AdaptDL Training Service

Now NNI supports running experiment on AdaptDL, which is a resource-adaptive deep learning training and scheduling framework. With AdaptDL training service, your trial program will run as AdaptDL job in Kubernetes cluster. AdaptDL aims to make distributed deep learning easy and efficient in dynamic-resource environments such as shared clusters and the cloud.


AdaptDL doesn’t support reuse mode.


Before starting to use NNI AdaptDL training service, you should have a Kubernetes cluster, either on-premises or Azure Kubernetes Service(AKS), a Ubuntu machine on which kubeconfig is setup to connect to your Kubernetes cluster.

  1. A Kubernetes cluster using Kubernetes 1.14 or later with storage. Follow this guideline to set up Kubernetes on Azure, or on-premise with cephfs, or microk8s with storage add-on enabled.

  2. Helm install AdaptDL Scheduler to your Kubernetes cluster. Follow this guideline to setup AdaptDL scheduler.

  3. Prepare a kubeconfig file, which will be used by NNI to interact with your Kubernetes API server. By default, NNI manager will use $(HOME)/.kube/config as kubeconfig file’s path. You can also specify other kubeconfig files by setting the KUBECONFIG environment variable. Refer this guideline to learn more about kubeconfig.

  4. If your NNI trial job needs GPU resource, you should follow this guideline to configure Nvidia device plugin for Kubernetes.

  5. (Optional) Prepare a NFS server and export a general purpose mount as external storage.

  6. Install NNI.

Verify the Prerequisites

nnictl --version
# Expected: <version_number>
kubectl version
# Expected that the kubectl client version matches the server version.
kubectl api-versions | grep adaptdl
# Expected:


We have a CIFAR10 example that fully leverages the AdaptDL scheduler under Github link: examples/trials/cifar10_pytorch folder. ( and config_adl.yaml)

Here is a template configuration specification to use AdaptDL as a training service.

authorName: default
experimentName: minimal_adl

trainingServicePlatform: adl
logCollection: http

  builtinTunerName: GridSearch
searchSpacePath: search_space.json

trialConcurrency: 2
maxTrialNum: 2

  adaptive: false # optional.
  image: <image_tag>
  imagePullSecrets:  # optional
    - name: stagingsecret
  codeDir: .
  command: python
  gpuNum: 1
  cpuNum: 1  # optional
  memorySize: 8Gi  # optional
  nfs: # optional
    path: /
    containerMountPath: /nfs
  checkpoint: # optional
    storageClass: dfs
    storageSize: 1Gi


This configuration is written following the specification of legacy experiment configuration. It is still supported, and will be updated to the latest version in future release.

The following explains the configuration fields of AdaptDL training service.

  • trainingServicePlatform: Choose adl to use the Kubernetes cluster with AdaptDL scheduler.

  • nniManagerIp: Required to get the correct info and metrics back from the cluster, for adl training service. IP address of the machine with NNI manager (NNICTL) that launches NNI experiment.

  • logCollection: Recommended to set as http. It will collect the trial logs on cluster back to your machine via http.

  • tuner: It supports the Tuun tuner and all NNI built-in tuners (only except for the checkpoint feature of the NNI PBT tuners).

  • trial: It defines the specs of an adl trial.

    • namespace: (Optional) Kubernetes namespace to launch the trials. Default to default namespace.

    • adaptive: (Optional) Boolean for AdaptDL trainer. While true, it the job is preemptible and adaptive.

    • image: Docker image for the trial

    • imagePullSecret: (Optional) If you are using a private registry, you need to provide the secret to successfully pull the image.

    • codeDir: the working directory of the container. . means the default working directory defined by the image.

    • command: the bash command to start the trial

    • gpuNum: the number of GPUs requested for this trial. It must be non-negative integer.

    • cpuNum: (Optional) the number of CPUs requested for this trial. It must be non-negative integer.

    • memorySize: (Optional) the size of memory requested for this trial. It must follow the Kubernetes default format.

    • nfs: (Optional) mounting external storage. For more information about using NFS please check the below paragraph.

    • checkpoint (Optional) storage settings for model checkpoints.

      • storageClass: check Kubernetes storage documentation for how to use the appropriate storageClass.

      • storageSize: this value should be large enough to fit your model’s checkpoints, or it could cause “disk quota exceeded” error.

More Features

NFS Storage

As you may have noticed in the above configuration spec, an optional section is available to configure NFS external storage. It is optional when no external storage is required, when for example an docker image is sufficient with codes and data inside.

Note that adl training service does NOT help mount an NFS to the local dev machine, so that one can manually mount it to local, manage the filesystem, copy the data or code etc. The adl training service can then mount it to the kubernetes for every trials, with the proper configurations:

  • server: NFS server address, e.g. IP address or domain

  • path: NFS server export path, i.e. the absolute path in NFS that can be mounted to trials

  • containerMountPath: In container absolute path to mount the NFS path above, so that every trial will have the access to the NFS. In the trial containers, you can access the NFS with this path.

Use cases:

  • If your training trials depend on a dataset of large size, you may want to download it first onto the NFS first, and mount it so that it can be shared across multiple trials.

  • The storage for containers are ephemeral and the trial containers will be deleted after a trial’s lifecycle is over. So if you want to export your trained models, you may mount the NFS to the trial to persist and export your trained models.

In short, it is not limited how a trial wants to read from or write on the NFS storage, so you may use it flexibly as per your needs.

Monitor via Log Stream

Follow the log streaming of a certain trial:

nnictl log trial --trial_id=TRIAL_ID
nnictl log trial EXPERIMENT_ID --trial_id=TRIAL_ID

Note that after a trial has done and its pod has been deleted, no logs can be retrieved then via this command. However you may still be able to access the past trial logs according to the following approach.

Monitor via TensorBoard

In the context of NNI, an experiment has multiple trials. For easy comparison across trials for a model tuning process, we support TensorBoard integration. Here one experiment has an independent TensorBoard logging directory thus dashboard.

You can only use the TensorBoard while the monitored experiment is running. In other words, it is not supported to monitor stopped experiments.

In the trial container you may have access to two environment variables:

  • ADAPTDL_TENSORBOARD_LOGDIR: the TensorBoard logging directory for the current experiment,

  • NNI_TRIAL_JOB_ID: the trial job id for the current trial.

It is recommended for to have them joined as the directory for trial, for example in Python:

import os
tensorboard_logdir = os.path.join(

If an experiment is stopped, the data logged here (defined by the above envs for monitoring with the following commands) will be lost. To persist the logged data, you can use the external storage (e.g. to mount an NFS) to export it and view the TensorBoard locally.

With the above setting, you can monitor the experiment easily via TensorBoard by

nnictl tensorboard start

If having multiple experiment running at the same time, you may use

nnictl tensorboard start EXPERIMENT_ID

It will provide you the web url to access the tensorboard.

Note that you have the flexibility to set up the local --port for the TensorBoard.