Manage Files

NeMo Helix provides a file storage interface through the Files service. The Files service supports multiple storage backends and can be used to store datasets for training, evaluation results, model artifacts, and other files.

Concepts

• Fileset: A named container that holds files.

Filesets are uniquely identified by a name within a given workspace.

• Storage Backend: Each fileset is backed by a storage backend where the files are actually persisted. Supported backends include:
• local: Local filesystem storage (default, read/write)
• s3: Amazon S3 or S3-compatible storage such as MinIO (read/write)
• ngc: NVIDIA GPU Cloud storage (read-only)
• huggingface: HuggingFace Hub repositories (read-only)

Read-only backends allow you to create a fileset that acts as a handle to external resources. This provides a unified interface to access files from different sources using the same SDK methods, and allows other Helix services to reference external data through a fileset.

• Purpose: A fileset field that indicates the intended use. Each purpose enables specific metadata fields under the corresponding key. Select a tab below to see the available metadata fields for each purpose:

  generic

  dataset

  model

  Use purpose="generic" (default) for other files that don’t fit the dataset or model categories.

  Metadata fields: No purpose-specific metadata fields.

  These fields are merged into the model entity spec by the model-spec background task.

• Custom Fields: Arbitrary key-value data attached to a fileset via custom_fields for user-defined metadata.

Managing Filesets

Fileset management operations (create, retrieve, list, delete) are available through the CLI (nemo files filesets) or the SDK (client.files, for example create_fileset, list_filesets, get_fileset, delete_fileset).

nemo files filesets list --workspace my-workspace

All Python SDK examples that follow assume this client initialization:

from nemo_helix_plugin.client.client import NemoClient
client = NemoClient.from_config()

Creating Filesets

Creating a fileset involves specifying a name and workspace. You can optionally provide a description, purpose, and custom storage configuration.

nemo files filesets create my-files \
--description "Training data for model fine-tuning"

{
  "id": "fileset-TeufFfapeKBrMtpBb42zdv",
  "created_at": "2026-01-20T03:00:00",
  "custom_fields": {},
  "description": "Training data for model fine-tuning",
  "metadata": {
    "dataset": null
  },
  "name": "my-files",
  "project": "",
  "purpose": "generic",
  "storage": {
    "path": "/var/mnt/filesets/default/my-files",
    "read_chunk_size": 16777216,
    "type": "local",
    "write_buffer_size": 16777216
  },
  "updated_at": "2026-01-20T03:00:00",
  "workspace": "default"
}

Listing Filesets

List all filesets in a given workspace:

nemo files filesets list

┏━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ name ┃ workspace ┃ created_at ┃
┡━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ my-files │ default │ 2026-01-20T03:00:00 │
└──────────┴───────────┴────────────────────────────┘

Filter filesets by purpose or storage type:

# List only dataset filesets
nemo files filesets list --filter.purpose dataset
# List filesets using local storage
nemo files filesets list --filter.storage-type local

Use pagination for large result sets:

# The "-" prefix sorts in descending order (newest first)
nemo files filesets list --page 1 --page-size 10 --sort "-created_at"

Deleting Filesets

Delete an entire fileset:

nemo files filesets delete my-files

✓ Deleted successfully

Managing Files Within Filesets

File operations are available through the CLI (nemo files) or the SDK (client.files), which provide methods for uploading, downloading, and listing files.

For advanced use cases, an fsspec-compatible filesystem is available through the filesets package (FilesetFileSystem). Refer to the fsspec documentation for additional methods.

Uploading Files

Upload files to a fileset:

# Upload a single file
nemo files upload ./data.jsonl my-files --remote-path training/data.jsonl
# Upload an entire directory
nemo files upload ./training_data/ my-files --remote-path training/

Uploading ━━━━━━━━━━━━━━━━ 100% • 3/3 files
Completed upload to my-files#training/

# Auto-creates a new fileset with a generated name (fileset-<8 hex chars>)
nemo files upload ./data.jsonl

Uploading ━━━━━━━━━━━━━━━━ 100% • 1/1 files
Completed upload to fileset-a1b2c3d4

Listing Files

List all files in a fileset:

nemo files list my-files

┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━┓
┃ PATH ┃ SIZE ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━┩
│ training/data.jsonl │ 1024 │
│ training/validation.jsonl │ 512 │
└────────────────────────────┴──────┘

List files under a specific directory:

nemo files list my-files --remote-path training/

Downloading Files

Download files to a local path:

# Download a single file
nemo files download my-files --remote-path training/data.jsonl -o ./data.jsonl
# Download an entire directory
nemo files download my-files --remote-path training/ -o ./training_data/

Downloading ━━━━━━━━━━━━━━━━ 100% • 2/2 files
Downloaded my-files#training/ to './training_data/'

Read file content into memory (SDK only):

content = client.files.download_file(
    name="my-files",
    path="config.json",
).read()
print(content.decode("utf-8"))

Deleting Files

Delete files from a fileset:

nemo files delete my-files --remote-path training/old-data.jsonl

Deleted my-files#training/old-data.jsonl

Using Progress Callbacks

Track progress during large file transfers using the RichProgressCallback context manager:

from filesets import FilesetFileSystem, RichProgressCallback
from nemo_helix_plugin.files.client import FilesClient
files = client.files
assert isinstance(files, FilesClient)
fs = FilesetFileSystem(client=files)
# Upload a directory with progress bar
with RichProgressCallback(description="Uploading dataset") as callback:
    fs.put(
        "./large_dataset/",
        "fileset://default/my-files",
        recursive=True,
        callback=callback,
    )
# Download all files from a fileset with progress bar
with RichProgressCallback(description="Downloading dataset") as callback:
    fs.get(
        "fileset://default/my-files",
        "./downloaded_data/",
        callback=callback,
    )

Use Cases

Using External Storage Backends

Connect to files stored in NVIDIA GPU Cloud (NGC):

# Create a secret to store your NGC API key
echo "$NGC_API_KEY" | nemo secrets create my-ngc-api-key --from-file -
# Create a fileset pointing to NGC storage
nemo files filesets create my-nemotron-personas-dataset-en_us \
--description "Nemotron Personas USA" \
--storage '{
"type": "ngc",
"org": "nvidia",
"team": "nemotron-personas",
"target": "nemotron-personas-dataset-en_us",
"version": "0.0.2",
"api_key_secret": "my-ngc-api-key"
}'

Connect to a HuggingFace repository:

# Create a secret to store your HuggingFace token (needed for gated and private repos)
echo "$HF_TOKEN" | nemo secrets create hf_token --from-file -
# Create a fileset pointing to a HuggingFace repo
nemo files filesets create hf-dataset \
--description "Dataset from HuggingFace" \
--storage '{
"type": "huggingface",
"repo_id": "nvidia/Nemotron-Personas-Japan",
"repo_type": "dataset",
"token_secret": "hf_token"
}'

Connect to an S3 bucket or S3-compatible storage (e.g., MinIO, Ceph):

# Create a fileset backed by S3 storage using SDK credential chain
# (uses AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY env vars, IRSA, instance profiles, etc.)
# The "prefix" field is optional - use it to scope the fileset to a folder within the bucket
nemo files filesets create s3-training-data \
--description "Training data stored in S3" \
--storage '{
"type": "s3",
"bucket": "my-ml-bucket",
"prefix": "datasets/training",
"region": "us-east-1",
"use_sdk_auth": true
}'
# Upload data to S3
nemo files upload ./training_data/ s3-training-data
# Download data from S3
nemo files download s3-training-data -o ./downloaded_data/

For S3-compatible storage like MinIO, use explicit credentials and a custom endpoint:

# Create secrets to store your S3 credentials
echo "$S3_ACCESS_KEY" | nemo secrets create s3_access_key --from-file -
echo "$S3_SECRET_KEY" | nemo secrets create s3_secret_key --from-file -
nemo files filesets create minio-fileset \
--description "Data stored in MinIO" \
--storage '{
"type": "s3",
"bucket": "my-bucket",
"endpoint_url": "http://minio.example.com:9000",
"region": "us-east-1",
"use_sdk_auth": false,
"access_key_id_secret": "s3_access_key",
"secret_access_key_secret": "s3_secret_key"
}'