tracebloc CLI - tracebloc

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.

Install

If you deployed with the Quick Start one-liner, the CLI is already installed. Otherwise:

macOS / Linux
Windows (PowerShell)

curl -fsSL https://github.com/tracebloc/cli/releases/latest/download/install.sh | sh

irm https://github.com/tracebloc/cli/releases/latest/download/install.ps1 | iex

Binaries are cosign-signed and multi-arch. Pin a version with --version vX.Y.Z (or $env:RELEASE_VERSION on Windows). Verify:

tracebloc version

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.

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

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

Command reference

Command	Does
`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 info`	Cluster, namespace, parent release, and token state
`tracebloc ingest validate <path>`	Validate an `ingest.yaml` against the v1 schema, locally
`tracebloc version`	CLI version, git SHA, and build date
`tracebloc completion`	Generate shell completion

Add --help to any command for the full flag list. For workspace lifecycle (upgrade, stop/start, uninstall), see Operations.

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.

Coming soon

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

​Install

​Operate your workspace

​Manage data

​Validate a config locally

​Command reference

​CLI or Helm chart?

​Coming soon

Install

Operate your workspace

Manage data

Validate a config locally

Command reference

CLI or Helm chart?

Coming soon