mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-07-29 15:23:51 +00:00
update to use AsyncLlamaStackClient, /v1/health and drop EventLogger
This commit is contained in:
Matthew Farrellee
parent
a325b38008
commit
417677814c
1 changed files with 236 additions and 259 deletions
|
|
@ -1,262 +1,239 @@
|
||||||
{
|
{
|
||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "markdown",
|
"cell_type": "markdown",
|
||||||
"id": "a0ed972d",
|
"id": "a0ed972d",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"source": [
|
"source": [
|
||||||
"# Switching between Local and Cloud Model with Llama Stack\n",
|
"# Switching between Local and Cloud Model with Llama Stack\n",
|
||||||
"\n",
|
"\n",
|
||||||
"This guide provides a streamlined setup to switch between local and cloud clients for text generation with Llama Stack’s `chat_completion` API. This setup enables automatic fallback to a cloud instance if the local client is unavailable.\n",
|
"This guide provides a streamlined setup to switch between local and cloud clients for text generation with Llama Stack’s `chat_completion` API. This setup enables automatic fallback to a cloud instance if the local client is unavailable.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"### Prerequisites\n",
|
"### Prerequisites\n",
|
||||||
"Before you begin, please ensure Llama Stack is installed and the distribution is set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/). You will need to run two distributions, a local and a cloud distribution, for this demo to work.\n",
|
"Before you begin, please ensure Llama Stack is installed and the distribution is set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/). You will need to run two distributions, a local and a cloud distribution, for this demo to work.\n",
|
||||||
"\n",
|
"\n",
|
||||||
"### Implementation"
|
"### Implementation"
|
||||||
]
|
]
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "bfac8382",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"### 1. Configuration\n",
|
|
||||||
"Set up your connection parameters:"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 1,
|
|
||||||
"id": "d80c0926",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"HOST = \"localhost\" # Replace with your host\n",
|
|
||||||
"LOCAL_PORT = 8321 # Replace with your local distro port\n",
|
|
||||||
"CLOUD_PORT = 8322 # Replace with your cloud distro port"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "df89cff7",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"#### 2. Set Up Local and Cloud Clients\n",
|
|
||||||
"\n",
|
|
||||||
"Initialize both clients, specifying the `base_url` for each instance. In this case, we have the local distribution running on `http://localhost:8321` and the cloud distribution running on `http://localhost:8322`.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 2,
|
|
||||||
"id": "7f868dfe",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from llama_stack_client import LlamaStackClient\n",
|
|
||||||
"\n",
|
|
||||||
"# Configure local and cloud clients\n",
|
|
||||||
"local_client = LlamaStackClient(base_url=f'http://{HOST}:{LOCAL_PORT}')\n",
|
|
||||||
"cloud_client = LlamaStackClient(base_url=f'http://{HOST}:{CLOUD_PORT}')"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "894689c1",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"#### 3. Client Selection with Fallback\n",
|
|
||||||
"\n",
|
|
||||||
"The `select_client` function checks if the local client is available using a lightweight `/health` check. If the local client is unavailable, it automatically switches to the cloud client.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 3,
|
|
||||||
"id": "ff0c8277",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\u001b[33mUsing local client.\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import httpx\n",
|
|
||||||
"from termcolor import cprint\n",
|
|
||||||
"\n",
|
|
||||||
"async def check_client_health(client, client_name: str) -> bool:\n",
|
|
||||||
" try:\n",
|
|
||||||
" async with httpx.AsyncClient() as http_client:\n",
|
|
||||||
" response = await http_client.get(f'{client.base_url}/health')\n",
|
|
||||||
" if response.status_code == 200:\n",
|
|
||||||
" cprint(f'Using {client_name} client.', 'yellow')\n",
|
|
||||||
" return True\n",
|
|
||||||
" else:\n",
|
|
||||||
" cprint(f'{client_name} client health check failed.', 'red')\n",
|
|
||||||
" return False\n",
|
|
||||||
" except httpx.RequestError:\n",
|
|
||||||
" cprint(f'Failed to connect to {client_name} client.', 'red')\n",
|
|
||||||
" return False\n",
|
|
||||||
"\n",
|
|
||||||
"async def select_client(use_local: bool) -> LlamaStackClient:\n",
|
|
||||||
" if use_local and await check_client_health(local_client, 'local'):\n",
|
|
||||||
" return local_client\n",
|
|
||||||
"\n",
|
|
||||||
" if await check_client_health(cloud_client, 'cloud'):\n",
|
|
||||||
" return cloud_client\n",
|
|
||||||
"\n",
|
|
||||||
" raise ConnectionError('Unable to connect to any client.')\n",
|
|
||||||
"\n",
|
|
||||||
"# Example usage: pass True for local, False for cloud\n",
|
|
||||||
"client = await select_client(use_local=True)\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "9ccfe66f",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"#### 4. Generate a Response\n",
|
|
||||||
"\n",
|
|
||||||
"After selecting the client, you can generate text using `chat_completion`. This example sends a sample prompt to the model and prints the response.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 4,
|
|
||||||
"id": "5e19cc20",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [],
|
|
||||||
"source": [
|
|
||||||
"from termcolor import cprint\n",
|
|
||||||
"from llama_stack_client.lib.inference.event_logger import EventLogger\n",
|
|
||||||
"\n",
|
|
||||||
"async def get_llama_response(stream: bool = True, use_local: bool = True):\n",
|
|
||||||
" client = await select_client(use_local) # Selects the available client\n",
|
|
||||||
" message = {\n",
|
|
||||||
" \"role\": \"user\",\n",
|
|
||||||
" \"content\": 'hello world, write me a 2 sentence poem about the moon'\n",
|
|
||||||
" }\n",
|
|
||||||
" cprint(f'User> {message[\"content\"]}', 'green')\n",
|
|
||||||
"\n",
|
|
||||||
" response = client.inference.chat_completion(\n",
|
|
||||||
" messages=[message],\n",
|
|
||||||
" model='Llama3.2-11B-Vision-Instruct',\n",
|
|
||||||
" stream=stream,\n",
|
|
||||||
" )\n",
|
|
||||||
"\n",
|
|
||||||
" if not stream:\n",
|
|
||||||
" cprint(f'> Response: {response.completion_message.content}', 'cyan')\n",
|
|
||||||
" else:\n",
|
|
||||||
" async for log in EventLogger().log(response):\n",
|
|
||||||
" log.print()\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "6edf5e57",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"#### 5. Run with Cloud Model\n",
|
|
||||||
"\n",
|
|
||||||
"Use `asyncio.run()` to execute `get_llama_response` in an asynchronous event loop.\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 7,
|
|
||||||
"id": "c10f487e",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\u001b[33mUsing cloud client.\u001b[0m\n",
|
|
||||||
"\u001b[32mUser> hello world, write me a 2 sentence poem about the moon\u001b[0m\n",
|
|
||||||
"\u001b[36mAssistant> \u001b[0m\u001b[33mSilver\u001b[0m\u001b[33m cres\u001b[0m\u001b[33mcent\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m midnight\u001b[0m\u001b[33m sky\u001b[0m\u001b[33m,\n",
|
|
||||||
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m glow\u001b[0m\u001b[33m that\u001b[0m\u001b[33m whispers\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mI\u001b[0m\u001b[33m'm\u001b[0m\u001b[33m passing\u001b[0m\u001b[33m by\u001b[0m\u001b[33m.\"\u001b[0m\u001b[97m\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import asyncio\n",
|
|
||||||
"\n",
|
|
||||||
"\n",
|
|
||||||
"# Run this function directly in a Jupyter Notebook cell with `await`\n",
|
|
||||||
"await get_llama_response(use_local=False)\n",
|
|
||||||
"# To run it in a python file, use this line instead\n",
|
|
||||||
"# asyncio.run(get_llama_response(use_local=False))"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "5c433511-9321-4718-ab7f-e21cf6b5ca79",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"#### 6. Run with Local Model\n"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "code",
|
|
||||||
"execution_count": 8,
|
|
||||||
"id": "02eacfaf-c7f1-494b-ac28-129d2a0258e3",
|
|
||||||
"metadata": {},
|
|
||||||
"outputs": [
|
|
||||||
{
|
|
||||||
"name": "stdout",
|
|
||||||
"output_type": "stream",
|
|
||||||
"text": [
|
|
||||||
"\u001b[33mUsing local client.\u001b[0m\n",
|
|
||||||
"\u001b[32mUser> hello world, write me a 2 sentence poem about the moon\u001b[0m\n",
|
|
||||||
"\u001b[36mAssistant> \u001b[0m\u001b[33mSilver\u001b[0m\u001b[33m cres\u001b[0m\u001b[33mcent\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m midnight\u001b[0m\u001b[33m sky\u001b[0m\u001b[33m,\n",
|
|
||||||
"\u001b[0m\u001b[33mA\u001b[0m\u001b[33m gentle\u001b[0m\u001b[33m glow\u001b[0m\u001b[33m that\u001b[0m\u001b[33m whispers\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mI\u001b[0m\u001b[33m'm\u001b[0m\u001b[33m passing\u001b[0m\u001b[33m by\u001b[0m\u001b[33m.\"\u001b[0m\u001b[97m\u001b[0m\n"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"source": [
|
|
||||||
"import asyncio\n",
|
|
||||||
"\n",
|
|
||||||
"await get_llama_response(use_local=True)"
|
|
||||||
]
|
|
||||||
},
|
|
||||||
{
|
|
||||||
"cell_type": "markdown",
|
|
||||||
"id": "7e3a3ffa",
|
|
||||||
"metadata": {},
|
|
||||||
"source": [
|
|
||||||
"Thanks for checking out this notebook! \n",
|
|
||||||
"\n",
|
|
||||||
"The next one will be a guide on [Prompt Engineering](./02_Prompt_Engineering101.ipynb), please continue learning!"
|
|
||||||
]
|
|
||||||
}
|
|
||||||
],
|
|
||||||
"metadata": {
|
|
||||||
"fileHeader": "",
|
|
||||||
"fileUid": "e11939ac-dfbc-4a1c-83be-e494c7f803b8",
|
|
||||||
"isAdHoc": false,
|
|
||||||
"kernelspec": {
|
|
||||||
"display_name": "Python 3 (ipykernel)",
|
|
||||||
"language": "python",
|
|
||||||
"name": "python3"
|
|
||||||
},
|
|
||||||
"language_info": {
|
|
||||||
"codemirror_mode": {
|
|
||||||
"name": "ipython",
|
|
||||||
"version": 3
|
|
||||||
},
|
|
||||||
"file_extension": ".py",
|
|
||||||
"mimetype": "text/x-python",
|
|
||||||
"name": "python",
|
|
||||||
"nbconvert_exporter": "python",
|
|
||||||
"pygments_lexer": "ipython3",
|
|
||||||
"version": "3.10.15"
|
|
||||||
}
|
|
||||||
},
|
},
|
||||||
"nbformat": 4,
|
{
|
||||||
"nbformat_minor": 5
|
"cell_type": "markdown",
|
||||||
|
"id": "bfac8382",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### 1. Configuration\n",
|
||||||
|
"Set up your connection parameters:"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "d80c0926",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"HOST = \"localhost\" # Replace with your host\n",
|
||||||
|
"LOCAL_PORT = 8321 # Replace with your local distro port\n",
|
||||||
|
"CLOUD_PORT = 8322 # Replace with your cloud distro port"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "df89cff7",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"#### 2. Set Up Local and Cloud Clients\n",
|
||||||
|
"\n",
|
||||||
|
"Initialize both clients, specifying the `base_url` for each instance. In this case, we have the local distribution running on `http://localhost:8321` and the cloud distribution running on `http://localhost:8322`.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "7f868dfe",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from llama_stack_client import AsyncLlamaStackClient\n",
|
||||||
|
"\n",
|
||||||
|
"# Configure local and cloud clients\n",
|
||||||
|
"local_client = AsyncLlamaStackClient(base_url=f'http://{HOST}:{LOCAL_PORT}')\n",
|
||||||
|
"cloud_client = AsyncLlamaStackClient(base_url=f'http://{HOST}:{CLOUD_PORT}')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "894689c1",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"#### 3. Client Selection with Fallback\n",
|
||||||
|
"\n",
|
||||||
|
"The `select_client` function checks if the local client is available using a lightweight `/v1/health` check. If the local client is unavailable, it automatically switches to the cloud client.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "ff0c8277",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import httpx\n",
|
||||||
|
"from termcolor import cprint\n",
|
||||||
|
"\n",
|
||||||
|
"async def check_client_health(client, client_name: str) -> bool:\n",
|
||||||
|
" try:\n",
|
||||||
|
" async with httpx.AsyncClient() as http_client:\n",
|
||||||
|
" response = await http_client.get(f'{client.base_url}/v1/health')\n",
|
||||||
|
" if response.status_code == 200:\n",
|
||||||
|
" cprint(f'Using {client_name} client.', 'yellow')\n",
|
||||||
|
" return True\n",
|
||||||
|
" else:\n",
|
||||||
|
" cprint(f'{client_name} client health check failed.', 'red')\n",
|
||||||
|
" return False\n",
|
||||||
|
" except httpx.RequestError:\n",
|
||||||
|
" cprint(f'Failed to connect to {client_name} client.', 'red')\n",
|
||||||
|
" return False\n",
|
||||||
|
"\n",
|
||||||
|
"async def select_client(use_local: bool) -> AsyncLlamaStackClient:\n",
|
||||||
|
" if use_local and await check_client_health(local_client, 'local'):\n",
|
||||||
|
" return local_client\n",
|
||||||
|
"\n",
|
||||||
|
" if await check_client_health(cloud_client, 'cloud'):\n",
|
||||||
|
" return cloud_client\n",
|
||||||
|
"\n",
|
||||||
|
" raise ConnectionError('Unable to connect to any client.')\n",
|
||||||
|
"\n",
|
||||||
|
"# Example usage: pass True for local, False for cloud\n",
|
||||||
|
"client = await select_client(use_local=True)\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "9ccfe66f",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"#### 4. Generate a Response\n",
|
||||||
|
"\n",
|
||||||
|
"After selecting the client, you can generate text using `chat_completion`. This example sends a sample prompt to the model and prints the response.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "5e19cc20",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"from termcolor import cprint\n",
|
||||||
|
"\n",
|
||||||
|
"async def get_llama_response(stream: bool = True, use_local: bool = True):\n",
|
||||||
|
" client = await select_client(use_local) # Selects the available client\n",
|
||||||
|
" message = {\n",
|
||||||
|
" \"role\": \"user\",\n",
|
||||||
|
" \"content\": 'hello world, write me a 2 sentence poem about the moon'\n",
|
||||||
|
" }\n",
|
||||||
|
" cprint(f'User> {message[\"content\"]}', 'green')\n",
|
||||||
|
"\n",
|
||||||
|
" response = await client.inference.chat_completion(\n",
|
||||||
|
" messages=[message],\n",
|
||||||
|
" model_id='meta-llama/Llama3.2-11B-Vision-Instruct',\n",
|
||||||
|
" stream=stream,\n",
|
||||||
|
" )\n",
|
||||||
|
"\n",
|
||||||
|
" cprint(f'Assistant> ', color='cyan', end='')\n",
|
||||||
|
" if not stream:\n",
|
||||||
|
" cprint(response.completion_message.content, color='yellow')\n",
|
||||||
|
" else:\n",
|
||||||
|
" async for chunk in response:\n",
|
||||||
|
" cprint(chunk.event.delta.text, color='yellow', end='')\n",
|
||||||
|
" cprint('')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "6edf5e57",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"#### 5. Run with Cloud Model\n",
|
||||||
|
"\n",
|
||||||
|
"Use `asyncio.run()` to execute `get_llama_response` in an asynchronous event loop.\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "c10f487e",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import asyncio\n",
|
||||||
|
"\n",
|
||||||
|
"\n",
|
||||||
|
"# Run this function directly in a Jupyter Notebook cell with `await`\n",
|
||||||
|
"await get_llama_response(use_local=False)\n",
|
||||||
|
"# To run it in a python file, use this line instead\n",
|
||||||
|
"# asyncio.run(get_llama_response(use_local=False))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "5c433511-9321-4718-ab7f-e21cf6b5ca79",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"#### 6. Run with Local Model\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"id": "02eacfaf-c7f1-494b-ac28-129d2a0258e3",
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import asyncio\n",
|
||||||
|
"\n",
|
||||||
|
"await get_llama_response(use_local=True)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "7e3a3ffa",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"Thanks for checking out this notebook! \n",
|
||||||
|
"\n",
|
||||||
|
"The next one will be a guide on [Prompt Engineering](./02_Prompt_Engineering101.ipynb), please continue learning!"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"id": "3ad6db48",
|
||||||
|
"metadata": {},
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"metadata": {
|
||||||
|
"fileHeader": "",
|
||||||
|
"fileUid": "e11939ac-dfbc-4a1c-83be-e494c7f803b8",
|
||||||
|
"isAdHoc": false,
|
||||||
|
"kernelspec": {
|
||||||
|
"display_name": "Python 3 (ipykernel)",
|
||||||
|
"language": "python",
|
||||||
|
"name": "python3"
|
||||||
|
},
|
||||||
|
"language_info": {
|
||||||
|
"codemirror_mode": {
|
||||||
|
"name": "ipython",
|
||||||
|
"version": 3
|
||||||
|
},
|
||||||
|
"file_extension": ".py",
|
||||||
|
"mimetype": "text/x-python",
|
||||||
|
"name": "python",
|
||||||
|
"nbconvert_exporter": "python",
|
||||||
|
"pygments_lexer": "ipython3",
|
||||||
|
"version": "3.10.15"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 5
|
||||||
}
|
}
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue