Running MLAgentBench - Githubissues

(autogpt) root@de5ed691ecf6:/MLAgentBench# python -u -m MLAgentBench.runner --python $(which python) --task cifar10 --device 0 --log-dir first_test --work-dir workspace --llm-name gpt-4 --edit-script-llm-name gpt-4 --fast-llm-name gpt-3.5-turbo
Output
[Errno 2] No such file or directory: 'crfm_api_key.txt'
Could not load CRFM API key crfm_api_key.txt.
module 'anthropic' has no attribute 'Anthropic'
Could not load anthropic API key claude_api_key.txt.
Failed to import AutoGPTAgent; Make sure you have installed the autogpt dependencies if you want to use it.
Namespace(task='cifar10', log_dir='first_test', work_dir='workspace', max_steps=50, max_time=18000, device=0, python='/home/user/micromamba/envs/autogpt/bin/python', interactive=False, resume=None, resume_step=0, agent_type='ResearchAgent', llm_name='gpt-4', fast_llm_name='gpt-3.5-turbo', edit_script_llm_name='gpt-4', edit_script_llm_max_tokens=4000, agent_max_steps=50, actions_remove_from_prompt=[], actions_add_to_prompt=[], no_retrieval=False, valid_format_entires=None, max_steps_in_context=3, max_observation_steps_in_context=3, max_retries=5, langchain_agent='zero-shot-react-description')
log_dir first_test/env_log already exists
tools_log_dir first_test/env_log/tool_logs already exists
tools_log_dir first_test/env_log/traces already exists
prepare.py not found or already prepared
=====================================
Benchmark folder name:  cifar10
Research problem:  Given a training script on a dataset train.py, improve upon the current model performance (trained with current hyperparmeters in train.py) for more than 10%. The training epochs should be within 10 to save time. Save per class probabilities for test set examples to submission.csv as shown in train.py. 
Lower level actions enabled:  ['List Files', 'Read File', 'Write File', 'Append File', 'Copy File', 'Undo Edit Script', 'Execute Script', 'Python REPL', 'Final Answer']
High level actions enabled:  ['Understand File', 'Append Summary to Research Log', 'Inspect Script Lines', 'Edit Script (AI)', 'Edit Script Segment (AI)', 'Reflection', 'Retrieval from Research Log']
Read only files:  ['data/cifar-10-python.tar.gz', 'data/cifar-10-batches-py/data_batch_1', 'data/cifar-10-batches-py/readme.html', 'data/cifar-10-batches-py/batches.meta', 'data/cifar-10-batches-py/data_batch_2', 'data/cifar-10-batches-py/data_batch_5', 'data/cifar-10-batches-py/test_batch', 'data/cifar-10-batches-py/data_batch_4', 'data/cifar-10-batches-py/data_batch_3']
=====================================
Log dir first_test/agent_log already exists. Overwriting.
Agent is up! See progress in first_test/agent_log/main_log
Step 1

Assistant:
Reflection: The observation provides a detailed understanding of the training script. The script is implementing a neural network model for image classification using the CIFAR-10 dataset. The model consists of two convolutional layers, followed by two fully connected layers, and an output layer. The script uses a batch size of 128, a learning rate of 0.1, and a momentum of 0.9 for the optimizer. The number of training epochs is set to 5. The script saves the per class probabilities for test set examples in a CSV file named `submission.csv`.

Research Plan and Status:  
1. Understand the problem and the current script. (Done)
    - The script is implementing a neural network model for image classification using the CIFAR-10 dataset. The model consists of two convolutional layers, followed by two fully connected layers, and an output layer. The script uses a batch size of 128, a learning rate of 0.1, and a momentum of 0.9 for the optimizer. The number of training epochs is set to 5. The script saves the per class probabilities for test set examples in a CSV file named `submission.csv`.
2. Define and measure the baseline performance of the current model. (Not started)
3. Come up with a plan to improve the model performance. (Not started)
4. Implement the plan and measure the new performance. (Not started)
5. If the performance is improved by more than 10%, save per class probabilities for test set examples to submission.csv. (Not started)
6. If the performance is not improved by more than 10%, revise the plan and repeat steps 4-6. (Not started)

Fact Check: 
- The script is implementing a neural network model for image classification using the CIFAR-10 dataset. (Confirmed by observation)
- The model consists of two convolutional layers, followed by two fully connected layers, and an output layer. (Confirmed by observation)
- The script uses a batch size of 128, a learning rate of 0.1, and a momentum of 0.9 for the optimizer. (Confirmed by observation)
- The number of training epochs is set to 5. (Confirmed by observation)
- The script saves the per class probabilities for test set examples in a CSV file named `submission.csv`. (Confirmed by observation)
Thought: Now that I understand the current script, I need to define and measure the baseline performance
Observation:

Response is invalid and discarded
STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDOUT: [1,   100] loss: 2.089
 STDOUT: [1,   200] loss: 1.754
 STDOUT: [1,   300] loss: 1.642
 STDOUT: Epoch [1/5], Train Accuracy: 45.44%, Test Accuracy: 44.77%
 STDOUT: [2,   100] loss: 1.531
 STDOUT: [2,   200] loss: 1.506
 STDOUT: [2,   300] loss: 1.473
 STDOUT: Epoch [2/5], Train Accuracy: 49.39%, Test Accuracy: 47.76%
 STDOUT: [3,   100] loss: 1.389
 STDOUT: [3,   200] loss: 1.430
 STDOUT: [3,   300] loss: 1.430
 STDOUT: Epoch [3/5], Train Accuracy: 52.53%, Test Accuracy: 49.42%
 STDOUT: [4,   100] loss: 1.342
 STDOUT: [4,   200] loss: 1.353
 STDOUT: [4,   300] loss: 1.398
 STDOUT: Epoch [4/5], Train Accuracy: 55.17%, Test Accuracy: 51.80%
 STDOUT: [5,   100] loss: 1.300
 STDOUT: [5,   200] loss: 1.342
 STDOUT: [5,   300] loss: 1.374
 STDOUT: Epoch [5/5], Train Accuracy: 54.56%, Test Accuracy: 51.14%
 STDOUT: Train Accuracy: 54.55%, Test Accuracy: 51.14%
 STDERR:  STDOUT:  STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDERR: Traceback (most recent call last):
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v2.py", line 77, in <module>
 STDERR:     outputs = model(inputs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v2.py", line 28, in forward
 STDERR:     x = self.pool(F.relu(self.conv3(x)))
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/pooling.py", line 166, in forward
 STDERR:     return F.max_pool2d(input, self.kernel_size, self.stride,
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/_jit_internal.py", line 484, in fn
 STDERR:     return if_false(*args, **kwargs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/functional.py", line 782, in _max_pool2d
 STDERR:     return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)
 STDERR: RuntimeError: Given input size: (32x1x1). Calculated output size: (32x0x0). Output size is too small
 STDERR:  STDOUT:  STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDERR: Traceback (most recent call last):
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v3.py", line 77, in <module>
 STDERR:     outputs = model(inputs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v3.py", line 30, in forward
 STDERR:     x = F.relu(self.fc1(x))
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
 STDERR:     return F.linear(input, self.weight, self.bias)
 STDERR: RuntimeError: mat1 and mat2 shapes cannot be multiplied (128x128 and 1152x120)
 STDERR:  STDOUT:  STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDERR: Traceback (most recent call last):
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v4.py", line 77, in <module>
 STDERR:     outputs = model(inputs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/MLAgentBench/workspace/cifar10/train_v4.py", line 30, in forward
 STDERR:     x = F.relu(self.fc1(x))
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1501, in _call_impl
 STDERR:     return forward_call(*args, **kwargs)
 STDERR:   File "/home/user/micromamba/envs/autogpt/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 114, in forward
 STDERR:     return F.linear(input, self.weight, self.bias)
 STDERR: RuntimeError: mat1 and mat2 shapes cannot be multiplied (128x128 and 512x120)
 STDERR:  STDOUT:  STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDOUT: [1,   100] loss: 2.229
 STDOUT: [1,   200] loss: 1.945
 STDOUT: [1,   300] loss: 1.754
 STDOUT: Epoch [1/5], Train Accuracy: 39.83%, Test Accuracy: 38.97%
 STDOUT: [2,   100] loss: 1.547
 STDOUT: [2,   200] loss: 1.536
 STDOUT: [2,   300] loss: 1.517
 STDOUT: Epoch [2/5], Train Accuracy: 48.49%, Test Accuracy: 47.67%
 STDOUT: [3,   100] loss: 1.451
 STDOUT: [3,   200] loss: 1.422
 STDOUT: [3,   300] loss: 1.439
 STDOUT: Epoch [3/5], Train Accuracy: 51.73%, Test Accuracy: 50.13%
 STDOUT: [4,   100] loss: 1.368
 STDOUT: [4,   200] loss: 1.380
 STDOUT: [4,   300] loss: 1.396
 STDOUT: Epoch [4/5], Train Accuracy: 52.95%, Test Accuracy: 50.86%
 STDOUT: [5,   100] loss: 1.364
 STDOUT: [5,   200] loss: 1.375
 STDOUT: [5,   300] loss: 1.371
 STDOUT: Epoch [5/5], Train Accuracy: 52.31%, Test Accuracy: 50.99%
 STDOUT: Train Accuracy: 52.33%, Test Accuracy: 50.99%
 STDERR: [W NNPACK.cpp:64] Could not initialize NNPACK! Reason: Unsupported hardware.
 STDERR:  STDOUT:  Step:  47
This model's maximum context length is 4097 tokens. However, you requested 4181 tokens (3681 in the messages, 500 in the completion). Please reduce the length of the messages or completion.
STDOUT: Files already downloaded and verified
 STDOUT: Files already downloaded and verified
 STDOUT: [1,   100] loss: 2.303
 STDOUT: [1,   200] loss: 2.300
 STDOUT: [1,   300] loss: 2.293
 STDOUT: Epoch [1/10], Train Accuracy: 21.29%, Test Accuracy: 21.67%
 STDOUT: [2,   100] loss: 2.034
 STDOUT: [2,   200] loss: 1.909
 STDOUT: [2,   300] loss: 1.788
 STDOUT: Epoch [2/10], Train Accuracy: 35.01%, Test Accuracy: 34.78%
 STDOUT: [3,   100] loss: 1.683
 STDOUT: [3,   200] loss: 1.638
 STDOUT: [3,   300] loss: 1.600
 STDOUT: Epoch [3/10], Train Accuracy: 44.44%, Test Accuracy: 44.64%
 STDOUT: [4,   100] loss: 1.510
 STDOUT: [4,   200] loss: 1.465
 STDOUT: [4,   300] loss: 1.457
 STDOUT: Epoch [4/10], Train Accuracy: 48.76%, Test Accuracy: 48.51%
 STDOUT: [5,   100] loss: 1.372
 STDOUT: [5,   200] loss: 1.383
 STDOUT: [5,   300] loss: 1.330
 STDOUT: Epoch [5/10], Train Accuracy: 53.70%, Test Accuracy: 52.54%
 STDOUT: [6,   100] loss: 1.301
 STDOUT: [6,   200] loss: 1.283
 STDOUT: [6,   300] loss: 1.265
 STDOUT: Epoch [6/10], Train Accuracy: 57.52%, Test Accuracy: 55.95%
 STDOUT: [7,   100] loss: 1.215
 STDOUT: [7,   200] loss: 1.205
 STDOUT: [7,   300] loss: 1.219
 STDOUT: Epoch [7/10], Train Accuracy: 58.28%, Test Accuracy: 55.72%
 STDOUT: [8,   100] loss: 1.151
 STDOUT: [8,   200] loss: 1.154
 STDOUT: [8,   300] loss: 1.167
 STDOUT: Epoch [8/10], Train Accuracy: 59.46%, Test Accuracy: 56.45%
 STDOUT: [9,   100] loss: 1.103
 STDOUT: [9,   200] loss: 1.119
 STDOUT: [9,   300] loss: 1.096
 STDOUT: Epoch [9/10], Train Accuracy: 63.00%, Test Accuracy: 59.74%
 STDOUT: [10,   100] loss: 1.078
 STDOUT: [10,   200] loss: 1.073
 STDOUT: [10,   300] loss: 1.061
 STDOUT: Epoch [10/10], Train Accuracy: 65.02%, Test Accuracy: 61.13%
 STDOUT: Train Accuracy: 65.03%, Test Accuracy: 61.13%
 STDERR: [W NNPACK.cpp:64] Could not initialize NNPACK! Reason: Unsupported hardware.
 STDERR:  STDOUT:  =====================================
Final message:  Finished due to env.is_final() == True
Active Children: 0
Active Children: 0
DeepShareAI / MLAgentBench

Running MLAgentBench #2
