Invalid credentials. Please try again.

Infrastructure Dashboard

Real-time monitoring and management of your AI services

🖥️
100%
Server Uptime
4
Active Services
🧠
2.4M
Requests Processed
📊
47GB
Data Processed Today

Active Services

🖥️

Dedicated Cloud Server - H100

Active
GPU
NVIDIA H100 80GB
CPU
48 vCPUs
RAM
512 GB
Storage
4 TB NVMe
Network
50 Gbps
Uptime
99.99%
GPU Utilization 67%
Storage Used 2.1 TB / 4 TB
🧠

Enterprise Model Build - Llama Fine-Tuning

Training in Progress
Base Model
Llama 3.1 70B
Dataset Size
2.3M Samples
Training Epochs
3 / 5
Est. Completion
14 Hours
Training Progress 60%
0.342
Training Loss
0.398
Validation Loss
87.3%
Accuracy
3.2k
Steps/Epoch
🌾

GPT Model - Agro-Food Sector Adaptation

Deployed
Model Version
AgriGPT v2.4
Specialization
Agro-Food
Response Quality
94.3%
Maintenance
Annual Plan
154k
API Calls (Today)
98.7%
Success Rate
142ms
Avg. Latency
99.99%
Uptime
🤖

AI Agents & Workflow Automation

4 Agents Active
Integration Type
RAG + CAG + SQL
ERP Connected
Keops, Hispatec
Documents Indexed
12,847
Automation Rules
23 Active
🔍 Document Search Agent
Processing 47 queries/hour
Active
💬 Customer Support Bot
23 conversations active
Active
📊 Data Analysis Agent
2 reports generated today
Active
🔄 Workflow Automator
14 tasks completed today
Active

Server Metrics - H100 GPU

GPU Temperature
72°C
Power Consumption
650W
Memory Used
67.3 GB
CPU Load
45%

GPU Utilization (Last 24 Hours)

65%
00:00
72%
04:00
58%
08:00
81%
12:00
76%
16:00
67%
20:00

Network Traffic

22 Gbps
Inbound
19 Gbps
Outbound

Server Console

cognitus-h100-01:~$ nvidia-smi

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03   Driver Version: 535.129.03   CUDA Version: 12.2     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  NVIDIA H100 80GB    On   | 00000000:01:00.0 Off |                    0 |
| N/A   72C    P0   650W / 700W |  67328MiB / 81920MiB |     67%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
|    0   N/A  N/A      1337      C   python3 llama_training.py      45123MiB |
|    0   N/A  N/A      2891      C   python3 inference_server.py    22205MiB |
+-----------------------------------------------------------------------------+

System Logs

[14:23:47] INFO GPU compute task completed successfully
[14:18:32] INFO Training checkpoint saved: epoch_3_step_9600.pt
[14:15:09] WARN GPU temperature approaching 75°C threshold
[14:10:55] INFO Batch processing completed: 2048 samples processed
[14:05:22] INFO Model inference request received from API gateway

Training Logs - Llama 3.1 Fine-Tuning

Current Epoch
3 / 5
Learning Rate
2.1e-5
Batch Size
32
Total Steps
9,600

Loss Over Time

1.24
Epoch 1
0.87
Epoch 2
0.56
Epoch 3
Est. 0.38
Epoch 4
Est. 0.29
Epoch 5

Training Log Output

[Training] Epoch 3/5 | Step 9600/16000
[Training] Loss: 0.342 | Val Loss: 0.398 | Accuracy: 87.3%
[Training] Tokens/sec: 45,230 | GPU Util: 98%
[Training] Est. time remaining: 14h 22m
[Training] Checkpoint saved: llama-3.1-70b-agrots-ep3-s9600.pt
[Training] Memory allocated: 67.3GB / 80GB
[Optimizer] Learning rate: 2.1e-5
[Dataset] Processed 2.3M / 2.3M samples
[Validation] Running validation on 50k samples...
[Validation] Accuracy: 87.3% | F1 Score: 0.891
[Training] Resuming training...

Model Checkpoints

Checkpoint Name Epoch Loss Size Action
llama-3.1-70b-agrots-ep3-s9600.pt 3 0.342 142 GB
llama-3.1-70b-agrots-ep2-s6400.pt 2 0.621 142 GB
llama-3.1-70b-agrots-ep1-s3200.pt 1 0.987 142 GB

Download Instructions

# Using AWS CLI
aws s3 cp s3://cognitus-models/llama-3.1-70b-agrots-ep3-s9600.pt ./

# Using curl
curl -O https://models.cognituslab.com/checkpoints/llama-3.1-70b-agrots-ep3-s9600.pt

# Verify checksum
sha256sum llama-3.1-70b-agrots-ep3-s9600.pt

API Documentation - AgriGPT v2.4

API Overview

The AgriGPT API provides access to our specialized GPT model optimized for the agro-food sector. The API supports real-time inference, batch processing, and streaming responses.

Base URL
api.cognituslab.com
Rate Limit
10,000/hour
Max Tokens
4,096
Response Time
~142ms

Authentication

curl -X POST https://api.cognituslab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json"
Endpoint Method Description
/v1/chat/completions POST Generate chat completions
/v1/embeddings POST Generate embeddings
/v1/models GET List available models
/v1/batch POST Submit batch processing job

Request Example

{
  "model": "agrigpt-v2.4",
  "messages": [
    {
      "role": "system",
      "content": "You are an expert in agro-food supply chain management."
    },
    {
      "role": "user",
      "content": "What are the optimal storage conditions for fresh tomatoes?"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 500
}

Response Example

{
  "id": "chatcmpl-8vQx2K4N",
  "object": "chat.completion",
  "model": "agrigpt-v2.4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "Optimal storage conditions for fresh tomatoes..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 45,
    "completion_tokens": 127,
    "total_tokens": 172
  }
}

API Test Console

Test Request

API Analytics

Total Requests
5.7M
Success Rate
98.7%
Avg Latency
142ms
Error Rate
1.3%

Daily API Calls

142k
Mon
156k
Tue
129k
Wed
172k
Thu
167k
Fri
85k
Sat
76k
Sun

Response Time Distribution

72%
<100ms
18%
100-200ms
7%
200-500ms
3%
>500ms

Manage AI Agents

🔍 Document Search Agent
RAG-based semantic search over 12,847 indexed documents
47
Queries/Hour
94.2%
Accuracy
1.2s
Avg Response
💬 Customer Support Bot
Multi-channel support with ERP integration
23
Active Chats
4.2
Avg Rating
87%
Resolution Rate
📊 Data Analysis Agent
Automated insights and report generation
2
Reports Today
127
Data Points
100%
Accuracy
🔄 Workflow Automator
Multi-step process automation with 23 active rules
14
Tasks Today
23
Active Rules
99.1%
Success Rate