> ## Documentation Index
> Fetch the complete documentation index at: https://docs.flex.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Checking the Training Job's Details
> Inspect the details and status of your training job
## Training Summary
### General Details about your Training Jobs
The "All Trainings" table in the FlexAI Console provides a summary of all your Training Jobs.
You can select the gear icon ⚙️ (labeled as *Configure*) in the `Actions` field of the Training Jobs list page. This will open a "Details" panel. The **Details** tab will be selected by default, showing all the relevant information about your Training Job.
| Field | Description |
| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------ |
| `Name` | The name you assigned to the Training Job. |
| `Status` | The current status of the Training Job (e.g., `pending`, `scheduling`, `building`, `in progress`, `succeeded`, `failed`, `stopped`, etc.). |
| `Created At` | Workload creation age. |
You can learn more about the different Training Job statuses on the [Lifecycle](/core-services/training/lifecycle) page.
### General Details about your Training Jobs
You can use the `list` command to get a table with general information about all the Training Jobs you have access to through your FlexAI account:
```bash theme={null}
flexai training list
```
This provides an output similar to the following:
```text theme={null}
NAME | DEVICE | NODE | ACCELERATOR | DATASET | REPOSITORY | STATUS | AGE
------------------------+--------+------+-------------+-----------------+-------------------------------------+----------+------
quickstart-training-job | nvidia | 1 | 1 | nanoGPT-dataset | https://github.com/flexaihq/nanogpt | building | 15s
```
## Training Configuration
| Field | Description |
| --------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `Dashboard URL` | The URL of the Training Job dashboard, where you can monitor the performance and resource usage of your Training Job. |
| `Tensorboard Dashboard URL` | The URL of the FlexAI-hosted TensorBoard dashboard, where you can visualize the training process of your models. |
| `Node Count` | The number of nodes allocated to the Training Job. |
| `Accelerator Count` | The number of accelerators (GPUs) allocated to the Training Job. |
| `Repository URL` | The URL of the Git repository containing your training code. |
| `Repository Revision` | The specific commit or branch of the repository that was used to create the Training Job. |
| `Repository Revision SHA` | The SHA hash of the specific commit or branch of the repository that was used to create the Training Job. |
| `Entry Point` | The entry point script along with its arguments. |
| `Datasets` | The datasets that were attached to the Training Job. |
| `Environment` | The environment variables and secrets that were set for the Training Job. Displayed in a Key-Value pai format where the Key is the name of the environment value within the Training Runtime, and the value is either the raw value (for Environment Variables) or the name of the **FlexAI secret** containing the secret value. |
| `Checkpoints` | The checkpoints that were created during the Training Job. These are stored in the FlexAI object storage and can be used to resume training or to create an Inference Endpoint (depending on the type of model). |
You can have a deeper look at the Training Job status using the [flexai training inspect](/cli/reference/training/inspect) command. Especially useful for debugging purposes:
```bash theme={null}
flexai training inspect quickstart-training-job
```
Below you will find an example of the output you will get when running the `inspect` command:
```yaml theme={null}
kind: Training
metadata:
name: quickstart-training-job
id: 75179cc2-ec63-4f93-b4da-44e49ea86049
creatorUserID: 16e289cc-c81b-4a15-91d9-0e2aae00a317
ownerOrgId: 270a5476-b91a-442f-8a13-852ef7bb5b9c
config:
device: nvidia
nodes: 1
accelerator: 1
entrypoint:
- train.py
- config/train_shakespeare_char.py
- --out_dir=/output-checkpoint
- --max_iters=1500
datasetsNames:
- nanoGPT-dataset
checkpointName: ""
sourceName: ""
repositoryURL: https://github.com/flexaihq/nanogpt
repositoryRevision: main
secrets: []
environment: []
runtime:
status: succeeded
queuePosition: 0
repositoryRevisionSha: 116799dbae7b0fe33caf1b90f73a72f84bc32adc
selectedAgentId: k8s-training-sesterce-001-CLIENT-PROD-client-prod
lifecycleEvents:
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: aws-cloud
AgentID: k8s-training-aws-001-CLIENT-PROD-client-prod
Response: NoAnswer
Conditions: [NonSchedulable: NoAnswer]
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: sesterce-h100-bm-01
AgentID: k8s-training-sesterce-001-CLIENT-PROD-client-prod
Response: OK
Conditions: []
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: sesterce-h200-bm-01
AgentID: k8s-training-sesterce-002-CLIENT-PROD-client-prod
Response: NoAnswer
Conditions: [NonSchedulable: NoAnswer]
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: sesterce-l40s-bm-01
AgentID: k8s-training-sesterce-003-CLIENT-PROD-client-prod
Response: NoAnswer
Conditions: [NonSchedulable: NoAnswer]
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: sesterce-a100-bm-01
AgentID: k8s-training-sesterce-004-CLIENT-PROD-client-prod
Response: NoAnswer
Conditions: [NonSchedulable: NoAnswer]
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: ResponseReceived
message: |-
Cluster Scheduling result{
Name: k8s-training-smc-001
AgentID: k8s-training-smc-001-CLIENT-PROD-client-prod
Response: NoAnswer
Conditions: [NonSchedulable: NoAnswer, OrgNotAuthorized]
}
raisedAt: "2025-06-30T11:41:54Z"
- type: AgentSelection
status: Completed
message: Selected agent k8s-training-sesterce-001-CLIENT-PROD-client-prod
raisedAt: "2025-06-30T11:41:54Z"
- type: BuildSubmission
status: Succeeded
message: Build request sent to flex-agent
raisedAt: "2025-06-30T11:41:54Z"
- type: BuildExecution
status: Succeeded
message: Build completed with image rg.fr-par.scw.cloud/paas-trainings-client-prod/9f9c379c-8d46-419b-8bf5-d0b0986a6dd9-arch_nvidia-1x1@sha256:0d854f75f698a549d2a8a0e024e930383b885bdac2863ee0cf74ebdc8a8f358c
raisedAt: "2025-06-30T11:41:54Z"
- type: TrainingPreparation
status: Succeeded
message: Training trainings-client-prod/training-75b79cc2-ec63-4f93-b4da-44e49a4a6049-zqg6d created
raisedAt: "2025-06-30T11:41:54Z"
- type: TrainingExecution
status: InProgress
message: Training in progress
raisedAt: "2025-06-30T11:42:00Z"
- type: TrainingExecution
status: Succeeded
message: Training complete, output available
raisedAt: "2025-06-30T11:43:48Z"
createdAt: "2025-06-30T11:41:54Z"
lastUpdate: "2025-06-30T11:43:48Z"
```
```json theme={null}
{
"kind": "Training",
"metadata": {
"name": "quickstart-training-job",
"id": "75179cc2-ec63-4f93-b4da-44e49ea86049",
"creatorUserID": "16e289cc-c81b-4a15-91d9-0e2aae00a317",
"ownerOrgId": "270a5476-b91a-442f-8a13-852ef7bb5b9c"
},
"config": {
"device": "nvidia",
"nodes": 1,
"accelerator": 1,
"entrypoint": [
"train.py",
"config/train_shakespeare_char.py",
"--out_dir=/output-checkpoint",
"--max_iters=1500"
],
"datasetsNames": ["nanoGPT-dataset"],
"checkpointName": "",
"sourceName": "",
"repositoryURL": "https://github.com/flexaihq/nanogpt",
"repositoryRevision": "main",
"secrets": [],
"environment": []
},
"runtime": {
"status": "succeeded",
"queuePosition": 0,
"repositoryRevisionSha": "116799dbae7b0fe33caf1b90f73a72f84bc32adc",
"selectedAgentId": "k8s-training-sesterce-001-CLIENT-PROD-client-prod",
"lifecycleEvents": [
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: aws-cloud\n AgentID: k8s-training-aws-001-CLIENT-PROD-client-prod\n Response: NoAnswer\n Conditions: [NonSchedulable: NoAnswer]\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: sesterce-h100-bm-01\n AgentID: k8s-training-sesterce-001-CLIENT-PROD-client-prod\n Response: OK\n Conditions: []\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: sesterce-h200-bm-01\n AgentID: k8s-training-sesterce-002-CLIENT-PROD-client-prod\n Response: NoAnswer\n Conditions: [NonSchedulable: NoAnswer]\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: sesterce-l40s-bm-01\n AgentID: k8s-training-sesterce-003-CLIENT-PROD-client-prod\n Response: NoAnswer\n Conditions: [NonSchedulable: NoAnswer]\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: sesterce-a100-bm-01\n AgentID: k8s-training-sesterce-004-CLIENT-PROD-client-prod\n Response: NoAnswer\n Conditions: [NonSchedulable: NoAnswer]\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "ResponseReceived",
"message": "Cluster Scheduling result{\n Name: k8s-training-smc-001\n AgentID: k8s-training-smc-001-CLIENT-PROD-client-prod\n Response: NoAnswer\n Conditions: [NonSchedulable: NoAnswer, OrgNotAuthorized]\n}\n",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "AgentSelection",
"status": "Completed",
"message": "Selected agent k8s-training-sesterce-001-CLIENT-PROD-client-prod",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "BuildSubmission",
"status": "Succeeded",
"message": "Build request sent to flex-agent",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "BuildExecution",
"status": "Succeeded",
"message": "Build completed with image rg.fr-par.scw.cloud/paas-trainings-client-prod/9f9c379c-8d46-419b-8bf5-d0b0986a6dd9-arch_nvidia-1x1@sha256:0d854f75f698a549d2a8a0e024e930383b885bdac2863ee0cf74ebdc8a8f358c",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "TrainingPreparation",
"status": "Succeeded",
"message": "Training trainings-client-prod/training-75b79cc2-ec63-4f93-b4da-44e49a4a6049-zqg6d created",
"raisedAt": "2025-06-30T11:41:54Z"
},
{
"type": "TrainingExecution",
"status": "InProgress",
"message": "Training in progress",
"raisedAt": "2025-06-30T11:42:00Z"
},
{
"type": "TrainingExecution",
"status": "Succeeded",
"message": "Training complete, output available",
"raisedAt": "2025-06-30T11:43:48Z"
}
],
"createdAt": "2025-06-30T11:41:54Z",
"lastUpdate": "2025-06-30T11:43:48Z"
}
}
```
## Next Steps
Once a Training Job is running, you can monitor its progress by checking its logs and leveraging the FlexAI Observability Services.
You'll learn more about this in the next step of the Quickstart Tutorial: [Monitoring a Training Job's Progress](/core-services/training/quickstart/monitoring-progress/).