I installed the ansible tower trial version "Basic version".
When I create a project via the tower GUI interface and tried to run it, a problem appeared saying that the feature " ha " is not configured in this version.
Please, how can I disable the ha option from my trial version.
The code seem like this:
"
{
"instances": {
"primary": "192.168.122.158",
"secondaries": [
"192.168.122.109",
"192.168.122.26"
]
},
"ha": true,
"role": "primary",
"version": "2.1.4"
}
"
I want the "ha": false.
Related
I'm trying to follow the instructions I found here for debugging a Python SAM application in VS Code
https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-sam-cli-using-debugging-python.html
I'm not sure why they don't use sam build in the expamle, and point to .aws-sam/build but that's what I'm attempting.
My launch.json looks like this:
{
"version": "0.2.0",
"configurations": [
{
"name": "SAM CLI Python debug test",
"type": "python",
"request": "attach",
"port": 5890,
"host": "localhost",
"pathMappings": [
{
"localRoot": "${workspaceFolder}/.aws-sam/build",
"remoteRoot": "/var/task"
}
]
}
]
}
I'm triggering the Lambda's directly for now and so I'm invoking them like this:
sam local invoke -d 5890
I'm then putting a breakpoint at the beginning of the Lambda I find in the build folder, but when I start the debugger in VS Code it executes the Lambda without stopping at the breakpoint.
I created a GitHub repo with the test project I'm using and description of how I'm using it.
https://github.com/rupe120/aws-sam-python-debugging-test
Could someone help point me at what I'm missing in my setup?
So, the recommended way to do this is with the AWS Toolkit extension.
https://github.com/awslabs/aws-sam-cli/issues/1926#issuecomment-616600799
So the docs suggest using a localRoot of "${workspaceFolder}/hello_world/build" (assuming one is using the Hello World template). However, it only works when removing the build at the end of the path:
"localRoot": "${workspaceFolder}/hello_world"
This way, I got it to work without the AWS Toolkit.
I am very new to AWS Step Functions and AWS Lambda Functions and could really use some help getting an EMR Cluster running through Step Functions. A sample of my current State Machine structure is shown by the following code
{
"Comment": "This is a test for running the structure of the CustomCreate job.",
"StartAt": "PreStep",
"States": {
"PreStep": {
"Comment": "Check that all the necessary files exist before running the job.",
"Type": "Task",
"Resource": "arn:aws:lambda:us-east-1:XXXXXXXXXX:function:CustomCreate-PreStep-Function",
"Next": "Run Job Choice"
},
"Run Job Choice": {
"Comment": "This step chooses whether or not to go forward with running the main job.",
"Type": "Choice",
"Choices": [
{
"Variable": "$.FoundNecessaryFiles",
"BooleanEquals": true,
"Next": "Spin Up Cluster"
},
{
"Variable": "$.FoundNecessaryFiles",
"BooleanEquals": false,
"Next": "Do Not Run Job"
}
]
},
"Do Not Run Job": {
"Comment": "This step triggers if the PreStep fails and the job should not run.",
"Type": "Fail",
"Cause": "PreStep unsuccessful"
},
"Spin Up Cluster": {
"Comment": "Spins up the EMR Cluster.",
"Type": "Pass",
"Next": "Update Env"
},
"Update Env": {
"Comment": "Update the environment variables in the EMR Cluster.",
"Type": "Pass",
"Next": "Run Job"
},
"Run Job": {
"Comment": "Add steps to the EMR Cluster.",
"Type": "Pass",
"End": true
}
}
}
Which is shown by the following workflow diagram
The PreStep and Run Job Choice tasks use a simple Lambda Function to check that the files necessary to run this job exist on my S3 Bucket, then go to spin up the cluster provided that the necessary files are found. These tasks are working properly.
What I am not sure about is how to handle the EMR Cluster related steps.
In my current structure, the first task is to spin up an EMR Cluster. this could be done through directly using the Step Function JSON, or preferably, using a JSON Cluster Config file (titled EMR-cluster-setup.json) I have located on my S3 Bucket.
My next task is to update the EMR Cluster environment variables. I have a .sh script located on my S3 Bucket that can do this. I also have a JSON file (titled EMR-RUN-Script.json) located on my S3 Bucket that will add a first step to the EMR Cluster that will run and source the .sh script. I just need to run that JSON file from within the EMR Cluster, which I do not know how to do using the Step Functions. The code for EMR-RUN-SCRIPT.json is shown below
[
{
"Name": "EMR-RUN-SCRIPT",
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "s3://us-east-1.elasticmapreduce/libs/script-runner/script-runner.jar",
"Args": [
"s3://PATH/TO/env_configs.sh"
]
}
}
]
My third task is to add a step that contains a spark-submit command to the EMR Cluster. This command is described in a JSON config file (titled EMR-RUN-STEP.json) located on my S3 Bucket that can be uploaded to the EMR Cluster in a similar manner to uploading the environment configs file in the previous step. The code for EMR-RUN-STEP.json is shown below
[
{
"Name": "EMR-RUN-STEP",
"ActionOnFailure": "CONTINUE",
"HadoopJarStep": {
"Jar": "command-runner.jar",
"Args": [
"bash", "-c",
"source /home/hadoop/.bashrc && spark-submit --master yarn --conf spark.yarn.submit.waitAppCompletion=false --class CLASSPATH.TO.MAIN s3://PATH/TO/JAR/FILE"
]
}
}
]
Finally, I want to have a task that makes sure the EMR Cluster terminates after it completes its run.
I know there may be a lot involved within this question, but I would greatly appreciate any assistance with any of the issues described above. Whether it be following the structure I outlined above, or if you know of another solution, I am open to any form of help. Thank you in advance.
You need a terminate cluster step,
as documentation states:
https://docs.aws.amazon.com/step-functions/latest/dg/connect-emr.html
createCluster uses the same request syntax as runJobFlow, except for the following:
The field Instances.KeepJobFlowAliveWhenNoSteps is mandatory,
and must have the Boolean value TRUE.
So, you need a step to do this for you:
terminateCluster.sync - for me this is preferable over the simple terminateCluster as it waits for the cluster to actually terminate and you can handle any hangs here - you'll be using Standard step functions so the bit of extra time will not be billed
Shuts down a cluster (job flow).
terminateJobFlows The same as terminateCluster, but waits for the cluster to terminate.
ps.: if you are using termination protection you'll need an extra step to turn if off before you can terminate your cluster ;)
'KeepJobFlowAliveWhenNoSteps': False
add the above configurations to emr cluster creation script. it will auto terminate emr clusters when all the steps are completed emr boto3 config
Google recently added support for GPUs in their cloud service.
I'm trying to follow the instructions found here to start a machine with a GPU. Running this script on Windows:
gcloud beta compute instances create gpu-instance-1^
--machine-type n1-standard-2^
--zone us-east1-d^
--accelerator type=nvidia-tesla-k80,count=1^
--image-family ubuntu-1604-lts^
--image-project ubuntu-os-cloud^
--maintenance-policy TERMINATE^
--restart-on-failure^
with gcloud command line tool version 146.0.0 fails, saying:
ERROR: (gcloud.beta.compute.instances.create) unknown collection [compute.acceleratorTypes]
Any ideas?
Was never able to get the gcloud utility working. Using the API did work. Of note, when posting the API request (instructions on the same page as the gcloud instructions, here) the key that creates an instance with a GPU is guestAccelerators. This key does not have an analogous option in gcloud.
Copying the API request as it appears on the instructions page linked above.
POST https://www.googleapis.com/compute/beta/projects/[PROJECT_ID]/zones/[ZONE]/instances?key={YOUR_API_KEY}
{
"machineType": "https://www.googleapis.com/compute/beta/projects/[PROJECT_ID]/zones/[ZONE]/machineTypes/n1-highmem-2",
"disks":
[
{
"type": "PERSISTENT",
"initializeParams":
{
"diskSizeGb": "[DISK_SIZE]",
"sourceImage": "https://www.googleapis.com/compute/beta/projects/[IMAGE_PROJECT]/global/images/family/[IMAGE_FAMILY]"
},
"boot": true
}
],
"name": "[INSTANCE_NAME]",
"networkInterfaces":
[
{
"network": "https://www.googleapis.com/compute/beta/projects/[PROJECT_ID]/global/networks/[NETWORK]"
}
],
"guestAccelerators":
[
{
"acceleratorCount": [ACCELERATOR_COUNT],
"acceleratorType": "https://www.googleapis.com/compute/beta/projects/[PROJECT_ID]/zones/[ZONE]/acceleratorTypes/[ACCELERATOR_TYPE]"
}
],
"scheduling":
{
"onHostMaintenance": "terminate",
"automaticRestart": true
},
"metadata":
{
"items":
[
{
"key": "startup-script",
"value": "[STARTUP_SCRIPT]"
}
]
}
}
Sometimes you need to ensure you have the latest version of the gcloud utility installed in order to use certain GCP features.
Try running this command or read the below docs on how to update your gcloud utility:
gcloud components update
https://cloud.google.com/sdk/gcloud/reference/components/update
It seems that by default EMR deploys the Spark driver to one of the CORE nodes, resulting in the MASTER node being virtually un-utilized. Is it possible to run the driver program on the MASTER node instead? I have experimented with the --deploy-mode arguments to no avail.
Here is my instance groups JSON definition:
[
{
"InstanceGroupType": "MASTER",
"InstanceCount": 1,
"InstanceType": "m3.xlarge",
"Name": "Spark Master"
},
{
"InstanceGroupType": "CORE",
"InstanceCount": 3,
"InstanceType": "m3.xlarge",
"Name": "Spark Executors"
}
]
Here is my configurations JSON definition:
[
{
"Classification": "spark",
"Properties": {
"maximizeResourceAllocation": "true"
},
"Configurations": []
},
{
"Classification": "spark-env",
"Properties": {
},
"Configurations": [
{
"Classification": "export",
"Properties": {
},
"Configurations": [
]
}
]
}
]
Here is my steps JSON definition:
[
{
"Name": "example",
"Type": "SPARK",
"Args": [
"--class", "com.name.of.Class",
"/home/hadoop/myjar-assembly-1.0.jar"
],
"ActionOnFailure": "TERMINATE_CLUSTER"
}
]
I am using aws emr create-cluster with --release-label emr-4.3.0.
Setting the location of the driver
With spark-submit, the flag --deploy-mode can be used to select the location of the driver.
Submitting applications in client mode is advantageous when you are debugging and wish to quickly see the output of your application. For applications in production, the best practice is to run the application in cluster mode. This mode offers you a guarantee that the driver is always available during application execution. However, if you do use client mode and you submit applications from outside your EMR cluster (such as locally, on a laptop), keep in mind that the driver is running outside your EMR cluster and there will be higher latency for driver-executor communication.
https://blogs.aws.amazon.com/bigdata/post/Tx578UTQUV7LRP/Submitting-User-Applications-with-spark-submit
I don't think it is a waste. When running Spark on EMR, the master node will run Yarn RM, Livy Server, and maybe other applications you selected. And if you run in client mode, the majority of the driver program will run on the master node as well.
Note that the driver program could be heavier than the tasks on executors, e.g. collecting all results from all executors, in which case you need to allocate enough resources to your master node if it is where the driver program is running.
What I'm trying to achieve: I have a docker container which contains a CMS, that CMS has a folder named 'assets'. I need the asset folder to be available to other containers, and also for the data to be safe from deletion when containers/images are removed.
How I've attempted to solve it: I have read all about mounting volumes in multi container environments and looked at a bunch of examples and came up with the following dockerrun.aws.json file
{
"AWSEBDockerrunVersion": 2,
"volumes": [
{
"name": "assets",
"host": {
"sourcePath": "/var/app/current/cms"
}
}
],
"containerDefinitions": [
{
//...
"mountPoints": [
{
"sourceVolume": "assets",
"containerPath": "/var/www/assets",
"readOnly": false
}
]
}
]
}
I can upload this via Beanstalk and everything builds and all boxes are green, however if I login to the EC2 instance and ls /var/app/current the directory is empty. I was expecting to see /var/app/current/cms/assets sitting there...
I think I'm missing a core concept or flag in my build file, any direction or better way of achieving what I'm trying to do would be appreciated.
Try taking a look at this link and try it out.
http://docs.aws.amazon.com/elasticbeanstalk/latest/dg/create_deploy_docker_v2config.html
I believe this is similar to what you are asking for.
J