ai.google.dev-এ দেখুন | একটি Colab নোটবুক ব্যবহার করে দেখুন | GitHub এ নোটবুক দেখুন | নোটবুক ডাউনলোড করুন |
এই নোটবুকে, আপনি শিখবেন কিভাবে PaLM API-এর জন্য পাইথন ক্লায়েন্ট লাইব্রেরি ব্যবহার করে টিউনিং পরিষেবা দিয়ে শুরু করবেন। এখানে, আপনি শিখবেন কিভাবে PaLM API এর টেক্সট জেনারেশন সার্ভিসের পিছনে টেক্সট মডেল টিউন করতে হয়।
সেটআপ
প্রমাণীকরণ
PaLM API আপনাকে আপনার নিজস্ব ডেটাতে মডেলগুলি টিউন করতে দেয়৷ যেহেতু এটি আপনার ডেটা এবং আপনার টিউন করা মডেলগুলি এর জন্য API-কীগুলি সরবরাহ করতে পারে তার চেয়ে কঠোর অ্যাক্সেস নিয়ন্ত্রণের প্রয়োজন৷
আপনি এই টিউটোরিয়ালটি চালানোর আগে, আপনাকে আপনার প্রকল্পের জন্য OAuth সেটআপ করতে হবে।
আপনি যদি এই নোটবুকটি Colab-এ চালাতে চান তাহলে "ফাইল > আপলোড" বিকল্প ব্যবহার করে আপনার client_secret*.json
ফাইল আপলোড করে শুরু করুন।
cp client_secret*.json client_secret.json
ls client_secret.json
client_secret.json
এই gcloud কমান্ডটি client_secret.json
ফাইলটিকে শংসাপত্রে পরিণত করে যা পরিষেবার সাথে প্রমাণীকরণের জন্য ব্যবহার করা যেতে পারে।
import os
if 'COLAB_RELEASE_TAG' in os.environ:
# Use `--no-browser` in colab
!gcloud auth application-default login --no-browser --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'
else:
!gcloud auth application-default login --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'
ক্লায়েন্ট লাইব্রেরি ইনস্টল করুন
pip install -q google-generativeai
লাইব্রেরি আমদানি করুন
import google.generativeai as genai
আপনি genai.list_tuned_model
পদ্ধতির মাধ্যমে আপনার বিদ্যমান টিউন করা মডেলগুলি পরীক্ষা করতে পারেন।
for i, m in zip(range(5), genai.list_tuned_models()):
print(m.name)
tunedModels/my-model-8527 tunedModels/my-model-7092 tunedModels/my-model-2778 tunedModels/my-model-1298 tunedModels/my-model-3883
টিউন করা মডেল তৈরি করুন
একটি টিউন করা মডেল তৈরি করতে, আপনাকে genai.create_tuned_model
পদ্ধতিতে মডেলটিতে আপনার ডেটাসেট পাস করতে হবে। আপনি কলের ইনপুট এবং আউটপুট মানগুলি সরাসরি সংজ্ঞায়িত করতে বা পদ্ধতিতে পাস করার জন্য একটি ডেটাফ্রেমে একটি ফাইল থেকে আমদানি করতে এটি করতে পারেন।
এই উদাহরণের জন্য, আপনি ক্রমানুসারে পরবর্তী সংখ্যা তৈরি করতে একটি মডেল টিউন করবেন। উদাহরণস্বরূপ, যদি ইনপুট 1
হয়, তাহলে মডেলটি 2
আউটপুট করবে। ইনপুট one hundred
হলে আউটপুট one hundred one
হতে হবে।
base_model = [
m for m in genai.list_models()
if "createTunedTextModel" in m.supported_generation_methods][0]
base_model.name
'models/text-bison-001'
import random
name = f'generate-num-{random.randint(0,10000)}'
operation = genai.create_tuned_model(
# You can use a tuned model here too. Set `source_model="tunedModels/..."`
source_model=base_model.name,
training_data=[
{
'text_input': '1',
'output': '2',
},{
'text_input': '3',
'output': '4',
},{
'text_input': '-3',
'output': '-2',
},{
'text_input': 'twenty two',
'output': 'twenty three',
},{
'text_input': 'two hundred',
'output': 'two hundred one',
},{
'text_input': 'ninety nine',
'output': 'one hundred',
},{
'text_input': '8',
'output': '9',
},{
'text_input': '-98',
'output': '-97',
},{
'text_input': '1,000',
'output': '1,001',
},{
'text_input': '10,100,000',
'output': '10,100,001',
},{
'text_input': 'thirteen',
'output': 'fourteen',
},{
'text_input': 'eighty',
'output': 'eighty one',
},{
'text_input': 'one',
'output': 'two',
},{
'text_input': 'three',
'output': 'four',
},{
'text_input': 'seven',
'output': 'eight',
}
],
id = name,
epoch_count = 100,
batch_size=4,
learning_rate=0.001,
)
আপনার টিউন করা মডেলটি অবিলম্বে টিউন করা মডেলের তালিকায় যোগ করা হয়েছে, কিন্তু মডেলটি টিউন করার সময় এর স্থিতি "তৈরি করা" তে সেট করা হয়েছে৷
model = genai.get_tuned_model(f'tunedModels/{name}')
model
TunedModel(name='tunedModels/generate-num-9028', source_model='tunedModels/generate-num-4110', base_model='models/text-bison-001', display_name='', description='', temperature=0.7, top_p=0.95, top_k=40, state=<State.CREATING: 1>, create_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc), update_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc), tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 734118, tzinfo=datetime.timezone.utc), complete_time=None, snapshots=[], hyperparameters=Hyperparameters(epoch_count=100, batch_size=4, learning_rate=0.001)))
model.state
<State.CREATING: 1>
টিউনিং অগ্রগতি পরীক্ষা করুন
অবস্থা পরীক্ষা করতে metadata
ব্যবহার করুন:
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
operation.result()
, বা operation.wait_bar()
ব্যবহার করে প্রশিক্ষণ শেষ হওয়ার জন্য অপেক্ষা করুন
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
আপনি cancel()
পদ্ধতি ব্যবহার করে যেকোনো সময় আপনার টিউনিং কাজ বাতিল করতে পারেন। নিচের লাইনটি আনকমেন্ট করুন এবং আপনার কাজ শেষ হওয়ার আগে বাতিল করতে কোড সেল চালান।
# operation.cancel()
টিউনিং সম্পূর্ণ হলে, আপনি টিউনিং ফলাফল থেকে ক্ষতি বক্ররেখা দেখতে পারেন। ক্ষতির বক্ররেখা দেখায় যে মডেলের ভবিষ্যদ্বাণী আদর্শ আউটপুট থেকে কতটা বিচ্যুত।
import pandas as pd
import seaborn as sns
model = operation.result()
snapshots = pd.DataFrame(model.tuning_task.snapshots)
sns.lineplot(data=snapshots, x = 'epoch', y='mean_loss')
<Axes: xlabel='epoch', ylabel='mean_loss'>
আপনার মডেল মূল্যায়ন
আপনি genai.generate_text
পদ্ধতি ব্যবহার করতে পারেন এবং আপনার মডেলের কার্যক্ষমতা পরীক্ষা করতে আপনার মডেলের নাম উল্লেখ করতে পারেন।
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='5')
completion.result
'6'
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='-9')
completion.result
'-8'
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='four')
completion.result
'four'
আপনি দেখতে পাচ্ছেন, শেষ প্রম্পটটি আদর্শ ফলাফল দেয়নি, five
। আরও ভাল ফলাফলের জন্য আপনি কয়েকটি ভিন্ন জিনিস চেষ্টা করতে পারেন যেমন আরও সামঞ্জস্যপূর্ণ ফলাফল পেতে তাপমাত্রা শূন্যের কাছাকাছি সামঞ্জস্য করা, আপনার ডেটাসেটে আরও গুণমানের উদাহরণ যোগ করা যা থেকে মডেল শিখতে পারে বা উদাহরণগুলিতে একটি প্রম্পট বা প্রস্তাবনা যোগ করা।
পারফরম্যান্সের উন্নতির বিষয়ে আরও নির্দেশনার জন্য টিউনিং গাইড দেখুন।
বর্ণনা আপডেট করুন
আপনি genai.update_tuned_model
পদ্ধতি ব্যবহার করে যেকোনো সময় আপনার টিউন করা মডেলের বিবরণ আপডেট করতে পারেন।
genai.update_tuned_model(f'tunedModels/{name}', {"description":"This is my model."})
TunedModel(name='', source_model=None, base_model=None, display_name='', description='This is my model.', temperature=None, top_p=None, top_k=None, state=<State.STATE_UNSPECIFIED: 0>, create_time=None, update_time=None, tuning_task=None)
model = genai.get_tuned_model(f'tunedModels/{name}')
model
TunedModel(name='tunedModels/generate-num-4668', source_model=None, base_model='models/text-bison-001', display_name='', description='This is my model.', temperature=0.7, top_p=0.95, top_k=40, state=<State.ACTIVE: 2>, create_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>), update_time=datetime.datetime(2023, 9, 19, 19, 11, 48, 101024, tzinfo=<UTC>), tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 562798, tzinfo=<UTC>), complete_time=datetime.datetime(2023, 9, 19, 19, 11, 48, 101024, tzinfo=<UTC>), snapshots=[{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 41, 221503, tzinfo=<UTC>), 'epoch': 0, 'mean_loss': 7.2774773, 'step': 1}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 42, 611142, tzinfo=<UTC>), 'epoch': 0, 'mean_loss': 6.178241, 'step': 2}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 43, 886844, tzinfo=<UTC>), 'epoch': 0, 'mean_loss': 5.505934, 'step': 3}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 45, 213316, tzinfo=<UTC>), 'epoch': 1, 'mean_loss': 7.9365344, 'step': 4}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 46, 719674, tzinfo=<UTC>), 'epoch': 1, 'mean_loss': 7.656596, 'step': 5}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 48, 51559, tzinfo=<UTC>), 'epoch': 1, 'mean_loss': 7.3750257, 'step': 6}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 49, 419247, tzinfo=<UTC>), 'epoch': 1, 'mean_loss': 4.579882, 'step': 7}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 50, 902477, tzinfo=<UTC>), 'epoch': 2, 'mean_loss': 6.776862, 'step': 8}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 52, 213448, tzinfo=<UTC>), 'epoch': 2, 'mean_loss': 6.3564157, 'step': 9}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 53, 679693, tzinfo=<UTC>), 'epoch': 2, 'mean_loss': 8.558726, 'step': 10}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 55, 2348, tzinfo=<UTC>), 'epoch': 2, 'mean_loss': 4.783774, 'step': 11}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 56, 322485, tzinfo=<UTC>), 'epoch': 3, 'mean_loss': 7.0234137, 'step': 12}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 58, 145081, tzinfo=<UTC>), 'epoch': 3, 'mean_loss': 7.317513, 'step': 13}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 59, 399317, tzinfo=<UTC>), 'epoch': 3, 'mean_loss': 5.85363, 'step': 14}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 0, 646995, tzinfo=<UTC>), 'epoch': 4, 'mean_loss': 4.21408, 'step': 15}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 1, 899798, tzinfo=<UTC>), 'epoch': 4, 'mean_loss': 6.6232214, 'step': 16}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 3, 167955, tzinfo=<UTC>), 'epoch': 4, 'mean_loss': 5.61497, 'step': 17}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 4, 407849, tzinfo=<UTC>), 'epoch': 4, 'mean_loss': 6.821261, 'step': 18}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 5, 649503, tzinfo=<UTC>), 'epoch': 5, 'mean_loss': 3.8338904, 'step': 19}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 7, 80497, tzinfo=<UTC>), 'epoch': 5, 'mean_loss': 5.0643735, 'step': 20}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 8, 401424, tzinfo=<UTC>), 'epoch': 5, 'mean_loss': 6.976447, 'step': 21}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 9, 688226, tzinfo=<UTC>), 'epoch': 5, 'mean_loss': 5.045044, 'step': 22}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 10, 942147, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 5.1944356, 'step': 23}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 12, 169501, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 5.342552, 'step': 24}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 13, 532023, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 7.360283, 'step': 25}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 14, 773265, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 2.874686, 'step': 26}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 16, 68826, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 5.0835795, 'step': 27}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 17, 328292, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 4.059507, 'step': 28}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 18, 683769, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 4.668791, 'step': 29}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 19, 917365, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 3.2776065, 'step': 30}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 21, 175338, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 4.1344976, 'step': 31}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 22, 510908, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 4.47365, 'step': 32}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 23, 972490, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 2.8087254, 'step': 33}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 25, 341109, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 3.581566, 'step': 34}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 26, 594799, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 3.3534799, 'step': 35}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 27, 857511, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 2.5248497, 'step': 36}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 29, 100872, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 1.8420736, 'step': 37}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 30, 356383, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 3.4610085, 'step': 38}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 31, 819918, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 3.2506752, 'step': 39}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 33, 77814, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 2.4844272, 'step': 40}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 34, 314311, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 2.3858242, 'step': 41}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 35, 572181, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 1.1961311, 'step': 42}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 36, 845239, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 3.5777583, 'step': 43}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 38, 120182, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 1.3613169, 'step': 44}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 39, 611773, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.7414228, 'step': 45}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 41, 835960, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.3301177, 'step': 46}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 43, 118015, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.3805578, 'step': 47}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 44, 383045, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 2.3191347, 'step': 48}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 45, 617675, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 1.7018254, 'step': 49}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 46, 856463, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 1.5530272, 'step': 50}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 48, 159606, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 2.1536818, 'step': 51}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 49, 388434, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 0.87225634, 'step': 52}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 50, 649576, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.6638466, 'step': 53}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 52, 113467, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.4672767, 'step': 54}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 53, 491995, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 0.66232294, 'step': 55}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 54, 849227, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.2151186, 'step': 56}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 56, 117613, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.75382125, 'step': 57}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 58, 244537, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.909588, 'step': 58}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 59, 495142, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.85212016, 'step': 59}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 0, 748073, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 1.0999682, 'step': 60}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 2, 9621, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 0.49189907, 'step': 61}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 3, 289800, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 1.2313881, 'step': 62}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 4, 542260, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 0.4186042, 'step': 63}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 5, 789722, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.5968985, 'step': 64}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 7, 21547, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.32776576, 'step': 65}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 8, 253903, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.085846476, 'step': 66}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 9, 503217, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.87150824, 'step': 67}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 10, 755627, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.50882834, 'step': 68}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 11, 981698, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.05643571, 'step': 69}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 13, 238454, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.11667071, 'step': 70}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 14, 474345, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.05200408, 'step': 71}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 15, 692710, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.21968448, 'step': 72}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 16, 930777, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.071391255, 'step': 73}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 18, 180590, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.39031163, 'step': 74}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 19, 433064, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.05084487, 'step': 75}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 20, 677200, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.04713744, 'step': 76}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 21, 901118, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.196708, 'step': 77}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 23, 166260, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.15159458, 'step': 78}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 24, 400680, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.0280451, 'step': 79}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 25, 644378, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.06759574, 'step': 80}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 27, 195128, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.03170073, 'step': 81}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 28, 546850, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.15327619, 'step': 82}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 29, 953511, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.14349619, 'step': 83}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 31, 334082, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.02684513, 'step': 84}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 32, 832994, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.019874452, 'step': 85}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 34, 88577, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.041133285, 'step': 86}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 35, 346650, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.06348712, 'step': 87}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 36, 585951, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.025213383, 'step': 88}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 37, 818161, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.018140253, 'step': 89}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 39, 72239, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.023763947, 'step': 90}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 40, 278252, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.008751405, 'step': 91}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 42, 747639, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.0082112085, 'step': 92}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 44, 8609, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.037568945, 'step': 93}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 45, 264108, tzinfo=<UTC>), 'epoch': 25, 'mean_loss': 0.027123686, 'step': 94}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 46, 526159, tzinfo=<UTC>), 'epoch': 25, 'mean_loss': 0.0142503055, 'step': 95}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 47, 768520, tzinfo=<UTC>), 'epoch': 25, 'mean_loss': 0.027518341, 'step': 96}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 49, 89441, tzinfo=<UTC>), 'epoch': 25, 'mean_loss': 0.013976067, 'step': 97}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 50, 342449, tzinfo=<UTC>), 'epoch': 26, 'mean_loss': 0.0036393465, 'step': 98}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 51, 613018, tzinfo=<UTC>), 'epoch': 26, 'mean_loss': 0.0058721625, 'step': 99}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 52, 847043, tzinfo=<UTC>), 'epoch': 26, 'mean_loss': 0.0008192812, 'step': 100}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 54, 81155, tzinfo=<UTC>), 'epoch': 26, 'mean_loss': 0.025449298, 'step': 101}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 55, 304198, tzinfo=<UTC>), 'epoch': 27, 'mean_loss': 0.0066863927, 'step': 102}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 56, 557883, tzinfo=<UTC>), 'epoch': 27, 'mean_loss': 0.002721126, 'step': 103}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 57, 787163, tzinfo=<UTC>), 'epoch': 27, 'mean_loss': 0.015793594, 'step': 104}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 59, 81532, tzinfo=<UTC>), 'epoch': 28, 'mean_loss': 0.005504051, 'step': 105}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 0, 328216, tzinfo=<UTC>), 'epoch': 28, 'mean_loss': 0.003016818, 'step': 106}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 1, 561541, tzinfo=<UTC>), 'epoch': 28, 'mean_loss': 0.014285667, 'step': 107}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 2, 782923, tzinfo=<UTC>), 'epoch': 28, 'mean_loss': 0.004257772, 'step': 108}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 4, 10208, tzinfo=<UTC>), 'epoch': 29, 'mean_loss': 0.010257841, 'step': 109}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 5, 268123, tzinfo=<UTC>), 'epoch': 29, 'mean_loss': 0.0075931884, 'step': 110}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 6, 485682, tzinfo=<UTC>), 'epoch': 29, 'mean_loss': 0.024589492, 'step': 111}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 7, 731568, tzinfo=<UTC>), 'epoch': 29, 'mean_loss': 0.0012908785, 'step': 112}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 8, 978691, tzinfo=<UTC>), 'epoch': 30, 'mean_loss': 0.011656972, 'step': 113}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 10, 254824, tzinfo=<UTC>), 'epoch': 30, 'mean_loss': 0.006077944, 'step': 114}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 11, 489143, tzinfo=<UTC>), 'epoch': 30, 'mean_loss': 0.004817399, 'step': 115}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 12, 772297, tzinfo=<UTC>), 'epoch': 30, 'mean_loss': 0.007809804, 'step': 116}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 14, 19305, tzinfo=<UTC>), 'epoch': 31, 'mean_loss': 0.006533157, 'step': 117}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 15, 257150, tzinfo=<UTC>), 'epoch': 31, 'mean_loss': 0.005006207, 'step': 118}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 16, 536830, tzinfo=<UTC>), 'epoch': 31, 'mean_loss': 0.0004677312, 'step': 119}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 17, 790581, tzinfo=<UTC>), 'epoch': 32, 'mean_loss': 0.007230793, 'step': 120}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 19, 41049, tzinfo=<UTC>), 'epoch': 32, 'mean_loss': 0.0020942457, 'step': 121}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 20, 306408, tzinfo=<UTC>), 'epoch': 32, 'mean_loss': 0.006205416, 'step': 122}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 21, 545345, tzinfo=<UTC>), 'epoch': 32, 'mean_loss': 0.0063284263, 'step': 123}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 22, 759006, tzinfo=<UTC>), 'epoch': 33, 'mean_loss': 0.0063140467, 'step': 124}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 23, 979661, tzinfo=<UTC>), 'epoch': 33, 'mean_loss': 0.00040962745, 'step': 125}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 25, 215222, tzinfo=<UTC>), 'epoch': 33, 'mean_loss': 0.0020570084, 'step': 126}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 26, 457055, tzinfo=<UTC>), 'epoch': 33, 'mean_loss': 0.011655594, 'step': 127}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 27, 705186, tzinfo=<UTC>), 'epoch': 34, 'mean_loss': 0.0038693137, 'step': 128}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 29, 602189, tzinfo=<UTC>), 'epoch': 34, 'mean_loss': 0.0092602, 'step': 129}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 30, 874690, tzinfo=<UTC>), 'epoch': 34, 'mean_loss': 0.0047834637, 'step': 130}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 32, 120016, tzinfo=<UTC>), 'epoch': 34, 'mean_loss': 0.00579008, 'step': 131}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 33, 432962, tzinfo=<UTC>), 'epoch': 35, 'mean_loss': 0.004663332, 'step': 132}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 34, 709379, tzinfo=<UTC>), 'epoch': 35, 'mean_loss': 0.004609281, 'step': 133}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 35, 948660, tzinfo=<UTC>), 'epoch': 35, 'mean_loss': 0.0044703777, 'step': 134}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 37, 173785, tzinfo=<UTC>), 'epoch': 36, 'mean_loss': 0.0016537609, 'step': 135}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 38, 412757, tzinfo=<UTC>), 'epoch': 36, 'mean_loss': 0.005695714, 'step': 136}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 39, 648122, tzinfo=<UTC>), 'epoch': 36, 'mean_loss': 0.004103207, 'step': 137}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 40, 885574, tzinfo=<UTC>), 'epoch': 36, 'mean_loss': 0.0018846963, 'step': 138}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 42, 993050, tzinfo=<UTC>), 'epoch': 37, 'mean_loss': 0.0023978357, 'step': 139}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 44, 304394, tzinfo=<UTC>), 'epoch': 37, 'mean_loss': 0.0033069355, 'step': 140}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 45, 565453, tzinfo=<UTC>), 'epoch': 37, 'mean_loss': 6.576523e-05, 'step': 141}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 47, 38618, tzinfo=<UTC>), 'epoch': 37, 'mean_loss': 0.0043391315, 'step': 142}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 48, 351795, tzinfo=<UTC>), 'epoch': 38, 'mean_loss': 0.0046651517, 'step': 143}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 49, 602102, tzinfo=<UTC>), 'epoch': 38, 'mean_loss': 0.0052327495, 'step': 144}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 50, 976143, tzinfo=<UTC>), 'epoch': 38, 'mean_loss': 0.013687609, 'step': 145}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 52, 227875, tzinfo=<UTC>), 'epoch': 38, 'mean_loss': 9.1750175e-05, 'step': 146}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 53, 495933, tzinfo=<UTC>), 'epoch': 39, 'mean_loss': 0.0025392435, 'step': 147}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 54, 736253, tzinfo=<UTC>), 'epoch': 39, 'mean_loss': 0.0011976548, 'step': 148}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 56, 2776, tzinfo=<UTC>), 'epoch': 39, 'mean_loss': 0.0025211202, 'step': 149}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 57, 245352, tzinfo=<UTC>), 'epoch': 40, 'mean_loss': 0.0018665651, 'step': 150}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 58, 506403, tzinfo=<UTC>), 'epoch': 40, 'mean_loss': 0.0033759787, 'step': 151}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 6, 59, 775888, tzinfo=<UTC>), 'epoch': 40, 'mean_loss': 0.00062831433, 'step': 152}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 1, 18295, tzinfo=<UTC>), 'epoch': 40, 'mean_loss': 0.0020364965, 'step': 153}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 2, 268012, tzinfo=<UTC>), 'epoch': 41, 'mean_loss': 0.0006586923, 'step': 154}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 3, 484102, tzinfo=<UTC>), 'epoch': 41, 'mean_loss': 0.0031121888, 'step': 155}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 4, 776770, tzinfo=<UTC>), 'epoch': 41, 'mean_loss': 0.0027823262, 'step': 156}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 6, 19324, tzinfo=<UTC>), 'epoch': 41, 'mean_loss': 0.0006065498, 'step': 157}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 7, 268435, tzinfo=<UTC>), 'epoch': 42, 'mean_loss': 0.0023070213, 'step': 158}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 8, 512407, tzinfo=<UTC>), 'epoch': 42, 'mean_loss': 0.0016650247, 'step': 159}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 9, 745756, tzinfo=<UTC>), 'epoch': 42, 'mean_loss': 7.3560514e-06, 'step': 160}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 10, 987670, tzinfo=<UTC>), 'epoch': 42, 'mean_loss': 0.012237127, 'step': 161}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 12, 219737, tzinfo=<UTC>), 'epoch': 43, 'mean_loss': 0.0013661574, 'step': 162}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 13, 484756, tzinfo=<UTC>), 'epoch': 43, 'mean_loss': 0.0064119953, 'step': 163}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 14, 730503, tzinfo=<UTC>), 'epoch': 43, 'mean_loss': 0.0035314618, 'step': 164}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 15, 947765, tzinfo=<UTC>), 'epoch': 44, 'mean_loss': 3.298011e-05, 'step': 165}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 17, 190398, tzinfo=<UTC>), 'epoch': 44, 'mean_loss': 0.00053371524, 'step': 166}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 18, 433536, tzinfo=<UTC>), 'epoch': 44, 'mean_loss': 0.00032033096, 'step': 167}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 19, 714514, tzinfo=<UTC>), 'epoch': 44, 'mean_loss': 0.0019390727, 'step': 168}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 20, 935080, tzinfo=<UTC>), 'epoch': 45, 'mean_loss': 2.4262117e-05, 'step': 169}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 22, 167896, tzinfo=<UTC>), 'epoch': 45, 'mean_loss': 0.00012971938, 'step': 170}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 23, 410928, tzinfo=<UTC>), 'epoch': 45, 'mean_loss': 0.0009971312, 'step': 171}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 24, 649509, tzinfo=<UTC>), 'epoch': 45, 'mean_loss': 0.0011956232, 'step': 172}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 25, 860363, tzinfo=<UTC>), 'epoch': 46, 'mean_loss': 8.339065e-05, 'step': 173}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 27, 129342, tzinfo=<UTC>), 'epoch': 46, 'mean_loss': 0.0013557925, 'step': 174}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 28, 356351, tzinfo=<UTC>), 'epoch': 46, 'mean_loss': 0.00077356555, 'step': 175}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 29, 568726, tzinfo=<UTC>), 'epoch': 46, 'mean_loss': 0.0017021206, 'step': 176}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 30, 835703, tzinfo=<UTC>), 'epoch': 47, 'mean_loss': 0.0010255948, 'step': 177}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 32, 82326, tzinfo=<UTC>), 'epoch': 47, 'mean_loss': 0.003208516, 'step': 178}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 33, 301035, tzinfo=<UTC>), 'epoch': 47, 'mean_loss': 0.0021095886, 'step': 179}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 34, 589684, tzinfo=<UTC>), 'epoch': 48, 'mean_loss': 0.0018085723, 'step': 180}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 35, 811121, tzinfo=<UTC>), 'epoch': 48, 'mean_loss': 0.003906385, 'step': 181}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 37, 51086, tzinfo=<UTC>), 'epoch': 48, 'mean_loss': 0.0081788115, 'step': 182}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 38, 296512, tzinfo=<UTC>), 'epoch': 48, 'mean_loss': 0.0021692181, 'step': 183}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 39, 576450, tzinfo=<UTC>), 'epoch': 49, 'mean_loss': 0.0007408549, 'step': 184}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 40, 818968, tzinfo=<UTC>), 'epoch': 49, 'mean_loss': 0.0005012541, 'step': 185}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 42, 115736, tzinfo=<UTC>), 'epoch': 49, 'mean_loss': 0.0015736766, 'step': 186}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 44, 177425, tzinfo=<UTC>), 'epoch': 49, 'mean_loss': 0.0010295139, 'step': 187}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 45, 663911, tzinfo=<UTC>), 'epoch': 50, 'mean_loss': 0.0003010271, 'step': 188}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 46, 907392, tzinfo=<UTC>), 'epoch': 50, 'mean_loss': 0.000984581, 'step': 189}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 48, 197363, tzinfo=<UTC>), 'epoch': 50, 'mean_loss': 0.0001981319, 'step': 190}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 49, 428991, tzinfo=<UTC>), 'epoch': 50, 'mean_loss': 0.00017777813, 'step': 191}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 50, 664645, tzinfo=<UTC>), 'epoch': 51, 'mean_loss': 0.00032808085, 'step': 192}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 51, 868913, tzinfo=<UTC>), 'epoch': 51, 'mean_loss': 0.00059463154, 'step': 193}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 53, 130216, tzinfo=<UTC>), 'epoch': 51, 'mean_loss': 0.0016690929, 'step': 194}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 54, 333529, tzinfo=<UTC>), 'epoch': 52, 'mean_loss': 0.00044837967, 'step': 195}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 55, 572466, tzinfo=<UTC>), 'epoch': 52, 'mean_loss': 0.0005696884, 'step': 196}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 56, 837266, tzinfo=<UTC>), 'epoch': 52, 'mean_loss': 0.0020454866, 'step': 197}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 58, 84490, tzinfo=<UTC>), 'epoch': 52, 'mean_loss': 0.008037148, 'step': 198}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 7, 59, 322229, tzinfo=<UTC>), 'epoch': 53, 'mean_loss': 0.00042736152, 'step': 199}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 0, 564415, tzinfo=<UTC>), 'epoch': 53, 'mean_loss': 0.0023432998, 'step': 200}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 1, 788247, tzinfo=<UTC>), 'epoch': 53, 'mean_loss': 0.0002715867, 'step': 201}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 3, 24660, tzinfo=<UTC>), 'epoch': 53, 'mean_loss': 0.00084764615, 'step': 202}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 4, 292818, tzinfo=<UTC>), 'epoch': 54, 'mean_loss': 0.00030835773, 'step': 203}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 5, 506320, tzinfo=<UTC>), 'epoch': 54, 'mean_loss': 0.00062151754, 'step': 204}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 6, 782668, tzinfo=<UTC>), 'epoch': 54, 'mean_loss': 3.3617685e-05, 'step': 205}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 8, 36202, tzinfo=<UTC>), 'epoch': 54, 'mean_loss': 0.00043850893, 'step': 206}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 9, 280914, tzinfo=<UTC>), 'epoch': 55, 'mean_loss': 0.0014309261, 'step': 207}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 10, 507550, tzinfo=<UTC>), 'epoch': 55, 'mean_loss': 0.00029372238, 'step': 208}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 11, 790404, tzinfo=<UTC>), 'epoch': 55, 'mean_loss': 0.000352273, 'step': 209}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 13, 28160, tzinfo=<UTC>), 'epoch': 56, 'mean_loss': 0.0034054378, 'step': 210}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 14, 285902, tzinfo=<UTC>), 'epoch': 56, 'mean_loss': 0.00038256973, 'step': 211}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 15, 554993, tzinfo=<UTC>), 'epoch': 56, 'mean_loss': 0.0029042722, 'step': 212}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 16, 782794, tzinfo=<UTC>), 'epoch': 56, 'mean_loss': 0.0013111946, 'step': 213}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 18, 18302, tzinfo=<UTC>), 'epoch': 57, 'mean_loss': 0.0005805618, 'step': 214}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 19, 256860, tzinfo=<UTC>), 'epoch': 57, 'mean_loss': 0.001555414, 'step': 215}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 20, 531737, tzinfo=<UTC>), 'epoch': 57, 'mean_loss': 0.0035115764, 'step': 216}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 21, 760323, tzinfo=<UTC>), 'epoch': 57, 'mean_loss': 0.00041363586, 'step': 217}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 23, 38254, tzinfo=<UTC>), 'epoch': 58, 'mean_loss': 8.1257895e-08, 'step': 218}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 24, 459123, tzinfo=<UTC>), 'epoch': 58, 'mean_loss': 0.0016267635, 'step': 219}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 25, 876654, tzinfo=<UTC>), 'epoch': 58, 'mean_loss': 0.0012277836, 'step': 220}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 27, 142763, tzinfo=<UTC>), 'epoch': 58, 'mean_loss': 0.0013576464, 'step': 221}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 28, 423880, tzinfo=<UTC>), 'epoch': 59, 'mean_loss': 0.0014309958, 'step': 222}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 29, 651501, tzinfo=<UTC>), 'epoch': 59, 'mean_loss': 0.00035945722, 'step': 223}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 30, 933277, tzinfo=<UTC>), 'epoch': 59, 'mean_loss': 0.0002977748, 'step': 224}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 32, 192499, tzinfo=<UTC>), 'epoch': 60, 'mean_loss': 0.0016817113, 'step': 225}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 33, 457414, tzinfo=<UTC>), 'epoch': 60, 'mean_loss': 0.0006552929, 'step': 226}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 34, 826380, tzinfo=<UTC>), 'epoch': 60, 'mean_loss': 0.00012618338, 'step': 227}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 36, 79976, tzinfo=<UTC>), 'epoch': 60, 'mean_loss': 0.00076779944, 'step': 228}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 37, 468400, tzinfo=<UTC>), 'epoch': 61, 'mean_loss': 0.0010934594, 'step': 229}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 38, 702382, tzinfo=<UTC>), 'epoch': 61, 'mean_loss': 0.0011660276, 'step': 230}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 39, 959625, tzinfo=<UTC>), 'epoch': 61, 'mean_loss': 8.885516e-06, 'step': 231}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 41, 195827, tzinfo=<UTC>), 'epoch': 61, 'mean_loss': 0.00091533817, 'step': 232}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 42, 428366, tzinfo=<UTC>), 'epoch': 62, 'mean_loss': 0.00090357044, 'step': 233}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 44, 734547, tzinfo=<UTC>), 'epoch': 62, 'mean_loss': 0.00091215235, 'step': 234}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 45, 975299, tzinfo=<UTC>), 'epoch': 62, 'mean_loss': 0.0008807101, 'step': 235}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 47, 381337, tzinfo=<UTC>), 'epoch': 62, 'mean_loss': 0.00038245833, 'step': 236}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 48, 654093, tzinfo=<UTC>), 'epoch': 63, 'mean_loss': 0.00080911745, 'step': 237}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 49, 882751, tzinfo=<UTC>), 'epoch': 63, 'mean_loss': 0.0003725673, 'step': 238}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 51, 110358, tzinfo=<UTC>), 'epoch': 63, 'mean_loss': 0.0012469762, 'step': 239}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 52, 679155, tzinfo=<UTC>), 'epoch': 64, 'mean_loss': 0.0005009441, 'step': 240}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 53, 911603, tzinfo=<UTC>), 'epoch': 64, 'mean_loss': 0.0005480327, 'step': 241}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 55, 184955, tzinfo=<UTC>), 'epoch': 64, 'mean_loss': -1.8597348e-06, 'step': 242}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 56, 578096, tzinfo=<UTC>), 'epoch': 64, 'mean_loss': 0.002287712, 'step': 243}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 57, 828452, tzinfo=<UTC>), 'epoch': 65, 'mean_loss': 6.471912e-05, 'step': 244}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 8, 59, 35379, tzinfo=<UTC>), 'epoch': 65, 'mean_loss': 0.00031904934, 'step': 245}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 0, 293344, tzinfo=<UTC>), 'epoch': 65, 'mean_loss': 1.0820688e-05, 'step': 246}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 1, 560935, tzinfo=<UTC>), 'epoch': 65, 'mean_loss': 0.0012223909, 'step': 247}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 2, 793184, tzinfo=<UTC>), 'epoch': 66, 'mean_loss': 0.0008803075, 'step': 248}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 4, 43681, tzinfo=<UTC>), 'epoch': 66, 'mean_loss': 0.0010418366, 'step': 249}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 5, 296877, tzinfo=<UTC>), 'epoch': 66, 'mean_loss': 0.0011515429, 'step': 250}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 6, 531123, tzinfo=<UTC>), 'epoch': 66, 'mean_loss': 9.024725e-05, 'step': 251}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 7, 793511, tzinfo=<UTC>), 'epoch': 67, 'mean_loss': 1.4540274e-06, 'step': 252}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 9, 25627, tzinfo=<UTC>), 'epoch': 67, 'mean_loss': 0.00012591947, 'step': 253}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 10, 275288, tzinfo=<UTC>), 'epoch': 67, 'mean_loss': 0.0008015272, 'step': 254}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 11, 522475, tzinfo=<UTC>), 'epoch': 68, 'mean_loss': 0.0008423489, 'step': 255}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 12, 764784, tzinfo=<UTC>), 'epoch': 68, 'mean_loss': 0.0011362592, 'step': 256}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 14, 58405, tzinfo=<UTC>), 'epoch': 68, 'mean_loss': 0.001567196, 'step': 257}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 15, 317351, tzinfo=<UTC>), 'epoch': 68, 'mean_loss': 0.0004869902, 'step': 258}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 16, 557683, tzinfo=<UTC>), 'epoch': 69, 'mean_loss': 0.00044071442, 'step': 259}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 17, 885247, tzinfo=<UTC>), 'epoch': 69, 'mean_loss': 0.0005702487, 'step': 260}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 19, 119570, tzinfo=<UTC>), 'epoch': 69, 'mean_loss': 0.0006433289, 'step': 261}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 20, 364831, tzinfo=<UTC>), 'epoch': 69, 'mean_loss': 0.00045972248, 'step': 262}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 21, 605030, tzinfo=<UTC>), 'epoch': 70, 'mean_loss': 0.0005696737, 'step': 263}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 22, 866124, tzinfo=<UTC>), 'epoch': 70, 'mean_loss': 0.0011296477, 'step': 264}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 24, 175216, tzinfo=<UTC>), 'epoch': 70, 'mean_loss': 0.00031165092, 'step': 265}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 25, 651260, tzinfo=<UTC>), 'epoch': 70, 'mean_loss': 0.0004441709, 'step': 266}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 27, 399823, tzinfo=<UTC>), 'epoch': 71, 'mean_loss': 0.0011137151, 'step': 267}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 28, 659278, tzinfo=<UTC>), 'epoch': 71, 'mean_loss': 0.0009634383, 'step': 268}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 29, 919579, tzinfo=<UTC>), 'epoch': 71, 'mean_loss': 9.269314e-05, 'step': 269}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 31, 271483, tzinfo=<UTC>), 'epoch': 72, 'mean_loss': 0.0005366412, 'step': 270}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 32, 557850, tzinfo=<UTC>), 'epoch': 72, 'mean_loss': 7.907781e-05, 'step': 271}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 33, 808536, tzinfo=<UTC>), 'epoch': 72, 'mean_loss': 0.00013625692, 'step': 272}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 35, 93333, tzinfo=<UTC>), 'epoch': 72, 'mean_loss': 0.00040144066, 'step': 273}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 36, 349643, tzinfo=<UTC>), 'epoch': 73, 'mean_loss': 0.000531957, 'step': 274}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 37, 601945, tzinfo=<UTC>), 'epoch': 73, 'mean_loss': 0.00064232247, 'step': 275}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 38, 855989, tzinfo=<UTC>), 'epoch': 73, 'mean_loss': 5.250331e-08, 'step': 276}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 40, 101868, tzinfo=<UTC>), 'epoch': 73, 'mean_loss': 0.00054669485, 'step': 277}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 41, 311587, tzinfo=<UTC>), 'epoch': 74, 'mean_loss': 0.0001149911, 'step': 278}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 42, 504293, tzinfo=<UTC>), 'epoch': 74, 'mean_loss': 0.0006582851, 'step': 279}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 44, 606424, tzinfo=<UTC>), 'epoch': 74, 'mean_loss': 0.00040794525, 'step': 280}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 45, 885918, tzinfo=<UTC>), 'epoch': 74, 'mean_loss': 0.00040962768, 'step': 281}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 47, 88903, tzinfo=<UTC>), 'epoch': 75, 'mean_loss': 0.00083329267, 'step': 282}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 48, 353963, tzinfo=<UTC>), 'epoch': 75, 'mean_loss': 0.0011541949, 'step': 283}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 49, 634966, tzinfo=<UTC>), 'epoch': 75, 'mean_loss': 0.00031403676, 'step': 284}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 50, 872769, tzinfo=<UTC>), 'epoch': 76, 'mean_loss': 0.0001571991, 'step': 285}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 52, 132997, tzinfo=<UTC>), 'epoch': 76, 'mean_loss': 3.2733406e-05, 'step': 286}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 53, 373001, tzinfo=<UTC>), 'epoch': 76, 'mean_loss': 9.974141e-06, 'step': 287}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 54, 731122, tzinfo=<UTC>), 'epoch': 76, 'mean_loss': 0.00092323206, 'step': 288}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 56, 5106, tzinfo=<UTC>), 'epoch': 77, 'mean_loss': 0.00027380337, 'step': 289}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 57, 958533, tzinfo=<UTC>), 'epoch': 77, 'mean_loss': 0.000730188, 'step': 290}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 9, 59, 218392, tzinfo=<UTC>), 'epoch': 77, 'mean_loss': 8.784898e-05, 'step': 291}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 0, 458718, tzinfo=<UTC>), 'epoch': 77, 'mean_loss': 0.00035266066, 'step': 292}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 1, 678532, tzinfo=<UTC>), 'epoch': 78, 'mean_loss': 0.00035272574, 'step': 293}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 2, 925032, tzinfo=<UTC>), 'epoch': 78, 'mean_loss': 0.00036251757, 'step': 294}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 4, 185515, tzinfo=<UTC>), 'epoch': 78, 'mean_loss': 0.00063127023, 'step': 295}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 5, 450029, tzinfo=<UTC>), 'epoch': 78, 'mean_loss': 0.00056570326, 'step': 296}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 6, 669796, tzinfo=<UTC>), 'epoch': 79, 'mean_loss': 0.00019644247, 'step': 297}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 7, 912769, tzinfo=<UTC>), 'epoch': 79, 'mean_loss': 1.2713252e-05, 'step': 298}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 9, 283472, tzinfo=<UTC>), 'epoch': 79, 'mean_loss': 0.00024443713, 'step': 299}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 10, 628408, tzinfo=<UTC>), 'epoch': 80, 'mean_loss': 0.001333565, 'step': 300}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 11, 890583, tzinfo=<UTC>), 'epoch': 80, 'mean_loss': 0.0007013555, 'step': 301}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 13, 592038, tzinfo=<UTC>), 'epoch': 80, 'mean_loss': 2.6616384e-05, 'step': 302}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 14, 822838, tzinfo=<UTC>), 'epoch': 80, 'mean_loss': 0.0005623731, 'step': 303}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 16, 205657, tzinfo=<UTC>), 'epoch': 81, 'mean_loss': 0.00032505486, 'step': 304}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 18, 71680, tzinfo=<UTC>), 'epoch': 81, 'mean_loss': 0.0005101152, 'step': 305}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 19, 294720, tzinfo=<UTC>), 'epoch': 81, 'mean_loss': 0.0010035196, 'step': 306}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 20, 524938, tzinfo=<UTC>), 'epoch': 81, 'mean_loss': 0.00033056142, 'step': 307}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 21, 762389, tzinfo=<UTC>), 'epoch': 82, 'mean_loss': 6.966456e-05, 'step': 308}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 23, 25594, tzinfo=<UTC>), 'epoch': 82, 'mean_loss': 0.00037358585, 'step': 309}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 24, 257330, tzinfo=<UTC>), 'epoch': 82, 'mean_loss': 0.0002650607, 'step': 310}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 25, 497375, tzinfo=<UTC>), 'epoch': 82, 'mean_loss': 0.0009153653, 'step': 311}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 26, 713095, tzinfo=<UTC>), 'epoch': 83, 'mean_loss': 0.0009658756, 'step': 312}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 27, 973220, tzinfo=<UTC>), 'epoch': 83, 'mean_loss': 0.00014872942, 'step': 313}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 29, 226304, tzinfo=<UTC>), 'epoch': 83, 'mean_loss': 9.968644e-06, 'step': 314}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 30, 493632, tzinfo=<UTC>), 'epoch': 84, 'mean_loss': 0.00027738986, 'step': 315}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 31, 735723, tzinfo=<UTC>), 'epoch': 84, 'mean_loss': 0.0004675896, 'step': 316}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 32, 954139, tzinfo=<UTC>), 'epoch': 84, 'mean_loss': 0.00014443416, 'step': 317}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 34, 196120, tzinfo=<UTC>), 'epoch': 84, 'mean_loss': 0.0006946635, 'step': 318}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 35, 443514, tzinfo=<UTC>), 'epoch': 85, 'mean_loss': 0.0007360133, 'step': 319}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 36, 687667, tzinfo=<UTC>), 'epoch': 85, 'mean_loss': 1.326669e-06, 'step': 320}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 37, 903099, tzinfo=<UTC>), 'epoch': 85, 'mean_loss': 0.0005314335, 'step': 321}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 39, 138629, tzinfo=<UTC>), 'epoch': 85, 'mean_loss': 6.947189e-05, 'step': 322}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 40, 371666, tzinfo=<UTC>), 'epoch': 86, 'mean_loss': 0.00053617253, 'step': 323}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 41, 602304, tzinfo=<UTC>), 'epoch': 86, 'mean_loss': 9.1956696e-05, 'step': 324}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 42, 836714, tzinfo=<UTC>), 'epoch': 86, 'mean_loss': 0.00018627953, 'step': 325}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 44, 874288, tzinfo=<UTC>), 'epoch': 86, 'mean_loss': 0.0002088271, 'step': 326}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 46, 162027, tzinfo=<UTC>), 'epoch': 87, 'mean_loss': 0.00075449655, 'step': 327}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 47, 431864, tzinfo=<UTC>), 'epoch': 87, 'mean_loss': 7.8588026e-05, 'step': 328}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 48, 680277, tzinfo=<UTC>), 'epoch': 87, 'mean_loss': -1.3336539e-06, 'step': 329}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 49, 968707, tzinfo=<UTC>), 'epoch': 88, 'mean_loss': 0.00012271712, 'step': 330}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 51, 259023, tzinfo=<UTC>), 'epoch': 88, 'mean_loss': 0.0017514592, 'step': 331}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 52, 511807, tzinfo=<UTC>), 'epoch': 88, 'mean_loss': 4.1678373e-05, 'step': 332}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 53, 808914, tzinfo=<UTC>), 'epoch': 88, 'mean_loss': 0.0006764167, 'step': 333}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 55, 179417, tzinfo=<UTC>), 'epoch': 89, 'mean_loss': 0.00013730745, 'step': 334}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 56, 395678, tzinfo=<UTC>), 'epoch': 89, 'mean_loss': 0.00032095844, 'step': 335}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 57, 654428, tzinfo=<UTC>), 'epoch': 89, 'mean_loss': 0.00015303271, 'step': 336}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 58, 891717, tzinfo=<UTC>), 'epoch': 89, 'mean_loss': 0.00012956047, 'step': 337}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 0, 151925, tzinfo=<UTC>), 'epoch': 90, 'mean_loss': 7.675003e-05, 'step': 338}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 1, 398761, tzinfo=<UTC>), 'epoch': 90, 'mean_loss': 0.00044489285, 'step': 339}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 2, 641274, tzinfo=<UTC>), 'epoch': 90, 'mean_loss': 1.4036312e-05, 'step': 340}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 3, 883546, tzinfo=<UTC>), 'epoch': 90, 'mean_loss': 0.00015219976, 'step': 341}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 5, 141535, tzinfo=<UTC>), 'epoch': 91, 'mean_loss': 0.00018677826, 'step': 342}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 6, 373784, tzinfo=<UTC>), 'epoch': 91, 'mean_loss': 0.000115128816, 'step': 343}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 7, 645535, tzinfo=<UTC>), 'epoch': 91, 'mean_loss': 0.00039966288, 'step': 344}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 8, 884670, tzinfo=<UTC>), 'epoch': 92, 'mean_loss': 0.0001351597, 'step': 345}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 10, 149429, tzinfo=<UTC>), 'epoch': 92, 'mean_loss': 6.1459374e-05, 'step': 346}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 11, 448963, tzinfo=<UTC>), 'epoch': 92, 'mean_loss': 0.00023051281, 'step': 347}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 12, 693703, tzinfo=<UTC>), 'epoch': 92, 'mean_loss': 0.00078510307, 'step': 348}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 13, 938911, tzinfo=<UTC>), 'epoch': 93, 'mean_loss': 8.103554e-06, 'step': 349}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 15, 460723, tzinfo=<UTC>), 'epoch': 93, 'mean_loss': 0.0019005266, 'step': 350}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 16, 766511, tzinfo=<UTC>), 'epoch': 93, 'mean_loss': 6.863149e-06, 'step': 351}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 18, 27508, tzinfo=<UTC>), 'epoch': 93, 'mean_loss': 0.0002926389, 'step': 352}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 19, 286497, tzinfo=<UTC>), 'epoch': 94, 'mean_loss': 0.00013998023, 'step': 353}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 20, 556785, tzinfo=<UTC>), 'epoch': 94, 'mean_loss': 2.2997847e-05, 'step': 354}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 21, 804735, tzinfo=<UTC>), 'epoch': 94, 'mean_loss': 0.0005936278, 'step': 355}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 23, 22367, tzinfo=<UTC>), 'epoch': 94, 'mean_loss': 3.43258e-05, 'step': 356}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 24, 284890, tzinfo=<UTC>), 'epoch': 95, 'mean_loss': 0.00010312116, 'step': 357}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 25, 525734, tzinfo=<UTC>), 'epoch': 95, 'mean_loss': 0.00015714776, 'step': 358}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 26, 780375, tzinfo=<UTC>), 'epoch': 95, 'mean_loss': 5.73016e-05, 'step': 359}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 28, 23466, tzinfo=<UTC>), 'epoch': 96, 'mean_loss': 0.00012817327, 'step': 360}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 29, 268204, tzinfo=<UTC>), 'epoch': 96, 'mean_loss': 3.9030332e-05, 'step': 361}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 30, 507390, tzinfo=<UTC>), 'epoch': 96, 'mean_loss': 0.0005360425, 'step': 362}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 31, 727121, tzinfo=<UTC>), 'epoch': 96, 'mean_loss': 0.00017444952, 'step': 363}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 33, 34563, tzinfo=<UTC>), 'epoch': 97, 'mean_loss': 0.0010171408, 'step': 364}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 34, 447584, tzinfo=<UTC>), 'epoch': 97, 'mean_loss': 0.0004899306, 'step': 365}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 35, 699821, tzinfo=<UTC>), 'epoch': 97, 'mean_loss': 0.00017226115, 'step': 366}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 36, 936549, tzinfo=<UTC>), 'epoch': 97, 'mean_loss': 4.2724423e-07, 'step': 367}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 38, 203783, tzinfo=<UTC>), 'epoch': 98, 'mean_loss': 1.9560219e-05, 'step': 368}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 39, 464518, tzinfo=<UTC>), 'epoch': 98, 'mean_loss': 0.00011098804, 'step': 369}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 40, 721560, tzinfo=<UTC>), 'epoch': 98, 'mean_loss': 0.0005288075, 'step': 370}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 41, 968386, tzinfo=<UTC>), 'epoch': 98, 'mean_loss': 4.2606727e-05, 'step': 371}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 43, 207703, tzinfo=<UTC>), 'epoch': 99, 'mean_loss': 1.1964934e-05, 'step': 372}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 45, 297458, tzinfo=<UTC>), 'epoch': 99, 'mean_loss': 0.00035788305, 'step': 373}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 46, 714760, tzinfo=<UTC>), 'epoch': 99, 'mean_loss': 7.525133e-05, 'step': 374}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 48, 17671, tzinfo=<UTC>), 'epoch': 100, 'mean_loss': 5.5355486e-06, 'step': 375}], hyperparameters=Hyperparameters(epoch_count=100, batch_size=4, learning_rate=0.001)))
model.description
'This is my model.'
মডেল মুছুন
আপনার আর প্রয়োজন নেই এমন মডেলগুলি মুছে দিয়ে আপনি আপনার টিউন করা মডেল তালিকা পরিষ্কার করতে পারেন৷ একটি মডেল মুছে ফেলার জন্য genai.delete_tuned_model
পদ্ধতি ব্যবহার করুন। আপনি যদি কোনো টিউনিং কাজ বাতিল করেন, আপনি সেগুলি মুছে দিতে চাইতে পারেন কারণ তাদের কর্মক্ষমতা অপ্রত্যাশিত হতে পারে।
genai.delete_tuned_model(f'tunedModels/{name}')
try:
m = genai.get_tuned_model(f'tunedModels/{name}')
print(m)
except Exception as e:
print(f"{type(e)}: {e}")
<class 'google.api_core.exceptions.NotFound'>: 404 Tuned model tunedModels/generate-num-4668 does not exist.