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TensorFlow is a free and open-source software library for machine learning and artificial intelligence.
TensorFlow pre-installed on servers in the Netherlands, Finland, Germany, Iceland, Turkey and the USA
Rent a virtual (VPS) or a dedicated server with pre-installed TensorFlow - a free and open-source software library for AI and ML. Simply choose the right plan, configure a server and start working in just 15 minutes.
TensorFlow is provided only for leased HOSTKEY servers. To get the TensorFlow license, select it in the "Software" tab while ordering the server.
Rent a reliable VPS in the Netherlands, Finland, Germany, Iceland, Turkey and the USA.
Server delivery ETA: ≈15 minutes.
Rent a dedicated server with a full out-of-band management in the Netherlands, Finland, Germany, Turkey and the USA.
Server delivery ETA: ≈15 minutes.
TensorFlow is a free and open-source software library distributed under the Apache License 2.0.
We guarantee that our servers are running safe and original software.
To install TensorFlow, you need to select a license while ordering a server on the HOSTKEY website. Our auto-deployment system will install the software on your server. Also you can read how to install this software by yourself.
If you have any difficulties or questions when installing and/or using the software, carefully learn the documentation on the official website of the developer, read about typical problems and how to solve them or contact TensorFlow support.
If you rent a server with TensorFlow installed, you won’t have to spend time on setup or worry about compatibility. You have access to a machine learning and AI environment that is already set up and supports all your GPU needs.
TensorFlow is open-source and can be used without charge. You will not be charged extra for using it on HOSTKEY servers; you pay only for the hardware and hosting.
If you are handling small tasks or development, a TensorFlow VPS is the better choice, but dedicated servers are needed for larger training or production jobs.
Absolutely. All TensorFlow servers include GPU support, along with the necessary drivers and libraries, so your models can be trained and used at full speed.
You may pick between Linux distributions like Ubuntu, Debian or CentOS. They all have TensorFlow and the most important ML tools already set up.
After you pay, your servers are set up within minutes. You will get your login details and can begin working right away using SSH.
Installation of TensorFlow with the support of GPUs requires appropriate drivers and CUDA toolkit and cuDNN libraries to run it on NVIDIA hardware or the ROCm stack to run it on AMD hardware. On the majority of systems, one can just do:
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Login pip install tensorflow-gpuAlternatively, pre-configured Docker images that are offered by TensorFlow can be used to prevent dependency conflict. You can even bypass the setup by use of HOSTKEY which provides ready TensorFlow GPU environments that take minutes to install.
TensorFlow needs a CUDA-compatible NVIDIA or an AMD graphics card with a minimum compute capability of 3.5 or supported AMD graphics cards running the ROCm software stack. To work efficiently, we suggest the use of the latest GPUs, as NVIDIA RTX 4090/5090, A5000/A6000, or A100/H100. AMD MI200/MI300 GPUs can be supported through ROCm. Large models require a large amount of VRAM (16GB or higher).
Yes. TensorFlow includes built-in distribution strategies for multi-GPU setups, such as MirroredStrategy and MultiWorkerMirroredStrategy. This allows you to scale training across multiple GPUs within a single server or across clusters. HOSTKEY offers tensorflow multi gpu servers pre-configured for distributed workloads.
Yes. AMD GPUs are now supported by TensorFlow using the ROCm platform. ROCm supports running training and inference on AMD MI200/MI300 hardware, so that an install of tensorflow gpus does not require NVIDIA hardware.
Run the following command inside Python:
import tensorflow as tf
print(tf.config.list_physical_devices('GPU'))In case of the detection of your GPU, it will be displayed in the output. TensorFlow will automatically move the computations to the GPU during the training as long as it is available.
The best GPU for TensorFlow depends on your use case:
Yes. Provided that TensorFlow can identify a supported GPU and the appropriate drivers/libraries are present then it will automatically switch to using the GPU. You are also able to set the placement of devices manually (when it is required).
Yes. HOSTKEY offers Tensorflow installed on the GPU servers. These environments are set up with CUDA/cuDNN or ROCm (based on the GPU vendor), such that you can train or deploy models on them right away without delays during set up.
TensorFlow is an esteemed library that offers a flexible and scalable ecosystem of tools, libraries, and community resources that empower researchers and developers to create and deploy machine learning-enabled applications.
Making TensorFlow run on a GPU is not an option anymore, it is a necessity in serious AI projects. Natural language processing and computer vision workloads are deep learning workloads that demand massive parallel computing. Training times on CPUs may take days or weeks, whereas, with an appropriately configured tensorflow gpu system, the same models can be trained in hours. Prediction is faster, which allows real-time predictions. And scalable multi gpu tensorflow configurations can be used to allocate workloads to multiple accelerators to achieve unprecedented performance.
TensorFlow is compatible with CUDA (NVIDIA) and ROCm (AMD), and you are free to select the most suitable hardware to use in your project. No matter whether you are developing models on tensorflow gpu windows, you are deploying at scale on the cloud, or just want the best gpu to use with tensorflow, optimized GPU support is what counts.
In HOSTKEY, we offer ready to use TensorFlow GPU servers that are pre-installed and can be deployed immediately. Our GPU servers are designed to minimize time-to-market, remove complicated installation procedures, and achieve the highest performance of AI, designed to serve research, startups, and enterprises. Our broad selection of GPU configurations, flexible billing, and infrastructure all over the world enable teams to expedite innovation without unjustified delays.
NVIDIA GPUs are still the leader in TensorFlow training and inference in the industry. CUDA giving the low-level interfaces to get the gpus to talk to and cuDNN giving the best primitives to do deep learning, your tensorflow gpu is going to become a simple and efficient install. These libraries have the highest compatibility, which allows convolutional networks, RNNs, and large-scale transformer models to have high throughput.
TensorFlow has now been developed to run on ROCm, the open-source AMD GPU computing stack. This provides new cost-effective opportunities to businesses and scientists who desire to use AMD hardware to do deep learning. ROCm compatibility also makes it possible to execute complex models in an environment that is not NVIDIA-based.
The current AI loads do not often execute on a single GPU. Using multi gpu with tensorflow, it is possible to divide the workload between several devices, which saves a lot of time in training. In very large models, this enables researchers and businesses to test at scale, previously only available to large tech companies.
Installation of TensorFlow with a GPU may be tricky, particularly in matching CUDA, cuDNN and TensorFlow. HOSTKEY does away with this difficulty with servers that come with tensorflow-gpu already installed and tested and so you can get down to research and deployment other than troubleshooting.
The needs of each project are not the same. There are teams that need burst of gpu power and those that need dedicated infrastructure in the long run. HOSTKEY offers VPS and dedicated servers that are ready to install tensorflow gpus and charge per hour or month to suit the size of any project.
All the GPU servers are placed in business-grade facilities that have stringent physical and cyber security. A 99.9% uptime SLA guarantees your workloads to be available, trustworthy and non-stop.
These cards are offering enormous computing capabilities at a comparatively reduced price than data-center GPUs. They are also suitable when one needs to develop prototypes faster, when training small-to-medium sized models, or when developing a tensorflow gpu windows set up.
These GPUs have high VRAM capacity, which makes them suitable with large datasets and complex models. They have applications in all areas of computer vision, NLP and generative AI workloads where consumer GPUs are bottlenecked by memory.
The A100 and H100 are the newest AI-oriented GPU accelerators of NVIDIA. They offer huge VRAM, FP16 and TF32 optimized tensor cores and unparalleled throughput. Fits well in AI research laboratories and businesses with billions of parameters in their models.
The new GPUs by AMD offer an alternative to enterprises that want a good performance-per-dollar. These GPUs are optimized to run with TensorFlow and ROCm and are very well suited to the case of installing gpus to run tensorflow when price and flexibility are important.
The most used method on NVIDIA hardware is to install tensorflow gpu using CUDA and cuDNN libraries. This offers full hardware acceleration and provides the best performance of TensorFlow.
AMD users have the opportunity to use tensorflow gpu install with the support of ROCm. The fact that ROCm is compatible with the TensorFlow framework, means that deep learning workloads can be executed on AMD MI-series GPUs.
TensorFlow has images pre-built with Docker in the form of drivers and libraries. This alternative makes deployment simple enough to keep you out of dependency problems.
Local deployment offers the greatest control and cloud environments offer speed, scale and convenience. HOSTKEY fills the void by providing ready to use instant TensorFlow GPU servers.
In data parallelism, data is divided into several GPUs, each of which processes its part independently. This accelerates training and does not compromise accuracy.
In cases where models are too big to fit in the memory of a single GPU, model parallelism partitions model layers between GPUs. This allows giant architectures such as GPT or vision transformers to be trained.
TensorFlow has in-built strategies like MirroredStrategy, Multiworker Mirrored strategy and Parameterserver Strategy to streamline scaling.
Under HOSTKEY infrastructure, it is possible to distribute TensorFlow to several nodes and create a real GPU cluster when running the workload of enterprise-scale AI.
HOSTKEY has solutions in the form of either consumer grade RTX cards or as data-center accelerators such as A100 and H100, and is the most appropriate gpu to run tensorflow workloads.
No manual installation will be necessary. Tensorflow-gpu is pre-installed on all of the servers.
Select hourly rates in case of short term training or monthly rates in case of long term training. Discounted reserved instances save up to 40 percent.
We have servers in Europe, North America, and Asia, which provide access to low-latency in all parts of the world.
Our technical support is 24 hours and 7 days a week and it offers installation, scaling, and optimization assistance.
Deep learning models can also require days or weeks to be trained on CPUs. TensorFlow gpu makes training just a matter of hours and the process of iteration is much faster and efficient.
Enterprise GPUs such as the A100 or the H100 may cost tens of thousands of dollars to buy. Cloud GPU hosting avails such resources at a fraction of the initial cost and does not have to worry about hardware maintenance.
The researchers access up to date hardware without making capital investments.
Scale from idea to production quickly with tensorflow gpu install environments.
Run transformer-based LLMs, train GANs, or fine-tune diffusion models efficiently.
Ai-optimized, secure, and compliant GPU clusters, at enterprise scale.
They are necessary in NVIDIA-based tensorflow gpu install work flows.
The open-source ROCm platform at AMD is associated with compatibility and performance.
Compare performance trade-offs between GPUs, TPUs, and CPUs.
Supports lots of CUDA version to become compatible with various TensorFlow versions.
Maintains TensorFlow in line with the changing ROCm ecosystem of AMD.
HOSTKEY is compatible with Ubuntu, tensorflow gpu windows, and macOS which have their own drivers installed.
The optimal gpu to use in tensorflow depends on workload. Greater VRAM will make it more expensive but allows higher model.
Multi-GPU hosting accelerates large-scale AI training pipelines.
Total cost can be reduced by 40 percent with reserved price.
Experiment with batch size for the best balance of speed and stability.
FP16 version Run FP16 will provide higher throughput at lower VRAM consumption.
Monitoring tools provided by TensorBoard and NVIDIA can be used to ensure that GPUs are used to their full capacity.
Stay updated with the latest CUDA versions for new features.
Stability in versions of TensorFlow is guaranteed by correct versions.
Complete support of AMD ROCm ecosystem.
Available on Linux, macOS and gpus on tensorflow windows..
Simplify reproducibility and deployment with Dockerized builds.
Eliminate dependency crises in operating multiple AI projects.
Use pip install tensorflow-gpu or Conda for controlled environments.
Confirm GPU access using tf.config.list_physical_devices('GPU').
TensorFlow GPU is pre-installed and servers are deployed in minutes. One- Click Marketplace Software. Billings of hourly and monthly, discounts of up to 40 percent.
Co-ordinates, synchronizes the updates of the gradient across the GPUs in one machine.
Trains on a variety of servers.
Training models with billions of parameters and across clusters are trained best with it.
Fluent flow between current orchestration and big data processes.
Have increased throughput and less VRAM consumption.
Optimize XLA to execute XLA graphs more quickly.
Save GPU memory Computing activations in between.
Visualize in TensorBoard to optimize values of workloads in graphics card.
Weigh flexibility, cost and control.
Launch the use of API-based (GPU backed) application deployment in real-time (such as in fraud detection).
Global GPU clusters enable cross-region AI deployments.
Security in a shared environment is guaranteed by virtualized separation.
Keep sensitive AI workloads secure with encryption.
Pass enterprise compliance criteria of regulated fields.
The selection of the environment where the tensorflow gpus will run is important in enhancing the optimum performance of AI. HOSTKEY offers fast and secure infrastructure whether you need to install tensorflow gpu, configure tensorflow gpu windows, scale with tensorflow multi gpu or simply looking to install gpus. Ease of use Traditionally, high-capacity servers rely on in-house hardware configurations to support training and inference models, along with the need for extensive data volume and storage (such as hard disks, flash disks, RAID drives, etc.). However, we can help to push this concept further by offering you finished TensorFlow GPU environments and flexible pricing as well as global coverage and security-first design.
Get our gpu hosting plans now and make the most of Tennesseeflow-gpu.
