ClearML
ClearML is a ML/DL development and production suite, it contains 5 main modules:
Experiment Manager- Automagical experiment tracking, environments and resultsMLOps- Orchestration, Automation & Pipelines solution for ML/DL jobs (K8s / Cloud / bare-metal)Data-Management- Fully differentiable data management & version control solution on top of object-storage (S3 / GS / Azure / NAS)Model-Serving- cloud-ready Scalable model serving solution! Deploy new model endpoints in under 5 minutes Includes optimized GPU serving support backed by Nvidia-Triton with out-of-the-box Model MonitoringFire Reports- Create and share rich MarkDown documents supporting embeddable online content
In order to properly keep track of your langchain experiments and their results, you can enable the ClearML integration. We use the ClearML Experiment Manager that neatly tracks and organizes all your experiment runs.
Installation and Setup
%pip install --upgrade --quiet clearml
%pip install --upgrade --quiet pandas
%pip install --upgrade --quiet textstat
%pip install --upgrade --quiet spacy
!python -m spacy download en_core_web_sm
Getting API Credentials
We'll be using quite some APIs in this notebook, here is a list and where to get them:
- ClearML: https://app.clear.ml/settings/workspace-configuration
- OpenAI: https://platform.openai.com/account/api-keys
- SerpAPI (google search): https://serpapi.com/dashboard
import os
os.environ["CLEARML_API_ACCESS_KEY"] = ""
os.environ["CLEARML_API_SECRET_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
Callbacks
from langchain_community.callbacks import ClearMLCallbackHandler
API Reference:ClearMLCallbackHandler
from langchain_core.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI
# Setup and use the ClearML Callback
clearml_callback = ClearMLCallbackHandler(
task_type="inference",
project_name="langchain_callback_demo",
task_name="llm",
tags=["test"],
# Change the following parameters based on the amount of detail you want tracked
visualize=True,
complexity_metrics=True,
stream_logs=True,
)
callbacks = [StdOutCallbackHandler(), clearml_callback]
# Get the OpenAI model ready to go
llm = OpenAI(temperature=0, callbacks=callbacks)
API Reference:StdOutCallbackHandler | OpenAI
The clearml callback is currently in beta and is subject to change based on updates to `langchain`. Please report any issues to https://github.com/allegroai/clearml/issues with the tag `langchain`.
Scenario 1: Just an LLM
First, let's just run a single LLM a few times and capture the resulting prompt-answer conversation in ClearML
# SCENARIO 1 - LLM
llm_result = llm.generate(["Tell me a joke", "Tell me a poem"] * 3)
# After every generation run, use flush to make sure all the metrics
# prompts and other output are properly saved separately
clearml_callback.flush_tracker(langchain_asset=llm, name="simple_sequential")
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a joke'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Tell me a poem'}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nQ: What did the fish say when it hit the wall?\nA: Dam!', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 109.04, 'flesch_kincaid_grade': 1.3, 'smog_index': 0.0, 'coleman_liau_index': -1.24, 'automated_readability_index': 0.3, 'dale_chall_readability_score': 5.5, 'difficult_words': 0, 'linsear_write_formula': 5.5, 'gunning_fog': 5.2, 'text_standard': '5th and 6th grade', 'fernandez_huerta': 133.58, 'szigriszt_pazos': 131.54, 'gutierrez_polini': 62.3, 'crawford': -0.2, 'gulpease_index': 79.8, 'osman': 116.91}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 24, 'token_usage_completion_tokens': 138, 'token_usage_total_tokens': 162, 'model_name': 'text-davinci-003', 'step': 4, 'starts': 2, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 0, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': '\n\nRoses are red,\nViolets are blue,\nSugar is sweet,\nAnd so are you.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 83.66, 'flesch_kincaid_grade': 4.8, 'smog_index': 0.0, 'coleman_liau_index': 3.23, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 6.71, 'difficult_words': 2, 'linsear_write_formula': 6.5, 'gunning_fog': 8.28, 'text_standard': '6th and 7th grade', 'fernandez_huerta': 115.58, 'szigriszt_pazos': 112.37, 'gutierrez_polini': 54.83, 'crawford': 1.4, 'gulpease_index': 72.1, 'osman': 100.17}
{'action_records': action name step starts ends errors text_ctr chain_starts \
0 on_llm_start OpenAI 1 1 0 0 0 0
1 on_llm_start OpenAI 1 1 0 0 0 0
2 on_llm_start OpenAI 1 1 0 0 0 0
3 on_llm_start OpenAI 1 1 0 0 0 0
4 on_llm_start OpenAI 1 1 0 0 0 0
5 on_llm_start OpenAI 1 1 0 0 0 0
6 on_llm_end NaN 2 1 1 0 0 0
7 on_llm_end NaN 2 1 1 0 0 0
8 on_llm_end NaN 2 1 1 0 0 0
9 on_llm_end NaN 2 1 1 0 0 0
10 on_llm_end NaN 2 1 1 0 0 0
11 on_llm_end NaN 2 1 1 0 0 0
12 on_llm_start OpenAI 3 2 1 0 0 0
13 on_llm_start OpenAI 3 2 1 0 0 0
14 on_llm_start OpenAI 3 2 1 0 0 0
15 on_llm_start OpenAI 3 2 1 0 0 0
16 on_llm_start OpenAI 3 2 1 0 0 0
17 on_llm_start OpenAI 3 2 1 0 0 0
18 on_llm_end NaN 4 2 2 0 0 0
19 on_llm_end NaN 4 2 2 0 0 0
20 on_llm_end NaN 4 2 2 0 0 0
21 on_llm_end NaN 4 2 2 0 0 0
22 on_llm_end NaN 4 2 2 0 0 0
23 on_llm_end NaN 4 2 2 0 0 0
chain_ends llm_starts ... difficult_words linsear_write_formula \
0 0 1 ... NaN NaN
1 0 1 ... NaN NaN
2 0 1 ... NaN NaN
3 0 1 ... NaN NaN
4 0 1 ... NaN NaN
5 0 1 ... NaN NaN
6 0 1 ... 0.0 5.5
7 0 1 ... 2.0 6.5
8 0 1 ... 0.0 5.5
9 0 1 ... 2.0 6.5
10 0 1 ... 0.0 5.5
11 0 1 ... 2.0 6.5
12 0 2 ... NaN NaN
13 0 2 ... NaN NaN
14 0 2 ... NaN NaN
15 0 2 ... NaN NaN
16 0 2 ... NaN NaN
17 0 2 ... NaN NaN
18 0 2 ... 0.0 5.5
19 0 2 ... 2.0 6.5
20 0 2 ... 0.0 5.5
21 0 2 ... 2.0 6.5
22 0 2 ... 0.0 5.5
23 0 2 ... 2.0 6.5
gunning_fog text_standard fernandez_huerta szigriszt_pazos \
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 5.20 5th and 6th grade 133.58 131.54
7 8.28 6th and 7th grade 115.58 112.37
8 5.20 5th and 6th grade 133.58 131.54
9 8.28 6th and 7th grade 115.58 112.37
10 5.20 5th and 6th grade 133.58 131.54
11 8.28 6th and 7th grade 115.58 112.37
12 NaN NaN NaN NaN
13 NaN NaN NaN NaN
14 NaN NaN NaN NaN
15 NaN NaN NaN NaN
16 NaN NaN NaN NaN
17 NaN NaN NaN NaN
18 5.20 5th and 6th grade 133.58 131.54
19 8.28 6th and 7th grade 115.58 112.37
20 5.20 5th and 6th grade 133.58 131.54
21 8.28 6th and 7th grade 115.58 112.37
22 5.20 5th and 6th grade 133.58 131.54
23 8.28 6th and 7th grade 115.58 112.37
gutierrez_polini crawford gulpease_index osman
0 NaN NaN NaN NaN
1 NaN NaN NaN NaN
2 NaN NaN NaN NaN
3 NaN NaN NaN NaN
4 NaN NaN NaN NaN
5 NaN NaN NaN NaN
6 62.30 -0.2 79.8 116.91
7 54.83 1.4 72.1 100.17
8 62.30 -0.2 79.8 116.91
9 54.83 1.4 72.1 100.17
10 62.30 -0.2 79.8 116.91
11 54.83 1.4 72.1 100.17
12 NaN NaN NaN NaN
13 NaN NaN NaN NaN
14 NaN NaN NaN NaN
15 NaN NaN NaN NaN
16 NaN NaN NaN NaN
17 NaN NaN NaN NaN
18 62.30 -0.2 79.8 116.91
19 54.83 1.4 72.1 100.17
20 62.30 -0.2 79.8 116.91
21 54.83 1.4 72.1 100.17
22 62.30 -0.2 79.8 116.91
23 54.83 1.4 72.1 100.17
[24 rows x 39 columns], 'session_analysis': prompt_step prompts name output_step \
0 1 Tell me a joke OpenAI 2
1 1 Tell me a poem OpenAI 2
2 1 Tell me a joke OpenAI 2
3 1 Tell me a poem OpenAI 2
4 1 Tell me a joke OpenAI 2
5 1 Tell me a poem OpenAI 2
6 3 Tell me a joke OpenAI 4
7 3 Tell me a poem OpenAI 4
8 3 Tell me a joke OpenAI 4
9 3 Tell me a poem OpenAI 4
10 3 Tell me a joke OpenAI 4
11 3 Tell me a poem OpenAI 4
output \
0 \n\nQ: What did the fish say when it hit the w...
1 \n\nRoses are red,\nViolets are blue,\nSugar i...
2 \n\nQ: What did the fish say when it hit the w...
3 \n\nRoses are red,\nViolets are blue,\nSugar i...
4 \n\nQ: What did the fish say when it hit the w...
5 \n\nRoses are red,\nViolets are blue,\nSugar i...
6 \n\nQ: What did the fish say when it hit the w...
7 \n\nRoses are red,\nViolets are blue,\nSugar i...
8 \n\nQ: What did the fish say when it hit the w...
9 \n\nRoses are red,\nViolets are blue,\nSugar i...
10 \n\nQ: What did the fish say when it hit the w...
11 \n\nRoses are red,\nViolets are blue,\nSugar i...
token_usage_total_tokens token_usage_prompt_tokens \
0 162 24
1 162 24
2 162 24
3 162 24
4 162 24
5 162 24
6 162 24
7 162 24
8 162 24
9 162 24
10 162 24
11 162 24
token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \
0 138 109.04 1.3
1 138 83.66 4.8
2 138 109.04 1.3
3 138 83.66 4.8
4 138 109.04 1.3
5 138 83.66 4.8
6 138 109.04 1.3
7 138 83.66 4.8
8 138 109.04 1.3
9 138 83.66 4.8
10 138 109.04 1.3
11 138 83.66 4.8
... difficult_words linsear_write_formula gunning_fog \
0 ... 0 5.5 5.20
1 ... 2 6.5 8.28
2 ... 0 5.5 5.20
3 ... 2 6.5 8.28
4 ... 0 5.5 5.20
5 ... 2 6.5 8.28
6 ... 0 5.5 5.20
7 ... 2 6.5 8.28
8 ... 0 5.5 5.20
9 ... 2 6.5 8.28
10 ... 0 5.5 5.20
11 ... 2 6.5 8.28
text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \
0 5th and 6th grade 133.58 131.54 62.30
1 6th and 7th grade 115.58 112.37 54.83
2 5th and 6th grade 133.58 131.54 62.30
3 6th and 7th grade 115.58 112.37 54.83
4 5th and 6th grade 133.58 131.54 62.30
5 6th and 7th grade 115.58 112.37 54.83
6 5th and 6th grade 133.58 131.54 62.30
7 6th and 7th grade 115.58 112.37 54.83
8 5th and 6th grade 133.58 131.54 62.30
9 6th and 7th grade 115.58 112.37 54.83
10 5th and 6th grade 133.58 131.54 62.30
11 6th and 7th grade 115.58 112.37 54.83
crawford gulpease_index osman
0 -0.2 79.8 116.91
1 1.4 72.1 100.17
2 -0.2 79.8 116.91
3 1.4 72.1 100.17
4 -0.2 79.8 116.91
5 1.4 72.1 100.17
6 -0.2 79.8 116.91
7 1.4 72.1 100.17
8 -0.2 79.8 116.91
9 1.4 72.1 100.17
10 -0.2 79.8 116.91
11 1.4 72.1 100.17
[12 rows x 24 columns]}
2023-03-29 14:00:25,948 - clearml.Task - INFO - Completed model upload to https://files.clear.ml/langchain_callback_demo/llm.988bd727b0e94a29a3ac0ee526813545/models/simple_sequential
At this point you can already go to https://app.clear.ml and take a look at the resulting ClearML Task that was created.
Among others, you should see that this notebook is saved along with any git information. The model JSON that contains the used parameters is saved as an artifact, there are also console logs and under the plots section, you'll find tables that represent the flow of the chain.
Finally, if you enabled visualizations, these are stored as HTML files under debug samples.
Scenario 2: Creating an agent with tools
To show a more advanced workflow, let's create an agent with access to tools. The way ClearML tracks the results is not different though, only the table will look slightly different as there are other types of actions taken when compared to the earlier, simpler example.
You can now also see the use of the finish=True keyword, which will fully close the ClearML Task, instead of just resetting the parameters and prompts for a new conversation.
from langchain.agents import AgentType, initialize_agent, load_tools
# SCENARIO 2 - Agent with Tools
tools = load_tools(["serpapi", "llm-math"], llm=llm, callbacks=callbacks)
agent = initialize_agent(
tools,
llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
callbacks=callbacks,
)
agent.run("Who is the wife of the person who sang summer of 69?")
clearml_callback.flush_tracker(
langchain_asset=agent, name="Agent with Tools", finish=True
)
[1m> Entering new AgentExecutor chain...[0m
{'action': 'on_chain_start', 'name': 'AgentExecutor', 'step': 1, 'starts': 1, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 0, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'input': 'Who is the wife of the person who sang summer of 69?'}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 2, 'starts': 2, 'ends': 0, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 0, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought:'}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 189, 'token_usage_completion_tokens': 34, 'token_usage_total_tokens': 223, 'model_name': 'text-davinci-003', 'step': 3, 'starts': 2, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 0, 'tool_ends': 0, 'agent_ends': 0, 'text': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 91.61, 'flesch_kincaid_grade': 3.8, 'smog_index': 0.0, 'coleman_liau_index': 3.41, 'automated_readability_index': 3.5, 'dale_chall_readability_score': 6.06, 'difficult_words': 2, 'linsear_write_formula': 5.75, 'gunning_fog': 5.4, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 121.07, 'szigriszt_pazos': 119.5, 'gutierrez_polini': 54.91, 'crawford': 0.9, 'gulpease_index': 72.7, 'osman': 92.16}
[32;1m[1;3m I need to find out who sang summer of 69 and then find out who their wife is.
Action: Search
Action Input: "Who sang summer of 69"[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who sang summer of 69', 'log': ' I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"', 'step': 4, 'starts': 3, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 1, 'tool_ends': 0, 'agent_ends': 0}
{'action': 'on_tool_start', 'input_str': 'Who sang summer of 69', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 5, 'starts': 4, 'ends': 1, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 0, 'agent_ends': 0}
Observation: [36;1m[1;3mBryan Adams - Summer Of 69 (Official Music Video).[0m
Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams - Summer Of 69 (Official Music Video).', 'step': 6, 'starts': 4, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 1, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 7, 'starts': 5, 'ends': 2, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 1, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought:'}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 242, 'token_usage_completion_tokens': 28, 'token_usage_total_tokens': 270, 'model_name': 'text-davinci-003', 'step': 8, 'starts': 5, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 2, 'tool_ends': 1, 'agent_ends': 0, 'text': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 94.66, 'flesch_kincaid_grade': 2.7, 'smog_index': 0.0, 'coleman_liau_index': 4.73, 'automated_readability_index': 4.0, 'dale_chall_readability_score': 7.16, 'difficult_words': 2, 'linsear_write_formula': 4.25, 'gunning_fog': 4.2, 'text_standard': '4th and 5th grade', 'fernandez_huerta': 124.13, 'szigriszt_pazos': 119.2, 'gutierrez_polini': 52.26, 'crawford': 0.7, 'gulpease_index': 74.7, 'osman': 84.2}
[32;1m[1;3m I need to find out who Bryan Adams is married to.
Action: Search
Action Input: "Who is Bryan Adams married to"[0m{'action': 'on_agent_action', 'tool': 'Search', 'tool_input': 'Who is Bryan Adams married to', 'log': ' I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"', 'step': 9, 'starts': 6, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 3, 'tool_ends': 1, 'agent_ends': 0}
{'action': 'on_tool_start', 'input_str': 'Who is Bryan Adams married to', 'name': 'Search', 'description': 'A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', 'step': 10, 'starts': 7, 'ends': 3, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 1, 'agent_ends': 0}
Observation: [36;1m[1;3mBryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...[0m
Thought:{'action': 'on_tool_end', 'output': 'Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...', 'step': 11, 'starts': 7, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 2, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0}
{'action': 'on_llm_start', 'name': 'OpenAI', 'step': 12, 'starts': 8, 'ends': 4, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 2, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'prompts': 'Answer the following questions as best you can. You have access to the following tools:\n\nSearch: A search engine. Useful for when you need to answer questions about current events. Input should be a search query.\nCalculator: Useful for when you need to answer questions about math.\n\nUse the following format:\n\nQuestion: the input question you must answer\nThought: you should always think about what to do\nAction: the action to take, should be one of [Search, Calculator]\nAction Input: the input to the action\nObservation: the result of the action\n... (this Thought/Action/Action Input/Observation can repeat N times)\nThought: I now know the final answer\nFinal Answer: the final answer to the original input question\n\nBegin!\n\nQuestion: Who is the wife of the person who sang summer of 69?\nThought: I need to find out who sang summer of 69 and then find out who their wife is.\nAction: Search\nAction Input: "Who sang summer of 69"\nObservation: Bryan Adams - Summer Of 69 (Official Music Video).\nThought: I need to find out who Bryan Adams is married to.\nAction: Search\nAction Input: "Who is Bryan Adams married to"\nObservation: Bryan Adams has never married. In the 1990s, he was in a relationship with Danish model Cecilie Thomsen. In 2011, Bryan and Alicia Grimaldi, his ...\nThought:'}
{'action': 'on_llm_end', 'token_usage_prompt_tokens': 314, 'token_usage_completion_tokens': 18, 'token_usage_total_tokens': 332, 'model_name': 'text-davinci-003', 'step': 13, 'starts': 8, 'ends': 5, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 0, 'text': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'generation_info_finish_reason': 'stop', 'generation_info_logprobs': None, 'flesch_reading_ease': 81.29, 'flesch_kincaid_grade': 3.7, 'smog_index': 0.0, 'coleman_liau_index': 5.75, 'automated_readability_index': 3.9, 'dale_chall_readability_score': 7.37, 'difficult_words': 1, 'linsear_write_formula': 2.5, 'gunning_fog': 2.8, 'text_standard': '3rd and 4th grade', 'fernandez_huerta': 115.7, 'szigriszt_pazos': 110.84, 'gutierrez_polini': 49.79, 'crawford': 0.7, 'gulpease_index': 85.4, 'osman': 83.14}
[32;1m[1;3m I now know the final answer.
Final Answer: Bryan Adams has never been married.[0m
{'action': 'on_agent_finish', 'output': 'Bryan Adams has never been married.', 'log': ' I now know the final answer.\nFinal Answer: Bryan Adams has never been married.', 'step': 14, 'starts': 8, 'ends': 6, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 0, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}
[1m> Finished chain.[0m
{'action': 'on_chain_end', 'outputs': 'Bryan Adams has never been married.', 'step': 15, 'starts': 8, 'ends': 7, 'errors': 0, 'text_ctr': 0, 'chain_starts': 1, 'chain_ends': 1, 'llm_starts': 3, 'llm_ends': 3, 'llm_streams': 0, 'tool_starts': 4, 'tool_ends': 2, 'agent_ends': 1}
{'action_records': action name step starts ends errors text_ctr \
0 on_llm_start OpenAI 1 1 0 0 0
1 on_llm_start OpenAI 1 1 0 0 0
2 on_llm_start OpenAI 1 1 0 0 0
3 on_llm_start OpenAI 1 1 0 0 0
4 on_llm_start OpenAI 1 1 0 0 0
.. ... ... ... ... ... ... ...
66 on_tool_end NaN 11 7 4 0 0
67 on_llm_start OpenAI 12 8 4 0 0
68 on_llm_end NaN 13 8 5 0 0
69 on_agent_finish NaN 14 8 6 0 0
70 on_chain_end NaN 15 8 7 0 0
chain_starts chain_ends llm_starts ... gulpease_index osman input \
0 0 0 1 ... NaN NaN NaN
1 0 0 1 ... NaN NaN NaN
2 0 0 1 ... NaN NaN NaN
3 0 0 1 ... NaN NaN NaN
4 0 0 1 ... NaN NaN NaN
.. ... ... ... ... ... ... ...
66 1 0 2 ... NaN NaN NaN
67 1 0 3 ... NaN NaN NaN
68 1 0 3 ... 85.4 83.14 NaN
69 1 0 3 ... NaN NaN NaN
70 1 1 3 ... NaN NaN NaN
tool tool_input log \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
.. ... ... ...
66 NaN NaN NaN
67 NaN NaN NaN
68 NaN NaN NaN
69 NaN NaN I now know the final answer.\nFinal Answer: B...
70 NaN NaN NaN
input_str description output \
0 NaN NaN NaN
1 NaN NaN NaN
2 NaN NaN NaN
3 NaN NaN NaN
4 NaN NaN NaN
.. ... ... ...
66 NaN NaN Bryan Adams has never married. In the 1990s, h...
67 NaN NaN NaN
68 NaN NaN NaN
69 NaN NaN Bryan Adams has never been married.
70 NaN NaN NaN
outputs
0 NaN
1 NaN
2 NaN
3 NaN
4 NaN
.. ...
66 NaN
67 NaN
68 NaN
69 NaN
70 Bryan Adams has never been married.
[71 rows x 47 columns], 'session_analysis': prompt_step prompts name \
0 2 Answer the following questions as best you can... OpenAI
1 7 Answer the following questions as best you can... OpenAI
2 12 Answer the following questions as best you can... OpenAI
output_step output \
0 3 I need to find out who sang summer of 69 and ...
1 8 I need to find out who Bryan Adams is married...
2 13 I now know the final answer.\nFinal Answer: B...
token_usage_total_tokens token_usage_prompt_tokens \
0 223 189
1 270 242
2 332 314
token_usage_completion_tokens flesch_reading_ease flesch_kincaid_grade \
0 34 91.61 3.8
1 28 94.66 2.7
2 18 81.29 3.7
... difficult_words linsear_write_formula gunning_fog \
0 ... 2 5.75 5.4
1 ... 2 4.25 4.2
2 ... 1 2.50 2.8
text_standard fernandez_huerta szigriszt_pazos gutierrez_polini \
0 3rd and 4th grade 121.07 119.50 54.91
1 4th and 5th grade 124.13 119.20 52.26
2 3rd and 4th grade 115.70 110.84 49.79
crawford gulpease_index osman
0 0.9 72.7 92.16
1 0.7 74.7 84.20
2 0.7 85.4 83.14
[3 rows x 24 columns]}
``````output
Could not update last created model in Task 988bd727b0e94a29a3ac0ee526813545, Task status 'completed' cannot be updated
Tips and Next Steps
-
Make sure you always use a unique
nameargument for theclearml_callback.flush_trackerfunction. If not, the model parameters used for a run will override the previous run! -
If you close the ClearML Callback using
clearml_callback.flush_tracker(..., finish=True)the Callback cannot be used anymore. Make a new one if you want to keep logging. -
Check out the rest of the open-source ClearML ecosystem, there is a data version manager, a remote execution agent, automated pipelines and much more!