TensorFlow + JupyterLab
Ubuntu 22.04, CUDA 12.4, TensorFlow (and-cuda), JupyterLab and SSH.
| Detail | Value |
|---|---|
| Image | ghcr.io/fairgpu/base-cuda-tensorflow:latest |
| Mode | interactive |
| Ports | 22/ssh, 8888/http |
| Needs | any GPU, 10 GB disk |
| Compatible machines online | 1 |
| Used | 0 times |
TensorFlow + JupyterLab
Same base as PyTorch + JupyterLab with tensorflow[and-cuda] instead of PyTorch.
Before you stop
Everything you create lives inside the container on the host's disk and is removed when the rental ends. Push results out before stopping (scp, rsync, git, HF Hub, S3...). From inside the container you can run fairgpu extend 60 to add time, fairgpu mark "epoch 3 done" to annotate the timeline and fairgpu stop when you are done.
Step by step
1. Connect
Open Terminal (browser) or Jupyter from the rental page, or use the SSH address line with your key.
2. Verify TensorFlow sees the GPU
python -c "import tensorflow as tf; print(tf.__version__); print(tf.config.list_physical_devices('GPU'))"
An empty list means no GPU: stop and pick another machine (nothing is charged for a failed start).
3. Bring your project
cd /workspace && git clone https://github.com/you/your-project.git
pip install -r your-project/requirements.txt # kept between runs
Upload data with scp -P <port> -r ./data root@<relay>:/workspace/ or drag files into JupyterLab's file browser.
4. Train
cd /workspace/your-project
python train.py --data /workspace/data --out /workspace/runs/exp1 2>&1 | tee /workspace/runs/exp1.log
Use tf.keras.callbacks.ModelCheckpoint('/workspace/runs/exp1/ckpt-{epoch}') so an interruption costs you an epoch, not a day. Mixed precision (tf.keras.mixed_precision.set_global_policy('mixed_float16')) roughly doubles throughput on RTX cards.
5. Keep installs
pip packages are kept in /workspace; system packages (apt-get) belong in the template's setup script.
Save your results before you stop
- Everything outside
/workspacedisappears when the rental stops;/workspacesurvives on the same host (persistent workspace) and anywhere with a FairGPU checkpoint. - Save checkpoint now on the rental page (or
fairgpu snapshot save "label"inside the container) archives /workspace to FairGPU cloud storage; Download workspace gets it to your computer. scp -P <port> root@<relay>:/workspace/results ./copies files out over SSH;rsyncand SFTP (WinSCP/FileZilla) work the same way.- Stop the rental to stop billing. Set an idle auto-stop if you tend to forget, or buy the $0.99 finish alert (SMS + email).
Machines that can run it
| Machine | GPU | VRAM | Price | Status | Reliability | Country | Host | Cached |
|---|---|---|---|---|---|---|---|---|
| WhiteBob | NVIDIA GeForce RTX 5060 Ti | 16 GB | $0.10/hr | available now | 62 % | - | Aharon Sela | - |