From 48c1224a4c3d0bf635454a26655d48c5f9f7f0b6 Mon Sep 17 00:00:00 2001 From: "Carlos Manzanedo Rueda (ruecarlo@)" Date: Sun, 7 Nov 2021 20:35:36 +0000 Subject: [PATCH] changes to the rendering workshop --- content/rendering-with-batch/_index.md | 2 +- content/rendering-with-batch/batch/_index.md | 34 ++++++++----- .../batch/compute_environment.md | 18 +++---- .../batch/job_definition.md | 8 +-- .../rendering-with-batch/batch/job_queue.md | 10 ++-- .../rendering-with-batch/batch/submit_job.md | 10 ++-- content/rendering-with-batch/cleanup.md | 2 +- .../rendering-with-batch/ecr-repository.md | 48 ++++++++++++------ .../rendering-with-batch/final_thoughts.md | 11 ++-- content/rendering-with-batch/monitor_job.md | 19 ++++++- .../rendering_pipeline.md | 4 +- static/images/rendering-with-batch/CE.png | Bin 70326 -> 154961 bytes 12 files changed, 105 insertions(+), 61 deletions(-) diff --git a/content/rendering-with-batch/_index.md b/content/rendering-with-batch/_index.md index e9d8883a..d63a32cc 100644 --- a/content/rendering-with-batch/_index.md +++ b/content/rendering-with-batch/_index.md @@ -14,7 +14,7 @@ The estimated completion time of this lab is **60 minutes**. Please note Renderi In this workshop you will learn to submit jobs with [AWS Batch](https://aws.amazon.com/batch/) following Spot best practices to [render](https://en.wikipedia.org/wiki/Rendering_(computer_graphics)) a [Blender](https://www.blender.org/) file in a distributed way. You will be creating a docker container and publishing it in Amazon Elastic Container Registry (ECR). Then you will use that container in AWS Batch using a mix of EC2 On-Demand and Spot instances. [Spot instances](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/using-spot-instances.html) are EC2 spare capacity offered at steep discounts compared to On-Demand instances and are a cost-effective choice for applications that can be interrupted, what makes them well-suited for the batch processing that we will run. After going through all the sections, you will have the following pipeline created: 1. A python script downloads the Blender file from S3 to extract the number of frames from the Blender project. -2. The script submits a Batch using an `array job` with as many tasks as number of frames. It also submits a single stitching job using [FFmpeg](https://ffmpeg.org/) to create a final video file. +2. The script submits a batch using an `array job` with as many tasks as number of frames. It also submits a single stitching job using [FFmpeg](https://ffmpeg.org/) to create a final video file. 3. Each of the jobs in the rendering job array run a Docker container that executes Blender to render a slice of frames, and after uploads them to S3. The outcome of the workshop would be the following animation. diff --git a/content/rendering-with-batch/batch/_index.md b/content/rendering-with-batch/batch/_index.md index 60d02b61..947c30a1 100644 --- a/content/rendering-with-batch/batch/_index.md +++ b/content/rendering-with-batch/batch/_index.md @@ -6,25 +6,33 @@ weight: 80 ## Overview -AWS Batch is a set of batch management capabilities that enables you to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized compute resources) based on the volume and specific resource requirements of the batch jobs submitted. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as AWS Fargate, Amazon EC2 and Spot Instances. There is no additional charge for AWS Batch. You only pay for the AWS resources you create to store and run your batch jobs. +AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized compute resources) based on the volume and specific resource requirements of the batch jobs submitted. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as AWS Fargate, Amazon EC2 and Spot Instances. There is no additional charge for AWS Batch. You only pay for the AWS resources you create to store and run your batch jobs. -Thanks to Batch computing, you can execute a series of programs ("jobs") on one or more computers without manual intervention. Input parameters are pre-defined through scripts, command-line arguments, control files, or job control language. A given batch job may depend on the completion of preceding jobs, or on the availability of certain inputs, making the sequencing and scheduling of multiple jobs important, and incompatible with interactive processing. +Thanks to AWS Batch computing, you can execute a series of "jobs" on one or more computers without manual intervention. Input parameters are pre-defined through scripts, command-line arguments, control files, or the AWS Batch job control language. You can make a batch job to +depend on the completion of preceding jobs, or on the availability of certain inputs, making the sequencing and scheduling of multiple jobs easy. ## AWS Batch features -- **Dynamic compute resource provisioning and scaling**: you only need to set up a few concepts in Batch (a Compute environment, job queue, and job definition), and you have a complete queue, scheduler, and compute architecture without managing a single piece of compute infrastructure. -- **AWS Batch with EC2 Spot instances**: Since Batch workloads are containerized, Batch is a perfect fit for Spot Instances. If a workload is interrupted, Batch will automatically spin-up another Spot Instance you’ve specified. -- **AWS Batch with Fargate**: AWS Batch with Fargate resources allows you to have a completely serverless architecture for your batch jobs. -- **Integration with EC2 Launch Templates**: AWS Batch now supports EC2 Launch Templates, allowing you to build customized templates for your compute resources, and enabling Batch to scale instances with those requirements. -- **Priority-based job scheduling**: AWS Batch enables you to set up multiple queues with different priority levels. Batch jobs are stored in the queues until compute resources are available to execute the job. The AWS Batch scheduler evaluates when, where, and how to run jobs that have been submitted to a queue based on the resource requirements of each job. +- **Dynamic compute resource provisioning and scaling**: you only need to set up a few concepts in AWS Batch (a Compute environment, job queue, and job definition), and you have a complete queue, scheduler, and compute architecture without managing a single piece of compute infrastructure. +- **AWS Batch with EC2 Spot instances**: AWS Batch workloads are usually a perfect fit for Spot Instances. If a workload is interrupted, AWS Batch will automatically retry the task and spin-up another Spot Instance using AWS EC2 Spot Best practices. +- **AWS Batch with Fargate**: AWS Batch integrates with [AWS Fargate](https://aws.amazon.com/fargate/). With AWS Fargate you can run batch workloads in a completely serverless environment. +- **Integration with EC2 Launch Templates**: AWS Batch does supports EC2 Launch Templates, allowing you to build customized compute resources, and enabling AWS Batch to scale instances with those requirements. +- **Priority-based job scheduling**: AWS Batch enables you to set up multiple queues with different priority levels. Batch jobs are queued until compute resources are available to execute the job. The AWS Batch scheduler evaluates when, where, and how to run jobs that have been submitted to a queue based on the resource requirements of each job. -These are just some examples of all the features AWS Batch has. If you want to learn in detail all its capabilities, visit [this web page](https://aws.amazon.com/batch/features/?nc=sn&loc=2). +If you want to learn in detail all its capabilities, visit [this web page](https://aws.amazon.com/batch/features/?nc=sn&loc=2). ## Understanding Batch components -### Typical job architecture +In this section we will go through each individual AWS Batch architecture component, starting with the typical AWS Batch job architecture. In the diagram below you can see how + +1. The user start triggering an event by uploading a S3 object +2. You can automate and trigger the submission of one or more jobs using [AWS Lambda](https://aws.amazon.com/lambda/) or manage [AWS Batch with Step functions](https://docs.aws.amazon.com/step-functions/latest/dg/connect-batch.html). The job gets submitted to a **Job queue** using a **Job Definition**. +3. AWS Batch will schedule the jobs submitted using **Compute Environments**. It will procure new resources when needed and allocate the job accordingly. +4. The Compute Environment resources (EC2 Instances or Fargate) will run the job and in this workshop and example, it will store the output of into S3. + ![Batch components](/images/rendering-with-batch/batch_architecture.png) + ### Compute environment An AWS Batch Compute Environment is a collection of compute resources on which jobs are executed. AWS Batch supports two types of Compute Environments; Managed Compute Environments which are provisioned and managed by AWS and Unmanaged Compute Environments which are managed by customers. @@ -52,12 +60,12 @@ To learn more about Job definitions, visit [AWS Batch Job definitions](https://d ## When you should use AWS Batch -AWS Batch can be used in several scenarios that imply executing tasks which can be tore down to small components, jobs, thus drastically reducing execution time by running programs in parallel, and also automating the workloads. These are some example scenarios: +AWS Batch can be used in several scenarios where you can split a workload into smaller jobs and run those in parallel thus drastically reducing execution time. These are some example scenarios: -- Post-trade analytics: trading desks require batch processing of large data sets to constantly analyse day's transaction costs and execution reporting, among other areas. AWS Batch enables the automation of these workloads so that you can understand the pertinent risk going into the next day’s trading cycle and make better decisions based on data. -- Drug screening: AWS Batch allows research scientists involved in drug discovery to more efficiently and rapidly search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. -- Rendering: AWS Batch provides content producers and post-production houses with tools to automate content rendering workloads and reduces the need for human intervention due to execution dependencies or resource scheduling. +- In Visual Effects, we cover this in this Rendering workshop: AWS Batch provides content producers and post-production houses with tools to automate content rendering workloads and reduces the need for human intervention due to execution dependencies or resource scheduling. - Media supply chain: AWS Batch simplifies complex media supply chain workflows by coordinating the execution of disparate and dependent jobs at different stages of processing, and supports a common framework for managing content preparation for different contributors to the media supply chain. +- In life-sciences: Drug screening. AWS Batch allows research scientists involved in drug discovery to more efficiently and rapidly search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. +- In Finance, Post-trade analytics: trading desks require batch processing of large data sets to constantly analyze day's transaction costs and execution reporting, among other areas. AWS Batch enables the automation of these workloads so that you can understand the pertinent risk going into the next day’s trading cycle and make better decisions based on data. ## Working with AWS Batch diff --git a/content/rendering-with-batch/batch/compute_environment.md b/content/rendering-with-batch/batch/compute_environment.md index 31bba435..61d567e2 100644 --- a/content/rendering-with-batch/batch/compute_environment.md +++ b/content/rendering-with-batch/batch/compute_environment.md @@ -6,13 +6,11 @@ weight: 90 ![Compute environment](/images/rendering-with-batch/CE.png) -We are going to create two compute environments: one using Spot instances and the other using On-Demand instances. The reason for this is that, on a real scenario, you may have to comply with several SLAs with regards to finishing the jobs under a specific time window. - -By using On-Demand instances, we are sure that the compute resources will remain throughout the whole execution. And by using Spot instances on top of that, we can reduce costs and execution time by acquiring compute capacity at steep discounts compared to On-Demand instances. +We are going to create two compute environments: one using **Spot** instances and the other using **On-Demand** instances. That way if your job need to comply with an SLAs the On-Demand proportion can be used to ensure a timely completion, while the Spot will be used to accelerate the run and reduce the overall cost. ### Spot Compute environment creation -Run the following to generate the configuration file that will be used to create the Batch compute environment that uses Spot instances: +Run the following to generate the configuration file that will be used to create the AWS Batch compute environment that uses Spot instances: ``` export SPOT_COMPUTE_ENV_NAME="RenderingSpotComputeEnvironment" @@ -44,13 +42,13 @@ EoF Let's explore the configuration parameters in the `computeResources` structure: - **type**: specifies the compute resource type to use. This compute environment will use Spot instances, hence the `SPOT` value. The other possible values are `EC2`, `FARGATE` and `FARGATE_SPOT`. -- **allocationStrategy**: the allocation strategy to use for the compute resource if not enough instances of the best fitting instance type can be allocated. The `SPOT_CAPACITY_OPTIMIZED` allocation strategy allocates instances from the Spot Instance pools with the optimal capacity for the number of instances that are launching, making use of real-time capacity data and optimizing the selection of used Spot Instances. You can read about the benefits of using `capcity-optimized` in the blog post [Capacity-Optimized Spot Instance allocation in action at Mobileye and Skyscanner](https://aws.amazon.com/blogs/aws/capacity-optimized-spot-instance-allocation-in-action-at-mobileye-and-skyscanner/). This way, Batch will select one or more instance types that are large enough to meet the requirements of the jobs in the queue, with a preference for instance types that are less likely to be interrupted. To learn more about allocation strategies, see [Allocation strategies](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html). +- **allocationStrategy**: the allocation strategy to use for the compute resource if not enough instances of the best fitting instance type can be allocated. The `SPOT_CAPACITY_OPTIMIZED` allocation strategy allocates instances from the Spot Instance pools with the optimal capacity for the number of instances that are launching, making use of real-time capacity data and optimizing the selection of used Spot Instances. You can read about the benefits of using `capcity-optimized` in the blog post [Capacity-Optimized Spot Instance allocation in action at Mobileye and Skyscanner](https://aws.amazon.com/blogs/aws/capacity-optimized-spot-instance-allocation-in-action-at-mobileye-and-skyscanner/). This way, AWS Batch will select one or more instance types that are large enough to meet the requirements of the jobs in the queue, with a preference for instance types that are less likely to be interrupted. To learn more about allocation strategies, see [Allocation strategies](https://docs.aws.amazon.com/batch/latest/userguide/allocation-strategies.html). - **instanceTypes**: the instances types that can be launched. By specifying `optimal`, instance types from the C4, M4, and R4 instance families are selected to match the demand of your job queues. - **subnets**: the VPC subnets where the compute resources are launched. - **launchTemplate**: the launch template to use when launching compute resources. We specify the one we created earlier so that the User data script is applied to every instance that is launched. - **instanceRole**: the Amazon ECS instance profile applied to Amazon EC2 instances in the compute environment. AWS Batch compute environments are populated with Amazon ECS container instances, and they run the Amazon ECS container agent locally. The Amazon ECS container agent makes calls to various AWS API operations on your behalf. Therefore, container instances that run the agent require an IAM policy and role for these services to recognize that the agent belongs to you. For more information read [Amazon ECS instance role](https://docs.aws.amazon.com/batch/latest/userguide/instance_IAM_role.html). -Execute this command to create the Batch compute environment and export its ARN to an environment variable. To learn more about this API, see [create-compute-environment CLI command reference](https://docs.aws.amazon.com/cli/latest/reference/batch/create-compute-environment.html). +Run this command to create the AWS Batch compute environment and export its ARN to an environment variable. To learn more about this API, see [create-compute-environment CLI command reference](https://docs.aws.amazon.com/cli/latest/reference/batch/create-compute-environment.html). ``` export SPOT_COMPUTE_ENV_ARN=$(aws batch create-compute-environment --cli-input-json file://spot-compute-environment-config.json | jq -r '.computeEnvironmentArn') @@ -59,7 +57,7 @@ echo "Spot compute environment Arn: ${SPOT_COMPUTE_ENV_ARN}" ### On-Demand Compute environment creation -Run the following to generate the configuration file that will be used to create the Batch compute environment that uses On-Demand instances: +Run the following to generate the configuration file that will be used to create the AWS Batch compute environment that uses On-Demand instances: ``` export ONDEMAND_COMPUTE_ENV_NAME="RenderingOnDemandComputeEnvironment" @@ -88,14 +86,14 @@ cat < ondemand-compute-environment-config.json EoF ``` -Let's explore the configuration parameters in the `computeResources` structure that differ from the previous Compute environment: +Lets explore the configuration parameters in the `computeResources` structure that differ from the previous Compute environment: - **type**: specifies the compute resource type to use. This compute environment will use On-demand instances, hence the `EC2` value. The other possible values are `SPOT`, `FARGATE` and `FARGATE_SPOT`. -- **allocationStrategy**: the allocation strategy to use for the compute resource if not enough instances of the best fitting instance type can be allocated. By specifying `BEST_FIT_PROGRESSIVE`, Batch selects an instance type that best fits the needs of the jobs with a preference for the lowest-cost instance type. +- **allocationStrategy**: the allocation strategy to use for the compute resource if not enough instances of the best fitting instance type can be allocated. By specifying `BEST_FIT_PROGRESSIVE`, AWS Batch selects an instance type that best fits the needs of the jobs with a preference for the lowest-cost instance type. Notice how we have specified lower values for `maxvCpus` and `desiredvCpus` in comparison to the same of the Spot compute environment. We do so to make sure that only the required compute capacity to finish the job in a finite amount of time is provisioned with On-demand capacity, using more Spot resources to reduce execution time and cost. -Execute this command to create the Batch compute environment and export its ARN to an environment variable. +Execute this command to create the AWS Batch compute environment and export its ARN to an environment variable. ``` export ONDEMAND_COMPUTE_ENV_ARN=$(aws batch create-compute-environment --cli-input-json file://ondemand-compute-environment-config.json | jq -r '.computeEnvironmentArn') diff --git a/content/rendering-with-batch/batch/job_definition.md b/content/rendering-with-batch/batch/job_definition.md index 89e17f1c..50203a43 100644 --- a/content/rendering-with-batch/batch/job_definition.md +++ b/content/rendering-with-batch/batch/job_definition.md @@ -4,7 +4,7 @@ date: 2021-09-06T08:51:33Z weight: 110 --- -As a last step to configuring Batch, we will register a job definition that will act as a template when we submit jobs. +As a last step to configuring AWS Batch, we will register a job definition that will act as a template when we submit jobs. Run the following to generate the configuration file that will be used to create the job definition: @@ -17,7 +17,7 @@ cat < job-definition-config.json "type": "container", "containerProperties": { "image": "${IMAGE}", - "vcpus": 2, + "vcpus": 1, "memory": 8000 }, "retryStrategy": { @@ -30,9 +30,9 @@ cat < job-definition-config.json EoF ``` -Let's explore the configuration parameters in the structure: +Lets explore the configuration parameters in the structure: -- **type**: how the job is going to be run. By specifying `container`, we are making the jobs launched using this definition to be run in a Docker container. The other possible value is `multinode`, that allows to run single jobs that span multiple EC2 instances. With AWS Batch multi-node parallel jobs, you can run large-scale, tightly coupled, high performance computing applications. That type of job is not supported with Spot instances. To learn more about multi-node jobs, visit [multi-node parallel jobs](https://docs.aws.amazon.com/batch/latest/userguide/multi-node-parallel-jobs.html). +- **type**: `container` is the default type and allows to run loosely coupled HPC workloads at scale. The type is `multinode`. With AWS Batch multi-node you can run large-scale, tightly coupled, high performance computing applications. Note `multi-node` jobs are not supported with Spot instances. To learn more about `multi-node` jobs, visit [multi-node parallel jobs](https://docs.aws.amazon.com/batch/latest/userguide/multi-node-parallel-jobs.html). - **image**: the image used to start a container, this value is passed directly to the Docker daemon. - **vcpus**: The number of vCPUs reserved for the job. Each vCPU is equivalent to 1,024 CPU shares. - **memory**: hard limit (in MiB) for a container. If your container attempts to exceed the specified number, it's terminated. diff --git a/content/rendering-with-batch/batch/job_queue.md b/content/rendering-with-batch/batch/job_queue.md index 78e331b7..6714a7a3 100644 --- a/content/rendering-with-batch/batch/job_queue.md +++ b/content/rendering-with-batch/batch/job_queue.md @@ -4,7 +4,9 @@ date: 2021-09-06T08:51:33Z weight: 100 --- -We are going to create a job queue that is going to be associated to the two compute environments that we just created. We will also assign an order of preference for the compute environments. Run the following to generate the configuration file that will be used to create the job queue: +You are now going to create a Job Queue. The Job Queue is going to be associated with the two compute environments that we just created. We will assign an order of use for the Compute Environments using first the OnDemand environment and then the Spot. + +Run the following to generate the configuration file that will be used to create the job queue: ``` export RENDERING_QUEUE_NAME=RenderingQueue @@ -28,12 +30,12 @@ cat < job-queue-config.json EoF ``` -Let's explore the configuration parameters in the structure: +Lets explore the configuration parameters in the structure: - **priority**: job queues with a higher priority are evaluated first when associated with the same compute environment. Priority is determined in descending order. - **computeEnvironmentOrder**: the set of compute environments mapped to a job queue and their order relative to each other. A compute environment with a **lower** value of `order` is tried for job placement first. We specify the On-demand compute environment to be tried first to ensure that we have compute capacity throughout the whole execution, thus being able to comply with SLAs should there be any. -{{% notice info %}} +{{% notice note %}} All the compute environments within a queue must be either (`SPOT` and/or `EC2`) or (`FARGATE` and/or `FARGATE_SPOT`). EC2 and Fargate compute environments can't be mixed. We will only work with `SPOT` and `EC2` compute environments. {{% /notice %}} @@ -43,4 +45,4 @@ Execute this command to create the job queue. To learn more about this API, see aws batch create-job-queue --cli-input-json file://job-queue-config.json ``` -Next, you are going to create a job definition that will be used to submit jobs. +Next, you are going to create a **Job Definition** that will be used to submit jobs. diff --git a/content/rendering-with-batch/batch/submit_job.md b/content/rendering-with-batch/batch/submit_job.md index 9ce54884..c3763eb0 100644 --- a/content/rendering-with-batch/batch/submit_job.md +++ b/content/rendering-with-batch/batch/submit_job.md @@ -4,14 +4,14 @@ date: 2021-09-06T08:51:33Z weight: 120 --- -You have now all the Batch components in place, and are ready to start submitting jobs that will be placed in a queue and processed by a compute environment when Batch's scheduler starts running them. The last step is to download a Python script that will take the bucket name, the rendering queue and the job definition as input parameters and will launch the two jobs of the rendering pipeline using the [AWS SDK for Python, Boto3](https://aws.amazon.com/sdk-for-python/). +You have now all the AWS Batch components in place, and are ready to start submitting jobs that will be placed in a queue and processed by a compute environment when AWS Batch's scheduler starts running them. The last step is to download a Python script that will take the bucket name, the rendering queue and the job definition as input parameters and will launch the two jobs of the rendering pipeline using the [AWS SDK for Python, Boto3](https://aws.amazon.com/sdk-for-python/). ## Downloading the python script To submit the jobs that will implement the rendering and stitching you are going to use a python script that has already been coded. Execute the following command to download it from GitHub. ``` -wget https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/batch/job_submission.py +wget https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/batch/batch.files/job_submission.py ``` ## Reviewing the command line arguments @@ -22,9 +22,9 @@ This script needs a couple of command line arguments as input, execute the follo python3 job_submission.py -h ``` -You see that the script needs to receive the location of the blender file and where the results should be uploaded, as well as the the job definition that will be used to submit the job, the queue where it will be placed and the name that will be used to submit it. +The script needs: (a) the location of the blender file, (b) the location where results will be uploaded, (c) as well as the the Job Definition that will be used to submit the job, (d) the Job Queue where it will be placed and the name that will be used to submit it. -Additionally, there's an extra argument `-f` that needs to be passed. Thanks to this argument you can specify how many frames each job should render. This will have a direct impact on the size of the array job that is submitted. E.g.: if you want to render a file that has 250 frames and you specify a value of 1 for that argument, the size of the array job will be 250. If you specify a value of 5, the size will be 50 and so on. As you can imagine, the less the frames each job has to render, the less the time it will take for the job to complete. +Additionally, there's an extra argument `-f` that needs to be passed. The `-f` argument can be used to specify how many frames each job should render. This will have a direct impact on the size of the array job that is submitted. E.g.: if you want to render a file that has 250 frames and you specify a value of 1 for that argument, the size of the array job will be 250. If you specify a value of 5, the size will be 50 and so on. As you can imagine, the less the frames each job has to render, the less the time it will take for the job to complete. ## Submitting the job @@ -42,6 +42,8 @@ At this point the jobs have been submitted and you are ready to monitor them. ## Optional: understanding the script +We have used a python program to programmatically submit the jobs to AWS Batch. Feel free to move to the next section and monitor the execution of your AWS Batch job. If at some point you are interested to know the details of how the `job_submission.py` python job submits the job, you can read the sections below. + ### Method submit_rendering_job Two important things to highlight from this method: diff --git a/content/rendering-with-batch/cleanup.md b/content/rendering-with-batch/cleanup.md index 9c735b41..cc67d9c2 100644 --- a/content/rendering-with-batch/cleanup.md +++ b/content/rendering-with-batch/cleanup.md @@ -32,7 +32,7 @@ aws batch update-compute-environment --compute-environment "${ONDEMAND_COMPUTE_E To learn more about these APIs, see [update-job-queue CLI Command Reference](https://docs.aws.amazon.com/cli/latest/reference/batch/update-job-queue.html) and [update-compute-environment CLI Command Reference](https://docs.aws.amazon.com/cli/latest/reference/batch/update-compute-environment.html). {{% notice info %}} -Job queues and compute environments cannot be deleted while being modified, so running the following commands might throw an error if the resources are still being disabled. +The previous operation may take up to 2 minutes. Job queues and compute environments cannot be deleted while being modified, so running the commands below while the compute environments and job queue are being disabled might result in an error with the message "resource is being modified" {{% /notice %}} To delete the components: diff --git a/content/rendering-with-batch/ecr-repository.md b/content/rendering-with-batch/ecr-repository.md index 5abf3785..e75ec2d7 100644 --- a/content/rendering-with-batch/ecr-repository.md +++ b/content/rendering-with-batch/ecr-repository.md @@ -4,7 +4,7 @@ date: 2021-07-07T08:51:33Z weight: 50 --- -The first step to implement the rendering pipeline is to generate a Docker image with the script that will run Blender and FFmpeg. As you will see later, this image will be run by Batch when running jobs. You are going to host that image in Amazon Elastic Container Registry. +The first step to implement the rendering pipeline is to generate a Docker image with the script that will run Blender and FFmpeg. This container image will be used by AWS Batch when running jobs. You are going to host that image in Amazon Elastic Container Registry. ## Amazon Elastic Container Registry @@ -16,13 +16,13 @@ If you want to learn more about containers, read [this containers deep dive](htt ### Download image files -To create the Docker image you will need two files; the DockerFile, which is a text document that contains all the commands a user could call on the command line to assemble an image, and the bash script that will be executed when running the Docker container. +To create the Docker image you will need two files; the DockerFile, which is a text document that contains all the commands a user could call on the command line to assemble an image, and the bash script that will be executed when running the Docker container. Download both files executing these commands: ``` -wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/docker-files/Dockerfile" -wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/docker-files/render.sh" +wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/rendering-with-batch.files/docker-files/Dockerfile" +wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/rendering-with-batch.files/docker-files/render.sh" ``` ### Push the image to ECR @@ -47,34 +47,50 @@ wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/conten 2. Build your Docker image using the following command. For information on building a Docker file from scratch see the instructions [here](https://docs.aws.amazon.com/AmazonECS/latest/developerguide/docker-basics.html). **The execution if this step might take a couple of minutes**. ``` - docker build -t "${RepositoryName}" . + docker build -t "${IMAGE}" . ``` -3. Tag your image so you can push the image to this repository. - - ``` - docker tag "${RepositoryName}:latest" "${IMAGE}" - ``` - -4. Push the image to your newly created AWS repository. +3. Push the image to ECR . ``` docker push "${IMAGE}" ``` -You are now done with the container part. Next, you will configure some environment variables needed to create resources in Batch. +You are now done with the container part. Next, you will configure some environment variables needed to create resources in AWS Batch. ## Optional: understanding the render.sh script -### Method render: +When we send a batch job, the container that we just created will be executed. The +Entrypoint of the container is the bash script `render.sh`. The scripts just takes +a few arguments that AWS Batch will pass to each task and does run either blender when a environment variable named `ACTION` is set to `render` or or ffmpeg when is set to `stitch`. + +The following section describes the `render.sh` script in more detail. You don't need to go through this to run this workshop, but if you are interested in fully understanding how Blender and Stitch are called it will give you a clear description. + +#### Method parse_argument: 1. Downloads the blender file from S3. + Read the environment variable `ACTION`and decide from it what's the type of job to run, either blender or ffmpeg. It also takes other arguments such as the the *input*, *output* + + {{< highlight go "linenos=table, linenostart=6" >}} + ACTION=$1 + + if [ "${ACTION}" != "render" ] && [ "${ACTION}" != "stitch" ] ; then + echo "Unrecognised action" + exit 2 + fi + ... +{{< / highlight >}} + + +#### Method render: + +2. Downloads the blender file from S3. {{< highlight go "linenos=table, linenostart=57" >}} aws s3 cp "${INPUT_URI}" file.blend {{< / highlight >}} -2. Calculates the slice of frames that has to render (we will se how in more detail when we talk about Batch). +3. Calculates the slice of frames that has to render (we will se how in more detail when we talk about AWS Batch). {{< highlight go "linenos=table, linenostart=43" >}} if [[ -z "${AWS_BATCH_JOB_ARRAY_INDEX}" ]]; then @@ -86,7 +102,7 @@ else fi {{< / highlight >}} -3. Executes Blender. +4. Executes Blender. {{< highlight go "linenos=table, linenostart=63, hl_lines=3" >}} mkdir frames diff --git a/content/rendering-with-batch/final_thoughts.md b/content/rendering-with-batch/final_thoughts.md index bc631a14..be2c9a20 100644 --- a/content/rendering-with-batch/final_thoughts.md +++ b/content/rendering-with-batch/final_thoughts.md @@ -6,11 +6,12 @@ weight: 160 **Congratulations!** You have successfully completed the workshop. Today you have: -1. Learned the principles of graphics rendering and what programs you can use to do it. -2. Built and published in ECR a Docker image that runs those programs. -3. Learned about AWS Batch, its use cases and components. -4. Learned the different types of jobs you find in AWS Batch (array and single). Also, you have learned how to define dependencies between them and how to work with the AWS_BATCH_JOB_ARRAY_INDEX environment variable. -5. Seen how to follow Spot best practices when working with Batch, by defining several compute environments with the appropriate allocation strategies for EC2 and Spot instances. +1. Learned about AWS Batch, its use cases and components. +2. Seen how easy it is to adopt Spot best practices when using AWS Batch, by defining several compute environments with the appropriate allocation strategies. +3. Learned the different types of jobs you find in AWS Batch (array and single). Also, you have learned how to define dependencies between them and how to work with the AWS_BATCH_JOB_ARRAY_INDEX environment variable. +4. You learned the concepts required to create a rendering pipeline using Blender and ffmpeg. +5. Built and published in ECR a Docker image that runs Blender and ffmpeg. + ## Savings diff --git a/content/rendering-with-batch/monitor_job.md b/content/rendering-with-batch/monitor_job.md index 257d364f..58e1e0ad 100644 --- a/content/rendering-with-batch/monitor_job.md +++ b/content/rendering-with-batch/monitor_job.md @@ -6,7 +6,7 @@ weight: 140 ## Results -You can check the rendering progress by running these commands: +You can check the rendering progress by running these commands in the Cloud9 terminal: ``` while [ true ] @@ -25,12 +25,29 @@ do done ``` +{{% notice tip %}} +This operation will take about 5 minutes. While it progresses, go to the AWS Batch Console, and explore the state of: (a) Compute Environments, (b) Jobs. You can also check in the EC2 Console the : \(c\) EC2 Instances, (d) Auto Scaling groups defined. +{{% /notice %}} + + When the progress reaches 100%, the output video will be available in the following URL: ``` echo "Output url: https://s3.console.aws.amazon.com/s3/buckets/${BucketName}?region=${AWS_DEFAULT_REGION}&prefix=${JOB_NAME}/output.mp4" ``` +Copy the output of the command into your browser. It will take you to the S3 page where the output file `output.mp4` has been stored. You can just click on the **Download** button to download it to your own computer and play it. + +{{% notice warning %}} +You will need the appropriate program and video codecs to watch the mp4 generated video. You can use [VLC media player](https://www.videolan.org/vlc/). +{{% /notice %}} + +{{% notice tip %}} +Explore also the rest of the S3 folders and check the frames that were created. +{{% /notice %}} + + + ## Monitoring ### Viewing the logs of a job diff --git a/content/rendering-with-batch/rendering_pipeline.md b/content/rendering-with-batch/rendering_pipeline.md index 4b167de3..30d02c04 100644 --- a/content/rendering-with-batch/rendering_pipeline.md +++ b/content/rendering-with-batch/rendering_pipeline.md @@ -37,7 +37,7 @@ If you want to learn more about Blender's command line rendering, visit [this we ### Gathering a Blender file -We will use a Blender file from [BlendSwap](https://blendswap.com/categories). In that web page, Blender-powered 3D artists can share, exchange, collaborate, and learn from other artists in the community. We will work with [the same file](https://blendswap.com/blend/28661) that was rendered to generate the animation that you've seen in the landing page. That file was created by [Prokster](https://blendswap.com/profile/1012752) and is licensed under [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0/). If you want to use a different one, feel free to do so! Just take into account the following: +We will use a Blender file from [BlendSwap](https://blendswap.com/categories). **BlenderSwap** is a site where Blender-powered 3D artists can share, exchange, collaborate, and learn from other artists. We will work with [the same file](https://blendswap.com/blend/28661) that was used to create the animation in the landing page. That file was created by [Prokster](https://blendswap.com/profile/1012752) and is licensed under [Creative Commons 0](https://creativecommons.org/share-your-work/public-domain/cc0/). If you want to use a different one, feel free to do so! Just take into account the following: - The file must be configured to render the frames as .png files. - The file must be named **blendfile.blend**. @@ -46,7 +46,7 @@ We will use a Blender file from [BlendSwap](https://blendswap.com/categories). I Run the following command to download the file and upload it to S3: ``` -wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/blendfile.blend" +wget "https://raw.githubusercontent.com/awslabs/ec2-spot-workshops/master/content/rendering-with-batch/rendering-with-batch.files/blendfile.blend" aws s3api put-object --bucket "${BucketName}" --key "${BlendFileName}" --body "${BlendFileName}" ``` diff --git a/static/images/rendering-with-batch/CE.png b/static/images/rendering-with-batch/CE.png index 46388739cd59af03da3bb5c5b0e293d95913a33b..3272571c789c73a3b7163c328cf0d5c46349e268 100644 GIT binary patch literal 154961 zcmeFZbyQT{`|u42Qc4O)cQ?{4-Q6A1-9v+dfPf&KgLHQZ3?bb^BQ;1jNH;v=_cre5 z7w`Y?S`TYx&E}lF_n95%iqCbOiBM6JevU$b0s{l{TvkRx4F(4O7zPIJ9nw?i5nrn; zYZw?=;o(tgCV_(; zBORk;9UWbtlTr)}zQ4ry{zRh_CRg=qqusM8vYQ%~{<0xEjay~`UeaU2QC;=xvk!;` zJ}){FhK(N`~xVdnT{Wmb1&?2B5-I`-BoFm|!A)?Xg(tjcw?QoW0t`hbOT 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