cloudtik 1.6.0
CloudTik: a cloud scale platform for distributed analytics and AI on public clouds
License: Apache 2.0
Requires Python: >=3.8
Keywords: Distributed Cloud Data Analytic AI Spark

# CloudTik: Cloud Scale Platform for Distributed Analytics and AI

## Introduction

Building and operating fully distributed and high performance data analytics and AI platform are complex and time-consuming.
This is usually hard for small or middle enterprises not saying individuals.

While the existing solutions for solving distributed analytics and AI problems on cloud
have major challenges on a combination of various aspects cared by users.
These include high cost for software services, non-optimal performance on the corresponding hardware,
the complexity of operating and running such a platform and lack of transparency.

CloudTik enables researchers, data scientists, and enterprises to easily create and manage analytics and AI platform on public clouds,
with out-of-box optimized functionalities and performance, and to go quickly to focus on running the business workloads
in hours or in even minutes instead of spending months to construct and optimize the platform.

### CloudTik Solution
CloudTik is designed for solving the above challenges by providing a platform to help user
focuses on business development and achieve "Develop once, run everywhere" with the following core capabilities:
- Scalable, robust, and unified control plane and runtimes for all environments:
  - Public cloud providers and Kubernetes
  - Single node virtual clustering
  - Local or on-premise clusters
- Out of box optimized runtimes for storage, database, analytics and AI
  - Optimized Spark runtime with CloudTik optimizations
  - Optimized AI runtime support both CPU and GPU
- Support of major public cloud providers and cloud native:
  - AWS - Amazon Elastic Compute Cloud (EC2) or Amazon Elastic Kubernetes Service (EKS)
  - Azure - Azure Virtual Machines or Azure Kubernetes Service (AKS)
  - GCP -  Google Compute Engine (GCE) or Google Kubernetes Engine (GKE)
  - Alibaba Cloud - Elastic Compute Service (ECS)
  - Kubernetes and more
- Microservices infrastructure and runtimes with:
  - Service discovery - service registry, service discover, service DNS naming
  - Load balancing -  Layer 4 or Layer 7 load balancer working with built-in service discovery
  - Metrics and monitoring
- A fully open architecture and open-sourced platform

## High Level Concepts
### Workspace
Workspace is the CloudTik concept to act as the container of a set of clusters and the shared Cloud
resources among these clusters.

When a workspace for specific cloud provider is created, all the shared resources for implementing the unified
design are created. These include network resources (like VPC, subnets, NAT gateways, firewall rules),
instance profiles, cloud storage and so on. Although the actual resources varies between cloud providers while
the design the resources achieved is consistent.

### Cluster
Within a workspace, one or more clusters with needed services(runtimes) can be started.
These clusters will share a lot of common configurations
such as network (they are in the same VPC) but vary on other aspects including instance types, scale of the cluster,
services running and so on. The services provided by one cluster can be discovered by other clusters
and be consumed.

### Providers
CloudTik provider abstracts the hardware infrastructure layer so that CloudTik common facilities and runtimes
can consistently run on every provider environments. The support of different public cloud are implemented as providers
(such as AWS, Azure, GCP providers). Beyond the public cloud environments, we also support
virtual single node clustering, local or on-premise clusters which are also implemented as providers
(for example, virtual, local and on-premise providers)

### Runtimes
CloudTik runtimes are functional components to provide virtually some services.
Although the runtimes are decoupled and can be selected to include in a cluster independently,
CloudTik runtimes are designed to connect and consume other runtime services in the same workspace
through various service discovery mechanisms.

For example, if you configure a cluster to run HDFS, MySQL, Metastore and Spark runtimes,
no need for additional configuration, Metastore will discover MySQL service
and will use it as Metastore database; Spark will discover HDFS and Metastore service
and will use HDFS as Spark storage and Metastore as Spark catalog store.
The same will work smartly even if the runtimes are in different clusters
as long as they are in the same workspace.

CloudTik supports a systematic of data, analytics and AI runtimes to efficiently solve
end-to-end and distributed data, analytics and AI problems as well as
the runtimes for running CloudTik as a platform with microservice architecture.

For each cluster started, user can configure very easily which runtimes
(such as Spark runtime or AI runtime) are needed.
CloudTik has designed the runtimes with the optimized configurations and libraries.
And when the cluster is running, the runtimes are properly configured and ready for running your workload.

## Getting Started with CloudTik

### 1. Preparing Python environment

CloudTik requires a Python environment on Linux. We recommend using Conda to manage Python environments and packages.

If you don't have Conda installed, please refer to `dev/install-conda.sh` to install Conda on Linux.

```
git clone https://github.com/cloudtik/cloudtik.git && cd cloudtik
bash dev/install-conda.sh
```

Once Conda is installed, create an environment with a specific Python version as below.
CloudTik currently supports Python 3.8 or above. Take Python 3.9 as an example,

```
conda create -n cloudtik -y python=3.9
conda activate cloudtik
```

### 2. Installing CloudTik

Execute the following `pip` commands to install CloudTik on your working machine for specific cloud providers. 

Take AWS for example,

```
pip install cloudtik[aws]
```

Replace `cloudtik[aws]` with `clouditk[azure]`, `cloudtik[gcp]`, `cloudtik[aliyun]`
if you want to create clusters on Azure, GCP, Alibaba Cloud respectively.

If you want to run on Kubernetes, install `cloudtik[kubernetes]`.
Or  `clouditk[eks]` or `cloudtik[gke]` if you are running on AWS EKS or GCP GKE cluster.
Use `cloudtik[all]` if you want to manage clusters with all supported Cloud providers.

If you don't have a public cloud account, you can also play with CloudTik
easily locally with the same clustering experiences using virtual, on-premise or local providers.
For this case, simply install cloudtik core as following command,
```
pip install cloudtik
```
Please refer to [User Guide: Running Clusters Locally](https://cloudtik.readthedocs.io/en/latest/UserGuide/running-locally.html)
for detailed guide for this case.


### 3. Authentication to Cloud Providers API

After CloudTik is installed on your working machine, you need to configure or log into your Cloud account to 
authenticate the cloud provider CLI on the working machine.

Take AWS for example, install AWS CLI (command line interface),
run `aws configure` command and input your *AWS Access Key ID* and *AWS Secret Access Key*.
CloudTik is able to pick up your client credentials you configured through `aws configure`.

For detailed information of how to authenticate with public cloud providers,
refer to [User Guide: Login to Cloud](https://cloudtik.readthedocs.io/en/latest/UserGuide/login-to-cloud.html)

### 4. Creating a Workspace for Clusters.
Once you authenticated with your cloud provider, you can start to create a Workspace.

CloudTik uses **Workspace** concept to easily manage shared Cloud resources such as VPC network resources,
identity and role resources, firewall or security groups, and cloud storage resources.
By default, CloudTik will create a workspace managed cloud storage
(S3 for AWS, Data Lake Storage Gen 2 for Azure, GCS for GCP) for use without any user configurations.

**Note: Some resources like NAT gateway or elastic IP resources in Workspace cost money.
The price policy may vary among cloud providers.
Please check the price policy of the specific cloud provider to avoid undesired cost.**

Within a workspace, you can start one or more clusters with different combination of runtime services.

Create a configuration workspace yaml file to specify the unique workspace name, cloud provider type and a few cloud 
provider properties. 

Take AWS for example,

```
# A unique identifier for the workspace.
workspace_name: example-workspace

# Cloud-provider specific configuration.
provider:
    type: aws
    region: us-west-2
    # Use allowed_ssh_sources to allow SSH access from your client machine
    allowed_ssh_sources:
      - 0.0.0.0/0
```
*NOTE:* `0.0.0.0/0` in `allowed_ssh_sources` will allow any IP addresses to connect to your cluster as long as it has the cluster private key.
For more security, you need to change from `0.0.0.0/0` to restricted CIDR ranges for your case.

Use the following command to create and provision a Workspace:

```
cloudtik workspace create /path/to/your-workspace-config.yaml
```

Check [Configuration Examples](https://github.com/cloudtik/cloudtik/tree/main/examples/cluster) folder for more Workspace configuration file examples
for AWS, Azure, GCP, Kubernetes (AWS EKS or GCP GKE).

If you encounter problems on creating a Workspace, a common cause is that your current login account
for the cloud doesn't have enough privileges to create some resources such as VPC, storages, public ip and so on.
Make sure your current account have enough privileges. An admin or owner role will give the latest chance to have
all these privileges.

### 5. Starting a cluster with runtimes

Now you can start a cluster with a combination of runtimes you want.
By default, it will include Spark runtime.

```
cloudtik start /path/to/your-cluster-config.yaml
```

A typical cluster configuration file is usually very simple thanks to design of CloudTik's templates with inheritance.

Take AWS for example,

```
# An example of standard 1 + 3 nodes cluster with standard instance type
from: aws/standard

# Workspace into which to launch the cluster
workspace_name: example-workspace

# A unique identifier for the cluster.
cluster_name: example

# Cloud-provider specific configuration.
provider:
    type: aws
    region: us-west-2

auth:
    ssh_user: ubuntu
    # Set proxy if you are in corporation network. For example,
    # ssh_proxy_command: "ncat --proxy-type socks5 --proxy your_proxy_host:your_proxy_port %h %p"

available_node_types:
    worker.default:
        # The minimum number of worker nodes to launch.
        min_workers: 3
```
This example can be found in CloudTik source code folder `examples/cluster/aws/example-standard.yaml`.

You need only a few key settings in the configuration file to launch a Spark cluster.

As for `auth` above, please set proxy if your working node is using corporation network.

```
auth:
    ssh_user: ubuntu
    ssh_proxy_command: "ncat --proxy-type socks5 --proxy <your_proxy_host>:<your_proxy_port> %h %p"
```

The cluster key will be created automatically for AWS and GCP if not specified.
The created private key file can be found in .ssh folder of your home folder.
For Azure, you need to generate an RSA key pair manually (use `ssh-keygen -t rsa -b 4096` to generate a new ssh key pair).
and configure the public and private key as following,

```
auth:
    ssh_private_key: ~/.ssh/my_cluster_rsa_key
    ssh_public_key: ~/.ssh/my_cluster_rsa_key.pub
```

If you need different runtime components in the cluster,
in the cluster configuration file, you can set the runtime types. For example,
```
runtime:
    types: [spark, ai]
```
It will run a cluster with spark and AI runtimes.

Refer to `examples/cluster` directory for more cluster configurations examples.

### 6. Running analytics and AI workloads

Once the cluster is started, you can run Spark analytics and AI workloads
which are designed to be distributed and large scale in nature.

Below provides the information of some basic examples to start with.
As to running optimized Spark and AI, you can refer to [Running Optimized Analytics with Spark](https://cloudtik.readthedocs.io/en/latest/UserGuide/running-optimized-ai.html)
and [Running Optimized AI](https://cloudtik.readthedocs.io/en/latest/UserGuide/running-optimized-ai.html) for more information.

#### Running spark PI example

Running a Spark job is very straight forward. Spark PI job for example,

```
cloudtik exec ./your-cluster-config.yaml "spark-submit --master yarn --deploy-mode cluster --name spark-pi --class org.apache.spark.examples.SparkPi --conf spark.yarn.submit.waitAppCompletion=false \$SPARK_HOME/examples/jars/spark-examples.jar 12345" --job-waiter=yarn
```

Refer to [Run Spark PI Example](examples/runtime/spark) for more details.

#### Running analytics benchmarks

CloudTik provides ready to use tools for running TPC-DS benchmark
on a CloudTik spark runtime cluster.

Refer to [Run TPC-DS performance benchmark for Spark](tools/benchmarks/spark)
for a detailed step-by-step guide.

#### Running machine learning and deep learning examples

CloudTik provides ready to run examples for demonstrating
how distributed AI jobs can be implemented in CloudTik Spark and AI runtime cluster.

Refer to [Distributed AI Examples](examples/runtime/ai)
for a detailed step-by-step guide.

#### Workflow examples
User can integrate CloudTik with external workflows using bash scripts or python
for running on-demand cluster and jobs.

Refer to [Workflow Integration Examples](examples/workflows) for example scripts.

### 7. Managing clusters

CloudTik provides very powerful capability to monitor and manage the cluster.

#### Cluster status and information

Use the following commands to show various cluster information.

```
# Check cluster status with:
cloudtik status /path/to/your-cluster-config.yaml

# Show cluster summary information and useful links to connect to cluster web UI.
cloudtik info /path/to/your-cluster-config.yaml
cloudtik head-ip /path/to/your-cluster-config.yaml
cloudtik worker-ips /path/to/your-cluster-config.yaml
```
#### Attach to the cluster head (or specific node)

Connect to a terminal of cluster head node.

```
cloudtik attach /path/to/your-cluster-config.yaml
```

#### Execute and Submit Jobs

Execute a command via SSH on cluster head node or a specified node.

```
cloudtik exec /path/to/your-cluster-config.yaml [command]
```

#### Manage Files

Upload files or directories to cluster.

``` 
cloudtik rsync-up /path/to/your-cluster-config.yaml [source] [target]
```
  
Download files or directories from cluster.

```
cloudtik rsync-down /path/to/your-cluster-config.yaml [source] [target]
```

### 8. Tearing Down

#### Terminate a Cluster

Stop and delete the cluster.

```
cloudtik stop /path/to/your-cluster-config.yaml
```

#### Delete the Workspace

Delete the workspace and all the network resources within it.

```
cloudtik workspace delete /path/to/your-workspace-config.yaml
```
Be default, the managed cloud storage will not be deleted.
Add --delete-managed-storage option to force deletion of manged cloud storage.

For more information as to the commands, you can use `cloudtik --help` or `cloudtik [command] --help` to get detailed instructions.