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version update post release v1.2.2 (#1005)

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Chi Wang 2023-04-24 21:48:17 -07:00 committed by GitHub
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4
notebook/autogen_chatgpt.ipynb
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@ -23,7 +23,7 @@
"\n", "\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai,blendsearch] option:\n", "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai,blendsearch] option:\n",
"```bash\n", "```bash\n",
"pip install flaml[openai,blendsearch]==1.2.1\n", "pip install flaml[openai,blendsearch]==1.2.2\n",
"```" "```"
] ]
}, },
@ -40,7 +40,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# %pip install flaml[openai,blendsearch]==1.2.1 datasets" "# %pip install flaml[openai,blendsearch]==1.2.2 datasets"
] ]
}, },
{ {

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notebook/autogen_openai.ipynb
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@ -23,7 +23,7 @@
"\n", "\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen,blendsearch] option:\n", "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen,blendsearch] option:\n",
"```bash\n", "```bash\n",
"pip install flaml[autogen,blendsearch]==1.2.1\n", "pip install flaml[autogen,blendsearch]==1.2.2\n",
"```" "```"
] ]
}, },
@ -40,7 +40,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# %pip install flaml[autogen,blendsearch]==1.2.1 datasets" "# %pip install flaml[autogen,blendsearch]==1.2.2 datasets"
] ]
}, },
{ {

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notebook/research/autogen_code.ipynb
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@ -21,7 +21,7 @@
"\n", "\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen] option:\n", "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [autogen] option:\n",
"```bash\n", "```bash\n",
"pip install flaml[autogen]==1.2.1\n", "pip install flaml[autogen]==1.2.2\n",
"```" "```"
] ]
}, },
@ -38,7 +38,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# %pip install flaml[autogen]==1.2.1 datasets" "# %pip install flaml[autogen]==1.2.2 datasets"
] ]
}, },
{ {

4
notebook/research/math_level5counting.ipynb
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@ -21,7 +21,7 @@
"\n", "\n",
"FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n", "FLAML requires `Python>=3.7`. To run this notebook example, please install flaml with the [openai] option:\n",
"```bash\n", "```bash\n",
"pip install flaml[openai]==1.2.1\n", "pip install flaml[openai]==1.2.2\n",
"```" "```"
] ]
}, },
@ -38,7 +38,7 @@
}, },
"outputs": [], "outputs": [],
"source": [ "source": [
"# %pip install flaml[openai]==1.2.1 datasets" "# %pip install flaml[openai]==1.2.2 datasets"
] ]
}, },
{ {

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website/docs/Examples/AutoGen-OpenAI.md
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@ -5,9 +5,9 @@ In this example, we will tune several hyperparameters for the OpenAI's completio
### Prerequisites ### Prerequisites
Install the [autogen,blendsearch] option. The OpenAI integration is in preview. Install the [autogen,blendsearch] option.
```bash ```bash
pip install "flaml[autogen,blendsearch]==1.2.1 datasets" pip install "flaml[autogen,blendsearch]==1.2.2 datasets"
``` ```
Setup your OpenAI key: Setup your OpenAI key:
@ -64,7 +64,9 @@ Before starting tuning, you need to define the metric for the optimization. For
from functools import partial from functools import partial
from flaml.autogen.code_utils import eval_function_completions, generate_assertions from flaml.autogen.code_utils import eval_function_completions, generate_assertions
eval_with_generated_assertions = partial(eval_function_completions, assertions=generate_assertions) eval_with_generated_assertions = partial(
eval_function_completions, assertions=generate_assertions,
)
``` ```
This function will first generate assertion statements for each problem. Then, it uses the assertions to select the generated responses. This function will first generate assertion statements for each problem. Then, it uses the assertions to select the generated responses.

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website/docs/Use-Cases/Auto-Generation.md
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@ -1,6 +1,6 @@
# Auto Generation # Auto Generation
`flaml.autogen` is a subpackage for automating generation tasks. It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints. `flaml.autogen` is a package for automating generation tasks (in preview). It uses [`flaml.tune`](../reference/tune/tune) to find good hyperparameter configurations under budget constraints.
Such optimization has several benefits: Such optimization has several benefits:
* Maximize the utility out of using expensive foundation models. * Maximize the utility out of using expensive foundation models.
* Reduce the inference cost by using cheaper models or configurations which achieve equal or better performance. * Reduce the inference cost by using cheaper models or configurations which achieve equal or better performance.