - but also over the cloud provider you provision the resources with. Your Docker image is cloud provider agnostic. - -If you have a DevOps team, they can build a Docker image suited for your needs, so you'll only need to use it in your -`iterative_task` script. We'll show you how to do this in the first part of this tutorial. But if you need (or want) to build -your own Docker image, we've got you too! The second part of this tutorial will illustrate some basics required for building -and using your own Docker image. Without further ado, let's get into it! +Recently we have +[published a tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) +that demonstrated how you can move a machine learning experiment from a local +computer to a more powerful cloud machine with some help from the +[Iterative Terraform Provider](https://registry.terraform.io/providers/iterative/iterative/latest). +We've talked about [Terraform](https://www.terraform.io), Iterative Terraform +Provider and how you can configure the `iterative_task` resource to provision +infrastructure, sync data and run your training script. + +To make it easy to get started, we used a pre-configured +[Deep Learning AMI from AWS](https://aws.amazon.com/machine-learning/amis/) to +simplify the environment setup. In most cases, however, instead of using a +pre-configured Deep Learning AMI, you will ship your environment to a remote +machine using [Docker](https://www.docker.com) which has become an essential +tool not only in traditional software development, but also in machine learning. +If you're not sure why it would be a good idea to use Docker for machine +learning development, check out +[this article from AWS](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) +that should answer this question for you. As a side note, using Docker to +encapsulate your environment doesn't mean you won't be using AMIs at all. In +fact, AMI (Amazon Machine Image) is actually a required component of setting up +an EC2, and at the minimum you will need an Ubuntu distribution AMI (which is a +configurable default for the `iterative_task`). However, we will use Docker for +the machine learning specific setup: drivers, GPU-specific libraries for +accelerated math, and Python libraries. This separation will not only give you +more control over the versions of the frameworks that you choose to use (or not +use), but also over the cloud provider you provision the resources with. Your +Docker image is cloud provider agnostic. + +If you have a DevOps team, they can build a Docker image suited for your needs, +so you'll only need to use it in your `iterative_task` script. We'll show you +how to do this in the first part of this tutorial. But if you need (or want) to +build your own Docker image, we've got you too! The second part of this tutorial +will illustrate some basics required for building and using your own Docker +image. Without further ado, let's get into it! ## Setting up environment for utilizing GPUs with Docker -If you haven't read the [previous tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) -yet and are not familiar with Terraform, I would encourage you to check it out and learn some basics, i.e. how to let Terraform -know that you'll be using the Iterative Provider, how to initialize the working directory with `terraform init`, how to use -other essential Terraform commands (`plan`, `apply`, `refresh`, `show`, `destroy`). All these will apply here, we'll only modify -the script part of the `main.tf`. - -Let's say we have a DevOps team that has carefully prepared a Docker image suitable for data science and machine learning. To -illustrate this, we can use a publicly available CML Docker image (`docker://iterativeai/cml`) provided by Iterative. This image -comes loaded with Python, CUDA, git, node and other essentials for full-stack data science. There are + +If you haven't read the +[previous tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) +yet and are not familiar with Terraform, I would encourage you to check it out +and learn some basics, i.e. how to let Terraform know that you'll be using the +Iterative Provider, how to initialize the working directory with +`terraform init`, how to use other essential Terraform commands (`plan`, +`apply`, `refresh`, `show`, `destroy`). All these will apply here, we'll only +modify the script part of the `main.tf`. + +Let's say we have a DevOps team that has carefully prepared a Docker image +suitable for data science and machine learning. To illustrate this, we can use a +publicly available CML Docker image (`docker://iterativeai/cml`) provided by +Iterative. This image comes loaded with Python, CUDA, git, node and other +essentials for full-stack data science. There are [several versions of this image](https://cml.dev/doc/self-hosted-runners#docker-images), -but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, which includes Ubuntu 20.04, Python 3.8 -(CUDA 11.0.3, CuDNN 8). +but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, +which includes Ubuntu 20.04, Python 3.8 (CUDA 11.0.3, CuDNN 8). -Here's what the script is going to look like: +Here's what the script is going to look like: ```hcl script = <<-END @@ -69,57 +87,76 @@ Here's what the script is going to look like: curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt update -qq && sudo apt install -yqq nvidia-docker2 sudo systemctl restart docker - + nvidia-smi docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ /bin/bash -c "pip install -r requirements.txt; python src/train.py" END ``` -As you can see, we still need to make sure to install the required drivers first. Then in order to use a Docker image, we'll need -to install Docker itself, and sort out users, groups and permissions. Next, we'll install the `nvidia-docker` that will enable us -to run GPU-accelerated Docker containers. Yes, GPUs will make you work for them before you can enjoy the accelerated training! +As you can see, we still need to make sure to install the required drivers +first. Then in order to use a Docker image, we'll need to install Docker itself, +and sort out users, groups and permissions. Next, we'll install the +`nvidia-docker` that will enable us to run GPU-accelerated Docker containers. +Yes, GPUs will make you work for them before you can enjoy the accelerated +training! + +Finally, we can use Iterative's image to run our own container with this line: -Finally, we can use Iterative's image to run our own container with this line: ``` docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ /bin/bash -c "pip install -r requirements.txt; python src/train.py" ``` -Let's unpack this line. -* `docker run`: this command will essentially create a container from the specified image, and then let us use it. -* `--rm` flag will make Docker automatically clean up the container and remove the file system when the container exits, -* `--gpus all` will allow us to use GPUs of the host machine from the container -* `-v "$PWD:/tpi"`: the Docker container we'll be running is only aware of what is included inside of it, and it doesn't have our -training script nor the data which we synced to the host machine. How do we pass the data and the training code into the Docker -container? We can do it by mounting a volume. The `-v` flag lets you configure a volume, and has the following structure: -`-v "[path on the host machine to files to be shared with Docker container]:[the path where the directory is mounted in the container]"` -* `iterativeai/cml:0-dvc2-base1-gpu`: full name of the Docker image including the tag -* `/bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py"`: bash commands to run inside of the container. -We'll need to navigate to the mounted volume, install the dependencies and launch the training script. - -Now if we've initialized the working directory, we can call `terraform apply` to provision infrastructure, sync data and code to -it, set up the environment, and run the training process. If you’d like to tinker with this example you can + +Let's unpack this line. + +- `docker run`: this command will essentially create a container from the + specified image, and then let us use it. +- `--rm` flag will make Docker automatically clean up the container and remove + the file system when the container exits, +- `--gpus all` will allow us to use GPUs of the host machine from the container +- `-v "$PWD:/tpi"`: the Docker container we'll be running is only aware of what + is included inside of it, and it doesn't have our training script nor the data + which we synced to the host machine. How do we pass the data and the training + code into the Docker container? We can do it by mounting a volume. The `-v` + flag lets you configure a volume, and has the following structure: + `-v "[path on the host machine to files to be shared with Docker container]:[the path where the directory is mounted in the container]"` +- `iterativeai/cml:0-dvc2-base1-gpu`: full name of the Docker image including + the tag +- `/bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py"`: + bash commands to run inside of the container. We'll need to navigate to the + mounted volume, install the dependencies and launch the training script. + +Now if we've initialized the working directory, we can call `terraform apply` to +provision infrastructure, sync data and code to it, set up the environment, and +run the training process. If you’d like to tinker with this example you can [find it on GitHub](https://github.com/iterative/blog-tpi-bees/blob/docker-examples/main.tf). ## Building your own Docker image -Suppose, we wanted our Docker container to include all the Python libraries required to run our training pipeline? Well, these would -be quite specific to our project, so we'd have to build our own Docker image. Luckily we don't have to build one completely from -scratch, instead we can use one of the existing publicly available images as a base to build on top of. -For this example, let's use `nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04` from NVIDIA. Earlier, we didn't need to install Docker -on the local machine because it was only used on the cloud machine, but if you're going to build your own image, you'll need to start by -[installing Docker locally](https://docs.docker.com/get-docker/). +Suppose, we wanted our Docker container to include all the Python libraries +required to run our training pipeline? Well, these would be quite specific to +our project, so we'd have to build our own Docker image. Luckily we don't have +to build one completely from scratch, instead we can use one of the existing +publicly available images as a base to build on top of. + +For this example, let's use `nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04` from +NVIDIA. Earlier, we didn't need to install Docker on the local machine because +it was only used on the cloud machine, but if you're going to build your own +image, you'll need to start by +[installing Docker locally](https://docs.docker.com/get-docker/). -To build an image, create a file called `Dockerfile` in your project's root directory. This is a text-based script of instructions -that will be used to create a container image. In our case it'll be quite simple as most of the complex setup is taken care of -by NVIDIA's image we're using as a base: +To build an image, create a file called `Dockerfile` in your project's root +directory. This is a text-based script of instructions that will be used to +create a container image. In our case it'll be quite simple as most of the +complex setup is taken care of by NVIDIA's image we're using as a base: ``` FROM --platform=linux/amd64 nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04 - + # Install wget and python RUN apt-get update && apt-get install -y wget python3-pip - + # Add the required packages to the environment COPY requirements.txt . RUN pip3 install -r requirements.txt && rm requirements.txt @@ -127,44 +164,57 @@ RUN pip3 install -r requirements.txt && rm requirements.txt WORKDIR /tpi ``` -Unlike Terraform, where you had a declarative description of the desired infrastructure in the main.tf, `Dockerfile` is a script, -so the order of the steps matters. The `FROM` instruction sets the base image for subsequent instructions, and it is a required first -instruction. To execute commands in a new layer on top of the current image and commit the results, we use the `RUN` instruction. -The resulting committed image will be used for the next step in the `Dockerfile`. +Unlike Terraform, where you had a declarative description of the desired +infrastructure in the main.tf, `Dockerfile` is a script, so the order of the +steps matters. The `FROM` instruction sets the base image for subsequent +instructions, and it is a required first instruction. To execute commands in a +new layer on top of the current image and commit the results, we use the `RUN` +instruction. The resulting committed image will be used for the next step in the +`Dockerfile`. -To install the project-specific Python packages, we'll need to copy the `requirements.txt` to the filesystem of the container, and -we can do this with the `COPY` instruction. Then we'll use `RUN` again to install them. +To install the project-specific Python packages, we'll need to copy the +`requirements.txt` to the filesystem of the container, and we can do this with +the `COPY` instruction. Then we'll use `RUN` again to install them. -Finally, with `WORKDIR` we can specify what we want to call the directory inside the container where we'll run things inside the container. +Finally, with `WORKDIR` we can specify what we want to call the directory inside +the container where we'll run things inside the container. -To build your own image locally use the following command: `docker build -t bees:latest .`, where `bees` is the name of my Docker image -but you can name yours however you like. +To build your own image locally use the following command: +`docker build -t bees:latest .`, where `bees` is the name of my Docker image but +you can name yours however you like. -Hooray! We've got our own Docker image, but we're not done just yet. To use this image from the Terraform config file, we should -publish it on [Docker Hub](https://hub.docker.com/). Your company may already have a business account for Docker Hub, but if you're learning -Docker and want to try it as an individual, you can create your own free [personal account here](https://hub.docker.com/). +Hooray! We've got our own Docker image, but we're not done just yet. To use this +image from the Terraform config file, we should publish it on +[Docker Hub](https://hub.docker.com/). Your company may already have a business +account for Docker Hub, but if you're learning Docker and want to try it as an +individual, you can create your own free +[personal account here](https://hub.docker.com/). Once you have ayour account, -1. Log in to the Docker public registry on your local machine from command line with `docker login`. -2. Tag your image (aka give it a name): `docker tag bees [YOUR_DOCKERHUB_ID]/bees:bees`. -3. Publish the image: `docker push [YOUR_DOCKERHUB_ID]/bees:bees` -Once complete, this bees image becomes publicly available which means we can now use it in our Terraform config, and it is going to be -nearly identical to the previous example: +1. Log in to the Docker public registry on your local machine from command line + with `docker login`. +2. Tag your image (aka give it a name): + `docker tag bees [YOUR_DOCKERHUB_ID]/bees:bees`. +3. Publish the image: `docker push [YOUR_DOCKERHUB_ID]/bees:bees` + +Once complete, this bees image becomes publicly available which means we can now +use it in our Terraform config, and it is going to be nearly identical to the +previous example: ```hcl terraform { required_providers { iterative = { source = "iterative/iterative"} } } provider "iterative" {} - + resource "iterative_task" "tpi-docker-examples" { name = "tpi-docker-examples" cloud = "aws" region = "us-east-2" machine = "m+k80" workdir { input = "." } - + script = <<-END #!/bin/bash sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common @@ -176,26 +226,20 @@ resource "iterative_task" "tpi-docker-examples" { curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt update -qq && sudo apt install -yqq nvidia-docker2 sudo systemctl restart docker - + nvidia-smi - - docker run --rm --gpus all -v "$PWD":/tpi [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py + + docker run --rm --gpus all -v "$PWD":/tpi [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py END } ``` -Note how we are mounting the volume to the working directory that we have specified when creating an image. This is not strictly required, -but makes things neat and tidy. Find this example together with the Dockerfile in this [GitHub repo](https://github.com/iterative/blog-tpi-bees/tree/own-docker-image). - -Now you know the basics of using Docker images together with Iterative Terraform Provider for provisioning your MLOps infrastructure, and you -can start building your own Docker images too! -Hope you found this tutorial useful, now it's time to build great things! - - - - - - - - +Note how we are mounting the volume to the working directory that we have +specified when creating an image. This is not strictly required, but makes +things neat and tidy. Find this example together with the Dockerfile in this +[GitHub repo](https://github.com/iterative/blog-tpi-bees/tree/own-docker-image). +Now you know the basics of using Docker images together with Iterative Terraform +Provider for provisioning your MLOps infrastructure, and you can start building +your own Docker images too! Hope you found this tutorial useful, now it's time +to build great things! From b6bd075b5c520d0f71ff13dd3411e657ed10b342 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 15:10:05 +0100 Subject: [PATCH 13/23] update tags --- ...2-28-using-iterative-terraform-provider-with-docker.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index c332e7ad95..00210dce49 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -12,9 +12,15 @@ author: maria_khalusova # todo: commentsUrl: tags: - MLOps - - Iterative Terraform Provider + - TPI - AWS - Docker + - Terraform + - Git + - Python + - Tutorial + - Cloud orchestration + - Spot instance management --- Recently we have From 2c3eda6b224dde1e2742346a954782fd07547c14 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 15:10:32 +0100 Subject: [PATCH 14/23] rewrite & shorten intro --- ...terative-terraform-provider-with-docker.md | 67 +++++++++---------- 1 file changed, 32 insertions(+), 35 deletions(-) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md index 00210dce49..67afa21c1b 100644 --- a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md +++ b/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md @@ -23,41 +23,38 @@ tags: - Spot instance management --- -Recently we have -[published a tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) -that demonstrated how you can move a machine learning experiment from a local -computer to a more powerful cloud machine with some help from the -[Iterative Terraform Provider](https://registry.terraform.io/providers/iterative/iterative/latest). -We've talked about [Terraform](https://www.terraform.io), Iterative Terraform -Provider and how you can configure the `iterative_task` resource to provision -infrastructure, sync data and run your training script. - -To make it easy to get started, we used a pre-configured -[Deep Learning AMI from AWS](https://aws.amazon.com/machine-learning/amis/) to -simplify the environment setup. In most cases, however, instead of using a -pre-configured Deep Learning AMI, you will ship your environment to a remote -machine using [Docker](https://www.docker.com) which has become an essential -tool not only in traditional software development, but also in machine learning. -If you're not sure why it would be a good idea to use Docker for machine -learning development, check out -[this article from AWS](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) -that should answer this question for you. As a side note, using Docker to -encapsulate your environment doesn't mean you won't be using AMIs at all. In -fact, AMI (Amazon Machine Image) is actually a required component of setting up -an EC2, and at the minimum you will need an Ubuntu distribution AMI (which is a -configurable default for the `iterative_task`). However, we will use Docker for -the machine learning specific setup: drivers, GPU-specific libraries for -accelerated math, and Python libraries. This separation will not only give you -more control over the versions of the frameworks that you choose to use (or not -use), but also over the cloud provider you provision the resources with. Your -Docker image is cloud provider agnostic. - -If you have a DevOps team, they can build a Docker image suited for your needs, -so you'll only need to use it in your `iterative_task` script. We'll show you -how to do this in the first part of this tutorial. But if you need (or want) to -build your own Docker image, we've got you too! The second part of this tutorial -will illustrate some basics required for building and using your own Docker -image. Without further ado, let's get into it! +We recently [published a tutorial][bees-part-1] on using [Terraform Provider +Iterative (TPI)][tpi] to move a machine learning experiment from your local +computer to a more powerful cloud machine. We've covered how you can use +[Terraform](https://www.terraform.io) & TPI to provision infrastructure, sync +data, and run training scripts. To simplify the setup, we used a pre-configured +[Ubuntu/NVIDIA image](https://registry.terraform.io/providers/iterative/iterative/latest/docs/resources/task#machine-image). +However, instead of using a pre-configured image, we can use custom +[Docker](https://www.docker.com) images. These are often +[recommended in machine learning](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) +as well as in traditional software development. + +[bees-part-1]: /blog/local-experiments-to-the-cloud-with-tpi +[tpi]: https://github.com/iterative/terraform-provider-iterative + + + +Using Docker to manage dependencies (e.g. Python packages) does not remove all +other setup requirements. You'll still need docker itself installed, as well as +GPU runtime drivers if applicable. Happily, TPI sets these up by default. + + + +Separation of dependencies into Docker images will give you more control over +the software versions, and also makes it trivial to switch between cloud +providers -- currently Amazon Web Services (AWS), Microsoft Azure, Google Cloud +Platform, and Kubernetes. Your Docker image is cloud provider-agnostic. There +are thousands of [pre-defined Docker images online](https://hub.docker.com/) +too. + +In the first part of this tutorial, we'll use an existing Docker image. The +second part of this tutorial will then cover some basics for building and using +your own Docker images. ## Setting up environment for utilizing GPUs with Docker From 71efd61fd07a31b36cf442a7cbf41ec91888510a Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 15:30:30 +0100 Subject: [PATCH 15/23] rename blog --- ...d => 2022-05-24-local-experiments-to-cloud-with-tpi-docker.md} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename content/blog/{2022-02-28-using-iterative-terraform-provider-with-docker.md => 2022-05-24-local-experiments-to-cloud-with-tpi-docker.md} (100%) diff --git a/content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md similarity index 100% rename from content/blog/2022-02-28-using-iterative-terraform-provider-with-docker.md rename to content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md From 65c387b184a3acef01c8da5c49ca6b71432d4b5e Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 18:49:10 +0100 Subject: [PATCH 16/23] rewrite metadata --- ...-24-local-experiments-to-cloud-with-tpi-docker.md | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index 67afa21c1b..0398f41af0 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -1,12 +1,14 @@ --- -title: Using Iterative Terraform Provider with Docker +title: + Moving Local Experiments to the Cloud with Terraform Provider Iterative (TPI) + and Docker date: 2022-02-28 description: > - In this tutorial you'll learn how you can move use an existing Docker image - with Iterative Terraform Provider, as well as build your own Docker image. + Tutorial for easily running experiments in the cloud with the help of + Terraform Provider Iterative (TPI) and Docker. descriptionLong: > - In this tutorial you'll learn how you can move use an existing Docker image - with Iterative Terraform Provider, as well as build your own Docker image. + In this tutorial, learn how to build a Docker image and use it to run + experiments in the cloud with Terraform Provider Iterative (TPI). picture: 2022-02-28/unsplash-containers.jpg author: maria_khalusova # todo: commentsUrl: From bf67acb7e72b06af0305a41ab4b8dc18a8778690 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 18:51:26 +0100 Subject: [PATCH 17/23] shorten & rewrite first section --- ...al-experiments-to-cloud-with-tpi-docker.md | 118 +++++++----------- 1 file changed, 47 insertions(+), 71 deletions(-) diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index 0398f41af0..275ecc2f48 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -58,84 +58,60 @@ In the first part of this tutorial, we'll use an existing Docker image. The second part of this tutorial will then cover some basics for building and using your own Docker images. -## Setting up environment for utilizing GPUs with Docker - -If you haven't read the -[previous tutorial](2022-01-15-moving-local-experiments-to-the-cloud-with-iterative-terraform-provider.md) -yet and are not familiar with Terraform, I would encourage you to check it out -and learn some basics, i.e. how to let Terraform know that you'll be using the -Iterative Provider, how to initialize the working directory with -`terraform init`, how to use other essential Terraform commands (`plan`, -`apply`, `refresh`, `show`, `destroy`). All these will apply here, we'll only -modify the script part of the `main.tf`. - -Let's say we have a DevOps team that has carefully prepared a Docker image -suitable for data science and machine learning. To illustrate this, we can use a -publicly available CML Docker image (`docker://iterativeai/cml`) provided by -Iterative. This image comes loaded with Python, CUDA, git, node and other -essentials for full-stack data science. There are -[several versions of this image](https://cml.dev/doc/self-hosted-runners#docker-images), -but for this example we'll take `docker://iterativeai/cml:0-dvc2-base1-gpu`, -which includes Ubuntu 20.04, Python 3.8 (CUDA 11.0.3, CuDNN 8). - -Here's what the script is going to look like: +## Run GPU-enabled Docker containers -```hcl - script = <<-END - #!/bin/bash - sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common - sudo ubuntu-drivers autoinstall - sudo curl -fsSL https://get.docker.com | sudo sh - - sudo usermod -aG docker ubuntu - sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock - curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - - curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list - sudo apt update -qq && sudo apt install -yqq nvidia-docker2 - sudo systemctl restart docker + - nvidia-smi - docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ - /bin/bash -c "pip install -r requirements.txt; python src/train.py" - END -``` +If you haven't read the [previous tutorial][bees-part-1], you should check out +the basics there first. This includes how to let Terraform know about TPI, and +essential commands (`init`, `apply`, `refresh`, `show`, and `destroy`). -As you can see, we still need to make sure to install the required drivers -first. Then in order to use a Docker image, we'll need to install Docker itself, -and sort out users, groups and permissions. Next, we'll install the -`nvidia-docker` that will enable us to run GPU-accelerated Docker containers. -Yes, GPUs will make you work for them before you can enjoy the accelerated -training! + -Finally, we can use Iterative's image to run our own container with this line: +The only modification from the [previous tutorial][bees-part-1] is the script +part of the `main.tf` config file. -``` -docker run --rm --gpus all -v "$PWD:/tpi" -w /tpi iterativeai/cml:0-dvc2-base1-gpu \ - /bin/bash -c "pip install -r requirements.txt; python src/train.py" +Let's say we've found a carefully prepared a Docker image suitable for data +science and machine learning -- in this case, +[`iterativeai/cml:0-dvc2-base1-gpu`](https://cml.dev/doc/self-hosted-runners#docker-images). +This image comes loaded with Ubuntu 20.04, Python 3.8, NodeJS, CUDA 11.0.3, +CuDNN 8, Git, [CML](https://cml.dev), [DVC](https://dvc.org), and other +essentials for full-stack data science. + +Our `script` block is now: + +```hcl +script = <<-END + #!/bin/bash + docker run --gpus all -v "$PWD:/tpi" -w /tpi -e TF_CPP_MIN_LOG_LEVEL \ + iterativeai/cml:0-dvc2-base1-gpu /bin/bash -c " + pip install -r requirements.txt tensorflow==2.8.0 + python train.py --output results-gpu/metrics.json + " +END ``` -Let's unpack this line. - -- `docker run`: this command will essentially create a container from the - specified image, and then let us use it. -- `--rm` flag will make Docker automatically clean up the container and remove - the file system when the container exits, -- `--gpus all` will allow us to use GPUs of the host machine from the container -- `-v "$PWD:/tpi"`: the Docker container we'll be running is only aware of what - is included inside of it, and it doesn't have our training script nor the data - which we synced to the host machine. How do we pass the data and the training - code into the Docker container? We can do it by mounting a volume. The `-v` - flag lets you configure a volume, and has the following structure: - `-v "[path on the host machine to files to be shared with Docker container]:[the path where the directory is mounted in the container]"` -- `iterativeai/cml:0-dvc2-base1-gpu`: full name of the Docker image including - the tag -- `/bin/bash -c "cd /tpi; pip install -r requirements.txt; python src/train.py"`: - bash commands to run inside of the container. We'll need to navigate to the - mounted volume, install the dependencies and launch the training script. - -Now if we've initialized the working directory, we can call `terraform apply` to -provision infrastructure, sync data and code to it, set up the environment, and -run the training process. If you’d like to tinker with this example you can -[find it on GitHub](https://github.com/iterative/blog-tpi-bees/blob/docker-examples/main.tf). +Yes, it's quite long for a one-liner. Let's looks at the components: + +- `docker run`: Download the specified image, create a container from the image, + and run it. +- `--gpus all`: Expose GPUs to the container. +- `-v "$PWD:/tpi"`: Expose our current working directory (`$PWD`) within the + container (as path `/tpi`). +- `-w /tpi`: Set the working directory of the container (to be `/tpi`). +- `-e TF_CPP_MIN_LOG_LEVEL`: Expose the environment variable + `TF_CPP_MIN_LOG_LEVEL` to the container (in this case to control TensorFlow's + verbosity). +- `iterativeai/cml:0-dvc2-base1-gpu`: The image we want to download & run a + container from. +- `/bin/bash -c "pip install -r requirements.txt ... python train.py ..."`: + Commands to run within the container's working directory. In this case, + install the dependencies and run the training script. + +We can now call `terraform init` and `terraform apply` to provision +infrastructure, sync data and code to it, set up the environment, and run the +training process. If you'd like to tinker with this example you can +[find it on GitHub](https://github.com/iterative/blog-tpi-bees/tree/docker). ## Building your own Docker image From baeaca6c48f58007cd543c7dd8039c85bbc73497 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Wed, 18 May 2022 19:18:54 +0100 Subject: [PATCH 18/23] who needs custom docker anyway --- ...al-experiments-to-cloud-with-tpi-docker.md | 130 +++--------------- 1 file changed, 18 insertions(+), 112 deletions(-) diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index 275ecc2f48..22a5ff5516 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -2,15 +2,15 @@ title: Moving Local Experiments to the Cloud with Terraform Provider Iterative (TPI) and Docker -date: 2022-02-28 +date: 2022-05-24 description: > Tutorial for easily running experiments in the cloud with the help of Terraform Provider Iterative (TPI) and Docker. descriptionLong: > - In this tutorial, learn how to build a Docker image and use it to run - experiments in the cloud with Terraform Provider Iterative (TPI). + In this tutorial, learn how to use Docker images to run experiments in the + cloud with Terraform Provider Iterative (TPI). picture: 2022-02-28/unsplash-containers.jpg -author: maria_khalusova +author: casper_dcl # todo: commentsUrl: tags: - MLOps @@ -54,9 +54,9 @@ Platform, and Kubernetes. Your Docker image is cloud provider-agnostic. There are thousands of [pre-defined Docker images online](https://hub.docker.com/) too. -In the first part of this tutorial, we'll use an existing Docker image. The -second part of this tutorial will then cover some basics for building and using -your own Docker images. +In this tutorial, we'll use an existing Docker image that comes with most of our +requirements already installed. We'll then add add a few more dependencies on +top and run our training pipeline in the cloud as before! ## Run GPU-enabled Docker containers @@ -113,114 +113,20 @@ infrastructure, sync data and code to it, set up the environment, and run the training process. If you'd like to tinker with this example you can [find it on GitHub](https://github.com/iterative/blog-tpi-bees/tree/docker). -## Building your own Docker image + -Suppose, we wanted our Docker container to include all the Python libraries -required to run our training pipeline? Well, these would be quite specific to -our project, so we'd have to build our own Docker image. Luckily we don't have -to build one completely from scratch, instead we can use one of the existing -publicly available images as a base to build on top of. +Don't forget to `terraform refresh && terraform show` to check the status, and +`terraform destroy` to download results & shut everything down. -For this example, let's use `nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04` from -NVIDIA. Earlier, we didn't need to install Docker on the local machine because -it was only used on the cloud machine, but if you're going to build your own -image, you'll need to start by -[installing Docker locally](https://docs.docker.com/get-docker/). - -To build an image, create a file called `Dockerfile` in your project's root -directory. This is a text-based script of instructions that will be used to -create a container image. In our case it'll be quite simple as most of the -complex setup is taken care of by NVIDIA's image we're using as a base: - -``` -FROM --platform=linux/amd64 nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu20.04 - -# Install wget and python -RUN apt-get update && apt-get install -y wget python3-pip - -# Add the required packages to the environment -COPY requirements.txt . -RUN pip3 install -r requirements.txt && rm requirements.txt - -WORKDIR /tpi -``` - -Unlike Terraform, where you had a declarative description of the desired -infrastructure in the main.tf, `Dockerfile` is a script, so the order of the -steps matters. The `FROM` instruction sets the base image for subsequent -instructions, and it is a required first instruction. To execute commands in a -new layer on top of the current image and commit the results, we use the `RUN` -instruction. The resulting committed image will be used for the next step in the -`Dockerfile`. - -To install the project-specific Python packages, we'll need to copy the -`requirements.txt` to the filesystem of the container, and we can do this with -the `COPY` instruction. Then we'll use `RUN` again to install them. - -Finally, with `WORKDIR` we can specify what we want to call the directory inside -the container where we'll run things inside the container. - -To build your own image locally use the following command: -`docker build -t bees:latest .`, where `bees` is the name of my Docker image but -you can name yours however you like. - -Hooray! We've got our own Docker image, but we're not done just yet. To use this -image from the Terraform config file, we should publish it on -[Docker Hub](https://hub.docker.com/). Your company may already have a business -account for Docker Hub, but if you're learning Docker and want to try it as an -individual, you can create your own free -[personal account here](https://hub.docker.com/). - -Once you have ayour account, - -1. Log in to the Docker public registry on your local machine from command line - with `docker login`. -2. Tag your image (aka give it a name): - `docker tag bees [YOUR_DOCKERHUB_ID]/bees:bees`. -3. Publish the image: `docker push [YOUR_DOCKERHUB_ID]/bees:bees` + -Once complete, this bees image becomes publicly available which means we can now -use it in our Terraform config, and it is going to be nearly identical to the -previous example: +Now you know the basics of using convenient Docker images together with +[TPI][tpi] for provisioning your MLOps infrastructure! -```hcl -terraform { - required_providers { iterative = { source = "iterative/iterative"} } -} -provider "iterative" {} - -resource "iterative_task" "tpi-docker-examples" { - name = "tpi-docker-examples" - cloud = "aws" - region = "us-east-2" - machine = "m+k80" - workdir { input = "." } - - script = <<-END - #!/bin/bash - sudo apt update -qq && sudo apt install -yqq software-properties-common build-essential ubuntu-drivers-common - sudo ubuntu-drivers autoinstall - sudo curl -fsSL https://get.docker.com | sudo sh - - sudo usermod -aG docker ubuntu - sudo setfacl --modify user:ubuntu:rw /var/run/docker.sock - curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - - curl -s -L https://nvidia.github.io/nvidia-docker/ubuntu20.04/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list - sudo apt update -qq && sudo apt install -yqq nvidia-docker2 - sudo systemctl restart docker - - nvidia-smi - - docker run --rm --gpus all -v "$PWD":/tpi [YOUR_DOCKERHUB_ID]/bees:bees python3 src/train.py - END -} -``` + -Note how we are mounting the volume to the working directory that we have -specified when creating an image. This is not strictly required, but makes -things neat and tidy. Find this example together with the Dockerfile in this -[GitHub repo](https://github.com/iterative/blog-tpi-bees/tree/own-docker-image). +If you have a lot of custom dependencies that rarely change (e.g. a large +`requirements.txt` that is rarely updated), it's a good idea to put build it +into your own custom Docker image. Let us know if you'd like a tutorial on this! -Now you know the basics of using Docker images together with Iterative Terraform -Provider for provisioning your MLOps infrastructure, and you can start building -your own Docker images too! Hope you found this tutorial useful, now it's time -to build great things! + From db3b99c9842396d1d6bebe3932b54b2e0a4a7119 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Thu, 19 May 2022 14:23:39 +0100 Subject: [PATCH 19/23] some typos, tidying, review comments --- ...ocal-experiments-to-cloud-with-tpi-docker.md | 17 +++++++++-------- 1 file changed, 9 insertions(+), 8 deletions(-) diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index 22a5ff5516..f2a0d6356b 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -42,13 +42,13 @@ as well as in traditional software development. Using Docker to manage dependencies (e.g. Python packages) does not remove all -other setup requirements. You'll still need docker itself installed, as well as -GPU runtime drivers if applicable. Happily, TPI sets these up by default. +other setup requirements. You'll still need Docker itself installed, as well as +GPU runtime drivers if applicable. Happily, TPI sets up all of this by default. Separation of dependencies into Docker images will give you more control over -the software versions, and also makes it trivial to switch between cloud +the software versions, and also makes it painless to switch between cloud providers -- currently Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform, and Kubernetes. Your Docker image is cloud provider-agnostic. There are thousands of [pre-defined Docker images online](https://hub.docker.com/) @@ -108,9 +108,10 @@ Yes, it's quite long for a one-liner. Let's looks at the components: Commands to run within the container's working directory. In this case, install the dependencies and run the training script. -We can now call `terraform init` and `terraform apply` to provision -infrastructure, sync data and code to it, set up the environment, and run the -training process. If you'd like to tinker with this example you can +We can now call `terraform init`, `export TF_LOG_PROVIDER=INFO`, and +`terraform apply` to provision infrastructure, upload our data and code, set up +the cloud environment, and run the training process. If you'd like to tinker +with this example you can [find it on GitHub](https://github.com/iterative/blog-tpi-bees/tree/docker). @@ -126,7 +127,7 @@ Now you know the basics of using convenient Docker images together with If you have a lot of custom dependencies that rarely change (e.g. a large -`requirements.txt` that is rarely updated), it's a good idea to put build it -into your own custom Docker image. Let us know if you'd like a tutorial on this! +`requirements.txt` that is rarely updated), it's a good idea to build it into +your own custom Docker image. Let us know if you'd like a tutorial on this! From 86c2eb53bdc7f9a5bf878f4e8514b7619bbc56bd Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Thu, 19 May 2022 14:26:34 +0100 Subject: [PATCH 20/23] update image --- ...al-experiments-to-cloud-with-tpi-docker.md | 2 +- .../images/2022-02-28/unsplash-containers.jpg | Bin 6397189 -> 0 bytes .../images/2022-05-19/tpi-docker-header.png | Bin 0 -> 170430 bytes 3 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 static/uploads/images/2022-02-28/unsplash-containers.jpg create mode 100644 static/uploads/images/2022-05-19/tpi-docker-header.png diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index f2a0d6356b..aebf79f96c 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -9,7 +9,7 @@ description: > descriptionLong: > In this tutorial, learn how to use Docker images to run experiments in the cloud with Terraform Provider Iterative (TPI). -picture: 2022-02-28/unsplash-containers.jpg +picture: 2022-05-19/tpi-docker-header.png author: casper_dcl # todo: commentsUrl: tags: diff --git a/static/uploads/images/2022-02-28/unsplash-containers.jpg b/static/uploads/images/2022-02-28/unsplash-containers.jpg deleted file mode 100644 index 18f40e10cb1ae7c66144f87c8c9e2e18806e2aa5..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6397189 zcmb^YcT^K!)ISPGQBhG)5m2fMNE5KpOOz^Ix=4vMY0^8SSSTVTNR=9C(uwq*sEBkT zHS|ag5FkK+5JJM6@9%lndhWZ{d;hxkOeWdcXRn#F&YYROKl`)iWa8u>;EJx6juzn5 zsZ)Swj16$IaGF;~T^;n?#7Ij=Uz5=R0Gz(`#?jON^c4WW(<{KwL|g6F3#*s6m}dZ| z05<_l05O1qy`#U+)926i0spt{V4PviXd{yU9qa$r?EinqMJH!}M@H8%W4Ckk@e2R| zPT4SImf!%N|72H&eBHt0|H$C~WIx6L8T!QkWS9Su3;&PJ|Hw`M$HvS=onh0@ke|5x zUu1{>i~RpRFT(`DW zI`4b{Kz^U0PyAnde`5%;zthOaU%2G5W7CF%~$0+*lRX6N?wWD~H{JT{_N(y{8}3 zjx32d^jSKMx(OOYLCR?)^xHv2qZU*%b6mi?*Sf%Xu65RwAs>i zykj4u11GpJ*?XqsLB$Ig7Dy>ZnSPCk->UK@=N8g1#NiV_Fh&N1f>5-vTOr~E#LTIp zQ*x)0UvB1haa01-4TjqW@{@;dAq{*j^K#WGtPRF9^=!4djlhrL*509MKlhUSMO

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zXX8t~2s4~a8!RlGEc{TIXP_~fN6+*2a46>kHy;mZpTrt>yfQEPwH-f1{gfUJ0z_$o zpMy_LGhO)R(6E;bFW-N9WE?!|l zKG4o7G&Hka+qz#O@dhkAz2g+iQquwUqN;ngpior@$U`|leBKySe3~g1kn7{@SF#&N z7$w4hK@`4ovc&`q{N&SeMS%(RNakjtBO?S*&Y%gN{heiu1};wQ(*_~pma}2E5&;pj zGb@kwLq5fzSQ+woz!L;qsC*zB!xYn_lt^I2A+;nVfd3x?_K=^Bq8QMASn=alK~dHY z1x^;HNnj|@p#6pqcm{v~>M$Bm-KPV{WFUY&IM{!%Ufch~|1CQKuK+X Date: Tue, 24 May 2022 14:44:00 +0100 Subject: [PATCH 21/23] add comments URL, crossrefs --- .../blog/2022-05-12-local-experiments-to-cloud-with-tpi.md | 4 ++-- .../2022-05-24-local-experiments-to-cloud-with-tpi-docker.md | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/blog/2022-05-12-local-experiments-to-cloud-with-tpi.md b/content/blog/2022-05-12-local-experiments-to-cloud-with-tpi.md index 09a3df8d3e..5553934383 100644 --- a/content/blog/2022-05-12-local-experiments-to-cloud-with-tpi.md +++ b/content/blog/2022-05-12-local-experiments-to-cloud-with-tpi.md @@ -338,5 +338,5 @@ this `main.tf` codification should also make the transition smoother. In this tutorial we covered the simplest example with no GPU, and one with GPUs. In many cases, deploying your pipelines would be easier with your own Docker image (both for prototyping and for production) and CI/CD workflows. If you'd -like to learn how to create your own Docker images and use them with TPI, let us -know and we'll write another tutorial about that! +like to learn how to create your own Docker images and use them with TPI, see +[part 2](/blog/local-experiments-to-cloud-with-tpi-docker) of this blog post! diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index aebf79f96c..3f0cd0e515 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -11,7 +11,7 @@ descriptionLong: > cloud with Terraform Provider Iterative (TPI). picture: 2022-05-19/tpi-docker-header.png author: casper_dcl -# todo: commentsUrl: +commentsUrl: https://discuss.dvc.org/t/moving-local-experiments-to-the-cloud-with-terraform-provider-iterative-tpi/1190 tags: - MLOps - TPI @@ -36,7 +36,7 @@ However, instead of using a pre-configured image, we can use custom [recommended in machine learning](https://aws.amazon.com/blogs/opensource/why-use-docker-containers-for-machine-learning-development/) as well as in traditional software development. -[bees-part-1]: /blog/local-experiments-to-the-cloud-with-tpi +[bees-part-1]: /blog/local-experiments-to-cloud-with-tpi [tpi]: https://github.com/iterative/terraform-provider-iterative From 950c9a3a1d516746beda8513e37381970ea7faea Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Tue, 24 May 2022 20:43:27 +0100 Subject: [PATCH 22/23] reference petunias --- ...-local-experiments-to-cloud-with-tpi-docker.md | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md index 3f0cd0e515..eda156dc04 100644 --- a/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md +++ b/content/blog/2022-05-24-local-experiments-to-cloud-with-tpi-docker.md @@ -47,12 +47,15 @@ GPU runtime drivers if applicable. Happily, TPI sets up all of this by default. -Separation of dependencies into Docker images will give you more control over -the software versions, and also makes it painless to switch between cloud -providers -- currently Amazon Web Services (AWS), Microsoft Azure, Google Cloud -Platform, and Kubernetes. Your Docker image is cloud provider-agnostic. There -are thousands of [pre-defined Docker images online](https://hub.docker.com/) -too. +When confronted with cloud infrastructure and dependencies, people often think +"oh no, not again" (much +[like the petunias](https://www.youtube.com/watch?v=THSY7-CxKnQ) in the cover +image). To solve this, separating dependencies into Docker images gives more +control over software versions, and also makes it painless to switch between +cloud providers -- currently Amazon Web Services (AWS), Microsoft Azure, Google +Cloud Platform, and Kubernetes. Your Docker image is cloud provider-agnostic. +There are thousands of +[pre-defined Docker images online](https://hub.docker.com/) too. In this tutorial, we'll use an existing Docker image that comes with most of our requirements already installed. We'll then add add a few more dependencies on From 6393f8620686bfba6ae337ea9437aebced5858d1 Mon Sep 17 00:00:00 2001 From: Casper da Costa-Luis Date: Tue, 24 May 2022 20:44:05 +0100 Subject: [PATCH 23/23] update image --- .../images/2022-05-19/tpi-docker-header.png | Bin 170430 -> 177113 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/static/uploads/images/2022-05-19/tpi-docker-header.png b/static/uploads/images/2022-05-19/tpi-docker-header.png index 80a972c24a2b1a97634607684e1e9d794a24d223..3c1c0130d650f2265b6a1b143cc409b5043631aa 100644 GIT binary patch literal 177113 zcmXt81yob-8^%CDkdQ_i=@LXyYAPv+bjJiqDQN*=(k0zFxftFyZN$sIgbR^Hm(-WF^fmnYwK4R=V!}D*Bt7ZYrClR zP2}PQ2Dms`KDnOWMJ2T!FU=#8lj4`oFxgYU)IQocW9RC~%HvHFQ|R3E_Kmfx*?si% z&9Q}?-$w(!?-4s{Qya+hHj=9_pI@-1#eMY59%^nEHGhbnKe=99n4Q^1MI7bC8ccp{ z0tSmC04%JT_5ImH^trh(X=%^wHgf9l`t%aDvAZ7@7ItuUxp#Cjv3@yodR^_S3*SGy zy1Y0(J|3@tBQ8+v2Bqv~HTyePtggNA&8t@yCDP@<(E;*t`1;u~@}o2lD`Z?Wd{R1K zM%$xqPRX-x`bZTr19qEPRs 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