For agents — source-of-truth: This skill is authored in roboflow/computer-vision-skills and shipped with the Roboflow plugin. If your client has loaded the plugin (you'll see roboflow:<name> skills in your available skills list), use those local skills — they're read fresh from disk every session. The same content served as MCP resources at roboflow://skills/<name>/... is a fallback for clients without the plugin and may lag this repo. Don't call ReadMcpResourceTool for roboflow://skills/... URIs when a local roboflow:<name> skill is available.

Batch Processing

Run a Roboflow Workflow over a very large set of images or videos on Roboflow's autoscaling compute. Every job processes a temporary Data Staging batch; the two input paths differ only in who fills that batch:

• Asset Library job — the files are already in Roboflow. You hand the platform a selection and it selects and stages them for you.
• Staged job — the files are outside Roboflow (local disk, cloud bucket, references file), so you stage them yourself with the inference-cli: only your machine and credentials can reach them. Nothing is imported into the workspace, and staged data expires after ~7 days.

Which path? Three questions

1. Are the files already in Roboflow (Asset Library)? Call batch_processing_asset_library_job_create with a stable idempotency key and exactly one selection: image_ids, query, or all_images=true. The platform performs access checks, selects the files, stages them, verifies Workflow compatibility, bills, and registers the durable job. Poll the returned taskId with batch_processing_asset_library_task_get until the task is terminal; then monitor its jobId with batch_processing_job_get.
2. Files outside Roboflow, and you only want the outputs? Stage them yourself and drive the run with batch_processing_guide + batch_processing_run as described below (the classic ETL shape: nothing lands in the workspace). Staging and result export must run on the machine that can access the files.
3. Files outside Roboflow that you want INSIDE it (labeling, curation, training)? That is an import, not a batch processing job: mirror the bucket with a datasource (connect_cloud_storage, see the cloud-storage skill). Once mirrored, the files are Asset Library images, so if you also want bulk predictions, run an Asset Library job over them (the classic ELT shape: load first, then transform).

"Datasource" and "bucket mirror" are one thing: a datasource is the user-facing name for a bucket-mirror config, the importer that fills the Asset Library. It never runs Workflows itself.

In pipeline terms: a staged job is the T of an ETL flow (no load ever happens), and an Asset Library job is the T of an ELT flow — the load already happened, via uploads or a datasource mirror.

Prerequisites

• The workspace needs the batch-processing feature. Gated calls fail with a 402 "Batch processing is not enabled for this workspace. Upgrade your plan or contact sales at https://roboflow.com/sales."
• Jobs consume credits; the workspace must have a positive balance.
• The Workflow must already exist in the workspace (workflows_list, workflows_create). Jobs reference it by workflow_id; there is no inline-spec option.
• API key: the inference-cli reads ROBOFLOW_API_KEY from the environment, and every command also accepts --api-key=<key>. A key stored by roboflow login (~/.config/roboflow/config.json) is NOT picked up by the inference-cli: export it or pass --api-key explicitly. Mint one with the api_keys_create MCP tool. Never have the user paste a private key into chat.

Where each step runs, and why

Two steps are inference-cli only, because the MCP server can neither read nor write the user's disk:

• Staging runs on the machine that can reach the files (local disk) or with the user's cloud credentials (bucket sources).
• Exporting results (export-batch) downloads into a local directory.

Everything in between needs only an API key, so it works either way: the MCP tools (batch_processing_run, batch_processing_staged_job_start, batch_processing_job_get, batch_processing_jobs_list, batch_processing_job_logs, batch_processing_job_abort, batch_processing_job_restart, plus the batch_processing_asset_library_* and batch_processing_staging_* families) or the equivalent CLI commands. Prefer the MCP tools when the host has no shell or the user has no local inference-cli; prefer the CLI when the user is already in a terminal. The full command set for both content types is in sections 1-5.

The batch_processing_guide MCP tool routes a request: it settles the batch id, picks the staging source, and returns the exact ordered commands. It never touches files or the network, and it does not repeat this document.

The master tool for staged batches: batch_processing_run

Prefer batch_processing_run(batch_id, workflow_id, content_type, ...) to drive the flow over a staging batch you name (for Asset Library selections, use batch_processing_asset_library_job_create instead). Each call reads current state, advances what it can, and returns immediately with a status:

• staging_required — no batch yet; the response contains the CLI commands for the whole run plus cloud-credential guidance. Run them, then call again.
• ingest_in_progress — files still registering.
• ingest_failed — ingest failed or returned an unknown shard state; no paid job was started.
• running — the job was started or is still working; includes stage progress.
• completed — includes the export batch id and the first result files with signed download URLs.
• failed — includes logs and a restart hint.

The server never waits on your behalf. Non-terminal responses carry retryAfterSeconds; sleep that long on your side, then call again with the returned job_id and the same batch_id to resume. Omit optional creation settings on a read-only resume: the paid job's stored definition is authoritative. The server holds no state between calls.

The job is started under an id derived from (workspace, batch, workflow), so retrying after a lost response re-registers the same job instead of paying for a second run. (The platform checks credits before that idempotency comparison, so a retry can still see a 429 first.) A 409 means that id already exists with different batch/Workflow identity or conflicts with an optional setting you explicitly supplied. Inspect the job, omit optional settings to monitor it as stored, or pass a new explicit job_id for a separate run.

For the advanced knobs (max_runtime_seconds, max_parallel_tasks, max_image_failure_rate, image_outputs_to_save) use batch_processing_staged_job_start directly.

Webhooks

Roboflow will POST job and ingest notifications to a URL you control. There is no MCP-side relay: pass notifications_url to batch_processing_staged_job_start, or --notifications-url on the CLI commands, pointing at your own receiver. The POST carries an Authorization header with your publishable key.

Caveat: local video staging does not support --notifications-url (the CLI prints a warning and drops it), and a small local image batch (32 files or fewer stages as a simple batch) ignores it too. Sharded local image, cloud-storage and references-file ingests all support it.

If you have no receiver, just poll batch_processing_job_get (or batch_processing_run, which reports progress on each call).

1. Install the CLI

pip install inference-cli
# For s3:// gs:// az:// sources:
pip install 'inference-cli[cloud-storage]'

Every inference rf-cloud command below authenticates via ROBOFLOW_API_KEY from the environment, or --api-key=<key> on the command itself.

Cloud credentials are picked up from the standard env chains on the machine running the CLI: AWS via the default credential chain (AWS_PROFILE honored; R2/MinIO work via AWS_ENDPOINT_URL, with AWS_REGION applied alongside it), GCS via GOOGLE_APPLICATION_CREDENTIALS, Azure via AZURE_STORAGE_ACCOUNT_NAME plus AZURE_STORAGE_ACCOUNT_KEY or AZURE_STORAGE_SAS_TOKEN. For S3/GCS the CLI generates presigned URLs (24h expiry); for Azure it appends your SAS token, so those URLs stay valid as long as the token does. Either way the URLs are handed to Roboflow and bucket secrets never leave the machine.

2. Stage a batch

Batch ids: lowercase letters, digits, - or _. Staged batches expire after ~7 days.

# Local images (>32 images are packed into tar shards automatically)
inference rf-cloud data-staging create-batch-of-images \
  --batch-id my-batch --images-dir ./images

# Local videos (uploaded one by one via signed URLs)
inference rf-cloud data-staging create-batch-of-videos \
  --batch-id my-batch --videos-dir ./videos

# Cloud bucket (S3/GCS/Azure; glob over object paths).
# Videos work exactly the same way: create-batch-of-videos.
inference rf-cloud data-staging create-batch-of-images \
  --batch-id my-batch --data-source cloud-storage \
  --bucket-path 's3://my-bucket/images/**/*.jpg'

inference rf-cloud data-staging create-batch-of-videos \
  --batch-id my-batch --data-source cloud-storage \
  --bucket-path 's3://my-bucket/videos/**/*.mp4'

# References file: JSONL lines of {"name": ..., "url": "https://..."}
inference rf-cloud data-staging create-batch-of-images \
  --batch-id my-batch --data-source references-file --references refs.jsonl

Images vs videos is a choice you make, not something inferred from the path: create-batch-of-images and create-batch-of-videos both accept every data source. Pick the one matching the content.

Sharded, cloud-storage, and references ingests are asynchronous. Wait until the batch is fully ingested before starting a job:

inference rf-cloud data-staging show-batch-details --batch-id my-batch
inference rf-cloud data-staging list-ingest-details --batch-id my-batch

or the batch_processing_staging_batch_get MCP tool, which returns the file count plus an ingest block with pending and failed flags. Do not start a job while pending is true or failed is true: the job costs credits and would run over incomplete input.

Practical limits: up to 20,000 image references per ingest request (auto-chunked; video references cap at 5,000 per request and are not chunked), ~1,000 videos per batch suggested, image formats jpg/png/webp/bmp/jp2, video formats mp4/mov/avi/mkv/flv/wmv/m4v. batch_processing_staging_batches_list shows every staged batch in the workspace (inputs and job results).

3. Start the job

CLI, images:

inference rf-cloud batch-processing process-images-with-workflow \
  --batch-id my-batch --workflow-id my-workflow --machine-type gpu

CLI, videos:

inference rf-cloud batch-processing process-videos-with-workflow \
  --batch-id my-batch --workflow-id my-workflow --machine-type gpu \
  --max-video-fps 5

Shared optional flags: --workers-per-machine 1|2|4|8, --aggregation-format jsonl|csv, --save-image-outputs, --image-outputs-to-save <name>, --image-input-name <name>, --workflow-params params.json, --max-runtime-seconds <n>, --max-parallel-tasks <n>, --job-id <id>, --job-name <name>, --notifications-url <url>, --part-name <part>.

Images only: --max-image-failure-rate 0.0-1.0 (the server rejects it on video jobs). Videos only: --max-video-fps <n>.

MCP equivalent:

batch_processing_staged_job_start(
  job_id="my-stable-job-id",           # required; reuse for retries
  batch_id="my-batch", workflow_id="my-workflow",
  content_type="images",            # or "videos"
  machine_type="gpu",               # cpu|gpu, optional
  workers_per_machine=4,            # 1, 2, 4 or 8, optional
  aggregation_format="jsonl",       # or "csv"
  save_image_outputs=True,          # persist crops/visualizations
)

For videos, set content_type="videos" and optionally max_video_fps=5 (prediction subsampling). The tool rejects max_video_fps on image jobs and max_image_failure_rate on video jobs, matching the platform.

Choosing cpu vs gpu (machine_type)

Default compute is CPU. Decide with two quick checks before starting a paid job over the whole batch:

1. Test-run the Workflow on one representative image (workflows_run MCP tool, or the hosted API) and measure the wall time. Run it twice and time the second call (the first may cold-start). If a single image takes more than about a second of model time, CPU workers will crawl through a large batch: take gpu.
2. Inspect the Workflow spec (workflows_get): count the model steps and note their sizes. One small fine-tuned detector/classifier: cpu is the cheapest and usually enough. Several models chained, or any large foundation model (SAM family, CLIP, OCR, VLM blocks): gpu.

Videos multiply per-frame work with --max-video-fps, so lean gpu there too. workers_per_machine (1/2/4/8) then scales throughput on one machine: more workers means better utilization but a higher OOM risk.

Same job_id plus an identical definition is idempotent; a divergent one is rejected with a 409. The tool checks that ingest is complete before the paid registration call. For a multipart input, pass part_name; it is also supported by batch_processing_run. Both tools default to the current inference-models backend; use inference_backend="old-inference" only for a known compatibility requirement.

4. Monitor

inference rf-cloud batch-processing show-job-details --job-id my-job
inference rf-cloud batch-processing fetch-logs --job-id my-job
inference rf-cloud batch-processing abort-job --job-id my-job
inference rf-cloud batch-processing restart-job --job-id my-job

MCP equivalents, which return JSON rather than a rendered table:

• batch_processing_job_get(job_id) — status, current/planned stages, per-stage progress, output batches; job.isTerminal + job.error are the end states.
• batch_processing_job_logs(job_id) — info/error logs for diagnosis.
• batch_processing_job_abort(job_id) / batch_processing_job_restart(job_id) — stop a run, or retry a failed one (optionally overriding machine type, workers, timeout).
• batch_processing_jobs_list(search=...) — the workspace's recent Workflow jobs, for finding a job id you did not keep. Internal TensorRT compilation jobs are excluded. Both this tool and batch_processing_job_get label each job with inputSource: asset-library (platform-staged selection) or staged-batch (a batch you staged yourself).

5. Download results

Results land in Data Staging as platform-generated batches: <job-id>-processing (raw per-shard outputs) and <job-id>-export (packaged, downloadable archives).

Downloading writes to disk, so this step is CLI only:

inference rf-cloud data-staging export-batch \
  --batch-id my-job-export --target-dir ./results

It is resumable; add --override-existing to re-pull content already exported, and --part-name <part> to fetch one part of a multipart batch.

The MCP tool batch_processing_staging_batch_files_list(batch_id="<job-id>-export") lists the same files with signed downloadURLs (~24h expiry) so a host with no shell can still fetch them. Export batches are multipart: the tool selects the part automatically when there is exactly one, and otherwise asks you to pass part_name (the parts are in batch_processing_staging_batch_get). Archives (.tar / .tar.gz) must be unpacked after download, and listings are capped at 10,000 entries per call — past that scale keep paginating with nextPageToken and per-part part_name listings, or pull everything with the resumable export-batch.

Do this within 7 days: staged inputs and results expire.