Comments by "Tony Zhou" (@ReflectionOcean) on "IBM Technology" channel.

  1. 1. Understanding the challenges with LLMs - 0:36 2. Introducing Retrieval-Augmented Generation (RAG) to solve LLM issues - 0:18 3. Using RAG to provide accurate, up-to-date information - 1:26 4. Demonstrating how RAG uses a content store to improve responses - 3:02 5. Explaining the three-part prompt in the RAG framework - 4:13 6. Addressing how RAG keeps LLMs current without retraining - 4:38 7. Highlighting the use of primary sources to prevent data hallucination - 5:02 8. Discussing the importance of improving both the retriever and the generative model - 6:01
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  2. - Explanation of LLMs and generative AI: 0:21 - Distinction between proprietary and open source LLMs: 0:39 - Benefits of open source LLMs including transparency and fine-tuning: 2:12 - Examples of open source LLM applications in various industries: 3:19 - Overview of Huggingface's open LLM leaderboard: 4:01 - Discussion of risks associated with LLMs: 5:19 - IBM's engagement with open source LLMs and Granite models: 6:07
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  3. Steps in creating a new pod: 1. A user submits a new configuration with kubectl to kube-API Server 2. Kube-API Server writes the configuration to etcd 3. Scheduler compares the configuration to actual state 4. Scheduler informs kube-API Server of which compute node(s) to schedule the workload 5. Kube-API Server requests corresponding Kubelets to create the new pod 6. Kubelet creates a new pod 7. Kubelets report latest status to kube-API Server 8. Kube-API Server updates the actual state in etcd
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  4. etcd is 1. replicated: each node in an etcd cluster has full access to the whole data store 2. consistent: every read is going to return the most recent write 3. highly available: no single point of failure and tolerance of network partition 4. fast: the speed upper bound of etcd is the individual node storage speed 5. secured: transport layer SSL 6. simple: http with JSON
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  5. When writing: 1. client makes a write request to the leader node 2. leader node forwards the request to its followers 3. followers make updates to their values 4. followers return success to the leader when values are updated 5. leader updates its own value when majority of the followers are updated successfully 6. leader returns success to the client
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  6. When reading from a follower that's not yet updated: 1. client makes a request to a follower 2. follower forwards the request to the leader 3. leader returns the latest value to the follower 4. follower returns the latest value to the client
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  7. The things that container orchestration will do: 1. Deployment 2. Scaling: schedule containers to the right worker node for the best resources utilization 3. Networking: create load balancers for external and internal services communication 4. Operations and Insight: automatically bring up instances of a services in failure; provide integration points for service mesh and logging
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  8. A Hyperviser is a software process running in a physical computer (powerful one) to schedule resources (CPU, RAM and Network) for VSIs (Virtual Server Instances).
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  9. kubelet does a few things: 1. register compute nodes 2. report status 3. start/stop workload
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  10. A service is a collection of pods that are loads balanced
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  11. K8S uses etcd "watch" function to compare configuration (desired) and state (actual)
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  12. 3 ways to debug a K8S deployment: 1. kubectl logs 2. kubectl describe 3. kubectl exec into a pod and debug with ps aux and so on
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  13. API enables app to app communications SDK simplifies it for the programmers
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  14. A few reasons to use containers rather than VMs: 1. Lighter weight: no need to run an OS in a container comparing to a VM 2. More resource efficient: 1. no extra OS needed to be run in containers; 2. resources in the hosted node can be shared dynamically
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  15. 00:01:13 Use Lang chain to streamline the programming of llm applications through abstractions. 00:01:56 Choose an llm of your choice, whether closed source like gp4 or open source like llama 2, within Lang chain. 00:02:25 Utilize prompts in Lang chain to give instructions to large language models without manually hardcoding context and queries. 00:02:54 Create sequential chains in Lang chain to combine llms with other components for executing functions in a sequence. 00:03:53 Implement document loaders in Lang chain to import data sources from various third-party applications like Dropbox, Google Drive, or databases. 00:04:55 Utilize text splitters in Lang chain to split text into small, meaningful chunks for further processing. 00:05:09 Enhance llms' long-term memory by using Lang chain utilities to retain conversations or their summarizations. 00:05:39 Employ agents in Lang chain to use a language model as a reasoning engine for decision-making in applications. 00:06:08 Apply Lang chain for chatbots to provide context and integrate them into existing communication channels. 00:06:29 Utilize Lang chain for summarization tasks, such as breaking down academic papers or providing digests of emails. 00:06:42 Leverage Lang chain for question answering by retrieving relevant information from specific documents or knowledge bases. 00:06:59 Explore data augmentation using llms in Lang chain to generate synthetic data for machine learning purposes. 00:07:18 Integrate virtual agents with the right workflows using Lang chain's agent modules for autonomous decision-making. 00:07:36 Utilize Lang chain's open-source tools and APIs to simplify building applications that leverage large language models.
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  16. Level of virtualization: VM: hardware level virtualization Container: OS level virtualization
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  17. The speed upper bound of etcd is the individual node storage speed
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  18. etcd stores k8s data for state, configuration and metadata
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  19. Portability and flexibility: VM: flexibility of hardware Container: portability of processes
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  20. Controller managers takes care of runtime failures of pods
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  21. The 3 essential resources in a compute host are: CPU, RAM and Network
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  22. etcd nodes are organized as leader and followers manner
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  23. kube-proxy is for communication between compute nodes
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  24. Helm helps to create templates for K8S configurations and install/upgrade K8S deployment at runtime
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  25. Level of isolation: VM: isolation of machines (hardware resources: CPU, RAM, and Network) Container: isolation of processes
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  26. POD < ReplicaSet < Deployment
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  27. How resources are access: VM: through Hypervisor Container: through Linux Kernel (namespace and cgroup)
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