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paperspace@pspx59qcl:~/client$ yea run functional_tests/imports/04-batch4.py
INFO: Timed out waiting for server to start... http://localhost:37211 1645464371.6856294
INFO: Timed out waiting for server to start... http://localhost:37211 1645464372.6893241
Traceback (most recent call last):
  File "/usr/lib/python3/dist-packages/urllib3/connection.py", line 159, in _new_conn
    conn = connection.create_connection(
  File "/usr/lib/python3/dist-packages/urllib3/util/connection.py", line 84, in create_connection
    raise err
  File "/usr/lib/python3/dist-packages/urllib3/util/connection.py", line 74, in create_connection
    sock.connect(sa)

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#!/usr/bin/env python
"""Test a batch of import telemetry

---
id: 0.imports.04-batch4
tag:
  shard: imports
plugin:
  - wandb
depend:

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# First table/ table for first iteration/epoch
original_table = ... # format [id, wandb.Image object, ...]
artifact.add(original_table, "my_table")

# Log the table as artifact. (optionally)Log the same table in dashboard if you want to
run.log_artifact(artifact)
run.log({"my_table": original_table})

# Use the logged artifact to get the reference of table
run.use_artifact(artifact)

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# Decision Boundary Utils
-# Modified from https://github.com/tmadl/highdimensional-decision-boundary-plot
-class DBPlot(BaseEstimator):
-    def __init__(
-        self,
-        estimator=KNeighborsClassifier(n_neighbors=10),
-        acceptance_threshold=0.03,
-        n_decision_boundary_keypoints=60,
-        n_connecting_keypoints=None,
-        n_interpolated_keypoints=None,

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# Needs datasets, albumentations

from PIL import Image
from datasets import load_dataset
from datasets.download.download_manager import DownloadMode #, REUSE_DATASET_IF_EXISTS, REUSE_CACHE_IF_EXISTS

import albumentations as A
from albumentations.pytorch.transforms import ToTensorV2
from albumentations.augmentations.transforms import Normalize

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from typing import Callable, Dict, Any, List
from fastapi import Depends, FastAPI
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
from lanarky.responses import StreamingResponse
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.base import AsyncCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain, SequentialChain, ConversationChain
from pydantic import BaseModel

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from typing import Callable, Dict, Any, List
from fastapi import Depends, FastAPI
from fastapi.encoders import jsonable_encoder
from fastapi.responses import JSONResponse
from lanarky.responses import StreamingResponse
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.base import AsyncCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain, SequentialChain, ConversationChain
from pydantic import BaseModel

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import langchain
import wandb

from typing import Any, Awaitable, Callable, Dict, Optional, Union
from fastapi.responses import StreamingResponse as _StreamingResponse
from langchain.chains.base import Chain
from starlette.background import BackgroundTask
from starlette.types import Send

from dotenv import load_dotenv

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#!/usr/bin/env python
# coding: utf-8

# In this notebook we will automatically generate a set of evaluation questions based on wandb docs

import random
import wandb
import re
import openai
import os