{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:3D6EGCK4PR56BZBATZUU6P7PPE","short_pith_number":"pith:3D6EGCK4","canonical_record":{"source":{"id":"1806.00187","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-01T04:33:16Z","cross_cats_sorted":[],"title_canon_sha256":"43b1c1dfb0b34120f467e4f9966fb93755d56ed8ddef740bfad296376af9ec47","abstract_canon_sha256":"c28a5cfb0236bd53d83766bf7c954567bae4f7cb2c6434d54eaaa0031e2fa9b8"},"schema_version":"1.0"},"canonical_sha256":"d8fc43095c7c7be0e4209e694f3fef790fb53902d3d4c2b4689090504da72647","source":{"kind":"arxiv","id":"1806.00187","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00187","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00187v3","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00187","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"pith_short_12","alias_value":"3D6EGCK4PR56","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3D6EGCK4PR56BZBA","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3D6EGCK4","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:3D6EGCK4PR56BZBATZUU6P7PPE","target":"record","payload":{"canonical_record":{"source":{"id":"1806.00187","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-01T04:33:16Z","cross_cats_sorted":[],"title_canon_sha256":"43b1c1dfb0b34120f467e4f9966fb93755d56ed8ddef740bfad296376af9ec47","abstract_canon_sha256":"c28a5cfb0236bd53d83766bf7c954567bae4f7cb2c6434d54eaaa0031e2fa9b8"},"schema_version":"1.0"},"canonical_sha256":"d8fc43095c7c7be0e4209e694f3fef790fb53902d3d4c2b4689090504da72647","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:28.623667Z","signature_b64":"Q84u4Ixii/nJNndX/Fmj2+0p9Yaz1txhDpg7yzIKzXw+5AC7VIwZ/vEY/BOzp8D3CGey2rktQdcjGqckdbc3Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8fc43095c7c7be0e4209e694f3fef790fb53902d3d4c2b4689090504da72647","last_reissued_at":"2026-05-18T00:06:28.622995Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:28.622995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.00187","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V4S7FV5ExiC/ll2SQES+QyunbKuFfHcwCPSNUbq24EPHVFpNXGwYKtG9UPF9DUR3o30JRgOZ/cKzYE8vq9lDCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T05:13:09.714439Z"},"content_sha256":"045b292e8f8325e97bb29a66b7f51908e9ad9ab4c04d4aa49611d380f7eeaa00","schema_version":"1.0","event_id":"sha256:045b292e8f8325e97bb29a66b7f51908e9ad9ab4c04d4aa49611d380f7eeaa00"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:3D6EGCK4PR56BZBATZUU6P7PPE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scaling Neural Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"David Grangier, Michael Auli, Myle Ott, Sergey Edunov","submitted_at":"2018-06-01T04:33:16Z","abstract_excerpt":"Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT'14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00187","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:06:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gbktxHQgfVSxA8fLndN8y3A/3LudSagrjB+CZliHSuTUpZsuwcPHReN/y3TUeI0Elb+Fgl2p2XyYYs9iY6FxBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T05:13:09.714873Z"},"content_sha256":"103cf89a385b09c5e688e2a55a91ed8cd4a86ffa46538829741e111a26ecd071","schema_version":"1.0","event_id":"sha256:103cf89a385b09c5e688e2a55a91ed8cd4a86ffa46538829741e111a26ecd071"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3D6EGCK4PR56BZBATZUU6P7PPE/bundle.json","state_url":"https://pith.science/pith/3D6EGCK4PR56BZBATZUU6P7PPE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3D6EGCK4PR56BZBATZUU6P7PPE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-22T05:13:09Z","links":{"resolver":"https://pith.science/pith/3D6EGCK4PR56BZBATZUU6P7PPE","bundle":"https://pith.science/pith/3D6EGCK4PR56BZBATZUU6P7PPE/bundle.json","state":"https://pith.science/pith/3D6EGCK4PR56BZBATZUU6P7PPE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3D6EGCK4PR56BZBATZUU6P7PPE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3D6EGCK4PR56BZBATZUU6P7PPE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c28a5cfb0236bd53d83766bf7c954567bae4f7cb2c6434d54eaaa0031e2fa9b8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-01T04:33:16Z","title_canon_sha256":"43b1c1dfb0b34120f467e4f9966fb93755d56ed8ddef740bfad296376af9ec47"},"schema_version":"1.0","source":{"id":"1806.00187","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00187","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00187v3","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00187","created_at":"2026-05-18T00:06:28Z"},{"alias_kind":"pith_short_12","alias_value":"3D6EGCK4PR56","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3D6EGCK4PR56BZBA","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3D6EGCK4","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:103cf89a385b09c5e688e2a55a91ed8cd4a86ffa46538829741e111a26ecd071","target":"graph","created_at":"2026-05-18T00:06:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Sequence to sequence learning models still require several days to reach state of the art performance on large benchmark datasets using a single machine. This paper shows that reduced precision and large batch training can speedup training by nearly 5x on a single 8-GPU machine with careful tuning and implementation. On WMT'14 English-German translation, we match the accuracy of Vaswani et al. (2017) in under 5 hours when training on 8 GPUs and we obtain a new state of the art of 29.3 BLEU after training for 85 minutes on 128 GPUs. We further improve these results to 29.8 BLEU by training on t","authors_text":"David Grangier, Michael Auli, Myle Ott, Sergey Edunov","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-01T04:33:16Z","title":"Scaling Neural Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00187","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:045b292e8f8325e97bb29a66b7f51908e9ad9ab4c04d4aa49611d380f7eeaa00","target":"record","created_at":"2026-05-18T00:06:28Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c28a5cfb0236bd53d83766bf7c954567bae4f7cb2c6434d54eaaa0031e2fa9b8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-06-01T04:33:16Z","title_canon_sha256":"43b1c1dfb0b34120f467e4f9966fb93755d56ed8ddef740bfad296376af9ec47"},"schema_version":"1.0","source":{"id":"1806.00187","kind":"arxiv","version":3}},"canonical_sha256":"d8fc43095c7c7be0e4209e694f3fef790fb53902d3d4c2b4689090504da72647","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d8fc43095c7c7be0e4209e694f3fef790fb53902d3d4c2b4689090504da72647","first_computed_at":"2026-05-18T00:06:28.622995Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:06:28.622995Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q84u4Ixii/nJNndX/Fmj2+0p9Yaz1txhDpg7yzIKzXw+5AC7VIwZ/vEY/BOzp8D3CGey2rktQdcjGqckdbc3Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:06:28.623667Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00187","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:045b292e8f8325e97bb29a66b7f51908e9ad9ab4c04d4aa49611d380f7eeaa00","sha256:103cf89a385b09c5e688e2a55a91ed8cd4a86ffa46538829741e111a26ecd071"],"state_sha256":"e3f4b034eea02c20e6d4a51179897999ca960d29c36a5f713ed19de027e248cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"j1Jb2tLMfDPMR+chIx0P/+C6822G3yz46kKKT/Buhj9kf91JAikZ/rfvOqlbeYsUE92lNIVtDA5Cb3XQ4Nu5CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T05:13:09.717222Z","bundle_sha256":"4ba1f11a64136f90e7d2cf05f7c96f845c0be2f654c0b4cd81f7a271a5a7d8fb"}}