cogniac

name

cogniac

description

Query and manage the Cogniac computer vision platform. Use when working with Cogniac tenants, applications, subjects, media, EdgeFlow/CloudFlow appliances, cameras, deployments, or workflows — including uploading media, checking fleet status, reading aggregate stats, or monitoring EdgeFlow/CloudFlow clusters. Triggers on references to Cogniac, CloudCore, EdgeFlow, CloudFlow, visual apps, or cogniac cli/sdk.

Cogniac SDK & CLI

Overview

Interact with the Cogniac enterprise AI computer vision platform.

Pick the right interface:

1. cogniac CLI — JSON output, designed for agent consumption. Use for any task it covers.
2. cogniac Python SDK — for anything the CLI doesn't expose. See references/python-sdk.md.

Key Concepts

• Tenant — an organization/account. Users may belong to multiple tenants.
• Application — an ML pipeline consuming input subjects and producing output subjects. Types: classification, box_detection, point_detection, point_count_detection, integration, http_input, camera_capture.
• Subject — a category or concept (e.g., "defect", "good part"). Media flows through the system via subject associations.
• Media — an image or video with metadata, detections, and subject associations.
• CloudCore — Cogniac's cloud SaaS platform: the API, web app, and core backend services that EdgeFlow and CloudFlow connect back to. Hosted at api.cogniac.io.
• EdgeFlow — an on-premise GPU appliance running inference. Hardware models include RM-ML80 and IPC-M20.
• CloudFlow — the cloud-hosted equivalent of EdgeFlow: same inference system, runs in CloudCore (server-side rather than on an EdgeFlow device).
• Deployment Group — a logical grouping of EdgeFlows for coordinated model rollout. A workflow is considered deployed if its ID appears as the target_workflow_id in any deployment group.
• Network camera — considered in use when an app of type camera_capture references its network_camera_id in app_type_config.camera_group_list.
• Detection — a model prediction or human judgment linking media to a subject with a probability (0-1).
• Consensus — the agreed label for a media-subject association: True, False, Sidelined, or None (undecided).
• Feedback — user-supplied corrections used to retrain models.

Setup

The cogniac CLI ships in the cogniac PyPI package (requires Python >= 3.11). Use >= 3.2.0 — earlier versions lack the nested command tree and the agent-ergonomics features (commands catalog, --format jsonl, structured error envelope, typed timestamps). Check with cogniac --version. If the command is missing or older, install or upgrade:

pip install 'cogniac>=3.2.0'

Authentication

In the common case credentials are already in place, so cogniac auth is all you need — it validates the stored credential and confirms you're ready to go. Only if that fails do you need to log in:

cogniac auth              # validate credentials
cogniac auth login        # only if `cogniac auth` fails: opens the browser, authenticates
                          # via your existing Cogniac web session (password or SAML SSO),
                          # and stores a per-user API key for subsequent use

After one browser round-trip the credential is stored and picked up automatically by the CLI and SDK on every subsequent run — no manual key handling.

Tenant selection: most commands also need a tenant. Set COG_TENANT, or pass the top-level --tenant <tenant_id> flag, which overrides COG_TENANT for a single invocation (handy when switching tenants without re-exporting env vars): cogniac --tenant <tenant_id> apps list. cogniac tenants and cogniac auth are the exceptions — they don't need a tenant.

Credential precedence (highest-to-lowest — the CLI/SDK uses the first it finds): explicit args → COG_API_KEY → stored login from cogniac auth login. So an exported COG_API_KEY takes priority over a stored login if both are present.

First steps for any task

1. Verify credentials: cogniac auth. If it errors, run cogniac auth login and try again.
2. Resolve the tenant if COG_TENANT is not set or the user didn't name one. List with cogniac tenants and ask which to use; filter by keyword with jq:

   cogniac tenants | jq '.tenants[] | select(.name | test("keyword"; "i")) | {tenant_id, name}'
   

   Then either export COG_TENANT=<tenant_id> for the rest of the session, or pass --tenant <tenant_id> on each invocation.
3. Run the task using the command reference below.

CLI Command Reference

All commands output JSON. Pipe into jq to filter, project, or aggregate.

Command surface & conventions (cogniac >= 3.2.0)

The CLI is a nested noun → sub-noun → verb tree — e.g. application consensus release list, edgeflow event reboot, media detection create, deployment target workflow set. Resource ids are --<resource>-id flags (--application-id, --subject-uid, --edgeflow-id, --media-id, …); the older positional spelling (apps get <id>) and the flat/hyphenated spellings still resolve as deprecated aliases, so the examples below remain valid. Singular/plural and synonym tokens are interchangeable in every position (app/apps/application, edgeflow/gateway, cert/certificate, eval/evaluation).

Discover the whole surface in one call rather than walking --help:

cogniac commands        # JSON array of every command: [{command, help, args:[{name, positional, required, type, choices, help}]}]
cogniac commands | jq -r '.[].command'                                 # list all command paths
cogniac commands | jq '.[] | select(.command=="application events")'   # exact args for one command

Cross-cutting behavior that applies everywhere:

• Output format — --format json (default) | jsonl | table. jsonl emits one object per line for large streams (compose with --limit, pipe to jq/head).
• Global flags anywhere — --format and --tenant are accepted before or after the command (cogniac application list --format jsonl).
• Request bodies — verbs that take --body accept inline JSON, @FILE, or - (stdin), so large payloads don't fight shell quoting. Update verbs also accept per-field flags as an alternative to --body.
• Timestamps — --start/--end accept epoch seconds or ISO 8601.
• Pagination — list verbs return the complete result set by default (the SDK follows the API's pagination automatically). Cap with --limit N, resume with --cursor. Two expensive reads keep a default cap: edgeflow status (--limit 10) and subject media (--limit 100) — pass a larger --limit to widen. When --limit truncates a result, a {"truncated": true, …} notice is written to stderr (stdout stays a clean array/stream).
• Errors — failures print a structured envelope to stderr and exit non-zero: {"error": {"type", "status", "message", "hint"}}, where type is one of auth / client / server / connection / rate_limit. Branch on type; hint is an optional self-heal suggestion, present only when an action applies (e.g. an auth error hints to run cogniac auth login) — don't assume it's always there.

Beyond the families documented below, the surface now also covers application sub-resources (events, event types, detections pending, replay, performance, feedback, consensus history/consensus release, evaluation metrics, model download/donate/export, build, type); edgeflow event device control (reboot/ping/upgrade/factory-reset/flush-upload-queue/time-bound-media-upload/trigger-camera-capture) and edgeflow certificate; media detection/embeddings/share; subject detections/consensus history/disassociate; deployment (edgeflows, history, prepull, target workflow set, capacity); workflow versions and deployment targets; and user + user api-key + tenant user management. Each name above is a namespace; the leaf commands that appear in cogniac commands are its verbs (e.g. media detection → media detection {list,create}), so filter on a full leaf path. Run cogniac commands (or <noun> --help) for the exact flags of any of these.

Auth & Tenants

cogniac auth login                              # browser login (preferred); stores a per-user API key
cogniac auth                                    # validate credentials, show tenant count
cogniac tenants                                 # list all authorized tenants (no COG_TENANT needed)
cogniac tenant                                  # current tenant info

Applications

cogniac apps list                               # list all apps in tenant
cogniac apps get <application_id>               # get app details
cogniac apps eval-metrics <application_id>      # list active evaluation-metric configs (point_count_F1, etc.) with detection thresholds, subject weights, primary/active flags
cogniac apps leaderboard <application_id>       # ranked candidate-model snapshot. Flags: --set-assignment {validation,training}, --snapshot-type {regular,int8}, --eval-metrics {primary,all}, --top N, --full

Subjects

cogniac subjects list                           # list all subjects
cogniac subjects get <subject_uid>              # get subject details
cogniac subjects search --name "exact name"     # exact name match
cogniac subjects media <subject_uid>            # list media associations
cogniac subjects media <subject_uid> --limit 10 --consensus True
cogniac subjects create "My Subject" --description "desc"
cogniac subjects associate <subject_uid> <media_id> --consensus True

Media

cogniac media get <media_id>                    # get media metadata
cogniac media download <media_id>               # download to <media_id>.<ext>
cogniac media download <media_id> -o out.jpg    # download to specific path
cogniac media search --filename "img.jpg"       # search by original filename
cogniac media search --external-media-id "EXT-001"  # search by customer-supplied external ID
cogniac media search --domain-unit "SN-123"     # search by domain unit (e.g. serial number / part ID the customer groups media under)
cogniac media upload /path/to/image.jpg         # upload media
cogniac media upload /path/to/image.jpg --subject-uid <subject_uid>  # upload and associate

external_media_id and domain_unit are customer-set fields on the media record. Use them when the user references their own identifier (e.g. a serial number, ticket ID) rather than a Cogniac media_id.

EdgeFlows

cogniac edgeflows list                          # list all EdgeFlow devices
cogniac edgeflows get <gateway_id>              # get device details
cogniac edgeflows status <gateway_id>           # recent status events (all subsystems)
cogniac edgeflows status <gateway_id> --subsystem gpus --limit 1            # latest GPU sample
cogniac edgeflows status <gateway_id> --list-subsystems                     # distinct subsystems a device reports
cogniac edgeflows status <gateway_id> --subsystem http-input-<app_id> --start <ts> --end <ts>   # time-range slice
cogniac edgeflows metrics names                                            # catalog of host/runtime metric names
cogniac edgeflows metrics list --metric-name <name> [--edgeflow-id <gateway_id>] [--start <ts> --end <ts>]

--subsystem is an exact-match filter. Built-in subsystems include gpus, upload, per-deployed-model detection counters named model_detections_<model_instance_id> (one per model), and http-input-<application_id> (one per deployed http_input app — see below).

To discover what a device reports, use --list-subsystems (requires cogniac >= 3.2.0): it returns {subsystem, last_seen, count} for each distinct subsystem regardless of sample frequency, then query a specific one — e.g. --subsystem model_detections_<model_instance_id> --limit 5. Don't page through unfiltered status for discovery: on a busy device the default page is entirely high-frequency model_detections_*, so low-frequency subsystems (http-input-*, gpus, cpu, memory) never appear. --list-subsystems scans up to --scan-limit records (default 8000); if it hits the cap it writes a {"scan_capped": true} notice to stderr (stdout stays pure JSON) — raise --scan-limit to widen the window.

Use --start/--end (epoch seconds or ISO 8601; requires cogniac >= 3.2.0) to slice a time window instead of paging --limit. They filter on the cloud-receipt clock (cc_timestamp), not device time — the two can diverge on a skewed or backfilling device.

The http-input-<application_id> subsystem carries ingest-side request counts. Its samples have a status.counters array of {path, method, status, count} entries, where count is cumulative; differencing the status == "200" / method == "POST" counts between two samples and dividing by elapsed time gives the ingest request rate (req/s) — the offered load on that input, distinct from model latency. Two things make this the right tool for throughput trends: EdgeFlow status is retained far longer than container logs (days), so you can see a multi-day trend the cluster's rolling logs can't; and the same payload also holds an http_request_duration_seconds histogram (whole-request HTTP time, not model inference).

Computing ingest rate (req/s). Pull a window with --start/--end, then for each /process endpoint difference its cumulative POST/200 count across the window (max − min, the upper envelope), sum across endpoints, and divide by elapsed time:

cogniac edgeflows status <gateway_id> --subsystem http-input-<app_id> --start <ts> --end <ts> \
  | jq '[.[] | {t: .gw_timestamp,
                counters: [.status.counters[]
                  | select(.method=="POST" and .status=="200" and (.path|test("/process")))
                  | {path, count}]}]
        | ((max_by(.t).t) - (min_by(.t).t)) as $dt
        | ([.[].counters[]] | group_by(.path)
           | map((max_by(.count).count) - (min_by(.count).count)) | add // 0) as $reqs
        | if $dt > 0 then $reqs / $dt else error("window needs >= 2 samples with /process traffic") end'

The group_by(.path) matters: an http_input app commonly exposes more than one /process endpoint, so you must difference each path independently and then sum — collapsing all paths into one max − min mixes counts across endpoints and gives a wrong rate. The add // 0 yields 0 for a window with no matching /process POST/200 traffic (instead of erroring on null), and the $dt guard gives a clear message when the window spans fewer than two distinct sample times rather than dividing by zero.

Multi-replica correctness. When an http_input app runs more than one replica, the cumulative counter jumps or regresses as the agent samples different pods. Track the per-endpoint cumulative max (upper envelope); never sum positive consecutive deltas — naive delta-summing overcounts by ~20×. The per-path max − min above is envelope-safe.

Timestamp schema. Each status record carries up to three clocks: gw_timestamp (gateway sample clock, always present — the correct denominator for rate math), cc_timestamp (cloud-receipt clock, always present — what --start/--end filter on), and timestamp (guaranteed present in cogniac >= 3.2.0, aliased to gw_timestamp when the backend omits it). Sort/diff on gw_timestamp; on older builds ~15% of records lack timestamp, so sort(key=lambda s: s["timestamp"]) KeyErrors partway through a window.

Counter conventions differ by subsystem — mixing them silently corrupts results:

Subsystem	Counter convention
http-input-<app_id>	cumulative request counts — difference between samples; use the envelope for multi-replica apps
model_detections_<id>	per-window counts (window_start/window_end) — don't difference across windows

For host/runtime telemetry (cpu/disk/connectivity — hundreds of metric names) use the edgeflows metrics family (requires cogniac >= 3.2.0) rather than hand-differencing status counters: metrics names lists the available names; metrics list --metric-name <name> returns Grafana-shaped [timestamp_seconds, value] series, tenant-wide or scoped to one device with --edgeflow-id. --start/--end are optional but must be supplied together (epoch seconds or ISO 8601).

For deeper debugging beyond cogniac edgeflows status — pod logs, events, deployment state on the cluster itself — fetch a read-only kubeconfig via the SDK + Rancher and drive kubectl directly. Works for both EdgeFlow and CloudFlow clusters. See references/edgeflow-kubectl-access.md.

Integration apps run frozen on the appliance. Apps of type: integration deploy via the /1/builds endpoint and run on EdgeFlow as part of a frozen deployment-workflow snapshot. Two consequences when one looks broken:

• A cloud-side active toggle generally does NOT reach the running on-appliance process — it may be a no-op against the actual process while risking a deployment re-sync of the whole appliance. Restarting/redeploying is an appliance-side action owned by the EdgeFlow operators; hand off rather than toggle.
• The only reliable liveness signal is media recency on the app's output subjects vs. its input subjects — input_queue_count is always 0 for EdgeFlow apps (processing is local, so the cloud queue number carries no liveness information). Live input + dead output = that app's process is dead; an identically-configured sibling producing on the same appliance proves the appliance itself is healthy.

Cameras

cogniac cameras list                            # list all network cameras
cogniac cameras get <network_camera_id>         # get camera details

# All camera IDs referenced by camera_capture apps (i.e. cameras in use)
cogniac apps list | jq '[.[] | select(.type == "camera_capture") | .app_type_config.camera_group_list[]?] | unique'

# Count cameras in use
cogniac apps list | jq '[.[] | select(.type == "camera_capture") | .app_type_config.camera_group_list[]?] | unique | length'

Deployments & Workflows

cogniac deployments list                        # list all deployment groups
cogniac deployments get <deployment_group_id>   # get specific deployment group
cogniac workflows get <workflow_id>              # get workflow details

System

cogniac --version                               # CLI/package version (the `cogniac version` subcommand was removed in 3.2.0)
cogniac commands                                # full machine-readable command catalog (see above)

Common Agent Workflows

Find apps using a specific subject

Substitute the real subject UID for s1a2b3c4d5:

cogniac apps list | jq --arg s "s1a2b3c4d5" '[.[] | select(.input_subjects[]? == $s or .output_subjects[]? == $s) | {application_id, name, type}]'

Check EdgeFlow fleet health

cogniac edgeflows list                                            # inspect JSON to pick the fields you need
cogniac edgeflows status <gateway_id> --subsystem gpus --limit 1  # latest GPU sample for one device

Trace media through the pipeline

cogniac media search --domain-unit "SERIAL-001" | jq '.[0].media_id'
cogniac media get <media_id> | jq '{media_id, filename, status}'

List deployed workflows

cogniac deployments list | jq -r '[.[].target_workflow_id // empty] | unique | .[]' | while read wid; do cogniac workflows get "$wid"; done

List / audit every workflow base in the tenant

The CLI's workflow namespace only covers a single base's versions and deployment targets — there's no bulk-list subcommand. Reconstructing the set from deployments list / deployment-group history only surfaces bases that were actually deployed to a group; it misses bases created but never deployed. To enumerate every workflow base in the tenant (e.g. to audit names or find orphaned bases), hit GET /1/tenants/current/workflows?base=True directly via the SDK's authenticated session, paginating on last_key:

from cogniac import CogniacConnection
cc = CogniacConnection()  # tenant from COG_TENANT / tenant_id=
url = cc.url_prefix.rstrip('/') + '/1/tenants/current/workflows'
bases, last_key = [], None
while True:
    params = {'base': True, **({'last_key': last_key} if last_key else {})}
    d = cc.session.get(url, params=params, timeout=cc.timeout).json()
    bases.extend(d['data'])
    last_key = d.get('last_key')
    if not last_key:
        break

See GET /1/tenants/current/workflows in ef-app-provisioning for the full param/response reference.

Python SDK

For anything the CLI doesn't cover, use the Python SDK. The full reference is in references/python-sdk.md.

Other CLIs shipped with the cogniac package

The cogniac PyPI package installs additional command-line utilities alongside the cogniac CLI:

Bulk upload (cogupload)

For uploading many files in parallel to an input subject:

cogupload <subject_uid> <directory_name>

cogupload uses sensible defaults for parallelism and retry. It does not set the consensus flag, so it is suitable for ingesting raw input data for training or inference, not for labeling correction.

Aggregate stats (cogstats)

cogstats -t "$COG_TENANT" [-g <gateway_id>] [-s <start_ts>] [-e <end_ts>]

Reports pixel counts processed and detections emitted in the time window. Default window is the last five minutes. Note: cogstats requires the tenant as a -t flag — it does not read COG_TENANT from the environment automatically. As of cogniac >= 3.2.0 it sums across all per-model model_detections_<id> subsystems, so it reports correct totals for CloudFlow and per-model devices; earlier versions returned 0 detections / 0 pixels for these even while they were actively processing.

Common pitfalls

• Multi-tenant accounts: omitting COG_TENANT (or tenant_id= in the SDK) on a user authorized for more than one tenant causes CogniacConnection() to raise ClientError(400): Unauthorized at construction. Always set it explicitly — via COG_TENANT, the CLI's --tenant flag, or tenant_id= to the SDK. (The CLI's cogniac auth and cogniac tenants are the exceptions — they don't need a tenant.)
• Authentication: prefer cogniac auth login (stored credential). If you also export COG_API_KEY, note it takes precedence over the stored login.
• Rate limits: the public API enforces rate limits. As of cogniac >= 3.2.0 the SDK/CLI retry 429 responses automatically with exponential backoff (honoring Retry-After); from the CLI a persistent 429 surfaces as an error envelope with type: "rate_limit". On earlier versions you must handle 429 and back off yourself.
• Region / URL prefix: the default https://api.cogniac.io points at Cogniac CloudCore. Override COG_URL_PREFIX (or pass url_prefix= to CogniacConnection) when targeting a different deployment. Either https://host or https://host/ is accepted — the SDK strips trailing slashes and any /<version> suffix on load.
• Subjects vs. applications: subjects describe what you care about; applications describe how media flows between subjects. Don't conflate them.
• Uploads are large: prefer cogupload for bulk ingestion over a custom loop; it handles parallelism and retries.
• Per-media fields default off: media_associations() defaults to abridged_media=True, which omits domain_unit, sequence_ix, and full app_data. Pass media_associations(abridged_media=False) to get the full per-media dict (keeps SDK retry) — see the method's entry in references/python-sdk.md.
• Deletion ordering: deactivate apps before deleting them (app.active = False, save, then .delete()), and delete subjects only after the apps referencing them are gone. The wrong order returns unhelpful 4xx errors.

References

• REST API reference: references/api/README.md — service catalog, core concepts, and per-service endpoint documentation.
• Python SDK reference: references/python-sdk.md.
• User guide: references/user-guide/INDEX.md — tutorial- and concept-oriented docs (app types, deployments, best practices, FAQ).
• Read-only kubectl access to EdgeFlow/CloudFlow clusters: references/edgeflow-kubectl-access.md.
• Python SDK source: https://github.com/Cogniac/cogniac-sdk-py
• PyPI package: https://pypi.org/project/cogniac/

Feedback

This skill is meant to make Cogniac easy to drive from an agent. If anything got in your way — a command that didn't behave as documented, a field that wasn't explained, a workflow you couldn't accomplish from what's here, or guidance you wish existed — please open an issue at https://github.com/Cogniac/skills with a short note on what you were trying to do and where the skill fell short.