> For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://nemo-platform.docs.buildwithfern.com/nemo/platform/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://nemo-platform.docs.buildwithfern.com/nemo/platform/_mcp/server. # Preview a Config This tutorial walks through the `anonymizer.preview` flow: defining a request, streaming a small anonymized sample, and inspecting the resulting frames. For detection and replacement strategy details, see the [open-source library documentation](https://github.com/NVIDIA-NeMo/Anonymizer/tree/main/docs). ## Prerequisites Complete the [tutorials prerequisites](/documentation/anonymize-data/tutorials#prerequisites), which cover: * A running NeMo Platform cluster with the `nemo anonymizer` CLI available (see [Setup](/documentation/get-started)). * An inference provider configured (default examples use `nvidia-build`). * A fileset named `anonymizer-inputs` with `anonymizer-input.csv` uploaded. ## What `preview` Does `preview` runs the Anonymizer pipeline on a small number of records and streams results back from the plugin service. Both surfaces (Python SDK and CLI) produce the same data; the SDK collects it into a `pandas` DataFrame while the CLI prints newline-delimited JSON frames. The plugin emits four frame kinds: | Frame | Purpose | | ---------------------------- | ------------------------------------------------------------------------------- | | `log` | Pipeline log lines forwarded from the library. | | `preview_dataset` | User-facing anonymized records. The final dataset you usually care about. | | `trace_dataset` | Internal trace records used by `display_record` in notebooks. | | `failed_records` | Per-record failures with reasons. Emitted only when at least one record failed. | | `heartbeat`, `done`, `error` | Generic stream control frames. | Available preview surfaces: | Surface | Where it runs | Local paths | `model_configs` required | | -------------------------------- | ---------------------------------- | ----------- | ------------------------ | | `sdk.anonymizer.preview(...)` | Anonymizer plugin service (remote) | Rejected | Required | | `nemo anonymizer preview run` | Local CLI process | Allowed | Optional | | `nemo anonymizer preview submit` | Anonymizer plugin service (remote) | Rejected | Required | ## Step 1: Build a `PreviewRequest` A preview request bundles the `AnonymizerConfig`, the input spec, and optional `model_configs` / `selected_models`. ```python import os from anonymizer.config.anonymizer_config import AnonymizerConfig from anonymizer.config.replace_strategies import Redact from data_designer.config import ModelConfig from nemo_anonymizer_plugin.app.input import AnonymizerInputSpec from nemo_anonymizer_plugin.app.task_config import PreviewRequest WORKSPACE = os.environ.get("NMP_WORKSPACE", "default") MODEL_PROVIDER = os.environ.get("NMP_ANON_PROVIDER", "nvidia-build") config = AnonymizerConfig( replace=Redact(format_template="[REDACTED_{label}]"), ) model_configs = [ ModelConfig(alias="gliner-pii-detector", provider=MODEL_PROVIDER, model="nvidia/gliner-pii"), ModelConfig(alias="gpt-oss-120b", provider=MODEL_PROVIDER, model="openai/gpt-oss-120b"), ModelConfig(alias="nemotron-30b-thinking", provider=MODEL_PROVIDER, model="nvidia/nemotron-3-nano-30b-a3b"), ] request = PreviewRequest( config=config, data=AnonymizerInputSpec( source=f"fileset://{WORKSPACE}/anonymizer-inputs#anonymizer-input.csv", text_column="biography", id_column="id", ), model_configs=model_configs, num_records=2, ) ``` Field reference: | Field | Type | Notes | | ------------------- | ------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `config` | [`AnonymizerConfig`](https://github.com/NVIDIA-NeMo/Anonymizer/tree/main/docs) | The library config. The example uses `Redact`; the plugin also supports `substitute`, `annotate`, `hash`, and `rewrite`. | | `data.source` | string | Local path, `http(s)` URL, or fileset reference. See [input source forms](#input-source-forms). | | `data.text_column` | string | Column containing text to anonymize. Defaults to `text`. | | `data.id_column` | string | Optional record identifier column. | | `data.data_summary` | string | Optional short description passed to Anonymizer library prompts. | | `model_configs` | list | Data Designer `ModelConfig` entries. `provider` must reference an Inference Gateway provider name (or `workspace/provider`). Omit to use Anonymizer library defaults (CLI local execution only). | | `selected_models` | object | Optional `detection` / `replace` / `rewrite` overrides on top of the bundled defaults. Requires `model_configs`. | | `num_records` | int (≥ 1) | Number of records to preview. Defaults to 10. | ## Step 2: Run with the Python SDK ```python import os from nemo_platform import NeMoPlatform sdk = NeMoPlatform( base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"), workspace=WORKSPACE, ) preview = sdk.anonymizer.preview(request) preview.dataset # pandas DataFrame of anonymized records preview.trace_dataset # detection trace preview.failed_records # list[dict] of per-record failures (usually empty) preview.display_record(0) # render record 0 with entity highlights in a notebook ``` `AnonymizerResource.preview` calls the plugin service, so the same constraints as `preview submit` apply: use a fileset or `http(s)` source, and include `model_configs`. `AsyncAnonymizerResource.preview` has the same signature and return type: ```python import os from nemo_platform import AsyncNeMoPlatform async_sdk = AsyncNeMoPlatform( base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"), workspace=WORKSPACE, ) preview = await async_sdk.anonymizer.preview(request) ``` ## Step 3: Persist Preview Records `AnonymizerPreviewResult.dataset` and `trace_dataset` are pandas DataFrames, so save them with standard pandas methods: ```python preview.dataset.to_csv("anonymized-preview.csv", index=False) preview.dataset.to_parquet("anonymized-preview.parquet", index=False) ``` `AnonymizerPreviewResult` stores everything in memory; nothing is persisted to disk by default. The `dataset` field is a regular pandas DataFrame and can be saved with `to_csv` or `to_parquet`. ## Step 4: Run from the CLI (Alternative) The CLI accepts the same request shape as a YAML spec file. Use `preview run` for local execution (allows local paths, model configs optional) or `preview submit` for the plugin service path (same as `sdk.anonymizer.preview`). Write the spec to YAML: ```python import yaml from pathlib import Path spec_path = Path("/tmp/anonymizer-preview.yaml") spec_path.write_text(yaml.safe_dump(request.model_dump(mode="json", exclude_none=True))) ``` Run preview locally: ```bash nemo anonymizer preview run \ --spec-file /tmp/anonymizer-preview.yaml \ --workspace "${NMP_WORKSPACE:-default}" ``` Or submit to the plugin service: ```bash nemo anonymizer preview submit \ --spec-file /tmp/anonymizer-preview.yaml \ --workspace "${NMP_WORKSPACE:-default}" \ --base-url "${NMP_BASE_URL:-http://localhost:8080}" ``` Both commands stream NDJSON frames to stdout. Filter with `jq`: ```bash nemo anonymizer preview run \ --spec-file /tmp/anonymizer-preview.yaml \ --workspace "${NMP_WORKSPACE:-default}" \ > /tmp/anonymizer-preview.ndjson jq -R 'fromjson? | select(.kind == "preview_dataset") | .records' \ /tmp/anonymizer-preview.ndjson ``` If `preview submit` returns 404 against the gateway, the plugin service isn't mounted. Restart `nemo services run` so the plugin is discovered and remounts `/apis/anonymizer/...`; see [Setup](/documentation/get-started). ## Input Source Forms The plugin accepts three forms for `data.source`: | Form | `sdk.anonymizer.preview` / `preview submit` | `preview run` | | ------------------------------------- | ------------------------------------------- | ------------- | | Local path (`/tmp/input.csv`) | No | Yes | | HTTP(S) URL (`https://.../input.csv`) | Yes | Yes | | Fileset reference | Yes | Yes | Fileset references take any of these forms; the workspace and fileset must already exist: ```text fileset:///# /# # ``` The `#` fragment must resolve to a single `.csv` or `.parquet` file. The plugin downloads the file before constructing the Anonymizer library input and cleans up the temp directory when the preview completes. ## Validate the Config Independently If you want to validate just the `AnonymizerConfig` (and optionally a `model_configs` YAML) without running a preview, use `nemo anonymizer validate`: ```python from pathlib import Path import yaml Path("/tmp/anonymizer-config.yaml").write_text(yaml.safe_dump({ "replace": {"kind": "redact", "format_template": "[REDACTED_{label}]"}, })) ``` ```bash nemo anonymizer validate --config /tmp/anonymizer-config.yaml ``` `validate` checks the config against the model selection (for example, that a `Substitute` strategy has a `replacement_generator` model defined) and prints `Config is valid.` on success. It does not accept `data.source`, so input-source validation happens during `preview` or `run`. ## Next Steps * Run a full job and inspect parquet output in the [run tutorial](/documentation/anonymize-data/tutorials/run-an-anonymizer-job). * Refer to [SDK Resources](/documentation/anonymize-data/sdk-resources) for `AnonymizerPreviewResult` and `AnonymizerResource.preview` details. * Learn about rewrite and replacement strategy parameters in the [library docs](https://github.com/NVIDIA-NeMo/Anonymizer/tree/main/docs).