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Martin Görner Profile picture
@martin_gorner
Sep 13, 2018 • 7 tweets • 3 min read • Read on X
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Google Cloud Platform now has preconfigured deep learning images with Tensorflow, PyTorch, Jupyter, Cuda and CuDNN already installed. It took me some time to figure out how to start Jupyter on such an instance. Turns out it's a one liner:
Detailed instructions:
1) Go to cloud.google.com/console and create an instance (pick the Tensorflow deep learning image and a powerful GPU)
2) Ssh into your instance using the "gcloud compute ssh" command in the pic (there will be additional install prompts to accept and a reboot on the first connection. Relaunch the command after that to reconnect). Replace PROJECT_NAME and INSTANCE_NAME with your own values.
3) You are now SSH'ed into your instance. Type "jupyter notebook". Jupyter starts and gives you a URL. Copy-paste it into your browser. That's it. The -L param in the SSH command sets up ssh tunnelling from localhost:8888 on your laptop to localhost:8888 on your instance.
Once again in copy-paste friendly text:
gcloud compute --project "PROJECT_NAME" ssh "INSTANCE_NAME" -- -L 8888:localhost:8888
Oh, and Jupyter lab is already running on port 8080 whenever a deep learning instance boots. You don't even need to start it. If you are into Jupyter Lab, start the instance and ssh right in:
gcloud compute ssh "INSTANCE_NAME" -- -L 8080:localhost:8080
It also works with multiple ports. You want jupyter notebooks (8888) and tensorboard (6006) ? No problem:

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More from @martin_gorner

Martin Görner Profile picture
Jan 16
The "Self-Extend" paper promises magic for your LLMs: extending the context window beyond what they were trained on. You can take an LLM trained on 2000 token sequences, feed it 5000 tokens and expect it to work. Thread 🧵
(SWA below=sliding window attn.) arxiv.org/abs/2401.01325
Image
To be fair, some LLMs can already do that, if they are trained with a specific positional encoding like Alibi (). And before LLMs, Recurrent Neural Networks (RNNs) could do this trick as well. But was lost in Transformers.arxiv.org/abs/2108.12409
So how does it work? It turns out that if you understand the self-attention mechanism, a bit of high-school math goes a long way. Here is how self-attention is computed in Transformers: Image
Read 19 tweets
Martin Görner Profile picture
Dec 29, 2022
Large Language Models are getting good at formal logic:
arxiv.org/abs/2212.13894 LAMBADA: Backward Chaining for Automated Reasoning.
This paper is, in part, a traditional algorithm, a "depth-first search algorithm over the facts and the rules", starting from the desired conclusion and trying to logically reach the premises (facts and rules).
And in part a sophisticated LLM prompting technique since the "Fact Check", "Decompose" and "Rule Selection" modules are specific prompts agains a Large Language Model, PaLM 540B in this case.
Read 11 tweets
Martin Görner Profile picture
Dec 8, 2022
How can you probe what a language model knows ? If you ask it directly, it might lie (for example because you prefixed your question with untruths, or many other reasons).
Contrast-Consistent Search (CCS) gives a way:
openreview.net/pdf?id=ETKGuby…
It takes advantage of a nice property of True/False statements: they cannot be True and False at the same time.
Take any statement "The Eiffel tower is a crab", add True/False to the end and you have two mutually exclusive statents.
You can then feed these into a language model and train a small neural network to classify them as True/False from just some of the internal activations of your model. The language model stays frozen.
Read 14 tweets
Martin Görner Profile picture
Dec 7, 2022
Here is @luke_wood_ml
explaining Stable Diffusion at #Devoxx

Stable Diffusion is the first end-to-end model in the new KerasCV library.
@luke_wood_ml The code to generate an image:
Here is also, the presentation:
lukewood.github.io/devoxx/
and a Colab notebook to try it out: colab.research.google.com/github/lukewoo…
#KerasCV Image
@luke_wood_ml A generated pic just for fun: "Photorealistic representation of a muscular barbarian wearing bunny ears wielding the sword Excalibur" "Photorealistic repres...
Read 4 tweets
Martin Görner Profile picture
Dec 5, 2022
Thought-provocative new paper from @geoffreyhinton: what if we could replace backpropagation with something better?
@geoffreyhinton I seems very unlikely that the human brain uses back propagation to learn. There is little evidence of backprop mechanics in biological brains (no error derivatives propagating backwards, no storage of neuron activities to use in a packprop pass, ...).
@geoffreyhinton Also, the brain can learn from a continuous stream of incoming data and does not need to stop to run a backprop pass. Yes, sleep is beneficial for learning somehow, but we can learn awake too.
Read 14 tweets
Martin Görner Profile picture
Nov 9, 2022
Contrastive Search is the new kid on the block for text generation from language models. Better than greedy or beam search, top-k, nucleus sampling etc

Can continue text from a prefix with quality indistinguishable from a human, as judged by humans
paper: arxiv.org/abs/2210.14140 Image
In the experiment results above, the model continues a given text and human raters evaluate the result.
The raters preferred text generated by contrastive search 60-70% of the time (green box). When comparing to human output, they were undecided (red ellipses).
Intuitively, contrastive search encourages the model to generate the most likely sequence of words without repeating itself. The decoding maximized likelihood and minimizes the cosine similarity with already generated tokens ("degeneration penalty"). Image
Read 6 tweets

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