Skip to content

Commit

Permalink
Update ntbk
Browse files Browse the repository at this point in the history
  • Loading branch information
@rlancemartin
rlancemartin committed Sep 18, 2024
1 parent a35f56f commit 77f2ac8
Showing 1 changed file with 114 additions and 43 deletions.
157 changes: 114 additions & 43 deletions ntbk/testing.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -38,7 +38,7 @@
"source": [
"from langgraph_sdk import get_client\n",
"\n",
"client = get_client(url=\"http://localhost:53394\")"
"client = get_client(url=\"http://localhost:63252\")"
]
},
{
Expand All @@ -56,27 +56,41 @@
"metadata": {},
"outputs": [],
"source": [
"schema = {\n",
"schema = {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"companies\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Names of top chip providers for LLM training\",\n",
" },\n",
" \"technologies\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Brief summary of key chip technologies used for LLM training\",\n",
" },\n",
" \"market_share\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Overview of market share distribution among top providers\",\n",
" },\n",
" \"future_outlook\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Brief summary of future prospects and developments in the field\",\n",
" },\n",
" \"type\": \"array\",\n",
" \"items\": {\n",
" \"type\": \"object\",\n",
" \"properties\": {\n",
" \"name\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Company name\"\n",
" },\n",
" \"technologies\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Brief summary of key technologies used by the company\"\n",
" },\n",
" \"market_share\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Overview of market share for this company\"\n",
" },\n",
" \"future_outlook\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Brief summary of future prospects and developments in the field for this company\"\n",
" },\n",
" \"key_powers\": {\n",
" \"type\": \"string\",\n",
" \"description\": \"Which of the 7 Powers (Scale Economies, Network Economies, Counter Positioning, Switching Costs, Branding, Cornered Resource, Process Power) best describe this company's competitive advantage\"\n",
" }\n",
" },\n",
" \"required\": [\"name\", \"technologies\", \"market_share\", \"future_outlook\"]\n",
" },\n",
" \"description\": \"List of companies\"\n",
" }\n",
" },\n",
" \"required\": [\"companies\", \"technologies\", \"market_share\", \"future_outlook\"],\n",
" \"required\": [\"companies\"]\n",
"}"
]
},
Expand Down Expand Up @@ -105,7 +119,7 @@
"# Stream\n",
"async for event in client.runs.stream(\n",
" thread[\"thread_id\"],\n",
" assistant_id=\"agent\",\n",
" assistant_id= \"agent\",\n",
" input={\n",
" \"topic\": topic,\n",
" \"extraction_schema\": schema,\n",
Expand All @@ -121,7 +135,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -130,26 +144,74 @@
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/markdown": [
"\n",
"# Top 5 Chip Providers for LLM Training\n",
"\n",
"## Companies\n",
"Nvidia, AMD, Google, OpenAI, Microsoft, Meta, Amazon, Baidu, Alibaba, Huawei\n",
"\n",
"## Key Technologies\n",
"GPUs (particularly Nvidia A100 and H100), TPUs (Google's custom AI chips), Large Language Models (LLMs)\n",
"## NVIDIA\n",
"\n",
"**Key Technologies:** A100 and H100 GPUs\n",
"\n",
"**Market Share:** Dominates the market with over 80% share in AI chips\n",
"\n",
"**Key Powers:** Scale Economies, Network Economies\n",
"\n",
"**Future Outlook:** Likely to maintain dominance but facing increasing competition from custom chips by cloud providers and other manufacturers\n",
"\n",
"---\n",
"\n",
"## AMD\n",
"\n",
"**Key Technologies:** MI250, MI300X, and Ryzen AI NPUs\n",
"\n",
"**Market Share:** Growing presence, estimated to be around 10-15% of the AI chip market\n",
"\n",
"**Key Powers:** Counter Positioning, Process Power\n",
"\n",
"**Future Outlook:** Expanding its GPU offerings to compete with NVIDIA, with potential for increased market share. Launching new chips like MI300X to target LLM training and inference\n",
"\n",
"---\n",
"\n",
"## Intel\n",
"\n",
"**Key Technologies:** Gaudi 2 and upcoming Gaudi 3 AI accelerators\n",
"\n",
"**Market Share:** Emerging player in the AI chip market, with competitive performance-per-dollar metrics\n",
"\n",
"**Key Powers:** Process Power, Counter Positioning\n",
"\n",
"**Future Outlook:** Promising outlook with Gaudi 2 showing strong performance and cost-effectiveness, and Gaudi 3 in development. Targeting both training and inference markets for LLMs\n",
"\n",
"---\n",
"\n",
"## Google\n",
"\n",
"**Key Technologies:** Custom Tensor Processing Units (TPUs), including TPU V5P\n",
"\n",
"**Market Share:** Significant in-house usage for Google's AI services, including Gemini LLM\n",
"\n",
"## Market Share\n",
"Nvidia dominates the market, particularly in data center GPUs. In 2023, the top five LLM developers acquired around 88.22% of the market revenue. The global LLM market is projected to grow from $1,590 million in 2023 to $259,800 million in 2030, with a CAGR of 79.80%.\n",
"**Key Powers:** Cornered Resource, Process Power\n",
"\n",
"## Future Outlook\n",
"The LLM market is expected to experience explosive growth, with projections reaching $35.43 billion by 2030 at a CAGR of 35.9%. Key developments include advanced pre-training techniques, multimodal models like Google's VideoPoet, and a focus on efficiency and specialized models. The industry is also seeing increased investment in AI infrastructure, prompt engineering, and MLOps (Machine Learning Operations) to support the growing demand for AI technologies.\n"
"**Future Outlook:** Continuing development of TPUs for internal use and potential expansion to cloud customers. TPU V5P offers improved performance for LLM training and inference\n",
"\n",
"---\n",
"\n",
"## Amazon Web Services (AWS)\n",
"\n",
"**Key Technologies:** Trainium2 and Graviton4 custom chips, Amazon SageMaker platform\n",
"\n",
"**Market Share:** Major cloud provider for AI services, exact market share in AI chips not specified\n",
"\n",
"**Key Powers:** Scale Economies, Network Economies\n",
"\n",
"**Future Outlook:** Expanding development and usage of in-house AI chips to reduce dependence on external providers. Focusing on custom silicon for AI fabric and specialized chips for LLM training and inference\n",
"\n",
"---\n"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
Expand All @@ -162,26 +224,26 @@
"source": [
"from IPython.display import Markdown, display\n",
"\n",
"\n",
"def format_llm_chip_info(data):\n",
" markdown_text = f\"\"\"\n",
"# Top 5 Chip Providers for LLM Training\n",
" markdown_text = \"# Top 5 Chip Providers for LLM Training\\n\\n\"\n",
" \n",
" for company in data['companies']:\n",
" markdown_text += f\"\"\"\n",
"## {company['name']}\n",
"\n",
"**Key Technologies:** {company['technologies']}\n",
"\n",
"## Companies\n",
"{', '.join(data['companies'].split(', '))}\n",
"**Market Share:** {company['market_share']}\n",
"\n",
"## Key Technologies\n",
"{data['technologies']}\n",
"**Key Powers:** {company.get('key_powers', 'Not specified')}\n",
"\n",
"## Market Share\n",
"{data['market_share']}\n",
"**Future Outlook:** {company['future_outlook']}\n",
"\n",
"## Future Outlook\n",
"{data['future_outlook']}\n",
"---\n",
"\"\"\"\n",
" \n",
" return Markdown(markdown_text)\n",
"\n",
"\n",
"# Display the formatted markdown\n",
"display(format_llm_chip_info(current_state[\"values\"][\"info\"]))"
]
Expand All @@ -198,8 +260,17 @@
]
},
{
"cell_type": "markdown",
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
Expand Down

0 comments on commit 77f2ac8

Please sign in to comment.