Verified claim · AI-ML · 100% confidence
DeepSpeed publicly released on: 2020-02-13 by Microsoft Research.
Last verified 2026-05-16 · Methodology veritas-v0.1 · 53cc193ef08fc5c0
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Structured fields
- Subject
- DeepSpeed
- Predicate
publicly_released_on- Object
- 2020-02-13 by Microsoft Research
- Confidence
- 100%
- Tags
- deepspeed · zero · microsoft · framework · distributed-training · open-source · released_on · 2020
Sources (2)
[1] official blog · Microsoft Research · 2020-02-13
ZeRO & DeepSpeed: New system optimizations enable training models with over 100 billion parameters“Today, we are happy to release an open-source library called DeepSpeed, which advances large model training by improving scale, speed, cost, and usability, unlocking the ability to train 100-billion-parameter models.”
[2] github release · Microsoft · 2020-02-13
DeepSpeed — official GitHub repository
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DeepSpeed publicly released on: 2020-02-13 by Microsoft Research. — SourceScore Claim 53cc193ef08fc5c0 (verified 2026-05-16). https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.jsonEmbed this claim
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Frequently asked questions
Is the claim "DeepSpeed publicly released on: 2020-02-13 by Microsoft Research." verified?
Yes — SourceScore verified this claim with 100% confidence as of 2026-05-16. The verification uses 2 primary sources cross-referenced against the SourceScore methodology (version veritas-v0.1). Full source list + signed JSON envelope linked below.
What is the evidence for "DeepSpeed publicly released on: 2020-02-13 by Microsoft Research."?
Evidence comes from 2 primary sources: Microsoft Research, Microsoft. Each source is listed below with verbatim excerpts and URLs. The signed JSON envelope at https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.json includes an HMAC-SHA256 signature for audit verification.
When was this claim last verified by SourceScore?
Last verified 2026-05-16 under methodology version veritas-v0.1. The signed JSON envelope is dated and cryptographically signed for audit trail. Re-verification cadence depends on the claim type and source freshness.
How can I cite this SourceScore claim in my code or article?
Fetch the signed JSON envelope from https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.json which includes the verbatim claim, primary sources, confidence, methodology version, last-verified date, and HMAC-SHA256 signature for audit. The CC-BY-4.0 license permits commercial use with attribution to SourceScore.
Use this claim in your code
Fetch this signed envelope from your application. The response includes the verbatim excerpt, primary source URLs, and an HMAC-SHA256 signature you can verify locally for audit trails.
cURL
curl https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.jsonJavaScript / TypeScript
const r = await fetch("https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.json");
const envelope = await r.json();
console.log(envelope.claim.statement);
// "DeepSpeed publicly released on: 2020-02-13 by Microsoft Research."Python
import httpx
r = httpx.get("https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.json")
envelope = r.json()
print(envelope["claim"]["statement"])
# "DeepSpeed publicly released on: 2020-02-13 by Microsoft Research."LangChain (retrieve-then-cite)
from langchain_core.tools import tool
import httpx
@tool
def get_deepspeed_fact() -> dict:
"""Fetch the verified SourceScore claim for DeepSpeed."""
r = httpx.get("https://sourcescore.org/api/v1/claims/53cc193ef08fc5c0.json")
return r.json()