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John Zedlewski

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@zstats

Accelerating data science and access @NVIDIA
Formerly RAPIDS, DL for self driving cars, medical imaging, health data, econometrics, and kernel hacking.
Berkeley rapids.ai Joined May 2011
229 Following 4.4K Followers 941 Posts

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John Zedlewski Verified account @zstats
I have a particularly nasty case of cancer. When I was hospitalized again at my lowest point last year, I told Jensen. He immediately reached out to contacts at Stanford, who were able to cut through red tape, and get me into a clinical trial for which I've turned out to be a perfect fit. I think everyone can have their own views about recent events. But any claims that Jensen did not really care about his staff is simply inaccurate. I, personally, owe him more than I can express.

I think the best tribute we can pay to Jensen's legacy is to drive NVIDIA's continued success and push the whole industry forward, by showing we've learned the lessons from years of working here. We will compare ourselves against Speed of Light. The mission will be the boss. And we will not be scared to take smart, bold risks and fail fast with ruthless intellectual honesty.

But if there is one management story I remember the most, it's a particular Jensen grilling from my time in autonomous vehicles that stayed with me. Luckily I was not in the hot seat for this one. Team A didn't want to use Team B's framework, because they thought it was too slow. Jensen was surprised - it's just applying a bunch of optimized CUDA kernels in sequence, how could it be slow?

So he asked the executive from team B to draw the entire data flow sequence from their framework on the whiteboard and highlight every memcpy. Sure enough, there were a bunch of unnecessary memcpy's surrounding each pipeline stage. Everyone in the room could see why it wasn't getting to SoL and a path to improve things. No arguing about fiefdoms or appeals to authority - the data got us all on the same page.

The lesson was not (just) that unnecessary memory copying is bad. It's that executives should not be afraid to probe into details if something doesn't add up, and leaders need to be technical enough and honest enough to engage in real time and not turn every meeting into "I'll get back to you in a week." If that means counting memcpy's with a billionaire CEO, so be it.

My best executive meetings at NVIDIA have been when I'm fully prepared to present every detail of my team's work, and my worst failures have come when I realized I couldn't go deep enough to really solve the problem.

But the thing that gives me confidence is that Jensen was never the only one to live this philosophy. Anyone who has presented to an SVP at NVIDIA knows they are at risk for getting grilled on fundamental constraints and technical details. I have been part of amazing conversations with both recent and old timer leaders where we were able to get to the core of a problem in an hour rather than a month, because we had the right people in the room and were not afraid to go deeper.

The company is filled with supremely talented people who have been trained for 3 decades to work at the highest level, and you can see it in what we produce every day. I'm so proud to be an NVIDIAn, and I can't wait to show off what we ship in the coming months and years.
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John Zedlewski Verified account @zstats
This has been such a great collaboration with @DataPolars ! It's a deep integration that makes GPU acceleration incredibly easy. Bring your queries from minutes down to seconds with virtually no code change.
Polars Verified account @DataPolars Sep 17, 2024
Together with NVIDIA RAPIDS we bring GPU acceleration to Polars!

Read more in our release blog:

pola.rs/posts/gpu-engi…
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John Zedlewski Verified account @zstats
Continued amazing work from @NVIDIAAI teams on embedding. Also check out the NVIDIA Retriever embed and rerank NIMs, which are really state-of-the art, and runnable anywhere, with commercial-safe licenses, on your own hardware or cloud: blogs.nvidia.com/blog/nemo-retr…
blogs.nvidia.com AI, Go Fetch! New NVIDIA NeMo Retriever Microservices Boost LLM Accuracy and Throughput Enterprises can unlock the value of business data with production-ready NVIDIA NIM inference microservices for retrieval-augmented generation, integrated in Cohesity, DataStax, NetApp and Snowflake...
NVIDIA AI Developer Verified account @NVIDIAAIDev Aug 30, 2024
We have reclaimed #1 on the MTEB Leaderboard 🏆

Our NV-Embed-v2, has achieved a record-breaking score of 72.31 across 56 text embedding/retrieval tasks, reclaiming the top spot on the Massive Text Embedding Benchmark (MTEB) leaderboard. It also holds the No. 1 in the retrieval sub-category (15 tasks) in the leaderboard, which is essential to the development of RAG technology.

➡️ huggingface.co/spaces/mteb/le… ✨
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John Zedlewski Verified account @zstats
The comprehensive pandas compatibility from @RAPIDSai cuDF made it really easy to accelerate existing pandas code, but we know a lot of you need to work with datasets larger than GPU RAM. Now that's easy too with unified memory support in RAPIDS 24.08 developer.nvidia.com/blog/rapids-cu…
developer.nvidia.com NVIDIA cuDF Unified Memory Accelerates pandas up to 30x on Large Datasets | NVIDIA Technical Blog NVIDIA has released NVIDIA cuDF unified memory and text data processing features that help data scientists continue to use pandas when working with larger and text-heavy datasets in demanding…
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John Zedlewski Verified account @zstats
NeMo Retriever includes SOTA embedding and reranking, ready to deploy on prem or in the cloud - wherever you do your compute. The services are lightweight and expose familiar APIs while not tying you to any particular generator or agent framework. Amazing building blocks!
NVIDIA AI Developer Verified account @NVIDIAAIDev Jul 23, 2024
You can now use our new NVIDIA NeMo Retriever to help build and deploy production-grade text retrieval pipelines with our collection of NIM #inference microservices, for high-accuracy and efficiency.

➡️ nvda.ws/3SmfXS5 Technical deep dive on using NeMo Retriever embedding and reranking NIMs.
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