Gradient Docs
Gradient HomeHelp DeskCommunitySign up free
1.0.0
1.0.0
  • About Paperspace Gradient
  • Get Started
    • Quick Start
    • Core Concepts
    • Install the Gradient CLI
    • Common Errors
  • Tutorials
    • Tutorials List
      • Getting Started with Notebooks
      • Train a Model with the Web UI
      • Train a Model with the CLI
      • Advanced: Distributed training sample project
      • Registering Models in Gradient
      • Using Gradient Deployments
      • Using Custom Containers
  • Notebooks
    • Overview
    • Using Notebooks
      • The Notebook interface
      • Notebook metrics
      • Share a Notebook
      • Fork a Notebook
      • Notebook Directories
      • Notebook Containers
        • Building a Custom Container
      • Notebook Workspace Include Files
      • Community (Public) Notebooks
    • ML Showcase
    • Run on Gradient (GitHub badge)
  • Projects
    • Overview
    • Managing Projects
    • GradientCI
      • GradientCI V1 (Deprecated)
  • Workflows
    • Overview
      • Getting Started with Workflows
      • Workflow Spec
      • Gradient Actions
  • Experiments
    • Overview
    • Using Experiments
      • Containers
      • Single-node & multi-node CLI options
      • Experiment options
      • Gradient Config File
      • Environment variables
      • Experiment datasets
      • Git Commit Tracking
      • Experiment metrics
        • System Metrics
        • Custom Metrics
      • Experiment Logs
      • Experiment Ports
      • GradientCI Experiments
      • Diff Viewer
      • Hyperparameter Tuning
    • Distributed Training
      • Distributed Machine Learning with Tensorflow
      • Distributed Machine Learning with MPI
        • Distributed Training using Horovod
        • Distributed Training Using ChainerMN
  • Jobs
    • Overview
    • Using Jobs
      • Stop a Job
      • Delete a Job
      • List Jobs
      • Job Logs
      • Job Metrics
        • System Metrics
        • Custom Metrics
      • Job Artifacts
      • Public Jobs
      • Building Docker Containers with Jobs
  • Models
    • Overview
    • Managing Models
      • Example: Prepare a TensorFlow Model for Deployments
      • Model Path, Parameters, & Metadata
    • Public Models
  • Deployments
    • Overview
    • Managing Deployments
      • Deployment Containers
        • Custom Deployment Containers
      • Deployment States
      • Deployment Logs
      • Deployment Metrics
      • A Deployed Model's API Endpoint
        • Gradient + TensorFlow Serving
      • Deployment Autoscaling
      • Optimize Models for Inference
  • Data
    • Types of Storage
      • Managing Data in Gradient
        • Managing Persistent Storage with VMs
    • Storage Providers
    • Versioned Datasets
    • Public Datasets Repository
  • TensorBoards
    • Overview
    • Using Tensorboards
      • TensorBoards getting started with Tensorflow
  • Metrics
    • Metrics Overview
    • View and Query Metrics
    • Push Metrics
  • Secrets
    • Overview
    • Using Secrets
  • Gradient SDK
    • Gradient SDK Overview
      • Projects Client
      • Experiments Client
      • Models Client
      • Deployments Client
      • Jobs Client
    • End to end tutorial
    • Full SDK Reference
  • Instances
    • Instance Types
      • Free Instances (Free Tier)
      • Instance Tiers
  • Gradient Cluster
    • Overview
    • Setup
      • Managed Private Clusters
      • Self-Hosted Clusters
        • Pre-installation steps
        • Gradient Installer CLI
        • Terraform
          • Pre-installation steps
          • Install on AWS
          • Install on bare metal / VMs
          • Install on NVIDIA DGX
        • Let's Encrypt DNS Providers
        • Updating your cluster
    • Usage
  • Tags
    • Overview
    • Using Tags
  • Machines (Paperspace CORE)
    • Overview
    • Using Machines
      • Start a Machine
      • Stop a Machine
      • Restart a Machine
      • Update a Machine
      • Destroy a Machine
      • List Machines
      • Show a Machine
      • Wait For a Machine
      • Check a Machine's utilization
      • Check availability
  • Paperspace Account
    • Overview
    • Public Profiles
    • Billing & Subscriptions
    • Hotkeys
    • Teams
      • Creating a Team
      • Upgrading to a Team Plan
  • Release Notes
    • Product release notes
    • CLI/SDK Release notes
Powered by GitBook
On this page
  1. Experiments
  2. Using Experiments

Experiment Logs

PreviousCustom MetricsNextExperiment Ports

Last updated 5 years ago

Log streaming is available in both the web UI and CLI. It is also possible to stream logs using the SDK.

Navigate to a Job run part of an Experiment to view its logs. These logs are streamed in realtime while the Experiment runs. There are a few options such as Search, View all Logs, and Download Logs available on the page.

You can stream logs while the Experiment is running or after it has stopped.‌

Example Use

gradient experiments logs --experimentId <id>

‌Example Output

Hello Paperspace
Creating file /artifacts/myoutput1.txt
Creating file /artifacts/myoutput2.txt
Finished; returning exit code 0

‌Properties

Name

Type

Attributes

Description

experimentId

string

Required

Id of the experiment to view logs for.

follow

string

<optional>

​Content

line

number

<optional>

Optional; if line is specified logs only logs after that line will be returned (up to limit).

limit

number

<optional>

Optional; number of log lines to retrieve on each request; default limit is 2000.

While using the SDK, you can print the logs of an Experiment.

#create a log stream & print all logs for the duration of experiment
log_stream = experiment_client.yield_logs(experiment_id)
print("Streaming logs of experiment")
try:
    while True:
        print(log_stream.send(None))
except:
    print("done streaming logs")