> ## Documentation Index > > Fetch the complete documentation index at: https://lithi.ai/llms.txt > > Use this file to discover all available pages before exploring further. --- title: Read Compute results from a PyTorch dataset description: >- Read an accepted result bundle from disjoint map-style or iterable datasets that preserve item identifiers and schemas. No arbitrary training executable is admitted to Compute. route: /docs/compute/integrations/pytorch page_id: docs-compute-integrations-pytorch page_type: howTo content_layer: guided documentation surface_profile: guided_docs audience: ML engineers loading Compute inputs and results into training code voice: D reader_question: How do I load a Compute result bundle into a PyTorch dataset correctly? primary_action: Read the manifest, then build a disjoint reader source_locale: en-US source_status: APPROVED source_version: docs-compute-integrations-pytorch-en-us-2026-09-05-v1 last_updated: '2026-09-05' robots: index claim_registry_pinned: true claim_registry_resync: "npm run governed:tsx -- scripts/build-compute-public-claim-registry.ts --write" --- ## What this connects Your own training code, as a reader. You build a dataset over an accepted result bundle, or over the immutable bundle you staged as an input. The conformance level is `import_compatible`, with a published conformance matrix for the file formats read. Your training runs on your own hardware; Compute produces the data it reads. ## Bind an exact input A dataset must be built over one frozen bundle, never over a directory that is still filling. Read `result-manifest.json` first. It is written last, it is the only complete-bundle marker, and it binds every file, count and schema you are about to iterate. Pin the same bundle identity in your experiment record: ```json "snapshot": { "version_id": "", "content_sha256": "" } ``` If your dataset can reach a file the manifest does not bind, your epochs are not reproducible. See [how to pin a snapshot that cannot change](/docs/compute/inputs/immutable-snapshots). ## Credentials Nothing in your training loop needs a Lithi secret. Download the bundle once, with a download authority you hold, then read from local storage. Where the loader reads staged storage directly, `input.credential_ref` names a registered credential reference. Never place a secret in a dataset class, a checkpoint, a log line or a launch script. ## Formats and limits Keep the readers disjoint, and make disjointness a property of the split rather than of chance. - For a map-style dataset, build the index once from the manifest and split the index across your workers. Every item then has exactly one owner. - For an iterable dataset, assign whole parts to workers. A part is the natural unit; splitting inside one invites duplicated items. - Never derive a split from a shuffle seed alone. Two workers with the same seed read the same rows. Preserve item identity and schema through the reader. Carry `item_id_field` into every batch, keep identifiers as strings, and take types from the manifest rather than from inference. The namespaced result columns, including `lithi_status`, `lithi_confidence` and `lithi_verification`, are what let you filter or weight a batch honestly. ## Commit the output Write predictions back as a table keyed by the same item identifier, then stage that table as its own immutable bundle if it becomes the input to another run. Record the bundle digest beside the checkpoint. A checkpoint without the digest of the data that produced it cannot be reproduced, only re-attempted. Reconcile before you report. Compare the item count you iterated against the manifest count, and account for the failure and abstention counts rather than treating the accepted rows as the whole set. ## What is not supported Compute is not a training platform, and holds no wire, SDK or endpoint compatibility claim about any framework. Outside the adapter: - Submitting an arbitrary training script, container or executable to run in Compute. No such input is admitted. - Distributed training, gradient exchange or checkpoint hosting. - Reading a bundle that has no `result-manifest.json` yet. Part files alone never mean the job finished. - Inferring types or identifiers from filenames. If a bundle is incomplete, the job reports `PARTIAL` with a next action. Take the accepted parts, resubmit the outstanding items, and rebuild the index from the newer manifest before you train again.