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Fabric for Deep Learning (FfDL)

This repository contains the core services of the FfDL (Fabric for Deep Learning) platform. FfDL is an operating system "fabric" for Deep Learning. It is a collaboration platform for:

• Framework-independent training of Deep Learning models on distributed hardware
• Open Deep Learning APIs
• Running Deep Learning hosting in user's private or public cloud

To know more about the architectural details, please read the design document. If you are looking for demos, slides, collaterals, blogs, webinars and other materials related to FfDL, please find them here

Prerequisites

• kubectl: The Kubernetes command line interface (https://kubernetes.io/docs/tasks/tools/install-kubectl/)
• helm: The Kubernetes package manager (https://helm.sh)
• docker: The Docker command-line interface (https://www.docker.com/)
• S3 CLI: The command-line interface to configure your Object Storage
• An existing Kubernetes cluster (e.g., Kubeadm-DIND for local testing or Follow the appropriate instructions for standing up your Kubernetes cluster using IBM Cloud Public or IBM Cloud Private). The minimum capacity requirement for FfDL is 4GB Memory and 3 CPUs.

Usage Scenarios

• If you are getting started and want to setup your own FfDL deployment, please follow the steps below.
• If you have a FfDL deployment up and running, you can jump to FfDL User Guide to use FfDL for training your deep learning models.
• If you want to leverage Jupyter notebooks to launch training on your FfDL cluster, please follow these instructions
• If you have FfDL configured to use GPUs, and want to train using GPUs, follow steps here
• To invoke Adversarial Robustness Toolbox to find vulnerabilities in your models, follow the instructions here
• To deploy your trained models, follow the integration guide with Seldon
• If you have used FfDL to train your models, and want to use a GPU enabled public cloud hosted service for further training and serving, please follow instructions here to train and serve your models using Watson Studio Deep Learning service.

Steps

1. Quick Start
   • 1.1 Installation using Kubernetes Cluster
   • 1.2 Installation using Kubeadm-DIND
2. Test
3. Monitoring
4. Development
5. Clean Up
6. Troubleshooting
7. References

1. Quick Start

There are multiple installation paths for installing FfDL into an existing Kubernetes cluster. Below are the steps for quick install. If you want to follow more detailed step by step instructions , please visit the detailed installation guide

• You need to initialize tiller with helm init before running the following commands.

1.1 Installation using Kubernetes Cluster

To install FfDL to any proper Kubernetes cluster, make sure kubectl points to the right namespace, then deploy the platform services:

export NAMESPACE=default # If your namespace does not exist yet, please create the namespace `kubectl create namespace $NAMESPACE` before running the make commands below
export SHARED_VOLUME_STORAGE_CLASS="ibmc-file-gold" # Change the storage class to what's available on your Cloud Kubernetes Cluster.

helm install ibmcloud-object-storage-plugin --name ibmcloud-object-storage-plugin --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE # Configure s3 driver on the cluster
helm install ffdl-helper --name ffdl-helper --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE,shared_volume_storage_class=$SHARED_VOLUME_STORAGE_CLASS --wait # Deploy all the helper micro-services for ffdl
helm install ffdl-core --name ffdl-core --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE,lcm.shared_volume_storage_class=$SHARED_VOLUME_STORAGE_CLASS --wait # Deploy all the core ffdl services.

1.2 Installation using Kubeadm-DIND

If you have Kubeadm-DIND installed on your machine, use these commands to deploy the FfDL platform:

export SHARED_VOLUME_STORAGE_CLASS=""
export NAMESPACE=default

./bin/s3_driver.sh # Copy the s3 drivers to each of the DIND node
helm install ibmcloud-object-storage-plugin --name ibmcloud-object-storage-plugin --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE,cloud=false
helm install ffdl-helper --name ffdl-helper --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE,shared_volume_storage_class=$SHARED_VOLUME_STORAGE_CLASS,localstorage=true --wait
helm install ffdl-core --name ffdl-core --repo https://ibm.github.io/FfDL/helm-charts --set namespace=$NAMESPACE,lcm.shared_volume_storage_class=$SHARED_VOLUME_STORAGE_CLASS --wait

# Forward the necessary microservices from the DIND cluster to your localhost.
./bin/dind-port-forward.sh

2. Test

To submit a simple example training job that is included in this repo (see etc/examples folder):

Note: For PUBLIC_IP, put down one of your Cluster Public IP that can access your Cluster's NodePorts. You can check your Cluster Public IP with kubectl get nodes -o wide. For IBM Cloud, you can get your Public IP with bx cs workers <cluster_name>.

export PUBLIC_IP=<Cluster Public IP> # Put down localhost if you are running with Kubeadm-DIND
make test-push-data-s3
make test-job-submit

3. Monitoring

The platform ships with a simple Grafana monitoring dashboard. The URL is printed out when running the status make target.

4. Development

Please refer to the developer guide for more details.

5. Clean Up

If you want to remove FfDL from your cluster, simply use the following commands.

helm delete --purge ffdl-core ffdl-helper

If you want to remove the storage driver from your cluster, run:

helm delete --purge ibmcloud-object-storage-plugin

For Kubeadm-DIND, you need to kill your forwarded ports. Note that the below command will kill all the ports that are created with kubectl.

kill $(lsof -i | grep kubectl | awk '{printf $2 " " }')

6. Troubleshooting

• FfDL has only been tested under Mac OS and Linux