Europe basketball predictions tomorrow

Moving Beyond Traditional Betting Markets

Beyond predicting match winners or point spreads lies an array of intriguing betting opportunities worth exploring:

• MVP Awards: Predict which player will have the most significant impact during the game.
• Highest Scorer: Place bets on which player will score the most points.
• Total Rebounds/Turnovers/Assists: These markets offer detailed insights into specific aspects of gameplay.

The Role of Injuries & Player Form in Predictions

Injuries can dramatically alter team dynamics while player form influences individual performances significantly:

• Injury Reports: Stay updated on injury reports before placing any bets; missing key players might change predicted outcomes considerably.
• Sudden Form Shifts:Avoid relying solely on past statistics; observe recent form changes that could impact tomorrow’s games.

Tactical Insights & Coaching Strategies Impacting Match Outcomes <|repo_name|>drewgreene/drewgreene.github.io<|file_sep|>/_posts/2017-11-09-adding-the-aws-cloudwatch-agent-to-a-aws-autoscaling-group.markdown --- layout: post title: "Adding The AWS CloudWatch Agent To A AWS Autoscaling Group" date: "2017-11-09T16:37:00+08:00" tags: - aws --- The AWS CloudWatch Agent is an open source tool that allows you to monitor metrics such as disk usage (including disk partitions) as well as logs from your EC2 instances. In this post I'll show you how you can use it together with AWS AutoScaling groups. ### Prerequisites You should already have: * An IAM role created that allows access to EC2 instances so they can send metrics/collect logs * An AutoScaling group ### Install The CloudWatch Agent On Your Instances The CloudWatch agent comes packaged with Python so you need at least version `2.6` installed. To install it use the following command: shell curl -O https://s3.amazonaws.com/amazoncloudwatch-agent/linux/amd64/latest/AmazonCloudWatchAgent.zip unzip AmazonCloudWatchAgent.zip sudo ./install.sh ### Create A Configuration File For The CloudWatch Agent The CloudWatch agent needs configuration so it knows what metrics/logs it should collect. Here's an example configuration file that collects memory usage metrics: json { "agent": { "metrics_collection_interval": 30, "logfile": "/opt/aws/amazon-cloudwatch-agent/logs/amazon-cloudwatch-agent.log" }, "metrics": { "metrics_collected": { "mem": { "measurement": [ "mem_used_percent" ], "metrics_collection_interval": 60, "resources": [ "*" ] } } } } Save this file as `config.json` somewhere on your instance. ### Start The CloudWatch Agent To start collecting metrics/logs using this configuration file run: shell sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a fetch-config -m ec2 -c file:/path/to/config.json -s To check if it started successfully run: shell sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -m ec2 -a status ### Adding The CloudWatch Agent To Your AutoScaling Group Now that we've verified that our instance can run the CloudWatch agent we can add it to our AutoScaling group. #### Create An IAM Instance Profile For The AutoScaling Group We need an IAM instance profile so our AutoScaling group knows what permissions it needs when launching new instances. To create one go into IAM -> Roles -> Create role -> EC2 -> Next step. Give it a name (eg `AutoScalingRole`) then select `CloudWatchAgentServerPolicy` from Policies then click Next step. Click Review policy then Create role.  Next go into IAM -> Instance Profiles -> Create Instance Profile -> Give it a name (eg `AutoScalingInstanceProfile`) -> Add role `AutoScalingRole` -> Create Instance Profile.  #### Add The IAM Instance Profile To Your AutoScaling Group Go into EC2 -> Auto Scaling -> Auto Scaling Groups -> Select your AutoScaling group -> Edit. Click Add policy then select `Instance profile` then select `AutoScalingInstanceProfile`.  Click Next step then Update. #### Add A Bootstrap Script To Your Launch Configuration The final step is adding some code to our launch configuration so that when new instances are launched they automatically install/run the CloudWatch agent. To do this go into EC2 -> Launch Configurations -> Select your Launch Configuration -> Edit. Scroll down to Advanced Details then paste the following code into User Data: bash #!/bin/bash yum install python26 -y curl -O https://s3.amazonaws.com/amazoncloudwatch-agent/linux/amd64/latest/AmazonCloudWatchAgent.zip unzip AmazonCloudWatchAgent.zip sudo ./install.sh sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a fetch-config -m ec2 -c file:/path/to/config.json -s Make sure you replace `/path/to/config.json` with the actual path where your config file will be located (eg `/home/ec2-user/config.json`). You can put your config file anywhere as long as it gets copied over when new instances are launched (eg using AWS CodeDeploy). Click Update at the bottom of the page once you're done. #### That's It! Now whenever new instances are launched they'll automatically install/run the CloudWatch agent using your config file. Thanks for reading! ### Useful Links * [Official Documentation](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/Install-CloudWatch-Agent.html) * [GitHub Repo](https://github.com/aws/amazon-cloudwatch-agent) * [List Of Available Metrics](https://docs.aws.amazon.com/AmazonCloudWatch/latest/monitoring/agent-metrics-collected.html) <|repo_name|>drewgreene/drewgreene.github.io<|file_sep|>/_posts/2017-05-03-setting-up-a-celery-cluster-with-docker-and-docker-compose.markdown --- layout: post title: "Setting Up A Celery Cluster With Docker And Docker Compose" date: "2017-05-03T19:57:00+08:00" tags: - docker --- I recently needed a way to process tasks asynchronously so I decided to try out Celery with RabbitMQ as my broker. To make things easier I also decided to use Docker Compose so I could easily spin up my Celery cluster whenever I needed it. Here's how I did it... ### Prerequisites You should already have Docker Compose installed. ### Project Structure My project structure looks like this: project/ │ docker-compose.yml # This file defines our docker containers/services. │ app.py # This file contains our celery tasks. └───celery/ │ celeryconfig.py # This file contains configuration for celery. ### Our Docker Compose File Here's what my docker-compose.yml looks like: yaml version: '2' services: rabbitmq: image: rabbitmq:management-alpine ports: - '5672' - '15672' environment: RABBITMQ_DEFAULT_USER: 'admin' RABBITMQ_DEFAULT_PASS: 'password' volumes: - rabbitmq_data:/var/lib/rabbitmq/ worker: build: context: . dockerfile: Dockerfile.celery-worker volumes: - .:/app/ environment: CELERY_BROKER_URL: amqp://admin:password@rabbitmq// CELERY_RESULT_BACKEND: redis://redis// links: - redis - rabbitmq beat: build: context: . dockerfile: Dockerfile.celery-beat volumes: - .:/app/ environment: CELERY_BROKER_URL: amqp://admin:password@rabbitmq// CELERY_RESULT_BACKEND: redis://redis// links: - redis - rabbitmq volumes: redis_data: rabbitmq_data: This sets up three services: * **rabbitmq**: Our message broker (using RabbitMQ). * **worker**: Our Celery worker which processes tasks asynchronously. * **beat**: Our Celery beat which schedules tasks periodically (eg every hour). ### Dockerfiles For Celery Worker And Beat Here are my Dockerfiles used by my worker & beat services: **Dockerfile.celery-worker** dockerfile FROM python:onbuild RUN pip install celery[redis] && pip install redis && pip install flower && pip install requests && apt-get update && apt-get install curl --yes && rm -rf /var/lib/apt/lists/* CMD celery worker --app=app.celery --concurrency=1 --loglevel=info --without-gossip --without-mingle --without-heartbeat && flower --port=5555 --broker=amqp://admin:@rabbitmq// --basic_auth=admin:@rabbitmq// & **Dockerfile.celery-beat** dockerfile FROM python:onbuild RUN pip install celery[redis] && pip install redis && apt-get update && apt-get install curl --yes && rm -rf /var/lib/apt/lists/* CMD celery beat --app=app.celery --loglevel=info --scheduler django_celery_beat.schedulers:DatabaseScheduler && curl http://localhost:$PORT/api/schedule/tasks/celery.add_every_hour_task/ Both Dockerfiles start by building from Python's official image then installing dependencies via pip (requests & flower are only needed by my worker).