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The tracebloc CLI is how you manage and operate your workspace and its data from your own machine: inspect the cluster, ingest datasets, remove them, validate configs — with more verbs on the way. It’s the friendly front end to the same ingestion protocol the Helm ingestor chart speaks, so it works against any workspace running the tracebloc/client chart.
You never touch Helm, edit YAML, or run kubectl cp. dataset push discovers your workspace, stages the files, submits, and streams the job for you.

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Install

If you deployed with the Quick Start one-liner, the CLI is already installed. Otherwise: 
curl -fsSL https://github.com/tracebloc/cli/releases/latest/download/install.sh | sh
Binaries are cosign-signed and multi-arch. Pin a version with --version vX.Y.Z (or $env:RELEASE_VERSION on Windows). Verify: 
tracebloc version

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Operate your workspace

tracebloc cluster info
Shows the cluster, namespace, parent release, and ingestor-token state — your first check that the CLI can reach your workspace.
If cluster info reports “no parent client release found in namespace default”, your client runs in another namespace — add -n <your-namespace>, or set it once with kubectl config set-context --current --namespace <your-namespace>. Every command below accepts -n.

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Manage data

Ingest a dataset — stage local data into your workspace and run ingestion in one step: 
tracebloc dataset push ./train.csv \
  --category tabular_classification \
  --table my_dataset_train \
  --intent train \
  --label-column label
Omit the flags to run guided (the CLI prompts you), or add --dry-run to preview without submitting. Under the hood it discovers your workspace, validates the schema locally, mints a token, stages the files to the shared volume, submits, and watches the job. It covers 9 task categories: image_classification, object_detection, keypoint_detection, text_classification, masked_language_modeling, tabular_classification, tabular_regression, time_series_forecasting, time_to_event_prediction. Remove a dataset — delete its table and staged files: 
tracebloc dataset rm my_dataset_train

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Validate a config locally

tracebloc ingest validate ingest.yaml
Checks an ingest.yaml against the embedded schema — no cluster required. The declarative form (also accepted by the Helm ingestor chart): 
apiVersion: tracebloc.io/v1
kind: IngestConfig
category: tabular_classification
table: my_dataset_train
intent: train
csv: /data/shared/my_dataset/train.csv
label: label

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Command reference

CommandDoes
tracebloc dataset push <path>Stage + ingest a local dataset (guided if you omit flags)
tracebloc dataset rm <table>Delete a pushed dataset (table + staged files)
tracebloc cluster infoCluster, namespace, parent release, and token state
tracebloc ingest validate <path>Validate an ingest.yaml against the v1 schema, locally
tracebloc versionCLI version, git SHA, and build date
tracebloc completionGenerate shell completion
Add --help to any command for the full flag list. For workspace lifecycle (upgrade, stop/start, uninstall), see Operations.

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CLI or Helm chart?

Both submit to the same ingestion protocol — pick the one that fits your workflow:
  • CLI — local data on your workstation; the everyday choice. Handles staging and submission for you.
  • Helm ingestor chart — Kubernetes-native / GitOps, when your data is already staged on the cluster.

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Coming soon

Cloud-source ingestion (S3 / GCS / HTTPS) for large datasets, a dataset list verb, and semantic_segmentation support.