Running NeMo Curator on Kubernetes

User Guide (Latest Version)

The following example demonstrates how to run the NeMo Curator with NVIDIA GPUs on a Kubernetes cluster, with PersistentVolumeClaims as the storage option.

Note

This project will download and install additional third-party open source software projects. Review the license terms of these open source projects before use.

To run NeMo Curator, we need to setup storage to upload and store the input files, as well as any processed outputs.

Here is an example of how to create a dynamic PV from a StorageClass setup by your cluster admin. Replace STORAGE_CLASS=<...> with the name of your StorageClass.

This example requests 150Gi of space. Adjust that number for your workloads and be aware that not all storage provisioners support volume resizing.

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STORAGE_CLASS=<...> PVC_NAME=nemo-workspace kubectl apply -f - <<EOF apiVersion: v1 kind: PersistentVolumeClaim metadata: name: ${PVC_NAME} spec: accessModes: - ReadWriteMany storageClassName: ${STORAGE_CLASS} resources: requests: # Requesting enough storage for a few experiments storage: 150Gi EOF

Note

The storage class must support ReadWriteMany because multiple Pods may need to access the PVC to concurrently read and write.

Setup PVC Busybox Helper Pod

Inspecting the PVC and copying to and from it is facilitated with a busybox container. Some examples below assume you have this Pod running to copy to and from the PVC.

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PVC_NAME=nemo-workspace MOUNT_PATH=/nemo-workspace kubectl create -f - <<EOF apiVersion: v1 kind: Pod metadata: name: nemo-workspace-busybox spec: containers: - name: busybox image: busybox command: ["sleep", "infinity"] volumeMounts: - name: workspace mountPath: ${MOUNT_PATH} volumes: - name: workspace persistentVolumeClaim: claimName: ${PVC_NAME} EOF

Feel free to delete this container if no longer needed, but it should use very little resources when idle.

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kubectl delete pod nemo-workspace-busybox

A Kubernetes Secret needs to be created on the k8s cluster to authenticate with the NGC private registry. If not done already, get an NGC key from ngc.nvidia.com. Create a secret key on the k8s cluster with (replace <NGC KEY HERE> with your NGC secret key. Note that if you have any special characters in your key you might need to wrap the key in single quotes (') so it can be parsed correctly by k8s):

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kubectl create secret docker-registry ngc-registry --docker-server=nvcr.io --docker-username=\$oauthtoken --docker-password=<NGC KEY HERE>

The environment to run the provided scripts in this example does not need the full nemo_curator package, so you can create a virtual environment with just the required packages as follows:

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python3 -m venv venv source venv/bin/activate pip install 'dask_kubernetes>=2024.4.1'

To copy into the nemo-workspace PVC, we will do so with kubectl exec. You may also use kubectl cp, but exec has fewer surprises regarding compressed files:

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# Replace <...> with a path on your local machine LOCAL_WORKSPACE=<...> # This copies $LOCAL_WORKSPACE/my_dataset to /my_dataset within the PVC. # Change foobar to the directory or file you wish to upload. ( cd $LOCAL_WORKSPACE; tar cf - my_dataset | kubectl exec -i nemo-workspace-busybox -- tar xf - -C /nemo-workspace )

Note

See Downloading and Extracting Text for an example of how to download local data that can be uploaded to the PVC with the above instruction.

Use the create_dask_cluster.py to create a CPU or GPU dask cluster.

Note

If you are creating another Dask cluster with the same --name <name>, first delete it via:

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kubectl delete daskcluster <name>

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# Creates a CPU Dask cluster with 1 worker python create_dask_cluster.py \ --name rapids-dask \ --n_workers 1 \ --image nvcr.io/nvidian/bignlp-train:nemofw-nightly \ --image_pull_secret ngc-registry \ --pvcs nemo-workspace:/nemo-workspace #╭───────────────────── Creating KubeCluster 'rapids-dask' ─────────────────────╮ #│ │ #│ DaskCluster Running │ #│ Scheduler Pod Running │ #│ Scheduler Service Created │ #│ Default Worker Group Created │ #│ │ #│ ⠧ Getting dashboard URL │ #╰──────────────────────────────────────────────────────────────────────────────╯ #cluster = KubeCluster(rapids-dask, 'tcp://localhost:61757', workers=2, threads=510, memory=3.94 TiB) # Creates a GPU Dask cluster with 2 workers with 1 GPU each python create_dask_cluster.py \ --name rapids-dask \ --n_workers 2 \ --n_gpus_per_worker 1 \ --image nvcr.io/nvidian/bignlp-train:nemofw-nightly \ --image_pull_secret ngc-registry \ --pvcs nemo-workspace:/nemo-workspace

After creating a cluster, you should be able to proceed after confirming the scheduler and the workers are all Running:

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# Set DASK_CLUSTER_NAME to the value of --name DASK_CLUSTER_NAME=rapids-dask kubectl get pods -l "dask.org/cluster-name=$DASK_CLUSTER_NAME" # NAME READY STATUS RESTARTS AGE # rapids-dask-default-worker-587238cf2c-7d685f4d75-k6rnq 1/1 Running 0 57m # rapids-dask-default-worker-f8ff963886-5577fff76b-qmvcd 1/1 Running 3 (52m ago) 57m # rapids-dask-scheduler-654799869d-9bw4z 1/1 Running 0 57m

Here is an example of running the existing gpu_exact_dedup Curator module. The arguments and script name will need to be changed according to the module you wish to run:

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# Set DASK_CLUSTER_NAME to the value of --name DASK_CLUSTER_NAME=rapids-dask SCHEDULER_POD=$(kubectl get pods -l "dask.org/cluster-name=$DASK_CLUSTER_NAME,dask.org/component=scheduler" -o name) # Starts an interactive shell session in the scheduler pod kubectl exec -it $SCHEDULER_POD -- bash ######################## # Inside SCHEDULER_POD # ######################## # Run the following inside the interactive shell to launch script in the background and # tee the logs to the /nemo-workspace PVC that was mounted in for persistence. # The command line flags will need to be replaced with whatever the module script accepts. # Recall that the PVC is mounted at /nemo-workspace, so any outputs should be written # to somewhere under /nemo-workspace. mkdir -p /nemo-workspace/curator/{output,log,profile} # Write logs to script.log and to a log file with a date suffix LOGS="/nemo-workspace/curator/script.log /nemo-workspace/curator/script.log.$(date +%y_%m_%d-%H-%M-%S)" ( echo "Writing to:$LOGS" gpu_exact_dedup \ --input-data-dirs /nemo-workspace/my_dataset \ --output-dir /nemo-workspace/curator/output \ --hash-method md5 \ --log-dir /nemo-workspace/curator/log \ --num-files -1 \ --files-per-partition 1 \ --profile-path /nemo-workspace/curator/profile \ --log-frequency 250 \ --scheduler-address localhost:8786 \ 2>&1 echo "Finished!" ) | tee $LOGS & # At this point, feel free to disconnect the shell via Ctrl+D or simply exit

At this point you can tail the logs and look for Finished! in /nemo-workspace/curator/script.log:

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# Command will follow the logs of the running module (Press ctrl+C to close) kubectl exec -it $SCHEDULER_POD -- tail -f /nemo-workspace/curator/script.log # Writing to: /nemo-workspace/curator/script.log /nemo-workspace/curator/script.log.24_03_27-15-52-31 # Computing hashes for /nemo-workspace/my_dataset # adlr_id _hashes # 0 cc-2023-14-0397113620 91b77eae49c10a65d485ac8ca18d6c43 # 1 cc-2023-14-0397113621 a266f0794cc8ffbd431823e6930e4f80 # 2 cc-2023-14-0397113622 baee533e2eddae764de2cd6faaa1286c # 3 cc-2023-14-0397113623 87dd52a468448b99078f97e76f528eab # 4 cc-2023-14-0397113624 a17664daf4f24be58e0e3a3dcf81124a # Finished!

In this example, we’ll demonstrate how to run a NeMo Curator module that you have defined locally.

Since your curator module may depend on version of the Curator that differs from what is in the container, we will need to build a custom image with your code installed:

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# Clone your repo. This example uses the official repo git clone https://github.com/NVIDIA/NeMo-Curator.git NeMo-Curator-dev # Checkout specific ref. This example uses a commit in the main branch git -C NeMo-Curator-dev checkout fc167a6edffd38a55c333742972a5a25b901cb26 # Example NeMo base image. Change it according to your requirements BASE_IMAGE=nvcr.io/nvidian/bignlp-train:nemofw-nightly docker build -t nemo-curator-custom ./NeMo-Curator-dev -f - <<EOF FROM ${BASE_IMAGE} COPY ./ /NeMo-Curator-dev/ RUN pip install -e /NeMo-Curator-dev EOF # Then push this image to your registry: Change <private-registry>/<image>:<tag> accordingly docker tag nemo-curator-custom <private-registry>/<image>:<tag> docker push <private-registry>/<image>:<tag>

Note

When using a custom image, you will likely need to create a different secret unless you pushed to a public registry:

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# Fill in <private-registry>/<username>/<password> kubectl create secret docker-registry my-private-registry --docker-server=<private-registry> --docker-username=<username> --docker-password=<password>

And with this new secret, you create your new dask cluster:

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# Fill in <private-registry>/<username>/<password> python create_dask_cluster.py \ --name rapids-dask \ --n_workers 2 \ --n_gpus_per_worker 1 \ --image <private-registry>/<image>:<tag> \ --image_pull_secret my-private-registry \ --pvcs nemo-workspace:/nemo-workspace

After the Dask cluster is deployed, you can proceed to run your module. In this example we’ll use the NeMo-Curator/nemo_curator/scripts/find_exact_duplicates.py module, but you can find other templates in NeMo-Curator/examples:

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# Set DASK_CLUSTER_NAME to the value of --name DASK_CLUSTER_NAME=rapids-dask SCHEDULER_POD=$(kubectl get pods -l "dask.org/cluster-name=$DASK_CLUSTER_NAME,dask.org/component=scheduler" -o name) # Starts an interactive shell session in the scheduler pod kubectl exec -it $SCHEDULER_POD -- bash ######################## # Inside SCHEDULER_POD # ######################## # Run the following inside the interactive shell to launch script in the background and # tee the logs to the /nemo-workspace PVC that was mounted in for persistence. # The command line flags will need to be replaced with whatever the module script accepts. # Recall that the PVC is mounted at /nemo-workspace, so any outputs should be written # to somewhere under /nemo-workspace. mkdir -p /nemo-workspace/curator/{output,log,profile} # Append logs to script.log and write to a log file with a date suffix LOGS="/nemo-workspace/curator/script.log /nemo-workspace/curator/script.log.$(date +%y_%m_%d-%H-%M-%S)" ( echo "Writing to:$LOGS" # Recall that /NeMo-Curator-dev was copied and installed in the Dockerfile above python3 -u /NeMo-Curator-dev/nemo_curator/scripts/find_exact_duplicates.py \ --input-data-dirs /nemo-workspace/my_dataset \ --output-dir /nemo-workspace/curator/output \ --hash-method md5 \ --log-dir /nemo-workspace/curator/log \ --files-per-partition 1 \ --profile-path /nemo-workspace/curator/profile \ --log-frequency 250 \ --scheduler-address localhost:8786 \ 2>&1 echo "Finished!" ) | tee $LOGS & # At this point, feel free to disconnect the shell via Ctrl+D or simply exit

At this point you can tail the logs and look for Finished! in /nemo-workspace/curator/script.log:

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# Command will follow the logs of the running module (Press ctrl+C to close) kubectl exec -it $SCHEDULER_POD -- tail -f /nemo-workspace/curator/script.log # Writing to: /nemo-workspace/curator/script.log /nemo-workspace/curator/script.log.24_03_27-20-52-07 # Reading 2 files # /NeMo-Curator-dev/nemo_curator/modules/exact_dedup.py:157: UserWarning: Output path f/nemo-workspace/curator/output/_exact_duplicates.parquet already exists and will be overwritten # warnings.warn( # Finished!

After you have finished using the created dask cluster, you can delete it to release the resources:

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# Where <name> is the flag passed to create_dask_cluster.py. Example: `--name <name>` kubectl delete daskcluster <name>

To download data from your PVC, you can use the nemo-workspace-busybox Pod created earlier:

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# Replace <...> with a path on your local machine LOCAL_WORKSPACE=<...> # Tar will fail if LOCAL_WORKSPACE doesn't exist mkdir -p $LOCAL_WORKSPACE # Copy file in PVC at /nemo-workspace/foobar.txt to local file-system at $LOCAL_WORKSPACE/nemo-workspace/foobar.txt kubectl exec nemo-workspace-busybox -- tar cf - /nemo-workspace/foobar.txt | tar xf - -C $LOCAL_WORKSPACE # Copy directory in PVC /nemo-workspace/fizzbuzz to local file-system at $LOCAL_WORKSPACE/fizzbuzz kubectl exec nemo-workspace-busybox -- tar cf - /nemo-workspace/fizzbuzz | tar xf - -C $LOCAL_WORKSPACE

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