Hugging Face

Hugging Face supports around 100,000 pre-trained language models that can be used for various NLP tasks. The Hugging Face transformers library, which is a popular choice for NLP tasks such as text classification and machine translation, currently supports over 100 pre-trained language models. These models include popular models such as BERT, GPT-2, and RoBERTa. In addition Hugging Face provides tools and libraries that allow users to fine-tune and customize these models for specific tasks or datasets.

A Hugging Face Course –

Hugging Face on AWS blog –

CEO Clement Delangue, calls it the “GitHub of machine learning.” Its emphasis on an open, collaborative approach that made investors confident in the company’s $2 billion valuation, he said. “That’s what is really important to us, makes us successful and makes us different from others in the space.” 

DistilBERT is a smaller, faster, and cheaper version of the BERT language model developed by Hugging Face by controlling the loss function during training of a ‘student model’ from a ‘teacher model’. It bucks the trend towards larger models, and instead focusses on training a more efficient model. It has been “distilled” to reduce its size and computational requirements, making it faster to train and more efficient to run. Despite being smaller than BERT, DistilBERT is able to achieve similar or even slightly better performance on many NLP tasks. The triple loss function is devised to include a distillation loss, a training loss and a cosine-distance loss.

Examples of generative models available on the Hugging Face platform include:

  1. GPT-2: GPT-2 (Generative Pre-training Transformer 2) is a large-scale language model developed by OpenAI that can be used for tasks such as language translation and text generation.
  2. BERT: BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that can be used for tasks such as language translation and text classification.
  3. RoBERTa: RoBERTa (Robustly Optimized BERT Approach) is a language model developed by Facebook that is based on the BERT model and can be used for tasks such as language translation and text classification.
  4. T5: T5 (Text-To-Text Transfer Transformer) is a language model developed by Google that can be used for tasks such as language translation and text summarization.
  5. DistilBERT, described above. To generate text with DistilBERT, you would typically fine-tune the model on a specific task, such as machine translation or language generation, using a dataset that is relevant to the task. Once the model has been fine-tuned, you can use it to generate text by providing it with a prompt or seed text and letting it predict the next word or sequence of words.

Docs on text generation –

Here’s an example of using transformers to generate some text.

import transformers

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('distilgpt2') 
model = AutoModelWithLMHead.from_pretrained('distilgpt2')  

# Encode the prompt
input_context_prompt = "Men on the moon "
input_ids = tokenizer.encode(input_context_prompt, return_tensors='pt')  # encode input context

# Generate text
outputs = model.generate(input_ids=input_ids, max_length=40, temperature=0.9, num_return_sequences=10, do_sample=True)  

# Sample candidate outputs and print
for i in range(10): #  10 output sequences were generated
    print('Generated {}: {}'.format(i, tokenizer.decode(outputs[i], skip_special_tokens=True)))

Note the temperature parameter during model.generate(). A temperature of zero means the generation process will choose the most likely next word . A higher temperature allows for less likely words to be included in the generation process.

Airflow and Orchestration

download_images >> train >> serve

This line sets the sequence of operations for an ML pipeline in Airflow. source

A metaphor to think of Airflow is that of an air-traffic controller that is orchestrating, sequencing, mediating, managing the flow of the flights of airplanes (source). It is an example of the mediator pattern which decouples dependencies in a complex system. The airplanes do not talk directly to each other, they talk to the air-traffic controller.

A functional alternative to Airflow is to use a bunch of cron jobs to schedule bash scripts. Airflow instead defines pipelines as Directed Acyclic Graphs (DAGs) in python code. This critical talk on “Don’t use Apache Airflow” describes it as cron on steroids.

A complete example of an ML pipeline built with airflow that outputs the results to a streamlit app –

Each operation calls an operator to do the job locally or remotely.

How does it perform an operation remotely on another node ? ssh/remote execution ? docker daemon ? k8s operator ? There can be many different ways – this logic is encapsulated by an Executor.

Local Executors

Remote Executors

A thread on airflow and alternatives- . – A number of pipeline tools for ETL

Intro talk on Airflow by Astronomer – ,

and on an ETL use case with Snowflake –

How can one compose these DAGs further and manage cross-DAG depedencies ? An approach is discussed in to define an explicit mediator between multiple DAGs.

Security of Solidity Smart Contracts using DistilBERT

Smart Contracts are relatively short blocks of code that run on the Ethereum Virtual Machine (EVM), and deal with tokens of value. For example a contract may release funds when certain preconditions such are met, such as time elapsed, or a signed request received. The number of smart contracts and the value of transactions in smart contracts has grown quite a bit in the last few years along with the prices of cryptocurrencies. The code of the Smart Contract is always publicly available as bytecode which can be reverse engineered, and often the source code in solidity language is often publicly available. As a result, bugs in smart contracts have become attractive exploit targets. EVMs are a distributed computing construct that run in parallel on a network of participating nodes, coordinating their actions by a consensus mechanism and protocol that runs between the nodes.

A collection of links on this topic – newsletter reporting high level analysis recent attacks. Solidity has 3 types of variables 1. local (inside function), 2. state (inside contract, outside function), 3. global (e.g. block.timestamp, msg.sender – chain level. provides info about the blockchain) (storage, memory, calldata) (public, private, internal, external) (onlyOwner to restrict access, validAddress to validate address, noReentrancy to prevent reentrancy) Incorrect reentrancy is a source of bugs. – instrumenting the blockchain to find gaps (EthDenver talk).

Security of Bridges. Bridges are implemented as smart contracts between two different chains.

Sequence diagram of a bridge operation in

Within the last year, bridges have accounted for a majority of the total funds stolen across all of the crypto ecosystem. Massive bridge hacks have occurred on average every few months, and each losing extremely large amounts of user funds. Some bridge hacks in the last couple of years have included the Axie Infinity Ronin bridge hack, losing users $625 million, the Wormhole bridge hack costing users $300 million, the Harmony bridge hack losing users $100 million, and just this last week the Nomad bridge hack, losing users almost $200 million.

Methods for Detecting attacks

  • Code reviews for reentrancy bugs
  • Detection of source of a txn as a bad actor
  • Using ML for code analysis and bad actor detection – This submission attempts using ML for detecting reentrancy attacks in Solidity code, by using transfer learning on DistilBERT, to train on good and bad smart contract code examples, and use the trained model to detect bad code on new code samples.

“from transformers import TFDistilBertModel, DistilBertTokenizerFast” # using a hugging face model

These guys had a funny presentation –

Machine Learning Security

Seven security concerns in Machine Learning (ML) –

  1. Data privacy and security: ML requires large amounts of data to be trained, and this data may contain sensitive or personal information. Appropriate measures need to be put in place to prevent data from being accessed by unauthorized parties.
  2. Notebooks security: ML typically requires Jupyter or similar notebooks to be served for data scientists to work on data, code, and models, both individually and collaboratively. These notebooks need to be access controlled and protected from unauthorized access. This includes the code and git repos that host the code, and the model artifacts that the notebook uses or creates.
  3. Model serving and inference security: ML models in production are commonly served and accessed over inference endpoints and such endpoints need authentication, authorization, encryption for protection against misuse. During model upgrades to an endpoint or changes to an endpoint and its configuration, a number of attacks are possible that are typical of a devops/devsecops pipeline. These need to be protected against.
  4. Model security: Models can be vulnerable to attacks such as adversarial inputs, such as when an attacker intentionally manipulates the input to the model in order to cause it to make incorrect predictions. Another example is when the model makes an egregiously bad decision on an input, for example a self-driving car hitting an obstacle instead of avoiding it. It is important to harden the model and bound the decisions that come from its use.
  5. Misuse: Even if a model works as designed, it can be misused, for example by generating fake or misleading content. It is important to consider the potential unintended consequences of using models and to put safeguards in place to prevent their misuse.
  6. Bias: ML models can sometimes exhibit biases due to the data they are trained on. There should be a plan to identify biases in a model and take steps to mitigate them.
  7. Intellectual property: ML models may be protected by intellectual property laws, and it is important to respect these laws and obtain the appropriate licenses when using language models developed by others.

Reinforcement learning

An Agent is in an Environment. a) Agent reads Input (State) from Environment. b) Agent produces Output (Action) that affects its State relative to Environment c) Agent receives reward (or feedback) for the Output produced. With the reward/feedback it receives it learns to produce better Output for given Input.

Where do neural networks come in ?

Optimal control theory considers control of a dynamical system such that an objective function is optimized (with applications including stability of rockets, helicopters). In optimal control theory, Pontryagin’s principle says: a necessary condition for solving the optimal control problem is that the control input should be chosen to minimize the control Hamiltonian. This “control Hamiltonian” is inspired by the classical Hamiltonian and the principle of least action. The goal is to find an optimal control policy function u∗(t) and, with it, an optimal trajectory of the state variable x∗(t) which by Pontryagin’s maximum principle are the arguments that maximize the Hamiltonian.

Derivatives are needed for the continuous optimizations. Deep learning models are capable of performing continuous linear and non-linear transformations, which in turn can compute derivatives and integrals. They can be trained automatically using real-world inputs, outputs and feedback. So a neural network can provide a system for sophisticated feedback-based non-linear optimization of the map from Input space to Output space.

The above could be accomplished by a feedforward neural network that is trained with a feedback (reward). Additionally a recurrent neural network could encode a memory into the system by making reference to previous states (likely with higher training and convergence costs).

Model-free reinforcement learning does not explicitly learn a model of the environment.

Manifestations of RL: Udacity self-driving course – lane detection. Karpathy’s RL blog post has an explanation of a network structure that can produce policies in a malleable manner, called policy gradients.

Practical issues in Reinforcement Learning –

Raw inputs vs model inputs: There is the problem of mapping inputs from real-world to the actual inputs to a computer algorithm. Volume/quality of information – high vs low requirement.

Exploitation vs exploration dilemma: Simple exploration methods are the most practical. With probability ε, exploration is chosen, and the action is chosen uniformly at random. With probability 1 − ε, exploitation is chosen, and the agent chooses the action that it believes has the best long-term effect (ties between actions are broken uniformly at random). ε is usually a fixed parameter but can be adjusted either according to a schedule (making the agent explore progressively less), or adaptively based on heuristics.

AWS DeepRacer. Allows exploration of RL. Simplifies the mapping of camera input to computer input, so one can focus more on the reward function and deep learning aspects. The car has a set of possible actions (change heading, change speed). The RL task is to predict the actions based on the inputs.

What are some of the strategies applied to winning DeepRacer ?

Reward function input parameters –

DeepRacer: Educational Autonomous Racing Platform for Experimentation with Sim2Real Reinforcement Learning” –

RL is not a fit for every problem. Alternative approaches with better explainability and determinism include behavior trees, vectorization/VectorNet, …

DeepMind says reinforcement learning is ‘enough’ to reach general AI

Richard Sutton and Andrew Barto’s book on RL: An introduction.

This paper explores incorporating Attention mechanism with Reinforcement learning – Reinforcement Learning with Attention that Works: A Self-Supervised Approach. A video review of the ‘Attention is all you need’ is here, the idea being to replace an RNN with a mechanism to selectivity track a few relevant things.

Multi agent Deep Deterministic Policy Gradients – cooperation between agents. Agents learn a centralized critic based on the observations and actions of all agents. .

Multi-vehicle RL for multi-lane driving.

Reinforcement learning in chip design

Deep learning is being applied to combinatorial optimization problems. A very intriguing talk by Anna Goldie discussed an application of RL to chip design that cuts down the time for layout optimization and which in turn enables optimizing of the chip design for a target software stack in simulation before the chip goes to production. Here’s a paper – graph placement methodology for fast chip design.

A snippet on how the research direction evolved to a learning problem.

Chip floorplanning as a learning problem

The underlying problem is a high-dimensional contextual bandits problem but, as in prior work, we have chosen to reformulate it as a sequential Markov decision process (MDP), because this allows us to more easily incorporate the problem constraints as described below. Our MDP consists of four key elements:
(1) States encode information about the partial placement, including the netlist (adjacency matrix), node features (width, height, type), edge features (number of connections), current node (macro) to be placed, and metadata of the netlist graph (routing allocations, total number of wires, macros and standard cell clusters).
(2) Actions are all possible locations (grid cells of the chip canvas) onto which the current macro can be placed without violating any hard constraints on density or blockages.
(3) State transitions define the probability distribution over next states, given a state and an action.
(4) Rewards are 0 for all actions except the last action, where the reward is a negative weighted sum of proxy wirelength, congestion and density, as described below.

We train a policy (an RL agent) modelled by a neural network that, through repeated episodes (sequences of states, actions and rewards), learns to take actions that will maximize cumulative reward (see Fig. 1).
We use proximal policy optimization (PPO) to update the parameters of the policy network, given the cumulative reward for each placement.”

Their diagram:

“An embedding layer encodes information about the netlist adjacency, node features and the current macro to be placed. The policy and value networks then output a probability distribution over available grid cells and an estimate of the expected reward for the current placement, respectively. id: identification number; fc: fullyconnected layer; de-conv: deconvolution layer”

A graph placement methodology for fast chip design | Nature

“Fig. 1 | Overview of our method and training regimen.In each training iteration, the RL agent places macros one at a time (actions, states and rewards are denoted byai, si and ri, respectively). Once all macros are placed, the standard cells are placed using a force-directed method. The intermediate rewards are zero. The reward at the end of each iteration is calculated as a linear combination of the approximate wirelength, congestion and density, and is provided as feedback to the agent to optimize its parameters for the next iteration.”

The references mention a number of applications of ML to chip design. A project exploring these is at at

ML for Forecasting

In this paper – “DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks”, the authors discuss a method for learning a global model from several individual time series.

Let’s break down some aspects of the approach and design.

“In probabilistic forecasting one is interested in the full predictive distribution, not just a single best realization, to be used in downstream decision making systems.”

The autoregressive model specifies that the output variable depends linearly on its own previous values and on a stochastic term (an imperfectly predictable term).

Recurrent Neural Network is used to refer to NNs with an infinite impulse response, and are used for speech recognition, handwriting recognition and such tasks involving sequences.

An LSTM or The Long Short-Term Memory (LSTM) is a type of RNN, that came about to solve a problem of vanishing gradients in previous RNN designs. An LSTM cell can process data sequentially and keep its hidden state through time.

A covariate is an independant random variable, with which the target random variable is assumed to have some covariance.

The approach has distinct features described in this snippet

“In addition to providing better forecast accuracy than previous methods, our approach has a number key advantages compared to classical approaches and other global methods: (i) As the model learns seasonal behavior and dependencies on given covariates across time series, minimal manual feature engineering is needed to capture complex, group-dependent behavior; (ii) DeepAR makes probabilistic forecasts in the form of Monte Carlo samples that can be used to compute consistent quantile estimates for all sub-ranges in the prediction horizon; (iii) By learning from similar items, our method is able to provide forecasts for items with little or no history at all, a case where traditional single-item forecasting methods fail; (vi) Our approach does not assume Gaussian noise, but can incorporate a wide range of likelihood functions, allowing the user to choose one that is appropriate for the statistical properties of the data.
Points (i) and (iii) are what set DeepAR apart from classical forecasting approaches, while (ii) and (iv) pertain to producing accurate, calibrated forecast distributions learned from the historical behavior of all of the time series jointly, which is not addressed by other global methods (see Sec. 2). Such probabilistic forecasts are of crucial importance in many applications, as they—in contrast to point forecasts—enable optimal decision making under uncertainty by minimizing risk functions, i.e. expectations of some loss function under the forecast distribution.”

Facebook Prophet is an open-source library for forecasting –

ARMA – AutoRegressive Moving Average Estimator

ARIMA estimator – AutoRegressive Integrated Moving Average is a generalization of ARMA and can better handle non-stationarity in a time series.

DevSecOps – Securing the Software Supply Chain

A position paper from CNCF on securing the software supply chain, talks about hardening the software construction process by hardening each of the links in the software production chain –

Quote – “To operationalize these principles in a secure software factory several stages are needed. The software factory must ensure that internal, first party source code repositories and the entities associated with them are protected and secured through commit signing, vulnerability scanning, contribution rules, and policy enforcement. Then it must critically examine all ingested second and third party materials, verify their contents, scan them for security issues, evaluate material trustworthiness, and material immutability. The validated materials should then be stored in a secure, internal repository from which all dependencies in the build process will be drawn. To further harden these materials for high assurance systems it is suggested they should be built directly from source.

Additionally, the build pipeline itself must be secured, requiring the “separation of concerns” between individual build steps and workers, each of which are concerned with a separate stage in the build process. Build Workers should consider hardened inputs, validation, and reproducibility at each build. Finally, the artifacts produced by the supply chain must be accompanied by signed metadata which attests to their contents and can be verified independently, as well as revalidated at consumption and deployment.”

The issue is that software development is a highly collaborative process. Walking down the chain and ensuring the ingested software packages are bug-free is where it gets challenging.

The Department of Defense Enterprise DevSecOps Reference design, speaks to the aspect of securing the build pipeline –

The DoD Container Hardening Guide referenced in the CNCF doc is at –

which has a visual Iron Bank flow diagram on p.20

Distributed Training – Parameter server, Data and Model parallelism

Distributed Training aims to reduce the training time of a model in machine learning, by splitting the training workload across multiple nodes. It has gained in importance as data sizes, model sizes and complexity of training have grown. Training consists of iteratively minimizing an objective function by running the data through a model and determining a) the error and the gradients with which to adjust the model parameters (forward path) and b) the updated model parameters using calculated gradients (reverse path). The reverse path always requires synchronization between the nodes, and in some cases the forward path also requires such communication.

There are three approaches to distributed training – data parallelism, model parallelism and data-model parallelism. Data parallelism is the more common approach and is preferred if the model fits in GPU memory (which is increasingly hard for large models).

In data parallelism, we partition the data on to different GPUs and and run the same model on these data partitions. The same model is present in all GPU nodes and no communication between nodes is needed on the forward path. The calculated parameters are sent to a parameter server, which averages them, and updated parameters are retrieved back by all the nodes to update their models to the same incrementally updated model.

In model parallelism, we partition the model itself into parts and run these on different GPUs.

A paper on Parameter Servers is here, on Scaling Distributed Machine Learning with the Parameter Server.

To communicate the intermediate results between nodes the MPI primitives are leveraged, including AllReduce.

The amount of training data for BERT is ~600GB. BERT-Tiny model is 17MB, BERT-Base model is ~400MB. During training a 16GB memory GPU sees an OOM error.

Some links to resources – (Horovod, an open source parameter server). Origin of General Language Understanding Evaluation.

Horovod core principles are based on the MPI concepts size, rank, local rank, allreduce, allgather, and broadcast. These are best explained by example. Say we launched a training script on 4 servers, each having 4 GPUs. If we launched one copy of the script per GPU:

  • Size would be the number of processes, in this case, 16.
  • Rank would be the unique process ID from 0 to 15 (size – 1).
  • Local rank would be the unique process ID within the server from 0 to 3.
  • Allreduce is an operation that aggregates data among multiple processes and distributes results back to them. Allreduce is used to average dense tensors. Here’s an illustration from the MPI Tutorial:
Allreduce Illustration
  • Allgather is an operation that gathers data from all processes in a group then sends data back to every process. Allgather is used to collect values of sparse tensors. Here’s an illustration from the MPI Tutorial:
Allgather Illustration
  • Broadcast is an operation that broadcasts data from one process, identified by root rank, onto every other process. Here’s an illustration from the MPI Tutorial:

Multimodal neurons typographic attacks

ML Training on images and text together leads to certain neurons holding information of both images and text – multimodal neurons.

When the type of the detected object can be changed by tricking the model into recognizing a textual description instead of a visual description- that can be called a typographic attack.

Intriguing concepts indicating that a fluid crossover from text to images and back is almost here.

Bitcoin market cap reaches $1T

Bitcoin reached a $1T market cap last month.

A Bitcoin halving event is scheduled to take place every 210,000 blocks. This reduces the payoff of securing a block by half. Three Bitcoin halvings have taken place so far in 2012, 2016, 2020. The next halving is predicted to occur in 2024. The corresponding block reward went from 50btc in 2009 to 25 in ‘12, 12.5 in ‘16, 6.25 in ‘20 and 3.125 in ‘24.

The rate of production of bitcoin over time is shown below. Mining will continue until 21million btc are created.

VeChain is a blockchain proposal/implementation for supply chain tracking.

EdgeChain is an architecture for placement of applications on the edge amongst multiple resources from multiple providers. It is built on Vechain for Mobile and Edge Computing use cases.

Disaster Recovery: Understanding and designing for RPO and RTO

Let’s take a disaster scenario where a system loses its data-in-transit (i.e. not yet persisted) at a certain point in time. and some time after this point, a recovery process kicks in, which restores the system back to normal functioning.

Recovery Point Objective refers to the amount of tolerable data loss measured in time. It can be measured in time based on the fact that it is in-transit data of a certain max velocity, so bounding the time bounds the amount of data that can be lost. This time figure, the RPO, is used to determine how frequently the data must be persisted and replicated. An RPO of 10 minutes implies the data must be backed up every 10 minutes. If there’s a crash the system can be restored to a point not more than 10 minutes prior to the time of crash. RPO determines frequency of backups, snapshots or transaction logs.

Recovery Time Objective refers to the amount of time required to restore a system to normal behavior after a disaster has happened. This includes restoration of all infrastructure components that provide a service, not just the restoration of data.

Lower RPO/RTO is higher cost.

Matrix of RPO – high/low vs RTO – high/low can be used to categorize applications.

Low RPO, Low RTO. Critical online application like a storefront.

Low RPO, High RTO. Data sensitive application but not online, like analytics.

High RPO, Low RTO. Redundantly available data or no data. Compute clusters that are highly available.

High RPO, High RPO. Non-prod systems – dev/test/qa ?

Amount of acceptable data loss <= App (data?) criticality.
One can expect a pyramid of apps – large number with less criticality, small number with high criticality

Repeatability. Backup and recovery procedures. Must be written. Must be tested. Automation.

HA/DR spectrum of solutions:

  • Backups, save transaction logs
  • Snapshots
  • Replication – synchronous, asynchronous
  • Storage only vs in-memory as well. Application level crash consistency of backups.
  • Multiple AZs
  • Hybrid

Tech: S3 versioning and DDB streams, Global tables.

Rules of thumb:

Related terms: RPA and RTA

3 types of disasters.

  • Natural disaster – e.g. floods, earthquakes, fire
  • Technical failure – e.g. loss of power, cable pulled
  • Human error – e.g. delete all files as admin

Replication – works for first two. Continuous snapshots/backup/versioning – for the last one. Replication will just delete the data on both sides. Need the ability to go back in time and restore data.

Cost – how to optimize cost of infrastructure and its maintenance.

Which region to choose ? Key considerations: What types of disasters are the concern (Risk). How much proximity is needed to end-customers and to primary region (Performance). What’s the cost of the region (Cost) ?

Processors for Deep Learning: Nvidia Ampere GPU, Tesla Dojo, AWS Inferentia, Cerebras

The NVidia Volta-100 GPU released in Dec 2017 was the first microprocessor with dedicated cores purely for matrix computations called Tensor Cores. The Ampere-100 GPU released May’20 is its successor. Ampere has 84 Streaming Multiprocessors (SMs) with 4 Tensor Cores (TCs) each for a total of 336 TCs. Tensor Cores reduce the cycle time for matrix multiplications, operating on 4×4 matrices of 16bit floating point numbers. These GPUs are aimed at Deep Learning use cases which consist of a pipeline of matrix operations.

Here’s an article on choosing the right EC2 instance type for DL – (G4 for inferencing, P4 for training).

How did the need for specialized DL chips arise, and why are Tensors important in DL ? In math, we have Scalars and Vectors. Scalars are used for magnitude and Vectors encode magnitude and direction. To transform Vectors, one applies Linear Transformations in the form of Matrices. Matrices for Linear Transformations have EigenVectors and EigenValues which describe the invariants of the transformation. A Tensor in math and physics is a concept that exhibits certain types invariance during transformations. In 3 dimensions, a Stress Tensor has 9 components, which can be representated as a 3×3 matrix; under a change of basis the components of the tensor change however the tensor itself does not.

In Deep Learning applications a Tensor is basically a Matrix. The Generalized Matrix Multiplication (GEMM) operation, D=AxB+C, is at the heart of Deep Learning, and Tensor Cores are designed to speed these up.

In Deep Learning, multilinear maps are interleaved with non-linear transforms to model arbitrary transforms of input to output and a specific model is arrived by a process of error reduction on training of actual data. This PyTorch Deep Learning page is an excellent resource to transition from traditional linear algebra to deep learning software – .

Tesla Dojo is planned to build a processor/computer dedicated for Deep Learning to train on vast amounts of video data. Launched on Tesla AI Day, Aug’20 2021, a video at

AWS Inferentia is a chip for deep learning inferencing, with its four Neuron Cores.

AWS Trainium is an ML chip for training.

Generally speaking the desire in deep learning community is to have simpler processing units in larger numbers.

Updates: Cerebras announced a chip which can handle neural networks with 120 trillion parameters, with 850,000 AI optimized cores per chip.

SambaNova, Anton, Cerebras and Graphcore presentations are at

SambaNova is building 400,000 AI cores per chip.

Delta Lake and Spark for threat detection and response at scale

Notes on a talk on the data platform for Threat detection and response at scale at Spark+AI Summit, 2018.

The threat detection problem, use-cases and scale.

  • It’s important to focus on and build the data platform first else one can get siloed into narrow set of addressable use-cases.
  • we want to detect attacks,
  • contextualize the attacks
  • determine root cause of an attack,
  • determine what the scope of the incident might be
  • determine what we need to contain it
  • Diverse threats require diverse data sets
  • the Threat signal can be concentrated or spread in time
  • Keylines visualization library is used to build a visualization of detection, contextualization, containment

Streaming is a simple pattern that takes us very far for detection

  • Streams are left-joined with context and filtered or inner-joined with indicators
  • Can do a lot with this but not everything
  • Graphs are key. Graphs at scale are super hard.

Enabling triage and containment with search and query

  • to triage the detection, it comes down to search and query.
  • ETM does 3.5million records/sec. 100TB of data a day. 300B events a day.
  • 11 trillion rows, 0.5PB of data.

Ingestion architecture – tries to solve all these problems and balance issues.

  • data comes into s3 in a consistent json wrapper
  • there’s a single ETL job that takes all the data and writes it into a single staging table which is partitioned by date and event-type, has a long retention
  • table is optimized to stream new data in and stream data out of, but can be queried as well. you can actually go and query it using sql function
  • highest value data – we write parsers, we have discrete parsing streams and put them into a common schema and put it into separate delta tables. well parsed, well structured.
  • use optimizations from delta, z-odering.. to index over columns that are common predicates. search by IP address, domain names – those are what we order by
  • indexing and z-ordering – take advantage of data skipping
  • sometimes we parser code gets messed up.
  • single staging table.. is great . we just let the fixed parser run forward, we have all the data corrected, then we are back-corrected. don’t have to repackage code and run as a batch job. we literally just fix code and run it in the same model that’s it.
  • off of these refined tables or parsed data sets, this is where the detection comes in.
  • we have a number of detection streams in batches, that do the logic and analysis. facet-faced or statistical.
  • alerts that come out of this go to their own alert table. goes to delta again. long retection, consistent schema. another streaming job then does de-duplication and whitelisting and writes out alerts to our alert management system. we durably store all the alerts, whether or not de-duped/whitelisted
  • allows us to go back and fix things if things are not quite correct, accidentally.
  • all this gives us operational sanity, and a nice feedback loop

Thanks to z-ordering, it can go from scanning 500TB to 36TB.

  • average case is searching over weeks or months. it makes it usable for ad-hoc refinements.
  • simple, unified platform.

Michael: Demo on interactive queries over months of data

  • first attempt is sql SELECT on raw data. takes too long, cancelled. second attempt uses HMS, still too long, cancelled. why is this so hard ?
  • observation: every data problem is actually two problems 1) data engineering and 2) data science. most projects fail on step 1.
  • doing it with delta – the following command takes 17s and fires off spark job to put the data in a common schema.

CREATE TABLE connections USING delta AS SELECT * from json.'/data/connections'


SELECT * FROM connections WHERE dest_port = 666

this is great to query the historical data quickly.. however batch alone is not going to cut it as we may have attacks going on right now. but delta plugs into streaming as well:

INSERT INTO connections SELECT * from kafkaStream

Now we’ve Indexed both batch and streaming data.

We can run a python netviz command to visualize the connections.

Here’s a paper on the Databricks Delta Lake approach – .