Amazon SageMaker Studio Lab - Free ML Environment
Source: https://studiolab.sagemaker.aws/
Description
Create account to comment on specific lines or Sign in
+ 1 Amazon SageMaker Studio Lab is a completely free, browser-based ML development environment powered by JupyterLab 4. It provides access to an NVIDIA T4 GPU (4 hours/day) and a T3.xlarge CPU (8 hours/day), 15 GB of persistent storage, and pre-installed ML frameworks -- all without needing an AWS account or credit card. It is one of the best zero-cost options for learning, prototyping, and experimenting with machine learning.
No comments on this line yet.
+ 2
No comments on this line yet.
+
3
No comments on this line yet.
+ 4
No comments on this line yet.
+ 6
No comments on this line yet.
+ 7 1. Go to studiolab.sagemaker.aws and click "Request free account"
No comments on this line yet.
+ 8 2. Fill in the request form: email address, first/last name, country, organization name, and occupation
No comments on this line yet.
+ 9 3. Click "Submit request" -- you will receive an email to verify your email address; click the verification link
No comments on this line yet.
+ 10 4. Wait for approval -- AWS reviews requests within 5 business days. In practice, most approvals come within a few hours to a few days
No comments on this line yet.
+ 11 5. Once approved, you receive an email with a registration link -- you have 7 days to claim your account before the link expires
No comments on this line yet.
+ 12 6. Click the link and create your account: choose a username and password (this is separate from any AWS account)
No comments on this line yet.
+ 13 7. Verify your email one more time via the confirmation email
No comments on this line yet.
+ 14 8. First runtime launch -- phone verification (one-time): Enter a mobile phone number, receive a 6-digit SMS code, and verify. This is required only once
No comments on this line yet.
+ 15 9. Choose CPU or GPU runtime and click "Start runtime" -- your JupyterLab environment loads in the browser
No comments on this line yet.
+ 16
No comments on this line yet.
+ 17 Important:
No comments on this line yet.
+ 18 • No AWS account or credit card is required at any step
No comments on this line yet.
+ 19 • One account per person/email
No comments on this line yet.
+ 20 • If your approval link expires after 7 days, you must submit a new request
No comments on this line yet.
+ 21 • Phone verification uses the AWS SMS channel, which supports 240+ countries but has known delivery issues in some regions (China, Colombia, UAE, Jordan have been reported). VoIP numbers typically do not work
No comments on this line yet.
+ 22 • Referral codes (used for workshops and hackathons) bypass the approval wait and grant instant access
No comments on this line yet.
+ 23
No comments on this line yet.
+
24
No comments on this line yet.
+ 25
No comments on this line yet.
+ 27
No comments on this line yet.
+ 28 ResourceInstance TypeSession LimitDaily Limit
No comments on this line yet.
+ 29 GPUG4dn.xlarge (NVIDIA T4, 16 GB VRAM)4 hours/session4 hours per 24-hour period
No comments on this line yet.
+ 30 CPUT3.xlarge (4 vCPUs, 16 GB RAM)4 hours/session8 hours per 24-hour period
No comments on this line yet.
+ 31
No comments on this line yet.
+ 32 Key details:
No comments on this line yet.
+ 33 • Only one runtime session can be active at a time (you cannot run CPU and GPU simultaneously)
No comments on this line yet.
+ 34 • When your session time runs out, all running computations stop -- but your files and installed packages are saved to persistent storage
No comments on this line yet.
+ 35 • Compute availability is not guaranteed and is subject to demand. During peak times, you may not be able to start a GPU session immediately
No comments on this line yet.
+ 36 • Time limit increases are not supported
No comments on this line yet.
+ 37 • You can switch between CPU and GPU runtimes between sessions
No comments on this line yet.
+ 38
No comments on this line yet.
+ 39 Note: The GPU provides an NVIDIA T4 with 16 GB of GPU memory, which is sufficient for training small-to-medium deep learning models, fine-tuning pre-trained models, and running inference on models like Stable Diffusion or smaller LLMs.
No comments on this line yet.
+ 40
No comments on this line yet.
+
41
No comments on this line yet.
+ 42
No comments on this line yet.
+ 44
No comments on this line yet.
+ 45 • 15 GB of persistent storage per user
No comments on this line yet.
+ 46 • Files, notebooks, conda environments, and installed packages persist across sessions and reboots
No comments on this line yet.
+ 47 • Environment state is automatically saved when you update packages or create new files
No comments on this line yet.
+ 48 • Manual save recommended: File edits are periodically auto-saved during a session, but are not saved when the runtime ends. Always save your work manually (Ctrl+S) before your session expires
No comments on this line yet.
+ 49 • No option to expand storage beyond 15 GB
No comments on this line yet.
+ 50
No comments on this line yet.
+ 51 Storage tips:
No comments on this line yet.
+ 52 • Avoid storing large datasets directly -- link to external sources (S3, Hugging Face Hub, GitHub) and load data in batches
No comments on this line yet.
+ 53 • Large model checkpoints and pre-trained weights can fill 15 GB quickly. Use model streaming or download only what you need per session
No comments on this line yet.
+
54
• Use .gitignore to keep your Git repos clean and avoid syncing large binary files
No comments on this line yet.
+ 55
No comments on this line yet.
+
56
No comments on this line yet.
+ 57
No comments on this line yet.
+ 59
No comments on this line yet.
+ 60 Studio Lab comes with a default conda environment and also supports the SageMaker Distribution, a curated environment designed to match full SageMaker Studio:
No comments on this line yet.
+ 61
No comments on this line yet.
+ 62 Framework / LibraryIncluded
No comments on this line yet.
+ 63 PyTorchYes
No comments on this line yet.
+ 64 TensorFlowYes
No comments on this line yet.
+ 65 KerasYes
No comments on this line yet.
+ 66 NumPyYes
No comments on this line yet.
+ 67 scikit-learnYes
No comments on this line yet.
+ 68 PandasYes
No comments on this line yet.
+ 69 Hugging Face TransformersInstallable via pip/conda
No comments on this line yet.
+ 70 OpenCVInstallable via pip/conda
No comments on this line yet.
+ 71 XGBoostInstallable via pip/conda
No comments on this line yet.
+ 72
No comments on this line yet.
+ 73 • JupyterLab 4 with full extension support
No comments on this line yet.
+ 74 • Python 3.9+ (default)
No comments on this line yet.
+ 75 • Package managers: conda, pip, and micromamba are all supported
No comments on this line yet.
+ 76 • Custom conda environments persist across sessions
No comments on this line yet.
+ 77 • Custom JupyterLab extensions persist across sessions
No comments on this line yet.
+ 78 • Git integration built-in -- clone repos, push/pull, manage branches directly from the UI
No comments on this line yet.
+ 79
No comments on this line yet.
+
80
No comments on this line yet.
+ 81
No comments on this line yet.
+ 83
No comments on this line yet.
+ 84 FeatureSageMaker Studio LabGoogle Colab (Free)Kaggle Notebooks
No comments on this line yet.
+ 85 Free GPUNVIDIA T4 (guaranteed type)T4 (not guaranteed)T4 or P100
No comments on this line yet.
+ 86 GPU time/day4 hours~4-12 hours (variable)30 hours/week
No comments on this line yet.
+ 87 Persistent storage15 GB (persistent)None (uses Google Drive)None (session-only)
No comments on this line yet.
+ 88 Sign-upApproval required (1-5 days)Google account (instant)Kaggle account (instant)
No comments on this line yet.
+ 89 EnvironmentJupyterLab 4 (fully customizable)Colab notebook (limited)Kaggle notebook
No comments on this line yet.
+ 90 Package persistenceYesNoNo
No comments on this line yet.
+ 91 Credit card neededNoNoNo
No comments on this line yet.
+ 92
No comments on this line yet.
+ 93 Key advantages of Studio Lab:
No comments on this line yet.
+ 94 • Persistent storage and environments -- your installed packages, datasets, and configurations survive between sessions, unlike Colab where you reinstall everything each time
No comments on this line yet.
+ 95 • Guaranteed GPU type -- you always get a T4, whereas Colab may downgrade you to a weaker GPU or deny access entirely
No comments on this line yet.
+ 96 • Full JupyterLab -- not a simplified notebook interface. You get a file browser, terminal, multiple tabs, and extension support
No comments on this line yet.
+ 97
No comments on this line yet.
+ 98 Key disadvantages:
No comments on this line yet.
+ 99 • Approval wait -- unlike Colab or Kaggle, you cannot start immediately
No comments on this line yet.
+ 100 • Shorter GPU sessions -- 4 hours/day vs. Colab's variable (but often longer) sessions
No comments on this line yet.
+ 101 • No real-time collaboration -- unlike Colab, you cannot share notebooks for live co-editing
No comments on this line yet.
+ 102
No comments on this line yet.
+
103
No comments on this line yet.
+ 104
No comments on this line yet.
+ 106
No comments on this line yet.
+ 107 Studio Lab is designed as an on-ramp to the full Amazon SageMaker platform:
No comments on this line yet.
+ 108
No comments on this line yet.
+ 109 1. Export your Studio Lab notebooks and environment files
No comments on this line yet.
+ 110 2. Create an AWS account (requires credit card)
No comments on this line yet.
+ 111 3. Open SageMaker Studio in the AWS Console
No comments on this line yet.
+ 112 4. Import your notebooks and recreate your conda environment using the SageMaker Distribution
No comments on this line yet.
+ 113 5. Access larger instances, SageMaker Pipelines (CI/CD), model deployment, and other production features
No comments on this line yet.
+ 114
No comments on this line yet.
+ 115 What Studio Lab does NOT include (available in full SageMaker):
No comments on this line yet.
+ 116 • SageMaker Pipelines for ML CI/CD
No comments on this line yet.
+ 117 • Real-time model endpoints / inference
No comments on this line yet.
+ 118 • SageMaker GroundTruth (data labeling)
No comments on this line yet.
+ 119 • Built-in SageMaker algorithms and estimators
No comments on this line yet.
+ 120 • Fine-grained IAM access control
No comments on this line yet.
+ 121 • Configurable instance types and storage
No comments on this line yet.
+ 122
No comments on this line yet.
+
123
No comments on this line yet.
+ 124
No comments on this line yet.
+ 126
No comments on this line yet.
+ 127 • Use checkpoints for GPU training -- save model state periodically so you can resume in the next 4-hour session rather than starting over
No comments on this line yet.
+ 128 • Prefer the CPU runtime for data preprocessing -- save your GPU hours for actual training. CPU sessions give you 8 hours/day
No comments on this line yet.
+ 129 • Install packages once, use them forever -- packages installed via conda/pip persist across sessions. No need to reinstall like in Colab
No comments on this line yet.
+ 130 • Clone GitHub repos for reproducibility -- the built-in Git integration makes it easy to version your work
No comments on this line yet.
+ 131 • Use the SageMaker Distribution environment if you plan to eventually migrate to full SageMaker -- it ensures compatibility
No comments on this line yet.
+ 132 • Keep an eye on storage -- 15 GB fills up fast with model weights. Clean up old checkpoints and datasets regularly
No comments on this line yet.
+ 133 • If GPU is unavailable, try again during off-peak hours (late night / early morning US time). Availability depends on demand
No comments on this line yet.
+ 134 • For workshops or classrooms, request referral codes from AWS to give participants instant access without the approval wait
No comments on this line yet.
+ 135
No comments on this line yet.
+
136
No comments on this line yet.
+ 137
No comments on this line yet.
+ 139
No comments on this line yet.
+ 140 • AWS Free Tier -- new AWS accounts get up to $200 in credits (post-July 2025), usable for SageMaker and Bedrock. Requires credit card
No comments on this line yet.
+ 141 • AWS Educate -- additional $200 in credits for students. Requires .edu email
No comments on this line yet.
+ 142 • AWS Activate -- up to $100K in credits for startups
No comments on this line yet.
+ 143 • Google Colab -- alternative free GPU environment with instant access but no persistent storage
No comments on this line yet.
+ 144 • Kaggle Notebooks -- 30 GPU hours/week, instant access, great dataset ecosystem
No comments on this line yet.
+ 145 • Lightning AI Studios -- free tier with 22 GPU hours/month on T4
No comments on this line yet.
+ 146
No comments on this line yet.
+
147
No comments on this line yet.
+ 148
No comments on this line yet.
+ 149 Sources:
No comments on this line yet.
+ 150 • SageMaker Studio Lab Homepage
No comments on this line yet.
+ 151 • SageMaker Studio Lab FAQ
No comments on this line yet.
+ 152 • AWS Docs: Studio Lab Overview
No comments on this line yet.
+ 153 • AWS Docs: Onboard to Studio Lab
No comments on this line yet.
+ 154 • AWS Blog: SageMaker Studio Lab Preview Announcement
No comments on this line yet.
+ 155 • DataCamp: SageMaker Studio Lab Guide
No comments on this line yet.
+ 156 • Roboflow: SageMaker Studio Lab vs Google Colab
No comments on this line yet.
+ 157 • GitHub: SageMaker Distribution
No comments on this line yet.
+ 158 • GitHub: Studio Lab Examples
No comments on this line yet.