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We provide a python CLI (open-source) for a convenient interface to the rest API. You can use the —explain option with any CLI command and it will print out the underlying API calls.

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PyPI Install

You can install the latest stable PyPI release with:
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pip install vastai

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Github

Alternatively you can get the very latest version directly from github:
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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 functionality of the vast.ai website GUI and uses the same underlying REST API. The CLI is self-contained in the single script file vast.py.

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Quickstart

In order to authenticate most commands you will need to first login to the vast.ai website and get an api-key. Go to https://cloud.vast.ai/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 a 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-keys with anyone as they authenticate commands from your account. Your default main key allows full access to all commands without limitations, but you can use the CLI to create additional keys with fine-grained access restrictions.

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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:
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vastai --help
To see how the API works you can use it to find machines for rent.
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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:
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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:
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vastai search offers 'reliability > 0.99 num_gpus>=4' -o 'num_gpus-'
The output of this command at the time of this writing is
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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

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Launching Instances

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vastai create instance --help
You create instances using the create instance command referencing an instance type ID returned from search offers. So to create an ssh direct instance of type 2459368 (using the ID returned from the search above for 4x 3090 on machine 4637) with the vastai/tensorflow image and 32 GB of disk storage:
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vastai create instance 2459368 --image vastai/tensorflow --disk 32 --ssh --direct
Once an instance is created, it then must first pull the image if it is not cached. After the image is loaded the instance boots and transititons to the running state. You are charged for the resources you reserve. As storage is reserved at creation, storage charges begin when the instance is created and end only when it is destroyed. GPU charges begin when the instance transitions to the running state, and end when it is stopped or destroyed.

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Get Instance Info

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vastai show instance --help
vastai show instances --help

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Starting Stopping

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vastai start instance --help
vastai stop  instance --help
You can stop an instance to avoid GPU charges, converting it into a storage unit - storage is usually very cheap compared to GPU. Starting an existing instance takes only a second or less whereas creating a new instance can take much longer (to pull a large docker image), so maintaining a pool of stopped instances is useful for many applications. You can call stop/destroy instance from inside the instance using a special autogenerated instance apikey, to avoid exposing your main apikey.

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Copy Data

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vastai copy --help
vastai cloud copy --help
You can copy data from a stopped instance to a running instance, to/from cloud storage, or to/from another machine.

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Destroy Instances

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vastai destroy instance --help
vastai destroy instances --help
Once you are done with an instance make sure to destroy it to avoid ongoing storage charges.