magics-python 0.0.2

Python client for Magics's Cloud Platform!

Magics Python API library

The Magics Python API Library is the official Python client for Magics's API platform, providing a convenient way for interacting with the REST APIs and enables easy integrations with Python 3.8+ applications with easy to use synchronous and asynchronous clients.

Installation

To install Magics Python Library from PyPI, simply run:

 pip install -e . 

Setting up API Key

Setting environment variable

export MAGICS_API_KEY=xxxxx

Using the client

from magics import Magics

client = Magics(api_key="xxxxx")

This repo contains both a Python Library and a CLI. We'll demonstrate how to use both below.

Usage – Python Client

Chat Completions

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

response = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[{"role": "user", "content": "tell me about new york"}],
)
print(response.choices[0].message.content)

Streaming

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))
stream = client.chat.completions.create(
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",
    messages=[{"role": "user", "content": "tell me about new york"}],
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Async usage

import os, asyncio
from magics import AsyncMagics

async_client = AsyncMagics(api_key=os.environ.get("MAGICS_API_KEY"))
messages = [
    "What are the top things to do in San Francisco?",
    "What country is Paris in?",
]

async def async_chat_completion(messages):
    async_client = AsyncMagics(api_key=os.environ.get("MAGICS_API_KEY"))
    tasks = [
        async_client.chat.completions.create(
            model="mistralai/Mixtral-8x7B-Instruct-v0.1",
            messages=[{"role": "user", "content": message}],
        )
        for message in messages
    ]
    responses = await asyncio.gather(*tasks)

    for response in responses:
        print(response.choices[0].message.content)

asyncio.run(async_chat_completion(messages))

Completions

Completions are for code and language models shown here. Below, a code model example is shown.

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

response = client.completions.create(
    model="codellama/CodeLlama-34b-Python-hf",
    prompt="Write a Next.js component with TailwindCSS for a header component.",
    max_tokens=200,
)
print(response.choices[0].text)

Streaming

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))
stream = client.completions.create(
    model="codellama/CodeLlama-34b-Python-hf",
    prompt="Write a Next.js component with TailwindCSS for a header component.",
    stream=True,
)

for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="", flush=True)

Async usage

import os, asyncio
from magics import AsyncMagics

async_client = AsyncMagics(api_key=os.environ.get("MAGICS_API_KEY"))
prompts = [
    "Write a Next.js component with TailwindCSS for a header component.",
    "Write a python function for the fibonacci sequence",
]

async def async_chat_completion(prompts):
    async_client = AsyncMagics(api_key=os.environ.get("MAGICS_API_KEY"))
    tasks = [
        async_client.completions.create(
            model="codellama/CodeLlama-34b-Python-hf",
            prompt=prompt,
        )
        for prompt in prompts
    ]
    responses = await asyncio.gather(*tasks)

    for response in responses:
        print(response.choices[0].text)

asyncio.run(async_chat_completion(prompts))

Image generation

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

response = client.images.generate(
    prompt="space robots",
    model="stabilityai/stable-diffusion-xl-base-1.0",
    steps=10,
    n=4,
)
print(response.data[0].b64_json)

Embeddings

from typing import List
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

def get_embeddings(texts: List[str], model: str) -> List[List[float]]:
    texts = [text.replace("\n", " ") for text in texts]
    outputs = client.embeddings.create(model=model, input = texts)
    return [outputs.data[i].embedding for i in range(len(texts))]

input_texts = ['Our solar system orbits the Milky Way galaxy at about 515,000 mph']
embeddings = get_embeddings(input_texts, model='magicscomputer/m2-bert-80M-8k-retrieval')

print(embeddings)

Files

The files API is used for fine-tuning and allows developers to upload data to fine-tune on. It also has several methods to list all files, retrive files, and delete files. Please refer to our fine-tuning docs here.

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

client.files.upload(file="somedata.jsonl") # uploads a file
client.files.list() # lists all uploaded files
client.files.retrieve(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # retrieves a specific file
client.files.retrieve_content(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # retrieves content of a specific file
client.files.delete(id="file-d0d318cb-b7d9-493a-bd70-1cfe089d3815") # deletes a file

Fine-tunes

The finetune API is used for fine-tuning and allows developers to create finetuning jobs. It also has several methods to list all jobs, retrive statuses and get checkpoints. Please refer to our fine-tuning docs here.

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

client.fine_tuning.create(
  training_file = 'file-d0d318cb-b7d9-493a-bd70-1cfe089d3815',
  model = 'mistralai/Mixtral-8x7B-Instruct-v0.1',
  n_epochs = 3,
  n_checkpoints = 1,
  batch_size = "max",
  learning_rate = 1e-5,
  suffix = 'my-demo-finetune',
  wandb_api_key = '1a2b3c4d5e.......',
)
client.fine_tuning.list() # lists all fine-tuned jobs
client.fine_tuning.retrieve(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # retrieves information on finetune event
client.fine_tuning.cancel(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # Cancels a fine-tuning job
client.fine_tuning.list_events(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") #  Lists events of a fine-tune job
client.fine_tuning.download(id="ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b") # downloads compressed fine-tuned model or checkpoint to local disk

Models

This lists all the models that Magics supports.

import os
from magics import Magics

client = Magics(api_key=os.environ.get("MAGICS_API_KEY"))

models = client.models.list()

for model in models:
    print(model)

Usage – CLI

Chat Completions

magics chat.completions \
  --message "system" "You are a helpful assistant named Magics" \
  --message "user" "What is your name?" \
  --model mistralai/Mixtral-8x7B-Instruct-v0.1

The Chat Completions CLI enables streaming tokens to stdout by default. To disable streaming, use --no-stream.

Completions

magics completions \
  "Large language models are " \
  --model mistralai/Mixtral-8x7B-v0.1 \
  --max-tokens 512 \
  --stop "."

The Completions CLI enables streaming tokens to stdout by default. To disable streaming, use --no-stream.

Image Generations

magics images generate \
  "space robots" \
  --model stabilityai/stable-diffusion-xl-base-1.0 \
  --n 4

The image is opened in the default image viewer by default. To disable this, use --no-show.

Files

# Help
magics files --help

# Check file
magics files check example.jsonl

# Upload file
magics files upload example.jsonl

# List files
magics files list

# Retrieve file metadata
magics files retrieve file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Retrieve file content
magics files retrieve-content file-6f50f9d1-5b95-416c-9040-0799b2b4b894

# Delete remote file
magics files delete file-6f50f9d1-5b95-416c-9040-0799b2b4b894

Fine-tuning

# Help
magics fine-tuning --help

# Create fine-tune job
magics fine-tuning create \
  --model magicscomputer/llama-2-7b-chat \
  --training-file file-711d8724-b3e3-4ae2-b516-94841958117d

# List fine-tune jobs
magics fine-tuning list

# Retrieve fine-tune job details
magics fine-tuning retrieve ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# List fine-tune job events
magics fine-tuning list-events ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Cancel running job
magics fine-tuning cancel ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

# Download fine-tuned model weights
magics fine-tuning download ft-c66a5c18-1d6d-43c9-94bd-32d756425b4b

Models

# Help
magics models --help

# List models
magics models list