Docs - CLI & API

Overview & Quickstart

PyPI Install #

You can install the latest stable PyPI release with:

pip install vastai

Github #

Alternatively you can get the very latest version directly from github:

wget https://raw.githubusercontent.com/vast-ai/vast-python/master/vast.py -O vast; chmod +x vast;

This repository contains the open source python command line interface for vast.ai. This CLI has all of the main functionality of the vast.ai website GUI and uses the same underlying REST API. Most of the functionality is self-contained in the single script file vast.py, although the invoice generating commands require installing an additional second script called vast_pdf.py.

Quickstart #

In order to authenticate most commands you will need to first login to the vast.ai website and get your api-key. Go to https://vast.ai/console/cli/. Copy the command under the heading "Login / Set API Key" and run it. The command will be something like:

vastai set api-key xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx

where the xxxx... is your unique api-key (a long hexadecimal number). Note that if the script is named "vast" in this command on the website and your installed script is named "vast.py" you will need to change the name of the script in the command you run. The set api-key command saves your api-key in a hidden file in your home directory. Do not share your api-key with anyone as it authenticates your other vast commands to your account.

Usage #

For the most up to date help, use 'vast.py --help'. You can then get a list of the available commands. Each command also typically has help documentation:

vastai --help

To see how the API works you can use it to find machines for rent.

vastai search offers --help

There are many parameters that can be used to filter the results. The search command supports all of the filters and sort options that the website GUI uses. To find GPU instances with compute capability 8.0 or higher:

vastai search offers 'compute_cap >= 800 '

To find instances with a reliability score >= 0.99 and at least 4 gpus, ordering by num of gpus descending:

vastai search offers 'reliability > 0.99 num_gpus>=4' -o 'num_gpus-'

The output of this command at the time of this writing is

ID CUDA Num Model PCIE_BW vCPUs RAM Storage $/hr DLPerf DLP/$ Nvidia Driver Version Net_up Net_down R Max_Days machine_id 1596177 11.4 10x GTX_1080 5.5 48.0 257.9 4628 2.0000 73.0 36.5 470.63.01 653.3 854.5 99.5 - 638 2459430 11.5 8x RTX_A5000 9.1 128.0 515.8 3094 4.0000 209.4 52.3 495.46 1844.2 2669.6 99.7 12.0 4384 2459380 11.4 8x RTX_3070 6.3 12.0 64.0 710 1.4200 67.2 47.3 470.86 0.0 0.0 99.8 - 4102 2456624 11.4 8x RTX_2080_Ti 10.7 32.0 257.9 1653 2.8000 126.4 45.2 470.82.00 14.6 214.2 99.8 28.7 3047 2456622 11.4 8x RTX_2080_Ti 10.8 32.0 128.9 1651 2.8000 127.1 45.4 470.82.00 14.9 214.7 99.1 28.7 1569 2456600 11.5 8x RTX_2080_Ti 10.9 48.0 256.6 1704 2.4000 125.5 52.3 495.29.05 169.0 169.8 99.7 25.7 4058 2455617 11.2 8x RTX_3090 21.7 64.0 515.8 6165 6.4000 261.1 40.8 460.67 477.6 707.2 99.8 28.7 2980 2454397 11.2 8x A100_SXM4 22.4 128.0 2064.1 21568 13.2000 300.1 22.7 460.106.00 708.7 1119.8 99.2 - 4762 2405590 11.4 8x RTX_2080_Ti 11.2 48.0 257.9 1629 3.8000 125.5 33.0 470.82.00 389.4 608.8 100.0 1.8 2776 2364579 11.4 8x A100_PCIE 18.5 128.0 515.8 4813 14.8000 278.8 18.8 470.74 472.4 699.0 99.9 28.7 3459 2281839 11.2 8x Tesla_V100 11.8 72.0 483.1 1171 5.6000 193.6 34.6 460.67 493.0 697.8 100.0 28.7 2744 2281832 11.2 8x A100_PCIE 17.7 64.0 515.9 5821 14.8000 276.7 18.7 460.91.03 478.2 655.5 99.9 28.7 2901 2452630 11.4 7x RTX_3090 6.3 28.0 64.0 61 3.5000 165.5 47.3 470.86 84.6 84.4 99.3 3.8 4420 2342561 11.4 7x RTX_3090 6.1 96.0 257.6 1664 4.5500 149.2 32.8 470.82.00 476.9 671.7 99.4 1.7 4202 2237983 11.4 7x RTX_3090 12.5 32.0 257.6 3228 3.1500 204.5 64.9 470.86 194.4 183.8 99.1 - 4207 2459511 11.4 6x RTX_3090 6.2 - 128.8 812 2.8200 150.2 53.2 470.94 374.4 271.4 99.0 6.7 3129 2448342 11.5 6x RTX_A6000 12.4 64.0 515.7 6695 3.6000 169.8 47.2 495.29.05 668.6 1082.6 99.6 - 3624 2437565 11.4 6x RTX_3090 23.0 16.0 128.8 1676 5.4000 196.8 36.5 470.94 34.1 131.5 99.4 - 4238 2332973 11.2 6x RTX_3090 11.9 48.0 193.3 1671 3.3000 180.3 54.6 460.84 582.1 737.6 99.9 25.6 3552 2459459 11.5 4x RTX_3090 23.1 32.0 257.8 1363 2.0000 131.2 65.6 495.46 1954.7 2725.8 99.6 12.0 3059 2459428 11.5 4x RTX_A5000 24.6 64.0 515.8 1547 2.0000 104.9 52.4 495.46 1844.2 2669.6 99.7 12.0 4384 2459368 11.4 4x RTX_3090 25.3 48.0 64.2 133 1.3967 130.5 93.4 470.86 0.0 0.0 99.4 - 4637 2458968 11.6 4x RTX_3090 11.7 16.0 128.5 752 1.4000 79.8 57.0 510.39.01 797.8 842.7 99.9 4.0 2555 2458878 11.6 4x RTX_3090 11.6 36.0 128.5 1531 1.4000 81.9 58.5 510.39.01 757.1 807.6 99.9 4.0 3646 2458845 11.6 4x RTX_3090 3.1 12.0 128.5 369 1.4000 92.4 66.0 510.39.01 725.7 852.2 99.8 4.0 700 2458838 11.6 4x RTX_3090 5.7 48.0 128.9 624 1.4000 85.3 60.9 510.39.01 574.9 731.7 99.8 4.0 2217 2454395 11.2 4x A100_SXM4 22.9 64.0 2064.1 10784 6.6000 150.0 22.7 460.106.00 708.7 1119.8 99.2 - 4762 2452632 11.4 4x RTX_3090 6.3 16.0 64.0 35 2.0000 123.5 61.8 470.86 84.6 84.4 99.3 3.8 4420 2450275 11.4 4x RTX_3080_Ti 12.5 32.0 128.7 817 1.8000 128.8 71.6 470.82.00 278.3 350.4 99.7 - 4260 2449210 11.5 4x RTX_3090 11.2 48.0 128.9 324 2.0000 89.7 44.9 495.29.05 688.3 775.4 99.8 - 2764 2445175 11.4 4x RTX_3090 11.9 32.0 257.6 1530 2.0000 135.4 67.7 470.86 868.6 887.1 99.7 25.9 3055 2444916 11.4 4x RTX_3090 11.9 16.0 128.7 1576 1.4000 131.8 94.2 470.82.00 39.4 402.3 99.9 - 3759 2437188 11.4 4x Tesla_P100 11.7 24.0 95.2 2945 0.7200 44.8 62.2 470.82.00 10.9 76.2 99.5 0.1 3969 2437179 11.4 4x Tesla_P100 11.7 32.0 192.1 3070 0.7200 44.8 62.3 470.82.00 11.1 66.0 99.2 0.0 4159 2431606 11.4 4x RTX_3090 17.9 32.0 110.7 330 1.8400 134.3 73.0 470.82.01 584.6 813.4 99.7 4.4 4079 2419191 11.4 4x RTX_2080_Ti 6.3 32.0 64.4 837 2.0000 64.7 32.4 470.63.01 40.5 205.9 99.7 - 162 2405589 11.4 4x RTX_2080_Ti 10.8 24.0 257.9 815 1.9000 62.8 33.0 470.82.00 389.4 608.8 100.0 1.8 2776 2392087 11.4 4x RTX_A6000 10.8 32.0 515.9 1247 1.8000 64.5 35.8 470.94 669.9 705.4 99.1 10.9 4782 2377227 11.2 4x RTX_3090 6.3 24.0 64.3 1638 2.0000 128.3 64.1 460.32.03 37.8 145.0 99.7 3.0 2672 2349173 11.4 4x RTX_3090 23.2 48.0 128.7 1475 2.0000 107.4 53.7 470.86 33.2 84.2 99.8 47.3 3949 2338635 11.4 4x RTX_3090 23.0 32.0 128.5 3151 1.6000 108.8 68.0 470.86 33.8 86.4 99.6 47.4 3948 2303959 11.2 4x RTX_3090 11.7 28.0 128.8 791 2.1200 131.3 61.9 460.32.03 519.7 570.7 99.5 - 3042 2281830 11.2 4x A100_PCIE 18.1 32.0 515.9 2910 7.4000 143.6 19.4 460.91.03 478.2 655.5 99.9 28.7 2901 2193726 11.4 4x RTX_3090 12.4 32.0 128.8 1646 3.6000 153.9 42.8 470.82.01 33.3 137.5 99.5 - 3434 1737692 11.2 4x RTX_3070 6.3 28.0 128.5 656 2.8000 37.5 13.4 460.91.03 452.6 703.2 99.6 - 3510

Launching Instances #

You create instances using the create instance command referencing a isntance type ID returned from search offers. So to create an instance of type 2459368 (using an ID from the search above for 4x 3090 machine 4637) with the vastai/tensorflow image and 32 GB of disk storage:

vastai create instance 2459368 --image vastai/tensorflow --disk 32
NumGenius AI
@ 2023 NumGenius Ai, all rights reserved